[type=author, orcid=0000-0001-5832-6087] [1]

[type=author, orcid=0000-0003-0302-5775] [type=author,
orcid=0000-0002-1352-9910]

1]organization=School of Computing, Engineering and Mathematical Sciences, La
Trobe University, city=Melbourne, citysep=, postcode=3086, country=Australia

[cor1]Corresponding author [cor2]Principal corresponding author

# A systematic review of the use of Deep Learning in Satellite Imagery for
Agriculture

Brandon Victor<EMAIL_ADDRESS>Zhen He  Aiden Nibali  [

###### Abstract

Agricultural research is essential for increasing food production to meet the
requirements of an increasing population in the coming decades. Recently,
satellite technology has been improving rapidly and deep learning has seen
much success in generic computer vision tasks and many application areas which
presents an important opportunity to improve analysis of agricultural land.
Here we present a systematic review of 150 studies to find the current uses of
deep learning on satellite imagery for agricultural research. Although we
identify 5 categories of agricultural monitoring tasks, the majority of the
research interest is in crop segmentation and yield prediction. We found that,
when used, modern deep learning methods consistently outperformed traditional
machine learning across most tasks; the only exception was that Long Short-
Term Memory (LSTM) Recurrent Neural Networks did not _consistently_ outperform
Random Forests (RF) for yield prediction. The reviewed studies have largely
adopted methodologies from generic computer vision, except for one major
omission: benchmark datasets are not utilised to evaluate models across
studies, making it difficult to compare results. Additionally, some studies
have specifically utilised the extra spectral resolution available in
satellite imagery, but other divergent properties of satellite images - such
as the hugely different scales of spatial patterns - are not being taken
advantage of in the reviewed studies.

###### keywords:

Systematic Review Deep learning Satellite imagery Agriculture Computer Vision

We review 150 studies that use deep learning on satellite imagery for
agriculture.

We identify 5 common tasks: LULC, soil health, plant physiology, crop damage,
yield.

We present a taxonomy describing how existing works utilise modern deep
learning.

We find that modern deep learning methods outperform other methods for most
tasks.

We uncover a need for public benchmarks to properly compare results across
studies.

##  1 Introduction

There are big agricultural challenges coming. The human population is expected
to increase significantly in the next decades (United Nations, 2019), which
will require an estimated global yield increase of 25-70% (Hunter et al.,
2017), along with a changing climate bringing additional challenges (Tirado et
al., 2010) and the need to reduce the environmental impact of agriculture
(Gebbers and Adamchuk, 2010).

Remote sensing is an extremely useful tool because it provides a non-
destructive and non-intrusive way to monitor crop growth and distributions
simultaneously at a fine level of detail and across wide areas and times.
Remote sensing can be used for fast and targeted interventions at a farm-level
(Griffin and Lowenberg-DeBoer, 2005), regional studies of ecological change
over time (Roughgarden et al., 1991), county-level yield prediction for
logistics (Fritz et al., 2019) and international trade decisions (MacDonald
and Hall, 1980). There are many sources of worldwide satellite imagery freely
available to the public and the most popular for agricultural purposes are:
Sentinel (Torres et al., 2012; Drusch et al., 2012), Landsat (Roy et al.,
2014) and MODIS. Each of these satellite programs store and manage enormous
collections of historical worldwide imagery, e.g. Sentinel added 7.34 PiB of
imagery to their archive in 2021 (SERCO, 2021).

The huge volume of data available in remote sensing is both a major boon and
challenge. Much research has gone into creating algorithms to make sense of
this volume of imagery at scale (Gómez et al., 2016; Weiss et al., 2020;
Kattenborn et al., 2021). In recent years, deep learning has become the
dominant algorithm used in generic computer vision tasks with all winning
submissions to large public datasets (Deng et al., 2009; Lin et al., 2014;
Kuznetsova et al., 2020) using deep learning, often with significantly better
performance than non-deep learning methods. Although the fundamentals have
been around for decades, the latest wave of deep learning has been largely
driven by a dramatic increase in available labelled data and processing power
(Goodfellow et al., 2016). Importantly, these advances on generic image tasks
have successfully been transferred to many application areas (Beyer et al.,
2020), including Earth Observation tasks (Ma and Mei, 2021; Brodrick et al.,
2019).

Table 1: The search terms used in the query for Clarivate’s Web of Science. There is an "AND" between each top-level concept (i.e. [Deep Learning] AND [Satellite] AND [Agriculture]), and an "OR" between each term under that. The list of specific crops comes from the CDL (USDA, 2022). The search interface enforces a limit to the number of "All" search terms, so only the abstract and topic were searched for specific agriculture terms. The full list of agricultural terms is available in the supplementary materials section. Deep Learning |  Satellite | Agriculture
---|---|---
All |  All |  All |  Abstract |  Topic
Deep Learn*, CNN, RNN, LSTM, GRU, Transformer, Neural Network, Deep Belief Network, Autoencoder |  Satellite |  Farm, Agri*, Crop |  Wheat, Corn, Maize, Orchard, Coffee, Vineyard, Soy, Rice, Cotton, Sorghum, Peanut*, Tobacco, Barley, Grain, Rye, Oat, Millet, Speltz, Canola, … [+52 more] |  *wheat, *flower*, *berries, *melon*, *berry

In just a few years, the number of studies using deep learning on satellite
imagery has exploded, with many different methods appearing. This systematic
review catalogues all of the current tasks and the many modern deep learning
methods on satellite imagery specifically for agriculture. In the 188 papers
reviewed, we see works using deep learning for each stage of a crop’s
lifecycle: locating crops/fields (Section 6.1), monitoring soil health
(Section 6.2) and plant physiology and growth (Section 6.3), detecting damage
from pests/disease (Section 6.4), and finally predicting yield (Section 6.5).
We observe that the majority of interest is in crop segmentation and yield
prediction. The other tasks have far fewer studies, likely because it is
harder to collect data, sometimes requiring specialised equipment. As such
there is very little use of modern deep learning and significantly smaller
datasets in the other tasks.

To our knowledge this is the first systematic review at the intersection of
deep learning techniques, satellite imagery, and agricultural applications.
This review will not describe how popular deep learning network architectures
such as Multi-Layer Perceptrons (MLPs), Convolutional Neural Networks (CNNs)
and Recurrent Neural Networks (RNNs) function (Goodfellow et al., 2016).
Instead, in this review, we systematically identify, categorise and describe
all agricultural applications at this intersection, as well as the different
ways that studies have used deep learning algorithms on satellite imagery,
including a taxonomy of methods to assist the reader to compare different
studies’ methodology at a high level (Section 5.1).

##  2 Search strategy

Our systematic search strategy started with formulating a search query for
Clarivate’s Web of Science. This query is described in Table 1. We used
generic terms for Deep Learning as well as specifically searching for the most
common names of deep learning algorithms to find studies that included deep
learning models. We included generic agricultural terms like “agri*” and
“farm”, as well as specific names of crops to ensure we found studies that
focused on individual crops. To ensure we covered all major crop types, we
used a manually filtered list of crop names from the Cropland Data Layer
(USDA, 2022). The resultant tagged library of studies is available as
supplementary materials.

The initial search yielded 770 studies. We performed an initial rapid pass
through the collection of studies to filter out studies that did not contain
tasks at the intersection of deep learning, satellite imagery and agriculture,
ultimately yielding 188 studies. The majority of these studies were for crop
segmentation and yield prediction, thus, the studies for these sections were
further filtered as follows:

  * •

2020 and earlier: study is included if it has at least x citations on Google
Scholar ($x=50$ for crop segmentation; $x=25$ for yield prediction)

  * •

2021 and later: all were included.

It is important to note that although this review is systematic, it is not
exhaustive, and not just because of the above filtering. By limiting the
review to studies indexed by Clarivate’s Web of Science, we are deliberately
selecting for higher profile works than if we included searches across all
published literature. Additionally, the search terms may not reveal all
possible relevant studies, however, we believe that they are sufficient to
return a representative sample of all relevant studies.

We chose not to include methods using UAV imagery because we were interested
in methods for resolving the tension between object size and pixel size in
satellite imagery. For crop segmentation studies (Section 6.1), we chose to
only include studies which used multiple agricultural classes. Soil monitoring
studies (Section 6.2) often only implied an agricultural significance, but,
since soil has such a strong influence on agriculture, and relatively few
studies, we chose to include soil monitoring studies which did not explicitly
have an agricultural motivation.

There was also some inconsistency in terminology in the reviewed studies. In
the interest of clarity, and to assist anyone unfamiliar with these terms, the
variations are summarised in Table 2.

Table 2: A collection of synonyms found in the literature. Words |  Idea
---|---
Radiative transfer; reflectance; backscatter |  The proportion of light which reflects off of a surface. This is a physical property of the surface, and can be measured in a laboratory.
Sub-pixel fractional estimation; Linear Unmixing Model; Linear Mixture Model |  A model of a pixel as being some proportion of just a few types of land cover, and thus every pixel’s colour is explainable as an (often linear) combination of these cover types (see Section 3)
Downscale; upsample; finer resolution |  Downscaling and upsampling can have the same meaning because there is a conflict in terminology between remote sensing scientists and computer scientists, with inverse meanings. In this review, we have used “coarser” or “finer” to avoid confusion.
Multitemporal images; time series; Satellite Image Time Series (SITS); temporal data |  Indicates the use of temporal data. Generally, stacked images of the same location over weeks/months (see Section 5)
Multi-layer perceptron; Artificial Neural Network (ANN); Deep Neural Network (DNN) |  Although ANN can technically refer to any Neural Network, it is typically used to refer to a small MLP. Generally, DNN refers to an MLP, but a DCNN refers to a CNN specifically.
Model inversion |  Training a statistical model to predict the inputs of a theoretical model from either ground-measured outputs, or outputs of the theoretical model itself. A good summary of the ways this is used is given in Weiss et al. (2020).
Object-based; field-based; parcel-based; superpixel |  Using aggregated colour information across a whole object or field or parcel or superpixel for prediction.

##  3 Satellite Images

Objects imaged by satellites are typically significantly smaller than the
ground spatial distance (GSD) covered by each pixel. For example, the colour
of each pixel in a satellite image of farmland might be aggregated from
hundreds, thousands or even millions of individual plants. This massive
difference in scale between object and pixel sizes has encouraged researchers
to focus on understanding the contents of individual pixels with per-pixel
algorithms (Blaschke and Strobl, 2001; Blaschke et al., 2014), rather than the
typical computer vision approaches which primarily use the structured pattern
of multiple spatially-related pixels to understand an image (Funck et al.,
2003; Wang et al., 2020a).

While the spatial resolution relative to the imaged objects is much worse for
satellite imagery, the spectral resolution is often significantly better.
Almost all satellite imagery have at least 4 colour channels (red, green, blue
and near-infrared), many have more than 10 colour channels (e.g. Sentinel-2),
and some have over 100 different colour channels (Song et al., 2018),
providing significantly more information per pixel than typical ground-based
sources. Additionally, satellite image sensors are calibrated to obtain
functions for converting from sensor brightness to reflectance - a physical
property of the imaged surface, independent of illumination and sensor - which
allows quantitative analysis of the Earth’s surface.

Theoretically, with a sufficiently precise sensor - and converting to
reflectance values - all surfaces could be uniquely identified by matching
each pixel to a spectral signature measured in a lab. Indeed, this ideal is
the basis of many hand-crafted models (e.g. Linear Mixture Model (Adams et
al., 1995)). However, such a precise sensor does not exist, and significant
noise is introduced by the lack of spatial and spectral resolutions,
atmospheric effects and other errors in calculating reflectance, leading to
the dominance of machine learning algorithms to learn the varied appearances
of surfaces from the data (Lu and Weng, 2007; Khatami et al., 2016).

There are currently many sources of freely available satellite imagery with
worldwide coverage to train these algorithms. In the reviewed studies, the
most common were:

  * •

Moderate Resolution Imaging Spectroradiometer
(MODIS) imagery at 250-1000m resolution which has been publicly available
since 2000, along with many model-based maps, such as land surface
temperatures, evapotranspiration and leaf area index (LAI).

  * •

Landsat imagery which has been freely available to the public since 2008 (Zhu
et al., 2019), of which most reviewed studies used Landsat-8 imagery at 30m
resolution.

  * •

Sentinel imagery from the Sentinel program of the European Space Agency which
has provided optical imagery at 10-60m resolution and Synthetic Aperture Radar
(SAR) imagery at 5-40m resolution since 2014.

The resolution of these data sources can dictate the resolution at which
analysis can be performed; for example, county-level yield prediction always
uses MODIS imagery and field-level yield prediction always uses
Landsat/Sentinel imagery. These are obvious pairings because MODIS pixels are
larger than individual fields, and images of entire counties using
Landsat/Sentinel imagery would require an unreasonable amount of disk space
and computation time. At the coarser resolutions, there was a strong
preference in the reviewed articles to pose the problem as just time series
analysis, rather than a spatio-temporal one. Further, in several works (Jin et
al., 2021; Shelestov et al., 2017; Ndikumana et al., 2018) and datasets like
LUCAS (d’Andrimont et al., 2021), the problem is posed as a single-pixel
problem, only providing labels for a set of sparsely distributed points.
Although this doesn’t preclude the use of CNNs (Ji et al., 2018), such a
dataset discourages it.

Spatial resolution in satellite imagery has increased over the years, such
that some commercial satellite providers now sell images with resolution as
fine as 34cm per pixel, a resolution sufficiently fine to detect individual
trees from satellite images (Gomez et al., 2010; Li et al., 2019; Ferreira et
al., 2021; Lin et al., 2021). This increased resolution has better aligned
satellite imagery analysis with typical computer vision, with algorithms
utilising both spatial information, as well as the higher spectral resolution
and reflectance calibration (e.g. Engen et al. 2021; Sagan et al. 2021;
Waldner et al. 2021). We note that although spatio-temporal input has the
richest information, it is not always available. For example, very-high
resolution commercial satellite imagery is expensive and sparsely collected,
thus most studies using commercial satellite imagery operated on a relatively
small number of individual images (e.g. Rahman et al. 2018; Choung and Jung
2021; Saralioglu and Gungor 2022).

##  4 Deep Learning

In many domains, machine learning has found accurate relationships in spite of
many variations in appearance and much noise. The data-driven nature of
machine learning techniques handles such variations and models arbitrarily
complex relationships while simultaneously including tools to prevent
overfitting to the noise. We found that in single-pixel problems, Random
Forests (RFs), Support Vector Machines (SVMs) and Multi-layer Perceptrons
(MLPs) were generally close competitors, with each method being more accurate
in different studies in roughly equal proportions (e.g. Feng et al. 2019; Ju
et al. 2021; Shelestov et al. 2017).

Compared to other machine learning methods, deep learning is known to be able
to construct significantly more complex models (Goodfellow et al., 2016),
allowing them to be more robust to noisy training data. Another major benefit
of deep learning models is that manual feature extraction is no longer
required because the model learns to create its own features. This comes at
the price of requiring larger datasets to observe this improved performance.
In all studies reviewed in all tasks except yield prediction, modern deep
learning methods outperformed traditional machine learning methods. In yield
prediction, 2DCNNs consistently outperformed traditional machine learning
methods, but LSTMs did not.

In the literature, various algorithms are called “deep learning”. In this
review, we refer to three main types of modern deep learning: CNNs, RNNs and
Attention. With a decade since AlexNet (Krizhevsky et al., 2012), and an
explosion of research, Convolutional Neural Networks (CNNs) are the current de
facto standard in generic computer vision tasks. Recurrent Neural Networks
(RNNs) are a common deep learning method for sequence modelling; almost all
cases of which use either a Long Short-term Memory (LSTM) or a Gated Recurrent
Unit (GRU) version of RNNs. “Attention” can mean many different things; here
we will use it to mean, specifically, multi-head attention as described by
Vaswani et al. (2017), as this is the basis for the recently popularised
Vision Transformers (Dosovitskiy et al., 2021) which have outperformed CNNs on
recent ImageNet competitions. In this review we use “deep learning” to mean
any neural network method, and “modern deep learning” to exclude MLP-only
algorithms. We will not discuss the technical details of these deep learning
algorithms in this review; instead we will mention the most significant modern
advances and refer the reader to existing explanations for more details
(Goodfellow et al., 2016; Brodrick et al., 2019; Kattenborn et al., 2021).

The ImageNet classification dataset (Deng et al., 2009) has had an enormous
influence on the trajectory of computer vision research. It is common - when
deep learning is applied to a new domain - for authors to use architectures
that achieved a high rank in the ImageNet competition. In particular, AlexNet
(Krizhevsky et al., 2012), VGG (Simonyan and Zisserman, 2015) and ResNet (He
et al., 2016) have received the most attention. Similarly for segmentation
models, models that performed well on the MS COCO and PASCALVOC datasets have
been adopted. The most popular segmentation architectures are based on UNet
(Ronneberger et al., 2015) and DeepLabv3 (Chen et al., 2017).

Two of the most significant innovations of modern deep learning are focused
around training deeper models: skip connections (He et al., 2016) and inter-
layer normalisation (e.g. BatchNorm (Ioffe and Szegedy, 2015), LayerNorm (Ba
et al., 2016), etc). These two ideas have been almost universally adopted by
all popular modern deep learning architectures, and with modern programming
libraries these are easily incorporated into custom architectures created by
individual studies (e.g. Benedetti et al. 2018; Gallo et al. 2021). Many works
reviewed also used Dropout (Hinton et al., 2012); another popular addition to
training deep learning models for training more robust models.

##  5 Common methods

###  5.1 Taxonomy

Table 3: Each model has a required input shape, and each data interpretation is intended to re-arrange the input to fit the model. Type X-f is any feature stack interpretation, Type X-s is any sequence interpretation, Type X-i is any image interpretation and Type X-c is any image cube interpretation (see Figure 2). |  Tree |  SVM |  MLP |  1DCNN |  2DCNN |  3DCNN |  RNN |  CNN + RNN |  Attention
---|---|---|---|---|---|---|---|---|---
Type X-f | ✓ | ✓ | ✓ |  |  |  |  |  |
Type X-s |  |  |  | ✓ |  |  | ✓ |  | ✓
Type X-i |  |  |  |  | ✓ |  |  |  | ✓
Type X-c |  |  |  |  |  | ✓ |  | ✓ | ✓
Figure 1: Depending on the images available, a problem can be posed as a
relationship between a single pixel input (blue cell) from a single image
(yellow grid of cells) and each prediction (red cell), or it can include
contextual pixels from spatial and/or temporal domains (blue grid of cells).
The bottom right situation is also called spatio-temporal (ST) data. Figure 2:
The data can be interpreted in different ways to allow the use of different
models. Each of these data shape/interpretations pairs is given a name like
Type X-x to denote the original type and it’s interpretation. The first column
of interpretations have no temporal component, while the second column does.
The top row treat the input as a 1D vector with no special ordering. The
second row treat the input as a 1D sequence. The third row treat the input as
an image. The fourth row treat the input as an image cube. The last row
aggregate non-square spatial information to produce fixed size features for
model input. Although spectral information is depicted here, several studies
replaced the spectral information with a set of other features: vegetation
indices, topographical, atmospheric, soil, etc.

Across the studies reviewed there were many models and methods in common. In
Figure 1, we show the different initial shapes of data that are used for
satellite imagery. The initial data shape can be one of: a single pixel (type
P-x), a temporal sequence of pixels (type T-x), a single image (type S-x) or a
temporal sequence of images (type ST-x). In Figure 2 we describe our taxonomy
of different interpretations of those data shapes which authors have used to
structure their data for use in modern deep learning algorithms. We name these
interpretations by their initial data shape as the first character and their
interpreted shape as the second character. The initial data shapes can then be
interpreted as one of: a vector of features (type X-f), a sequence of pixels
(type X-s), a single image (type X-i), an image cube (type X-c), or an object
(type X-o). For example, type T-s denotes an interpretation of a temporal
sequence of pixels (T) for models that take advantage of the fact that it is a
sequence (s).

Traditional machine learning methods such as Random Forests (RFs), Support
Vector Machines (SVMs) and Multi-layer Perceptrons (MLPs) generally require a
1D vector of features as input (types P-f and T-f), but modern deep learning
methods generally have a multi-dimensional / sequential interpretation of the
data (see Table 3). Several papers reframed the data to make specific modern
deep learning methods more obvious candidates. In particular, treating
spectral information as a sequence in order to apply sequence analysis
algorithms. For example, type P-f and type P-s have precisely the same data,
but by interpreting a single pixel (type P) as a sequence of colours (type
P-s), 1DCNNs and LSTMs become obvious model choices. Similarly, temporal data
(type T) can be interpreted as a spectral/temporal “image” (type T-i), and
spatial data (type S) can be interpreted as a spatial/spectral image cube
(type S-c) to use higher dimensional CNN kernels. Interpreting spectral
information as a sequence is rarely theoretically justified, but multiple
authors noted improved performance doing so (Kussul et al., 2017; Debella-Gilo
and Gjertsen, 2021; Saralioglu and Gungor, 2022).

Sometimes the interpretation comes about because of labels. In yield
prediction especially (see Section 6.5), ground labels are often annotated at
a field or county or country level (collectively, “objects”) which are shown
as types S-o and ST-o, depending on whether temporal data was included. In
such cases, the relationship between the colour in the pixels and the final
prediction are not necessarily present in each and every pixel contained
within the the object, so a common method is to aggregate the information
within an object before a model sees the data. Most commonly, the colour
information is averaged, however Khaki et al. (2021) showed good performance
by computing a colour histogram per county instead. These interpretations
remove the spatial component, so after aggregation, S-o is subsequently re-
interpreted as either P-f or P-s, and ST-o is subsequently re-interpreted as
one of T-f, T-s or T-i.

It is worth noting that a single 2D image is actually a three-dimensional
tensor of data: two spatial and one spectral. A 2DCNN operates on this 3D data
by treating the spectral information as the “channels” dimension. Similarly, a
temporal stack of 2D images is a four-dimensional tensor of data: two spatial,
one spectral and one temporal. A 3DCNN operates on a 4D “image cube”. Thus, in
the literature, an “image cube” may refer to the 3D data from a single 2D
image after adding a singleton “channels” dimension (type S-c), or it may
refer to the standard 4D data, using the spectral information as the
“channels” dimension (type ST-c).

The terminology used for this taxonomy can be used for combinations not shown
in Figure 2. For example, Chelali et al. (2021) and Paul et al. (2022) both
interpreted spatiotemporal as an image (type ST-i) without aggregation and in
completely different ways. This taxonomy does not exhaustively describe every
possible way to use data with modern deep learning algorithms, but describes
the main cases with a consistent terminology.

###  5.2 Clouds

Cloud cover and other atmospheric effects present significant challenges to
Earth Observation tasks using optical imagery by obscuring the targets of
interest. It is common to simply exclude single and multitemporal pixels that
are occluded by clouds (Kussul et al., 2017) when operating at a pixel level
(Type P or Type T), or interpolate the colour information from earlier and
later images (e.g. Inglada et al. 2015; Benedetti et al. 2018; Interdonato et
al. 2019) when using temporal data (Type T or ST). Another common strategy is
to create single image composites from images taken at different times (e.g.
Waldner and Diakogiannis 2019; Lu et al. 2022). Some authors remove entire
images from their training set if there is too much cloud cover (Moreno-Revelo
et al., 2021). Discarding entire samples reduces the number of examples
available for model training, and when using multitemporal data, discarding
either the whole multitemporal pixel or individual images in the sequence
degrades performance. Rawat et al. (2021) collected both Landsat-8 and
Sentinel-2 images, and when a Sentinel-2 image was too cloudy, replaced it
with a Landsat-8 image. And Metzger et al. (2022) show a method to explicitly
encode variable temporal sampling into a recurrent model’s architecture to
prevent performance degradation. Other authors argue that deep learning is
robust to occasional entire images being completely covered in cloud and use
the data without explicitly handling clouds (Engen et al., 2021; Turkoglu et
al., 2021).

The main benefit of using Synthetic Aperture Radar (SAR) imagery is that it
easily images the surface in spite of any atmospheric interference, so cloud
cover can be mitigated by using both optical and SAR imagery (e.g. Wang et al.
2020b). Few works took advantage of Sentinel-1 SAR imagery in addition to
their own, but those that did noted improved performance (Wang et al., 2020b;
Moumni and Lahrouni, 2021; Thorp and Drajat, 2021; Ofori-Ampofo et al., 2021).

###  5.3 Temporal mismatch

Ground-based in situ measurements are generally not coordinated with satellite
imagery. Instead the measurements are taken, and then paired with images at
the closest available time, leading to a temporal mismatch. In the reviewed
studies there are three methods for handling this temporal mismatch (roughly
in order of popularity): ignore it; interpolate ground labels to match image
collection date (Kira et al., 2017); or interpolate images to match
measurement date (Bocco et al., 2012). Ignoring the mismatch is popular
because it is a reasonable action to take if the property is not expected to
change significantly during the timescale of mismatch, and most properties of
interest do not change much over a few days to a week.

##  6 Tasks

The reviewed studies were manually categorised into one of 5 groups of tasks.
These categories were determined based on the papers reviewed, rather than
being imposed from the beginning and are roughly arranged in order of the
plant’s life cycle: first, we consider how deep learning is used to describe
where crops are being grown and the location of other farm equipment/landmarks
(Section 6.1). Next, we explore how deep learning is used to evaluate the
soil’s health (Section 6.2). We then consider methods for observing plants
after establishment: evaluating the plant’s growth (Section 6.3), monitoring
damage from disease or disaster (Section 6.4), and finally estimating crop
yield (Section 6.5).

###  6.1 Land Use and Land Cover

Table 4: Studies on crop segmentation (2022 only; see supplementary materials for other years). N is the number of classes that the study distinguished between (number of agricultural classes in brackets). VI stands for vegetative indices, and indicates whether VIs were used (possibly in combination with other features). GSD = Ground Spatial Distance/resolution. In the labels column, a $\dagger$ indicates that the labels are from a dataset (see Table 6). The best performing model type in each study is underlined. Where no model is underlined, no one model performed best. Where two model types are underlined, it means the model had components of both types. In the Tree column: RF = Random Forest, and a tick means any other kind of tree. In the CNN column: nD = nDCNN, including non-spatial CNNs. In the RNN column: L = LSTM, G = GRU, and nD = nD ConvRNN (ConvGRU or ConvLSTM). In the Attn column: T = Transformer, P = PSE-TAE (Garnot et al., 2020) and a tick indicates any other transformer-like attention. |  |  |  |  |  | Model |
---|---|---|---|---|---|---|---
Study |  N |  Images | VI |  GSD |  Labels |  Tree |  SVM |  MLP |  CNN |  RNN |  Attn |  Type
Asming et al. (2022) |  9 (2) |  Landsat-8, Sentinel-2 |  |  30m, 10m |  Image Survey |  |  | ✓ |  |  |  |  P-f
Desloires et al. (2022) |  2 (1) |  Sentinel-2 |  |  10m |  Pub. govt. |  |  |  |  |  G |  |  T-s
Fontanelli et al. (2022) |  10 (9) |  COSMO-SkyMed |  |  15m |  Field Survey |  |  |  |  1D 3D |  |  |  T-s
ST-c
Garnot et al. (2022) |  30 (27) |  Sentinel-1/2 |  |  10m |  Pub. govt. |  |  |  |  2D |  | P |  ST-c
ST-o
(T-s)
Jiang et al. (2022) |  10 (3) |  Sentinel-2 |  |  10m |  CDL |  RF ✓ | ✓ | ✓ |  |  |  |  P-f
Lei et al. (2022) |  2 (1) |  SPOT, Sentinel-1 | ✓ |  6m, 10m |  Pub. govt. |  | ✓ | ✓ |  |  |  |  ST-f
Li et al. (2022a) |  3 (2) |  RadarSat-2 | ✓ |  8m |  Field Survey |  |  |  |  1D |  |  |  P-f
Li et al. (2022c) |  15 (3) |  Jilin-1, GaoFen-2 |  |  1m, 4m |  Image Survey |  |  |  |  2D |  | ✓ |  S-i
Metzger et al. (2022) |  48 (41) |  Sentinel-2 |  |  10m |  ZeuriCrops$\dagger$ |  |  |  |  |  L G | T |  ST-s
Nyborg et al. (2022) |  11 (10) |  Sentinel-2 | ✓ |  10m |  Pub. govt. |  |  |  |  |  | P |  ST-o
(T-s)
Paul et al. (2022) |  6 (5) |  Sentinel-1 |  |  10m |  Field Survey |  ✓ | ✓ |  |  2D |  |  |  ?-f
ST-i
Pavlovic et al. (2022) |  10 (2) |  Sentinel-2 |  |  10m |  Image Survey |  |  |  |  2D |  |  |  S-i
Rauf et al. (2022) |  3 (2) |  Sentinel-2 | ✓ |  10m |  Field Survey, Model-based |  |  |  |  2D |  |  |  T-i
Rawat et al. (2022) |  2 (1) |  Sentinel-2 | ✓ |  10m |  Field Survey |  |  |  |  1D |  |  |  T-s
Saralioglu and Gungor (2022) |  7 (2) |  [Commercial] |  |  0.5m |  Image Survey |  RF | ✓ |  |  2D 3D |  |  |  P-f
S-i
S-c
Sharma (2022) |  101 (4) |  Sentinel-2 | ✓ |  10m |  Pub. govt. |  |  |  |  1D |  |  |  T-s
Sykas et al. (2022) |  11 (11) |  Sentinel-2 | ✓ |  10m |  Dataset: Sen4AgriNet |  |  |  |  1D 2D |  2D L | T |  ST-c
ST-o
(T-s)
Tang et al. (2022) |  13 (8) |  Sentinel-2 |  |  10m |  Breizhcrops$\dagger$ |  RF |  |  |  1D |  L | ✓ |  T-f
T-s
ST-c
Teimouri et al. (2022) |  7 (7) |  Sentinel-1/2 |  |  10m |  Pub. govt. |  | ✓ | ✓ |  2D 3D |  |  |  P-f
S-i
ST-c
Wang et al. (2022a) |  4 (3) |  Sentinel-2 |  |  10m |  Pub. govt. |  |  |  |  2D |  | T |  S-i
Wang et al. (2022b) |  10 (5) |  Sentinel-2 | ✓ |  10m |  Field Survey |  |  |  |  2D |  |  |  S-i
Wang et al. (2022d) |  5 (5) |  MODIS | ✓ |  1km |  Model-based |  RF | ✓ |  |  1D |  L |  |  T-f
T-s
Yang et al. (2022) |  4 (3) |  Sentinel-2 |  |  10m |  Model-based |  |  |  |  1D 3D |  |  |  T-s
ST-c
Table 5: Studies on specific land cover tasks other than segmentation. The best performing model in each study is underlined. See Table 4 for table key. |  |  |  |  |  | Model |
---|---|---|---|---|---|---|---
Study |  Task/N |  Images | VI |  GSD |  Labels |  Tree |  SVM |  MLP |  CNN |  RNN |  Attn |  Type
Field Boundary Detection
Persello et al. (2019) |  - |  WV-2/3 |  |  0.3m |  Image Survey |  |  |  |  2D |  |  | S-i
Waldner and Diakogiannis (2019) |  - |  Sentinel-2 |  |  10m |  Image Survey |  |  |  |  2D |  |  | S-i
Masoud et al. (2020) |  - |  Sentinel-2 |  |  10m |  Pub. govt. |  |  |  |  2D |  |  | S-i
Waldner et al. (2021) |  - |  Sentinel-2 |  |  10m |  Image Survey |  |  |  |  2D |  | ✓ | S-i
Jong et al. (2022) |  - |  MODIS, Landsat, Sentinel |  |  10m |  Pub. govt., Image Survey |  |  |  |  2D |  |  | S-i
Long et al. (2022) |  - |  GF-1/2 |  |  4m |  Image Survey |  |  |  |  2D |  |  | S-i
Sharifi et al. (2022) |  - |  Sentinel-2 |  |  10m |  Image Survey |  |  |  |  2D |  |  | S-i
Mei et al. (2022) |  - |  WV-3 |  |  0.5m |  Image Survey |  |  |  |  2D |  |  | S-i
Dam Detection
Carvajal et al. (2016) |  - |  Quickbird |  |  1m |  ??? |  |  | ✓ |  |  |  | P-f
Malerba et al. (2021) |  - |  [Maps] |  |  - |  Image Survey |  |  |  |  2D |  |  | S-i
Ma et al. (2022) |  - |  GF1, GF2 |  |  15m, 4m |  Image Survey |  |  |  |  2D |  |  | S-i
Malerba et al. (2022) |  - |  [Maps] |  |  - |  Image Survey |  |  |  |  2D |  |  | S-i
Center Pivot Irrigation Detection
Zhang et al. (2018) |  Classify |  Landsat-8 |  |  30m |  Image Survey |  |  |  |  2D |  |  | S-i
de Albuquerque et al. (2020) |  Segment |  Landsat-8 |  |  30m |  Pub. govt. |  |  |  |  2D |  |  | S-i
Saraiva et al. (2020) |  Segment |  PlanetScope |  |  3m |  Image Survey |  |  |  |  2D |  |  | S-i
Li et al. (2022b) |  Classify |  Landsat |  |  30m |  Image Survey |  |  |  |  2D |  |  | S-i
Irrigation Detection
Colligan et al. (2022) |  Segment |  Landsat |  |  30m |  Field/Image Survey |  |  |  |  2D |  |  | S-i
Greenhouse Detection
Li et al. (2020) |  Detection |  GF1, GF2 |  |  2m, 1m |  Image Survey |  |  |  |  2D |  |  | S-i
Zhang et al. (2021b) |  Segment Boundary |  GF2 |  |  0.8m |  Image Survey |  |  |  |  2D |  |  | S-i
Hedgerow Detection
Ahlswede et al. (2021) |  Segment |  IKONOS |  |  1m |  Field/Image Survey |  |  |  |  2D |  |  | S-i
Tree Crown Delineation
Gomez et al. (2010) |  Segment |  QuickBird |  |  0.6m |  ??? |  |  | ✓ |  |  |  | P-f
Li et al. (2019) |  Classify |  QuickBird |  |  0.6m |  Image Survey |  |  |  |  2D |  |  | S-i
Ferreira et al. (2021) |  Segment |  WV-3 |  |  0.3m |  Field/Image Survey |  |  |  |  2D |  |  | S-i
Lin et al. (2021) |  Classify |  WV-3, PlanetScope |  |  0.3m, 3m |  Image Survey |  |  |  |  2D |  L |  | S-i
Abozeid et al. (2022) |  Segment |  Satellites.pro |  |  ? |  Image Survey |  |  |  |  |  | T | S-i
Lu et al. (2022) |  Segment |  Sentinel | ✓ |  10m |  Field Survey |  |  | ✓ |  |  |  | P-f

The task of Land Use and Land Cover (LULC) is to either classify each pixel of
satellite/UAV imagery (segment), or detect specific regions/objects in the
images. LULC is by far the most common use of deep learning in satellite
imagery in the reviewed articles, and within LULC crop segmentation was the
most popular subtask (see Table 4 for 2022 studies and supplementary materials
for earlier studies). This popularity is not surprising, as land cover mapping
is considered the most important descriptor of the environment (Herold et al.,
2006). The power of LULC is widely recognised and governments across the world
publicly provide large-scale LULC maps (e.g. USDA 2022; d’Andrimont et al.
2021; ABARES 2021), created through a combination of visual inspection of
satellite imagery, census information, local expertise and automated methods
(e.g. linear unmixing model). Automated methods are the least reliable of
these, but are the easiest to scale up. Thus, the goal of research into LULC
and crop segmentation is to improve the quality of the automated methods so
that eventually they become at least as reliable as expert humans.

When creating data sources for developing LULC models it is impossible to both
manually annotate every pixel and then manually verify each field at ground
level. There is also inherent ambiguity of what is visible in these images,
so, even with the huge manual effort that goes into creating these data
sources, they are known to have significant error in them; e.g. the Cropland
Data Layer (CDL) (USDA, 2022) has an error rate of 5-15% for major crop types
(Boryan et al., 2011). Many European countries require that farmers register
what crops they grow - along with farm boundaries - when applying for
government rebates. Notably, France, Switzerland, Norway and others have
started to provide this data publicly in a quasi-anonymised form providing
huge data sources of labels to train machine learning models, and is the basis
of most public benchmark datasets for LULC. These initiatives create high
quality data sources with few errors, although it should be noted that farmers
may accidentally or deliberately misreport the crops they are growing (Hamer
et al., 2021), so even these are not error-free.

Table 6: Identified crop segmentation satellite benchmark datasets and data sources. Benchmark datasets (those with a tick in B) have a clearly defined size, and thus can be used to compare different methods. Only the most popular public government (non-benchmark) sources are included here. Name | Labels |  Location |  Years |  Size |  Source | B
---|---|---|---|---|---|---
LUCAS (d’Andrimont et al., 2021) | Point Data |  Europe |  2006-2018 |  63,000 points; 11.8MB |  In Situ / Visual | ✓
Munich dataset (Rußwurm and Körner, 2018) | Shapefile |  Munich |  2016-2017 |  137,000 fields; 41.5GB |  Govt. | ✓
BreizhCrops (Rußwurm et al., 2020) | Superpixels |  Brittany |  2017 |  750,000 superpixel sequences; 4.6GB |  Govt. | ✓
ZueriCrop (Turkoglu et al., 2021) | Shapefile |  Switzerland |  2019 |  116,000 fields; 38.5GB |  Govt. | ✓
Reunion Island (Dupuy and Gaetano, 2020) | Shapefile |  Reunion Island |  2019 |  50,000 fields; 290MB |  Model | ✓
Campo Verde (Feitosa, 2017) | Shapefile |  Brazil |  2015-2016 |  513 fields; 6.7GB |  Visual | ✓
UOS2 (Pedrayes et al., 2021) | Segmentation |  Spain |  2020 |  1958x 256x256 tiles; 5.5GB |  Visual | ✓
PASTIS-R (Garnot et al., 2022) | Segmentation |  France |  2019 |  2433x 128x128 tiles; 54GB |  Govt. | ✓
Sen4AgriNet (Sykas et al., 2022) | Segmentation |  France,
Catalonia |  2019-2020 |  250,000x S2 images; 10TB |  Govt. | ✓
French Land Parcel Identification System | Shapefile |  France |  - |  - |  - |
Cropland Data Layer (USDA, 2022) | Segmentation |  US |  - |  - |  - |
Corine Land Cover | Segmentation |  Europe |  - |  - |  - |

This review makes a distinction between a _data source_ and a _benchmark
dataset_. The former is a living collection of data that is frequently updated
and covers wide areas, whereas a benchmark dataset is a fixed set of images
and labels on which different methods can be compared directly across studies.
LULC was the only task to have any benchmark dataset or named data sources.
See Table 6 for the list of data sources and benchmark datasets used by the
reviewed studies.

Despite the availability of this data, only 22 out of the 73 reviewed crop
segmentation studies used continually updated government data sources, and 9
used benchmark datasets (mostly, their own). Over half (46) described methods
for obtaining their own segmentation maps. There are three methods studies
used to do this: manual field survey (26), manual image survey (17), and
automated methods (8). With the exception of crowdsourcing (e.g. Thorp and
Drajat 2021), manual field surveys are the more difficult, expensive and
reliable way to obtain ground data for training models. Using an automated
method to obtain training data for models is cheaper and less reliable, and
largely limits deep learning models to matching the automated method’s
dynamics, rather than allowing it to learn the true distribution, so a few
works used a combination of each. For example, Rahimi-Ajdadi and Khani (2021)
gathered 55 data points from field surveys, 360 from visual inspection and 550
from automated methods. And Zhou et al. (2019) and Hamer et al. (2021) used
automated systems to initially label their satellite images, before doing a
manual pass to fix the errors. Several works relied entirely on manual image
surveys (e.g. Ji et al. 2018; Ndikumana et al. 2018), which is less reliable
than ground surveys, however only Saralioglu and Gungor (2022) described a
validation method for their manual image survey.

####  6.1.1 Trends and recommendations

The most common data sources for the reviewed LULC studies were the
Sentinel-1/Sentinel-2/Landsat-8 satellites. The Sentinel-1 satellites provides
SAR imagery (active microwave imaging), and the Sentinel-2 and Landsat-8
satellites provides multispectral optical imagery (passive imaging).
Conceptually, these different modalities contain complementary information,
and 9 studies used both data sources. Of these, 6 studies evaluated the
benefit of using both image sources, and consistently found that it improved
performance compared to using only one source (Wang et al., 2020b; Moumni and
Lahrouni, 2021; Thorp and Drajat, 2021). Specifically, while Ofori-Ampofo et
al. (2021) found that late fusion (averaging two model’s outputs) was the most
effective, Garnot et al. (2022) compared fusion types in many different
scenarios and concluded with recommendations for each scenario.

Out of all of the tasks reviewed in this paper, the deep learning methods for
LULC were the most varied and novel. There seems to be two main reasons for
this: first is the popularity of the task, and second is the relative ease of
obtaining data, allowing datasets large enough to train modern deep learning
methods. Most works reviewed were using at least 2000 data points, and many
were in the 10,000s, which - while small by deep learning standards - are
significantly larger than datasets used for the other reviewed satellite
tasks. All major modern deep learning algorithms are represented in the
reviewed papers: 1DCNNs (19 studies), 2DCNNs (27), 3DCNNs (8), LSTMs (22),
GRUs (9), ConvLSTM/ConvGRU (7) and transformers (8)/other attention (6), with
many studies comparing between these methods. In the reviewed articles, the
best modern deep learning methods (CNN/RNN/Attn) outperformed all tree (22
comparisons) (including RF (21 comparisons)), SVM (12 comparisons) and MLP (6
comparisons) methods in all cases. Anecdotally, it was observed that in many
studies there were several deep learning algorithms tested, and almost all of
these outperformed trees, SVMs and MLPs as well.

The first use of modern deep learning techniques in the reviewed studies was
Kussul et al. (2017), who used their own custom architectures with five
convolution layers using a dataset of 100,000s of labelled pixels from field
surveys and found that 2DCNNs outperformed RF, MLP and 1DCNNs. From this time
onwards, the majority of the papers reviewed used modern deep learning
(excluding purely MLP methods): there were 28 deep learning studies reviewed
from 2021 and 21 of those used modern deep learning methods.

Most studies in crop segmentation utilising 2DCNNs created their own
architectures / arrangement of layers, typically favouring shallower networks
(<10 layers), with only a few using common architectures used in generic
computer vision like VGG, ResNet and UNet (e.g. Ji et al. 2018; Sagan et al.
2021; Waldner et al. 2021). However, using those existing architectures was
extremely common in all other LULC tasks, with all but two studies (Persello
et al., 2019; Masoud et al., 2020) using an existing architecture, and several
using pretrained weights from ImageNet (e.g. Li et al. 2020; Ferreira et al.
2021). There were two main algorithms used to process spatiotemporal data
(type ST) the first is to use a 3DCNN (e.g. Ji et al. 2018; Wang et al. 2020b;
Gallo et al. 2021), and the second is to use 2DCNNs on the images and then use
RNNs (GRUs/LSTMs) on the outputs (e.g. Rußwurm and Koerner 2020; Li et al.
2021; Teimouri et al. 2019), however these methods were not compared against
each other.

In generic computer vision, transformers (Dosovitskiy et al., 2021) are state-
of-the-art on some major benchmarks, so one might wonder about their
performance on satellite tasks. There were 8 studies that directly referenced
transformer networks, and a further 6 that used a similar multi-head attention
mechanism as is used in transformers. In all works that compared a transformer
network to other modern deep learning methods, the transformer did not perform
better (Rußwurm and Koerner, 2020; Xu et al., 2020; Turkoglu et al., 2021;
Metzger et al., 2022; Tang et al., 2022; Sykas et al., 2022). The main other
attention method used is a model called Pixel-Set Encoder and Temporal
Attention Encoder (PSE-TAE) (Garnot et al., 2020). This was only compared
against other methods in a single study, and it was out-performed by a 1DCNN
with a Squeeze and Excitation module (Tang et al., 2022). It has been noted
that typical transformer networks require even more training data than CNNs
(Dosovitskiy et al., 2021), stating that even ImageNet (1 million labelled
images) is too small, so it is possible that the datasets used in these
satellite studies are simply not large enough for transformer networks to
begin to outperform CNNs.

LSTMs and GRUs are drop-in replacements for one another when using RNNs, so it
is possible to compare these directly. There were 4 studies that found GRUs to
perform better than LSTMs, and 2 studies that found the inverse.

###  6.2 Soil health

Table 7: Studies on soil health; mostly soil moisture and soil salinity studies. VI stands for vegetative indices, and indicates whether VIs were used (possibly in combination with other features). GSD = Ground Spatial Distance/resolution. The best performing model type in each study is underlined. In the Tree column: RF = Random Forest, and a tick means any other kind of tree. In the CNN column: 2D = 2DCNN. |  |  |  |  |  | Model |
---|---|---|---|---|---|---|---
Study |  Task |  Images | VI |  GSD |  Labels |  Tree |  SVM |  MLP |  CNN |  RNN |  Type
Soil Moisture |
Del Frate et al. (2004) |  SM |  ERS-2 |  |  30m |  Model-based (train), Field survey (eval) |  |  | ✓ |  |  |  P-f
Santi et al. (2013) |  SM |  ASAR |  |  30m |  Model-based (train), Field survey (eval) |  |  | ✓ |  |  |  P-f
Baghdadi et al. (2016) |  SM |  RADARSAT-2, Landsat-7/8 |  |  12m, 30m |  Field Survey |  |  | ✓ |  |  |  S-o
(P-f)
Kolassa et al. (2018) |  SM |  [Many] |  |  3km, 16km, 36km |  Model-based (train), Field survey (eval) |  |  | ✓ |  |  |  P-f
van der Schalie et al. (2018) |  Agreement |  AMSR-E, MIRAS, ASCAT |  |  25km, 35km, 25km |  Self-labelled |  |  | ✓ |  |  |  P-f
Eroglu et al. (2019) |  SM |  CYGNSS | ✓ |  9km |  Government |  |  | ✓ |  |  |  P-f
Kumar et al. (2019) |  SM |  Sentinel-1 |  |  20m |  Field Survey |  RF | ✓ | ✓ |  |  |  P-f
Zhang et al. (2020b) |  SM |  Landsat, Terra-SAR | ✓ |  30m, 3m |  Model-based (train), Field survey (eval) |  |  | ✓ |  |  |  P-f
Rabiei et al. (2021) |  SM |  Sentinel-1, Sentinel-2 |  |  10m |  Field Survey |  |  | ✓ | 2D |  |  P-f
S-i
Senanayake et al. (2021) |  SM |  MODIS | ✓ |  1km |  Field Survey |  ✓ |  | ✓ |  |  |  P-f
Ghasemloo et al. (2022) |  SM |  Landsat, Sentinel-1 | ✓ |  30m, 10m |  Field Survey |  |  | ✓ |  |  |  P-f
Tripathi et al. (2022) |  SM, salinity, etc. |  Sentinel-1/2 | ✓ |  10m |  Field Survey |  RF ✓ | ✓ | ✓ |  |  |  P-f
Xu et al. (2022) |  SM |  SMAP, MODIS | ✓ |  36km, 1km |  Model-based (train), Government (eval) |  |  | ✓ |  |  |  P-f
Zeynoddin and Bonakdari (2022) |  SM |  SMAP |  |  36km |  Self-labelled |  |  |  |  | L |  T-s
Soil Nutrients/Minerals |
Gautam et al. (2011) |  Nitrate |  Landsat |  |  30m |  Field Survey |  |  | ✓ |  |  |  S-o
(P-f)
Song et al. (2018) |  Nitrogen |  HJ-1 |  |  100m |  Field Survey |  RF | ✓ | ✓ |  |  |  ?-f
Zhang et al. (2020a) |  Potassium |  Landsat |  |  30m |  Field Survey |  |  | ✓ |  |  |  P-f
Peng et al. (2022) |  Fertility |  Sentinel-2 | ✓ |  10m |  Field Survey |  ✓ |  | ✓ |  |  |  P-f
Wang et al. (2022c) |  Pb, Cd |  Sentinel-2 | ✓ |  10m |  Field Survey |  RF |  | ✓ |  |  |  P-f
Zhang et al. (2022) |  Zn, Ni, Cu |  GaoFen-5 |  |  30m |  Field Survey |  RF | ✓ | ✓ |  |  |  P-f
Soil Salinity |
Wang et al. (2018) |  Regress |  Landsat, HJ-1 | ✓ |  30m, 100m |  Field Survey |  |  | ✓ |  |  |  P-f
Qi et al. (2020) |  Regress |  Sentinel-2 | ✓ |  10m |  Field Survey / Model-based |  RF | ✓ | ✓ |  |  |  P-f
Habibi et al. (2021) |  Regress |  Landsat |  |  30m |  Field Survey |  |  | ✓ |  |  |  P-f
Akca and Gungor (2022) |  Classify |  RapidEye | ✓ |  6.5m |  Field Survey |  | ✓ |  | 2D |  |  S-i

Soil health has an impact on all surface vegetation, however it can be
difficult to measure from space. We are - as always - limited to measuring
properties that cause a visible change within the spectra used to image the
surface. There were not many studies using deep learning to measure soil
properties using satellite images. Those that did can be put into three
groups: measuring soil moisture, soil nutrients or soil salinity. Generally,
studies focused on measuring several soil samples at a site, but some studies
used more permanent soil sensor stations that take ongoing measurements. In
this section we include several papers which were not specifically on
agricultural fields, since the distinction between “agricultural” and not
isn’t very clear.

Mapping soil moisture is useful for understanding hydrologic processes,
vegetation states, and climatic conditions (Entekhabi et al., 1996). Active
microwave imaging is often used to predict soil moisture because the
dielectric properties of soil changes with moisture for microwaves (Dobson et
al., 1985). There are several satellite-based worldwide spatially and
temporally continuous active microwave imaging satellites used to estimate
soil moisture from space (Njoku et al., 2003; Entekhabi et al., 2010; Kerr et
al., 2012; Wagner et al., 2013). However Rabiei et al. (2021) provide a
summary of different benefits and drawbacks to using different remote sensing
techniques. They point out that much existing work utilising the active
microwave satellites and distributed networks of soil monitoring stations are
at a much coarser resolution than is useful for a single field. In their work,
they used relatively fine resolution optical imagery (10m) with CNNs, which
they found to be superior to other machine learning methods.

There were six studies which attempted to measure soil nutrients using deep
learning on satellite imagery, with all of them using MLPs, but only three of
them compared methods. Wang et al. (2022c) and Zhang et al. (2022) found that
RFs worked better than MLPs and Song et al. (2018) found that an MLP worked
better than an RF or SVM. None of these studies used CNNs or other modern deep
learning architectures.

High soil salinity is a growing problem worldwide, and there is growing
interest in mapping this environmental hazard using satellite imagery
(Metternicht and Zinck, 2003). Despite the dire impacts of high soil salinity
and the relatively high interest in the topic, there were only 4 studies that
investigated predicting soil salinity from satellite imagery using deep
learning. Only one used a modern deep learning approach (Akca and Gungor,
2022), and they found that it performed significantly better than SVM models
using over 500 data points. The three other studies (Wang et al., 2018; Qi et
al., 2020; Habibi et al., 2021) only used MLPs and were small in scope, with
less than 100 data points each.

###  6.3 Plant physiology

Table 8: Studies on plant physiology; mostly canopy cover/LAI estimates. VI stands for vegetative indices, and indicates whether VIs were used (possibly in combination with other features). GSD = Ground Spatial Distance/resolution. The best performing model type in each study is underlined. LAI = Leaf Area Index, LCC = Leaf Chlorophyl Content, LWC = Leaf Water Content, GPP = Gross Primary Production, SIF = Solar-Induced Fluorescence. In the RNN column: L = LSTM, G = GRU, and 2D = 2D ConvRNN (ConvGRU or ConvLSTM). |  |  |  |  |  | Model |
---|---|---|---|---|---|---|---
Study |  Task |  Images | VI |  GSD |  Labels |  Tree |  SVM |  MLP |  CNN |  RNN |  Type
Canopy Cover/LAI
Del Frate et al. (2004) |  Regress LAI |  ERS-2 |  |  30m |  Model-based (train), Field survey (eval) |  |  | ✓ |  |  |  P-f
Gascon et al. (2007) |  Regress LAI |  POLDER |  |  60m |  Model-based (train), Field survey (eval) |  |  | ✓ |  |  |  P-f
Bsaibes et al. (2009) |  Regress LAI |  FORMOSAT-2 |  |  8m |  Field Survey |  |  | ✓ |  |  |  S-o
(P-f)
Richter et al. (2009) |  Regress LAI |  CASI |  |  10m |  Model-based (train), Field survey (eval) |  |  | ✓ |  |  |  P-f
Verger et al. (2011) |  Regress LAI / Canopy Cover |  PROBA-1 |  |  34m |  Model-based (train), Field survey (eval) |  |  | ✓ |  |  |  P-f
Bocco et al. (2012) |  Regress Canopy Cover |  MODIS, Landsat | ✓ |  500m, 30m |  Field Survey |  |  | ✓ |  |  |  S-o
(P-f)
Wu et al. (2015) |  Regress LAI |  HJ-1 | ✓ |  30m |  Field Survey |  |  | ✓ |  |  |  P-f
Baghdadi et al. (2016) |  Regress LAI |  RADARSAT-2, Landsat-7/8 |  |  12m, 30m |  Field Survey |  |  | ✓ |  |  |  S-o
(P-f)
Kira et al. (2017) |  Regress LAI |  MODIS, Landsat, MERIS |  |  500m, 30m, 250m |  Field Survey |  | ✓ | ✓ |  |  |  S-o
(P-f)
Delloye et al. (2018) |  Regress LAI |  Sentinel-2 |  |  10m |  Model-based (train), Field survey (eval) |  |  | ✓ |  |  |  P-f
Sun et al. (2021) |  Regress LAI |  MODIS |  |  500m |  Model-based (train), Field survey (eval) |  |  | ✓ |  |  |  P-f
Tomicek et al. (2021) |  Regress LAI, LCC, LWC |  Sentinel-2 |  |  10m |  Model-based (train), Field survey (eval) |  |  | ✓ |  |  |  P-f
Elmetwalli et al. (2022) |  Regress LAI, height, AGB, SPAD |  QuickBird | ✓ |  0.6m |  Field Survey | RF |  | ✓ |  |  |  P-f
Igder et al. (2022) |  Regress LAI |  Sentinel-2 |  |  10m |  Model-based |  |  | ✓ |  |  |  P-f
Other
Wagle et al. (2016) |  Regress Light / Water Use Efficiency |  MODIS | ✓ |  500m |  Government |  |  | ✓ |  |  |  T-f
Wolanin et al. (2019) |  Regress GPP |  Sentinel-2 | ✓ |  10m |  Model-based (train), Field survey (eval) |  |  | ✓ |  |  |  P-f
Kira and Sun (2020) |  Regress SIF |  MODIS |  |  500m |  Government |  |  | ✓ |  |  |  P-f
Thorp and Drajat (2021) |  Classify Growth Stage |  Sentinel-1, Sentinel-2 | ✓ |  10m |  Government | RF | ✓ | ✓ | 2D |  L G 2D |  P-f
S-i
T-s
ST-c
Zhao et al. (2022) |  Classify Growth Stage |  Sentinel-1/2 | ✓ |  10m |  Field survey, Model-based |  |  |  | 2D |  |  S-i

Leaf Area Index (LAI) and Canopy Cover have a history of being a proxy for
overall plant growth (Wu et al., 2015), and are the most commonly predicted
plant physiology measurements from satellite imagery using deep learning (See
Table 8). Canopy cover is simply a measurement of the proportion of light
being intercepted by the plant, and thus is fairly straight forward to measure
with remote sensing. LAI is half of the green leaf area per unit ground
surface area (Chen and Black, 1992), which has the capacity to measure the
extent of vertical/overlapping canopy cover. At ground level, LAI is typically
measured with a device such as the LAI-2000. This device measures the canopy
cover at multiple angles, and uses the assumption of uniformly randomly
distributed leaves to calculate LAI. Canopy cover and LAI are highly
correlated metrics (Nielsen et al., 2012), so these are considered together
for this review.

All but three of the plant physiology studies reviewed (16/19) used only MLPs
as their primary model, and didn’t compare against other machine learning
methods. The only study that made a comparison or used any modern deep
learning was Thorp and Drajat (2021), who found that LSTMs outperformed MLPs.

####  6.3.1 Radiative Transfer models

A radiative transfer model (RTM) is a method to estimate the reflectance from
physical properties of the object being imaged. The PROSAIL model (Jacquemoud
et al., 2009) was the most used RTM in these studies, and creates a model of
the plants and the soil beneath them. It takes as input: the chlorophyll
content, the water/dry matter content, the Leaf Area Index, average leaf
inclination, ratio of diffuse to direct incident radiation, soil brightness,
solar zenith angle, sensor zenith angle and relative azimuth angle. Since the
physiological properties are the input to the PROSAIL model, the process of
determining one of the physiological properties (e.g. LAI) from the
reflectance is called “model inversion” in these contexts.

Almost half of the studies (8/19) collected ground-level physical properties
and used an RTM in forward mode to generate training pairs of LAI/reflectance
for their MLPs, and then tested them on real data (noted as "Model-Based
(train), Field Survey (eval)" in Table 8). However, Tomicek et al. (2021)
found that the PROSAIL RTM predicted reflectances only correlated with
Sentinel-2 images between $R^{2}>0.6$ for red, green and blue bands and up to
$R^{2}>0.9$ for red-edge and NIR bands, which implies fundamental limits on
the accuracy of LAI estimation using this method.

No studies compared PROSAIL in reverse mode to a deep learning model trained
only on field survey data.

###  6.4 Crop Damage

Table 9: Studies on crop damage. VI stands for vegetative indices, and indicates whether VIs were used (possibly in combination with other features). GSD = Ground Spatial Distance/resolution. In the Tree column: RF = Random Forest, and a tick means any other kind of tree. In the RNN column: G = GRU. The best performing model type in each study is underlined. |  |  |  |  |  | Model |
---|---|---|---|---|---|---|---
Study |  Task |  Images | VI |  GSD |  Labels |  Tree |  SVM |  MLP |  CNN |  RNN |  Type
Disease
Yuan et al. (2014) |  Segment powdery mildew |  Spot-6 | ✓ |  1.5m |  Field Survey |  |  | ✓ |  |  |  S-o
(P-f)
Ma et al. (2019) |  Segment powdery mildew and aphids |  Landsat | ✓ |  30m |  Field Survey |  | ✓ | ✓ |  |  |  P-f
Bi et al. (2020) |  Segment SDS in soybeans |  PlanetScope |  |  3m |  Field Survey | ✓ |  | ✓ |  | G |  T-f
T-s
Pignatti et al. (2021) |  Segment P. syringae and Yellow Rust |  Sentinel-2, RapidEye-1, PRISMA | ✓ |  10m, 6.5m, 30m |  Field Survey |  |  | ✓ |  |  |  S-o
(P-f)
Ruan et al. (2021) |  Segment wheat stripe rust |  Sentinel-2 | ✓ |  10m |  Field Survey |  | ✓ | ✓ |  |  |  P-f
Guo et al. (2022) |  Segment Yellow Leaf Disease |  PlanetScope | ✓ |  3.1m |  Field Survey | RF |  | ✓ |  |  |  P-f
Generic Damage
Muradyan et al. (2020) |  Regress heavy metal conc. |  SPOT-7 |  |  1.5m |  Field Survey |  |  | ✓ |  |  |  S-o
(P-f)
Rodriguez et al. (2021) |  Regress coconut tree density |  Sentinel-2 |  |  10m |  Image Survey |  |  |  | 2D |  |  S-i
Virnodkar et al. (2021) |  Segment water stress |  Sentinel-2 |  |  10m |  Field Survey |  |  |  | 2D |  |  S-i
Boroughani et al. (2022) |  Segment dusty day |  MODIS |  |  250m |  Field/Image Survey | ✓ |  | ✓ |  |  |  P-f

There are few works using deep learning to detect crop damage using satellite
data (see Table 9). Although pests and diseases are features on the scale of
the plants, when they become wide-spread enough, they become visible via
remote sensing (Ye et al., 2020).

All but one of the found studies posed the damage caused by disease and
natural disasters as a classification/segmentation problem. In these cases,
the models were segmenting between "damage" and "no damage". The exception was
Rodriguez et al. (2021), who used a modified ResNet-18 (He et al., 2016) CNN
model to detect damage to Coconut crops in the Philippines by comparing tree
density estimates before and after Typhoon Goni, making it the only study to
measure the extent of the damage per unit area.

The threshold for considering an area to be infected with a disease varied
significantly between studies, with Yuan et al. (2014) requiring 80% of the
plants to have visible pustules on the top of the canopy to be considered
"infected" with powdery mildew, and Ma et al. (2019) requiring more than 10%
of the leafs to be infected to be considered "infected". Obviously a more
extensive spread of a disease makes the disease more visible, and hence,
predictable. For example, Pignatti et al. (2021) explicitly tested their MLP
model at different times after initial infection and noted significantly
improved accuracy for later predictions.

These studies were all straight-forward applications of well-known deep
learning algorithms (mostly MLPs on single-pixel data) mostly using field
survey data. All of the studies used medium resolution images between 1.5m and
30m resolution to match the resolution of the fields in the field surveys.

###  6.5 Yield

Table 10: Studies on county-level yield (2021 onwards; see supplementary materials for full table). VI stands for vegetative indices, and indicates whether VIs were used (possibly in combination with other features). GSD = Ground Spatial Distance/resolution. “Clim.?” indicates whether additional climate data was used in the prediction. The best performing model type in each study is underlined. Where two model types are underlined, it means the model had components of both types. In the Tree column: RF = Random Forest, and a tick means any other kind of tree. In the CNN column: nD = nDCNN, including non-spatial CNNs. In the RNN column: L = LSTM |  |  |  |  |  |  | Model |
---|---|---|---|---|---|---|---|---
Study |  Crop |  Images |  VI |  GSD |  Clim.? |  Labels |  Tree |  SVM |  MLP |  CNN |  RNN |  Type
County-level yield
Cao et al. (2021) |  Wheat |  MODIS |  ✓ |  1km |  ✓ |  Pub. govt. |  RF |  |  ✓ |  1D |  L |  ST-o
(T-f
T-s)
Feng et al. (2021) |  Wheat |  MODIS |  ✓ |  500m |  ✓ |  Pub. govt. |  |  |  ✓ |  |  |  ST-o
(T-f)
Ju et al. (2021) |  Maize, Soy, Rice |  MODIS |  ✓ |  250m, 1km |  ✓ |  Pub. govt. |  RF |  ✓ |  ✓ |  2D |  L |  ST-o
(T-f
T-s)
Khaki et al. (2021) |  Maize, Soy |  MODIS |  |  1km |  |  Pub. govt. |  RF |  |  ✓ |  2D 3D |  |  ST-o
(T-f
T-i
T-c)
Ma et al. (2021) |  Maize |  MODIS |  ✓ |  500m |  ✓ |  Pub. govt. |  |  |  ✓ |  |  |  ST-o
(T-f)
Tian et al. (2021) |  Wheat |  MODIS |  ✓ |  1km |  ✓ |  Pub. govt., Field Survey |  |  |  |  |  L |  ST-o
(T-s)
Qiao et al. (2021) |  Wheat |  MODIS |  |  500m |  |  Pub. govt. |  |  |  |  3D |  |  ST-o
(T-c)
Xie and Huang (2021) |  Wheat |  MODIS |  ✓ |  250m |  ✓ |  Model-based, Pub. govt. |  RF |  |  |  1D |  L |  ST-o
(T-f
T-s)
Ji et al. (2022) |  Maize |  MODIS |  ✓ |  1km |  ✓ |  Pub. govt. |  RF |  |  |  |  L |  ST-o
(T-f
T-s)
Liu et al. (2022) |  Wheat |  MODIS |  ✓ |  1km |  ✓ |  Pub. govt. |  RF ✓ |  ✓ |  |  |  L |  ST-o
(T-f
T-s)
Luo et al. (2022) |  Wheat |  AVHRR, MODIS |  ✓ |  1km |  ✓ |  Pub. govt. |  RF ✓ |  |  |  |  L |  ST-o
(T-f
T-s)
Watson-Hernandez et al. (2022) |  Palm Oil |  Landsat-5/6/7/8 |  ✓ |  15m, 30m |  ✓ |  Business |  RF ✓ |  |  ✓ |  |  |  ST-o
(T-f)
Xie (2022) |  Wheat |  Sentinel-2 |  ✓ |  10m |  ✓ |  Model-based, Pub. govt. |  |  |  |  |  L |  ST-o
(T-s)
Table 11: Studies on yield at smaller than county-level. VI stands for vegetative indices, and indicates whether VIs were used (possibly in combination with other features). GSD = Ground Spatial Distance/resolution. “Clim.?” indicates whether additional climate data was used in the prediction. The best performing model type in each study is underlined. Where two model types are underlined, it means the model had components of both types. In the Tree column: RF = Random Forest. In the CNN column: nD = nDCNN, including non-spatial CNNs. In the RNN column: L = LSTM, G = GRU |  |  |  |  |  |  | Model |
---|---|---|---|---|---|---|---|---
Study |  Crop |  Images |  VI |  GSD |  Clim.? |  Labels |  Tree |  SVM |  MLP |  CNN |  RNN |  Type
Field-level yield
Khan et al. (2020) |  Mentha |  Landsat |  ✓ |  30m |  |  Field Survey |  |  |  ✓ |  |  |  S-o
(P-f)
Arab et al. (2021) |  Grapes |  Landsat |  ✓ |  30m |  |  Field Survey |  |  |  ✓ |  |  |  S-o
(P-f)
Evans and Shen (2021) |  Wheat |  Landsat |  ✓ |  30m |  ✓ |  Field Survey |  RF |  ✓ |  ✓ |  |  |  ST-o
(P-f)
Gbodjo et al. (2021) |  Millet |  Sentinel, Planetscope |  ✓ |  10m, 3.1m |  |  Field Survey |  RF |  |  ✓ |  2D |  L |  ST-o
(T-f
T-s
?-i)
Zhang et al. (2021a) |  Maize |  Landsat |  ✓ |  30m |  ✓ |  Priv. govt. |  ✓ |  |  |  |  L |  ST-o
(T-f
T-s)
Abebe et al. (2022) |  Sugarcane |  Sentinel-2, Landsat |  ✓ |  30m |  |  Field Survey |  |  ✓ |  ✓ |  |  |  ST-o
(P-f)
Krupavathi et al. (2022) |  Sugarcane |  Landsat |  ✓ |  30m |  |  Field Survey |  |  |  ✓ |  |  |  ST-o
(P-f)
Farm-level yield
Engen et al. (2021) |  Any |  Sentinel-2 |  ✓ |  10m |  ✓ |  Pub. govt. |  |  |  ✓ |  2D |  L G |  ST-c
Plot-level yield
Haghverdi et al. (2018) |  Cotton |  Landsat |  ✓ |  30m |  ✓ |  Field Survey |  |  |  ✓ |  |  |  T-f
Sagan et al. (2021) |  Maize, Soy |  WorldView-3, PlanetScope |  |  0.3m, 3.1m |  |  Field Survey |  RF |  ✓ |  ✓ |  2D 3D |  |  ST-o
(P-f)
ST-c
Tree-level yield
Rahman et al. (2018) |  Mango |  Worldview-3 |  ✓ |  0.3m |  |  Field Survey |  |  |  ✓ |  |  |  S-i
Pixel-level yield
Jeong et al. (2022) |  Rice |  MODIS |  ✓ |  1km |  ✓ |  Model-based |  |  |  |  1D |  L |  T-s
Tripathi et al. (2022) |  Wheat |  Sentinel-1/2 |  ✓ |  10m |  |  Priv. govt. |  |  |  ✓ |  |  |  P-f
Grassland Biomass
Li et al. (2016) |  Grass |  Landsat |  |  30m |  |  Field Survey |  |  |  ✓ |  |  |  P-f
Ali et al. (2017) |  Grass |  MODIS |  ✓ |  500m |  |  Field Survey |  |  |  ✓ |  |  |  P-f
Barnetson et al. (2021) |  Grass |  PlanetScope |  ✓ |  3m |  |  Field Survey |  |  |  ✓ |  |  |  T-f
Nickmilder et al. (2021) |  Grass |  Sentinel-1/2 |  |  |  ✓ |  Field Survey |  RF |  ✓ |  ✓ |  |  |  T-f
County-level cumulative NDVI
Zambrano et al. (2018) |  All |  MODIS |  ✓ |  500m, 18km |  ✓ |  Images |  |  |  ✓ |  |  |  P-f

The task of yield prediction is to predict the average yield per hectare for a
parcel of land. Unlike LULC, which can be identified by a human expert from a
satellite image moderately reliably (McNairn et al., 2009), yield is generally
measured at either the county-level by government bodies, or at the field-
level by individual researchers. For the purposes of this review, "county"
refers to anything larger than a field (e.g. “municipality”, “province”,
“state”, etc.) due to their varying meaning across different
papers/governments. We note that almost universally, county-level yield uses
MODIS imagery with yield measurements published by governments; while field-
level yield uses Sentinel/Landsat imagery and field surveys, where the data
was collected specifically for the study. However, the methodology appears
similar in both cases for the vast majority of studies. Since the yield is
measured with respect to large shapes (counties/fields) and not with respect
to pixels, the input to the models is an aggregated colour value from the
imagery (or NDVI) across the county/field, effectively removing all spatial
information from the data (Type T-o and ST-o in Figure 2). There are multiple
ways one could aggregate across a whole county or field, yet several papers
did not describe their method for doing so. The most commonly mentioned method
is to take an average, so it is likely that the studies that did not specify
the aggregation method simply used an average.

Aggregating all pixels of a county by averaging naively will incorporate a lot
of non-crop information from bodies of water, arid land, residential areas,
and so on. Since the process reduces the data for a whole county to just a few
numbers (one per colour band; often just NDVI), it is important to ensure that
the recorded aggregate is as accurate as possible. For the US and China, there
are nation-wide estimates of crop types aligned to Landsat imagery (e.g. CDL).
Since Landsat imagery is at a significantly finer resolution than MODIS, it’s
possible to calculate an estimated proportion of each crop within each MODIS
pixel using these crop maps and filter them for each county to only select the
pixels that relate to the crop of interest. For example Li et al. (2007)
describe filtering out MODIS pixels with less than 0.1 NDVI or less than 75%
of the area covered by the crop of interest, as indicated by the CDL. Even
when using such a method, there are still significant sources of error in the
NDVI aggregation: the pixels still contain non-crop information, the fields
may be mislabelled, and fields that are double-cropped may or may not contain
the crop of interest at the time the image is taken.

County-level yield prediction is the most popular task in the papers surveyed;
this is likely because several governments publicly provide yield measurements
aggregated at a county/state level and thus, the data for such work is readily
accessible. This provides data for a whole country at once, but is highly
aggregated. It is likely that MODIS imagery is popular for this task because
the relatively large pixels makes aggregation of VIs, etc. a less
computationally intensive task than using Landsat. Conversely, field-level
data is generally more difficult to obtain as there are no public databases to
work from. We note that Landsat imagery is used at a field-level in much the
same way as MODIS imagery is used at a county-level (e.g. Zhang et al. 2021a),
and when field-level prediction is formulated in the same way as county-level,
the input is the same shape, and a single model can operate seamlessly at both
granularities (Cao et al., 2021; Tian et al., 2021). However, aggregating over
pixels removes all spatial information, and thus the models cannot learn
spatial relationships.

While averaging over pixels within the field/county was the most popular
method, two works (Haghverdi et al., 2018; Sagan et al., 2021) used combine
harvesters equipped with yield measuring technology to obtain a yield map
across a whole field. This provides yield at a significantly finer resolution
than other methods, and also provides an obvious pathway to include a CNN in
the solution as the yield measurements can now be aligned to the uniform pixel
grid of the imagery. More specifically, Haghverdi et al. (2018) collected many
densely predicted yields along with combine harvester locations, and then
averaged these to match Landsat imagery, and Sagan et al. (2021) tasked
commercial satellites to take 30cm resolution images of their crops during
their study to obtain an extremely fine resolution dataset to train on. They
were then able to train CNNs on dense spatiotemporal data (Type ST-c) and
compare against traditional ML methods (trees, SVMs) and found that 2D and 3D
CNNs worked better. Although they only used a single site, each pixel is
effectively another example, meaning they had many thousands of examples to
train on.

It is still possible to use a 2D CNN on public government data. Khaki et al.
(2021) and Qiao et al. (2021) chose to aggregate the county-level data using a
histogram, providing a much richer understanding of the information within a
county than just taking the average. This creates a 3D block of data (bins,
time, bands) which can be used with a 2D CNN (Type ST-o interpreted as Type
T-i). In these cases, the 2D CNNs performed better than traditional machine
learning methods. However, this method of using 2D CNNs doesn’t use spatial
data - instead convolving over the temporal and spectral dimensions. Engen et
al. (2021) collated several Norwegian government sources to create a dataset
of farm-level yields and applied novel architectures including both a 2D CNN
and GRU. They used the field shape files to only include the pixels for the
field being predicted, setting the other pixels to zero. They compared these
to aggregating data across the whole field and using an LSTM and found that
their novel 2DCNN/GRU architecture performed better.

There were several studies which compared performance between models, but even
for county-level prediction, which had the most homogeneous approach, there
was no model which always performed the best. Unlike for the other tasks,
there is a slight majority of LSTMs performing better than tree methods, but
they still appear to be comparable models. It is not clear why this is the
case; the dataset sizes were approximately the same for studies where LSTMs
performed better. Each of these studies compared different times, places and
crops types, so comparing RMSE or $R^{2}$ values would also not be applicable.
There were only two studies that explicitly used CNNs on spatial data (Engen
et al., 2021; Sagan et al., 2021), and in both cases the CNN models performed
better.

Several of the county-level studies have over 1000 counties recorded for over
10 years. This data is easy to obtain for a researcher, as it is all publicly
downloadable. In contrast, with the exception of Zhang et al. (2021a) (11000
data points), the field-level studies used small datasets containing less than
100 datapoints; presumably because the data was so expensive to obtain.

Only one of the reviewed studies attempted to predict the yield outside the
current season (forecast) (Watson-Hernandez et al., 2022).

####  6.5.1 Climatic and soil variables

Nearly all of the county-level studies used climatic variables (e.g. ground
temperature and precipitation) and soil properties (e.g. soil particle size,
soil moisture content) in addition to the reflectances/VIs when aggregating at
a county level, and universally noted improved performance when compared to
not using these variables (Cai et al., 2019; Wang et al., 2020c; Wolanin et
al., 2020). However, different studies obtained different soil variables and
the temperature was sometimes encoded differently. Plants behave differently
under different temperatures, and sometimes the temperature is
classified/aggregated into Growing Degree Days, Killing Degree Days and
Freezing Degree Days before being fed into the model (e.g. Zhang et al.
2021a). No works compared their climatic data encoding to other encodings. It
was also common to use temperature statistics (e.g. min/mean/max). Some
studies combined multiple sources of meteorological data (e.g Ju et al. 2021).

Meteorological stations are not uniformly distributed, and don’t necessarily
cover everywhere in the study area. When operating at smaller than county-
level, Engen et al. (2021) showed that the meteorological information can be
interpolated between meteorological stations to provide local climatic
variables.

##  7 Discussion

In this review, we have observed that modern deep learning methods have become
dramatically more popular in the last few years. This trend is likely to
continue, as the research appears to be following generic computer vision.
However, generic computer vision research has been partially propelled by
large public benchmark datasets, which are mostly lacking in the agricultural
tasks identified in this review, especially monitoring soil health, plant
physiology and crop damage. Without accessible large datasets, methods are not
easily comparable and deep learning in these areas is constrained to those who
have the resources to conduct large field surveys.

Crop segmentation is the only identified task with benchmark datasets (see
Table 6; Section 6.1), and this data is mostly derived from large public data
sources. Even within crop segmentation, there are very few examples of
comparisons on these benchmark datasets. And county-level yield prediction -
despite also being quite popular, and having public data sources - does not
have _any_ benchmark datasets. More accessible data makes more research easier
and enables reliable comparisons. For example, the recommendation to always
use Sentinel-1 data along with Sentinel-2 data is viable because all Sentinel
data is always freely available, and reliable because many studies were able
to independently find the same conclusion. Public benchmark datasets would
provide an accessible collection of data for many different researchers to
train deep learning models, which would lead to more reproducible results, and
allow researchers to more easily build off of each others work. Provided, of
course, that researchers utilise them.

Crop segmentation and county-level yield prediction are the two tasks with the
most interest, and the only ones with existing country-wide data sources which
could be utilised for creating benchmark datasets easily. This is likely
because crop segmentation is relatively easy to label, and monitoring yield
country-wide is already well-established in many countries. The other tasks
suffer from being more difficult to measure and not being widely tracked or
collated, which has resulted in relatively small-scale studies, and less
research interest - at least as far as deep learning is concerned. There were
51 studies identified across soil health, plant physiology and crop damage
tasks, and among these only eight of them used modern deep learning algorithms
and most of those were after 2020. Of these eight, only four compared the
modern deep learning algorithm to a tree/forest, SVM or MLP, but in all four
cases the modern deep learning algorithm performed better.

The vast majority of modern deep learning methods were for LULC tasks, in
which there was a very noticeable trend of modern deep learning algorithms
performing better. In crop segmentation, modern deep learning algorithms were
compared with trees, SVMs or MLPs in 25 studies, and performed better in all
but one case. However, the Vision Transformer (Dosovitskiy et al., 2021) model
and variants, which have performed very well in generic computer vision tasks,
did not show any particular improvement over CNNs and RNNs. Transformers have
been noted to require much more data than CNNs to achieve state-of-the-art
performance, and it’s possible that the transformers used in the reviewed
studies have not been trained with enough data. So, there is room for further
investigation of this phenomenon, perhaps utilising unsupervised or semi-
supervised training methods to bridge the gap in data scale.

For as many examples of straightforward uses of standardised forms of modern
deep learning algorithms (e.g. UNet fine-tuned on type S-i data (Pedrayes et
al., 2021)), there were also many examples of specific ideas being directly
transferred and adapted from generic computer vision to the satellite setting
(e.g. discriminator loss (Li et al., 2021), neural ordinary differential
equations (Metzger et al., 2022), channel attention maps (Tang et al., 2022)).
However, there was only one main variation observed that explicitly took
advantage of the differences between satellite imagery and ground-based
imagery: several works utilised the higher spectral resolution and convolved
over the spectral dimension (types P-s, T-i and S-c; e.g. Kussul et al. 2017;
Debella-Gilo and Gjertsen 2021; Saralioglu and Gungor 2022). There is a gap
here; satellite imagery is not the same as ground-based images or video. There
is temporal continuity at every pixel. There are spatial patterns at hugely
varied scales. There is an order of magnitude more data available, and it is
all associated with physical locations. All of these properties are not being
taken advantage of in existing work tackling agricultural tasks, and are
interesting directions for future research.

## Acknowledgements

The funding for this work comes from the SmartSat Cooperative Research Centre.

Certain data included herein are derived from Clarivate Web of Science. ©
Copyright Clarivate 2022. All rights reserved.

## References

  * ABARES (2021) ABARES, 2021.  Catchment Scale Land Use of Australia - Update December 2020.  Australian Bureau of Agricultural and Resource Economics and Sciences, Canberra doi:10.25814/aqjw-rq15.
  * Abebe et al. (2022) Abebe, G., Tadesse, T., Gessesse, B., 2022.  Combined Use of Landsat 8 and Sentinel 2A Imagery for Improved Sugarcane Yield Estimation in Wonji-Shoa, Ethiopia.  JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING 50, 143–157.  doi:10.1007/s12524-021-01466-8.
  * Abozeid et al. (2022) Abozeid, A., Alanazi, R., Elhadad, A., Taloba, A.I., Abd El-Aziz, R.M., 2022.  A large-scale dataset and deep learning model for detecting and counting olive trees in satellite imagery.  COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022\.  doi:10.1155/2022/1549842.
  * Adams et al. (1995) Adams, J.B., Sabol, D.E., Kapos, V., Almeida Filho, R., Roberts, D.A., Smith, M.O., Gillespie, A.R., 1995.  Classification of multispectral images based on fractions of endmembers: Application to land-cover change in the Brazilian Amazon.  Remote Sensing of Environment 52, 137–154.  doi:10.1016/0034-4257(94)00098-8.
  * Ahlswede et al. (2021) Ahlswede, S., Asam, S., Roeder, A., 2021.  Hedgerow object detection in very high-resolution satellite images using convolutional neural networks.  JOURNAL OF APPLIED REMOTE SENSING 15\.  doi:10.1117/1.JRS.15.018501.
  * Akca and Gungor (2022) Akca, S., Gungor, O., 2022.  Semantic segmentation of soil salinity using in-situ EC measurements and deep learning based U-NET architecture.  CATENA 218.  doi:10.1016/j.catena.2022.106529.
  * Ali et al. (2017) Ali, I., Cawkwell, F., Dwyer, E., Green, S., 2017\.  Modeling Managed Grassland Biomass Estimation by Using Multitemporal Remote Sensing Data-A Machine Learning Approach.  IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 10, 3254–3264.  doi:10.1109/JSTARS.2016.2561618.
  * Arab et al. (2021) Arab, S.T., Noguchi, R., Matsushita, S., Ahamed, T., 2021\.  Prediction of grape yields from time-series vegetation indices using satellite remote sensing and a machine-learning approach.  REMOTE SENSING APPLICATIONS-SOCIETY AND ENVIRONMENT 22.  doi:10.1016/j.rsase.2021.100485.
  * Asming et al. (2022) Asming, M.A.A., Ibrahim, A.M., Abir, I.M., 2022.  Processing and classification of landsat and sentinel images for oil palm plantation detection.  REMOTE SENSING APPLICATIONS-SOCIETY AND ENVIRONMENT 26.  doi:10.1016/j.rsase.2022.100747.
  * Ba et al. (2016) Ba, J.L., Kiros, J.R., Hinton, G.E., 2016.  Layer Normalization.  doi:10.48550/arXiv.1607.06450, arXiv:1607.06450.
  * Baghdadi et al. (2016) Baghdadi, N.N., El Hajj, M., Zribi, M., Fayad, I., 2016\.  Coupling SAR C-Band and Optical Data for Soil Moisture and Leaf Area Index Retrieval Over Irrigated Grasslands.  IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 9, 1229--1243.  doi:10.1109/JSTARS.2015.2464698.
  * Barnetson et al. (2021) Barnetson, J., Phinn, S., Scarth, P., 2021.  Climate-Resilient Grazing in the Pastures of Queensland: An Integrated Remotely Piloted Aircraft System and Satellite-Based Deep-Learning Method for Estimating Pasture Yield.  AGRIENGINEERING 3, 681--702.  doi:10.3390/agriengineering3030044.
  * Benedetti et al. (2018) Benedetti, P., Ienco, D., Gaetano, R., Ose, K., Pensa, R.G., Dupuy, S., 2018\.  M(3)Fusion: A Deep Learning Architecture for Multiscale Multimodal Multitemporal Satellite Data Fusion.  IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 11, 4939--4949.  doi:10.1109/JSTARS.2018.2876357.
  * Beyer et al. (2020) Beyer, L., Hénaff, O.J., Kolesnikov, A., Zhai, X., van den Oord, A., 2020.  Are we done with ImageNet?  arXiv:2006.07159 [cs] arXiv:2006.07159.
  * Bi et al. (2020) Bi, L., Hu, G., Raza, M.M., Kandel, Y., Leandro, L., Mueller, D., 2020.  A Gated Recurrent Units (GRU)-Based Model for Early Detection of Soybean Sudden Death Syndrome through Time-Series Satellite Imagery.  REMOTE SENSING 12.  doi:10.3390/rs12213621.
  * Blaschke et al. (2014) Blaschke, T., Hay, G.J., Kelly, M., Lang, S., Hofmann, P., Addink, E., Queiroz Feitosa, R., van der Meer, F., van der Werff, H., van Coillie, F., Tiede, D., 2014.  Geographic Object-Based Image Analysis - Towards a new paradigm.  ISPRS Journal of Photogrammetry and Remote Sensing 87, 180--191.  doi:10.1016/j.isprsjprs.2013.09.014.
  * Blaschke and Strobl (2001) Blaschke, T., Strobl, J., 2001\.  What’s wrong with pixels? Some recent developments interfacing remote sensing and GIS.  GIS - Zeitschrift für Geoinformationssysteme 14, 12--17.
  * Bocco et al. (2012) Bocco, M., Ovando, G., Sayago, S., Willington, E., Heredia, S., 2012.  Estimating soybean ground cover from satellite images using neural-networks models.  INTERNATIONAL JOURNAL OF REMOTE SENSING 33, 1717--1728.  doi:10.1080/01431161.2011.600347.
  * Boroughani et al. (2022) Boroughani, M., Mohammadi, M., Mirchooli, F., Fiedler, S., 2022\.  Assessment of the impact of dust aerosols on crop and water loss in the Great Salt Desert in Iran.  COMPUTERS AND ELECTRONICS IN AGRICULTURE 192\.  doi:10.1016/j.compag.2021.106605.
  * Boryan et al. (2011) Boryan, C., Yang, Z., Mueller, R., Craig, M., 2011\.  Monitoring US agriculture: The US Department of Agriculture, National Agricultural Statistics Service, Cropland Data Layer Program.  Geocarto International 26, 341--358.  doi:10.1080/10106049.2011.562309.
  * Brodrick et al. (2019) Brodrick, P.G., Davies, A.B., Asner, G.P., 2019.  Uncovering Ecological Patterns with Convolutional Neural Networks.  Trends in Ecology and Evolution doi:10.1016/j.tree.2019.03.006.
  * Bsaibes et al. (2009) Bsaibes, A., Courault, D., Baret, F., Weiss, M., Olioso, A., Jacob, F., Hagolle, O., Marloie, O., Bertrand, N., Desfond, V., Kzemipour, F., 2009.  Albedo and LAI estimates from FORMOSAT-2 data for crop monitoring.  REMOTE SENSING OF ENVIRONMENT 113, 716--729.  doi:10.1016/j.rse.2008.11.014.
  * Cai et al. (2019) Cai, Y., Guan, K., Lobell, D., Potgieter, A.B., Wang, S., Peng, J., Xu, T., Asseng, S., Zhang, Y., You, L., Peng, B., 2019.  Integrating satellite and climate data to predict wheat yield in Australia using machine learning approaches.  AGRICULTURAL AND FOREST METEOROLOGY 274, 144--159.  doi:10.1016/j.agrformet.2019.03.010.
  * Cao et al. (2021) Cao, J., Zhang, Z., Luo, Y., Zhang, L., Zhang, J., Li, Z., Tao, F., 2021.  Wheat yield predictions at a county and field scale with deep learning, machine learning, and google earth engine.  EUROPEAN JOURNAL OF AGRONOMY 123\.  doi:10.1016/j.eja.2020.126204.
  * Carvajal et al. (2016) Carvajal, F., Aguera, F., Sanchez-Hermosilla, J., 2016.  Estimating the Evaporation from Irrigation Reservoirs of Greenhouses Using Satellite Imagery.  ENVIRONMENTAL PROGRESS & SUSTAINABLE ENERGY 35, 1750--1757.  doi:10.1002/ep.12419.
  * Chelali et al. (2021) Chelali, M., Kurtz, C., Puissant, A., Vincent, N., 2021\.  Deep-STaR: Classification of image time series based on spatio-temporal representations.  COMPUTER VISION AND IMAGE UNDERSTANDING 208\.  doi:10.1016/j.cviu.2021.103221.
  * Chen and Black (1992) Chen, J.M., Black, T.A., 1992\.  Defining leaf area index for non-flat leaves.  Plant, Cell & Environment 15, 421--429.  doi:10.1111/j.1365-3040.1992.tb00992.x.
  * Chen et al. (2017) Chen, L.C., Papandreou, G., Schroff, F., Adam, H., 2017\.  Rethinking Atrous Convolution for Semantic Image Segmentation.  doi:10.48550/arXiv.1706.05587, arXiv:1706.05587.
  * Choung and Jung (2021) Choung, Y.J., Jung, D., 2021\.  Comparison of Machine and Deep Learning Methods for Mapping Sea Farms Using High-Resolution Satellite Image.  Journal of Coastal Research 114, 420--423.  doi:10.2112/JCR-SI114-085.1.
  * Colligan et al. (2022) Colligan, T., Ketchum, D., Brinkerhoff, D., Maneta, M., 2022\.  A deep learning approach to mapping irrigation using landsat: IrrMapper U-net.  IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 60\.  doi:10.1109/TGRS.2022.3175635.
  * d’Andrimont et al. (2021) d’Andrimont, R., Verhegghen, A., Meroni, M., Lemoine, G., Strobl, P., Eiselt, B., Yordanov, M., Martinez-Sanchez, L., van der Velde, M., 2021.  LUCAS Copernicus 2018: Earth-observation-relevant in situ data on land cover and use throughout the European Union.  Earth System Science Data 13, 1119--1133.  doi:10.5194/essd-13-1119-2021.
  * de Albuquerque et al. (2020) de Albuquerque, A.O., de Carvalho Junior, O.A., Ferreira de Carvalho, O.L., de Bem, P.P., Guimaraes Ferreira, P.H., de Moura, R.d.S., Silva, C.R., Trancoso Gomes, R.A., Guimaraes, R.F., 2020\.  Deep Semantic Segmentation of Center Pivot Irrigation Systems from Remotely Sensed Data.  REMOTE SENSING 12.  doi:10.3390/rs12132159.
  * Debella-Gilo and Gjertsen (2021) Debella-Gilo, M., Gjertsen, A.K., 2021\.  Mapping Seasonal Agricultural Land Use Types Using Deep Learning on Sentinel-2 Image Time Series.  REMOTE SENSING 13.  doi:10.3390/rs13020289.
  * Del Frate et al. (2004) Del Frate, F., Ferrazzoli, P., Guerriero, L., Strozzi, T., Wegmuller, U., Cookmartin, G., Quegan, S., 2004.  Wheat cycle monitoring using radar data and a neural network trained by a model.  IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 42, 35--44.  doi:10.1109/TGRS.2003.817200.
  * Delloye et al. (2018) Delloye, C., Weiss, M., Defourny, P., 2018.  Retrieval of the canopy chlorophyll content from Sentinel-2 spectral bands to estimate nitrogen uptake in intensive winter wheat cropping systems.  REMOTE SENSING OF ENVIRONMENT 216, 245--261.  doi:10.1016/j.rse.2018.06.037.
  * Deng et al. (2009) Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L., 2009\.  ImageNet: A large-scale hierarchical image database, in: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248--255.  doi:10.1109/CVPR.2009.5206848.
  * Desloires et al. (2022) Desloires, J., Ienco, D., Botrel, A., Ranc, N., 2022\.  Positive Unlabelled Learning for Satellite Images’Time Series Analysis: An Application to Cereal and Forest Mapping.  REMOTE SENSING 14.  doi:10.3390/rs14010140.
  * Dobson et al. (1985) Dobson, M.C., Ulaby, F.T., Hallikainen, M.T., El-rayes, M.A., 1985\.  Microwave Dielectric Behavior of Wet Soil-Part II: Dielectric Mixing Models.  IEEE Transactions on Geoscience and Remote Sensing GE-23, 35--46.  doi:10.1109/TGRS.1985.289498.
  * Dosovitskiy et al. (2021) Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N., 2021\.  An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.  arXiv:2010.11929 [cs] arXiv:2010.11929.
  * Drusch et al. (2012) Drusch, M., Del Bello, U., Carlier, S., Colin, O., Fernandez, V., Gascon, F., Hoersch, B., Isola, C., Laberinti, P., Martimort, P., Meygret, A., Spoto, F., Sy, O., Marchese, F., Bargellini, P., 2012.  Sentinel-2: ESA’s Optical High-Resolution Mission for GMES Operational Services.  Remote Sensing of Environment 120, 25--36.  doi:10.1016/j.rse.2011.11.026.
  * Dupuy and Gaetano (2020) Dupuy, S., Gaetano, R., 2020\.  Reunion island - 2019, Land cover map (Spot6/7) - 1.5m.  doi:10.18167/DVN1/YZJQ7Q.
  * Elmetwalli et al. (2022) Elmetwalli, A.H., Mazrou, Y.S.A., Tyler, A.N., Hunter, P.D., Elsherbiny, O., Yaseen, Z.M., Elsayed, S., 2022.  Assessing the efficiency of remote sensing and machine learning algorithms to quantify wheat characteristics in the nile delta region of egypt.  AGRICULTURE-BASEL 12.  doi:10.3390/agriculture12030332.
  * Engen et al. (2021) Engen, M., Sandø, E., Sjølander, B.L.O., Arenberg, S., Gupta, R., Goodwin, M., 2021\.  Farm-Scale Crop Yield Prediction from Multi-Temporal Data Using Deep Hybrid Neural Networks.  Agronomy 11, 2576\.  doi:10.3390/agronomy11122576.
  * Entekhabi et al. (2010) Entekhabi, D., Njoku, E.G., O’Neill, P.E., Kellogg, K.H., Crow, W.T., Edelstein, W.N., Entin, J.K., Goodman, S.D., Jackson, T.J., Johnson, J., Kimball, J., Piepmeier, J.R., Koster, R.D., Martin, N., McDonald, K.C., Moghaddam, M., Moran, S., Reichle, R., Shi, J.C., Spencer, M.W., Thurman, S.W., Tsang, L., Van Zyl, J., 2010.  The Soil Moisture Active Passive (SMAP) Mission.  Proceedings of the IEEE 98, 704--716.  doi:10.1109/JPROC.2010.2043918.
  * Entekhabi et al. (1996) Entekhabi, D., Rodriguez-Iturbe, I., Castelli, F., 1996.  Mutual interaction of soil moisture state and atmospheric processes.  Journal of Hydrology 184, 3--17.  doi:10.1016/0022-1694(95)02965-6.
  * Eroglu et al. (2019) Eroglu, O., Kurum, M., Boyd, D., Gurbuz, A.C., 2019\.  High Spatio-Temporal Resolution CYGNSS Soil Moisture Estimates Using Artificial Neural Networks.  REMOTE SENSING 11.  doi:10.3390/rs11192272.
  * Evans and Shen (2021) Evans, F.H., Shen, J., 2021\.  Long-Term Hindcasts of Wheat Yield in Fields Using Remotely Sensed Phenology, Climate Data and Machine Learning.  REMOTE SENSING 13.  doi:10.3390/rs13132435.
  * Feitosa (2017) Feitosa, R.Q., 2017.  Campo Verde Database.
  * Feng et al. (2021) Feng, L., Wang, Y., Zhang, Z., Du, Q., 2021.  Geographically and temporally weighted neural network for winter wheat yield prediction.  REMOTE SENSING OF ENVIRONMENT 262\.  doi:10.1016/j.rse.2021.112514.
  * Feng et al. (2019) Feng, P., Wang, B., Liu, D.L., Yu, Q., 2019.  Machine learning-based integration of remotely-sensed drought factors can improve the estimation of agricultural drought in South-Eastern Australia.  AGRICULTURAL SYSTEMS 173, 303--316.  doi:10.1016/j.agsy.2019.03.015.
  * Ferreira et al. (2021) Ferreira, M.P., Lotte, R.G., D’Elia, V, F., Stamatopoulos, C., Kim, D.H., Benjamin, A.R., 2021\.  Accurate mapping of Brazil nut trees (Bertholletia excelsa) in Amazonian forests using WorldView-3 satellite images and convolutional neural networks.  ECOLOGICAL INFORMATICS 63.  doi:10.1016/j.ecoinf.2021.101302.
  * Fontanelli et al. (2022) Fontanelli, G., Lapini, A., Santurri, L., Pettinato, S., Santi, E., Ramat, G., Pilia, S., Baroni, F., Tapete, D., Cigna, F., Paloscia, S., 2022.  Early-season crop mapping on an agricultural area in italy using X-band dual-polarization SAR satellite data and convolutional neural networks.  IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 15, 6789--6803.  doi:10.1109/JSTARS.2022.3198475.
  * Fritz et al. (2019) Fritz, S., See, L., Bayas, J.C.L., Waldner, F., Jacques, D., Becker-Reshef, I., Whitcraft, A., Baruth, B., Bonifacio, R., Crutchfield, J., Rembold, F., Rojas, O., Schucknecht, A., Van der Velde, M., Verdin, J., Wu, B., Yan, N., You, L., Gilliams, S., Mücher, S., Tetrault, R., Moorthy, I., McCallum, I., 2019.  A comparison of global agricultural monitoring systems and current gaps.  Agricultural Systems 168, 258--272.  doi:10.1016/j.agsy.2018.05.010.
  * Funck et al. (2003) Funck, J.W., Zhong, Y., Butler, D.A., Brunner, C.C., Forrer, J.B., 2003.  Image segmentation algorithms applied to wood defect detection.  Computers and Electronics in Agriculture 41, 157--179.  doi:10.1016/S0168-1699(03)00049-8.
  * Gallo et al. (2021) Gallo, I., La Grassa, R., Landro, N., Boschetti, M., 2021\.  Sentinel 2 Time Series Analysis with 3D Feature Pyramid Network and Time Domain Class Activation Intervals for Crop Mapping.  ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 10\.  doi:10.3390/ijgi10070483.
  * Garnot et al. (2022) Garnot, V.S.F., Landrieu, L., Chehata, N., 2022.  Multi-modal temporal attention models for crop mapping from satellite time series.  ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 187, 294--305.  doi:10.1016/j.isprsjprs.2022.03.012.
  * Garnot et al. (2020) Garnot, V.S.F., Landrieu, L., Giordano, S., Chehata, N., 2020\.  Satellite Image Time Series Classification With Pixel-Set Encoders and Temporal Self-Attention, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 12325--12334.
  * Gascon et al. (2007) Gascon, F., Gastellu-Etchegorry, J.P., Leroy, M., 2007.  Using multi-directional high-resolution imagery from POLDER sensor to retrieve leaf area index.  INTERNATIONAL JOURNAL OF REMOTE SENSING 28, 167--181.  doi:10.1080/01431160600647217.
  * Gautam et al. (2011) Gautam, R., Panigrahi, S., Franzen, D., Sims, A., 2011\.  Residual soil nitrate prediction from imagery and non-imagery information using neural network technique.  BIOSYSTEMS ENGINEERING 110, 20--28.  doi:10.1016/j.biosystemseng.2011.06.002.
  * Gbodjo et al. (2021) Gbodjo, Y.J.E., Ienco, D., Leroux, L., 2021.  Benchmarking statistical modelling approaches with multi-source remote sensing data for millet yield monitoring: A case study of the groundnut basin in central Senegal.  INTERNATIONAL JOURNAL OF REMOTE SENSING 42, 9277--9300.  doi:10.1080/01431161.2021.1993465.
  * Gebbers and Adamchuk (2010) Gebbers, R., Adamchuk, V.I., 2010\.  Precision Agriculture and Food Security.  Science 327, 828--831.  doi:10.1126/science.1183899.
  * Ghasemloo et al. (2022) Ghasemloo, N., Matkan, A.A., Alimohammadi, A., Aghighi, H., Mirbagheri, B., 2022.  Estimating the agricultural farm soil moisture using spectral indices of landsat 8, and sentinel-1, and artificial neural networks.  JOURNAL OF GEOVISUALIZATION AND SPATIAL ANALYSIS 6\.  doi:10.1007/s41651-022-00110-4.
  * Gomez et al. (2010) Gomez, C., Mangeas, M., Petit, M., Corbane, C., Hamon, P., Hamon, S., De Kochko, A., Le Pierres, D., Poncet, V., Despinoy, M., 2010\.  Use of high-resolution satellite imagery in an integrated model to predict the distribution of shade coffee tree hybrid zones.  REMOTE SENSING OF ENVIRONMENT 114, 2731--2744.  doi:10.1016/j.rse.2010.06.007.
  * Gómez et al. (2016) Gómez, C., White, J.C., Wulder, M.A., 2016.  Optical remotely sensed time series data for land cover classification: A review.  ISPRS Journal of Photogrammetry and Remote Sensing 116, 55--72.  doi:10.1016/j.isprsjprs.2016.03.008.
  * Goodfellow et al. (2016) Goodfellow, I., Bengio, Y., Courville, A., 2016.  Deep Learning.  MIT Press.
  * Griffin and Lowenberg-DeBoer (2005) Griffin, T.W., Lowenberg-DeBoer, J., 2005\.  Worldwide adoption and profitability of precision agriculture Implications for Brazil.  Revista de Política Agrícola 14, 20--37.
  * Guo et al. (2022) Guo, J., Jin, Y., Ye, H., Huang, W., Zhao, J., Cui, B., Liu, F., Deng, J., 2022.  Recognition of Areca Leaf Yellow Disease Based on PlanetScope Satellite Imagery.  AGRONOMY-BASEL 12.  doi:10.3390/agronomy12010014.
  * Habibi et al. (2021) Habibi, V., Ahmadi, H., Jafari, M., Moeini, A., 2021\.  Quantitative assessment of soil salinity using remote sensing data based on the artificial neural network, case study: Sharif Abad Plain, Central Iran.  MODELING EARTH SYSTEMS AND ENVIRONMENT 7, 1373--1383.  doi:10.1007/s40808-020-01015-1.
  * Haghverdi et al. (2018) Haghverdi, A., Washington-Allen, R.A., Leib, B.G., 2018.  Prediction of cotton lint yield from phenology of crop indices using artificial neural networks.  COMPUTERS AND ELECTRONICS IN AGRICULTURE 152, 186--197.  doi:10.1016/j.compag.2018.07.021.
  * Hamer et al. (2021) Hamer, A.M., Simms, D.M., Waine, T.W., 2021.  Replacing human interpretation of agricultural land in Afghanistan with a deep convolutional neural network.  INTERNATIONAL JOURNAL OF REMOTE SENSING 42, 3017--3038.  doi:10.1080/01431161.2020.1864059.
  * He et al. (2016) He, K., Zhang, X., Ren, S., Sun, J., 2016.  Identity Mappings in Deep Residual Networks, in: Leibe, B., Matas, J., Sebe, N., Welling, M. (Eds.), Computer Vision - ECCV 2016, Springer International Publishing, Cham. pp. 630--645.  doi:10.1007/978-3-319-46493-0_38.
  * Herold et al. (2006) Herold, M., Latham, J.S., Di Gregorio, A., Schmullius, C.C., 2006\.  Evolving standards in land cover characterization.  Journal of Land Use Science 1, 157--168.  doi:10.1080/17474230601079316.
  * Hinton et al. (2012) Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.R., 2012.  Improving neural networks by preventing co-adaptation of feature detectors.  doi:10.48550/arXiv.1207.0580, arXiv:1207.0580.
  * Hunter et al. (2017) Hunter, M.C., Smith, R.G., Schipanski, M.E., Atwood, L.W., Mortensen, D.A., 2017.  Agriculture in 2050: Recalibrating Targets for Sustainable Intensification.  BioScience 67, 386--391.  doi:10.1093/biosci/bix010.
  * Igder et al. (2022) Igder, O.M., Alizadeh, H., Mojaradi, B., Bayat, M., 2022\.  Multivariate assimilation of satellite-based leaf area index and ground-based river streamflow for hydrological modelling of irrigated watersheds using SWAT.  JOURNAL OF HYDROLOGY 610.  doi:10.1016/j.jhydrol.2022.128012.
  * Inglada et al. (2015) Inglada, J., Arias, M., Tardy, B., Hagolle, O., Valero, S., Morin, D., Dedieu, G., Sepulcre, G., Bontemps, S., Defourny, P., Koetz, B., 2015.  Assessment of an Operational System for Crop Type Map Production Using High Temporal and Spatial Resolution Satellite Optical Imagery.  Remote Sensing 7, 12356--12379.  doi:10.3390/rs70912356.
  * Interdonato et al. (2019) Interdonato, R., Ienco, D., Gaetano, R., Ose, K., 2019\.  DuPLO: A DUal view Point deep Learning architecture for time series classificatiOn.  ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 149, 91--104.  doi:10.1016/j.isprsjprs.2019.01.011.
  * Ioffe and Szegedy (2015) Ioffe, S., Szegedy, C., 2015\.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, in: Proceedings of the 32nd International Conference on Machine Learning, PMLR. pp. 448--456.
  * Jacquemoud et al. (2009) Jacquemoud, S., Verhoef, W., Baret, F., Bacour, C., Zarco-Tejada, P.J., Asner, G.P., François, C., Ustin, S.L., 2009\.  PROSPECT+SAIL models: A review of use for vegetation characterization.  Remote Sensing of Environment 113, S56--S66.  doi:10.1016/j.rse.2008.01.026.
  * Jeong et al. (2022) Jeong, S., Ko, J., Yeom, J.M., 2022.  Predicting rice yield at pixel scale through synthetic use of crop and deep learning models with satellite data in South and North Korea.  SCIENCE OF THE TOTAL ENVIRONMENT 802\.  doi:10.1016/j.scitotenv.2021.149726.
  * Ji et al. (2018) Ji, S., Zhang, C., Xu, A., Shi, Y., Duan, Y., 2018\.  3D Convolutional Neural Networks for Crop Classification with Multi-Temporal Remote Sensing Images.  REMOTE SENSING 10.  doi:10.3390/rs10010075.
  * Ji et al. (2022) Ji, Z., Pan, Y., Zhu, X., Zhang, D., Dai, J., 2022.  Prediction of corn yield in the USA corn belt using satellite data and machine learning: From an evapotranspiration perspective.  AGRICULTURE-BASEL 12.  doi:10.3390/agriculture12081263.
  * Jiang et al. (2022) Jiang, D., Chen, S., Useya, J., Cao, L., Lu, T., 2022.  Crop mapping using the historical crop data layer and deep neural networks: A case study in jilin province, china.  SENSORS 22.  doi:10.3390/s22155853.
  * Jin et al. (2021) Jin, Y., Guo, J., Ye, H., Zhao, J., Huang, W., Cui, B., 2021.  Extraction of Arecanut Planting Distribution Based on the Feature Space Optimization of PlanetScope Imagery.  AGRICULTURE-BASEL 11.  doi:10.3390/agriculture11040371.
  * Jong et al. (2022) Jong, M., Guan, K., Wang, S., Huang, Y., Peng, B., 2022\.  Improving field boundary delineation in ResUNets via adversarial deep learning.  INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 112.  doi:10.1016/j.jag.2022.102877.
  * Ju et al. (2021) Ju, S., Lim, H., Ma, J.W., Kim, S., Lee, K., Zhao, S., Heo, J., 2021.  Optimal county-level crop yield prediction using MODIS-based variables and weather data: A comparative study on machine learning models.  AGRICULTURAL AND FOREST METEOROLOGY 307\.  doi:10.1016/j.agrformet.2021.108530.
  * Kattenborn et al. (2021) Kattenborn, T., Leitloff, J., Schiefer, F., Hinz, S., 2021\.  Review on Convolutional Neural Networks (CNN) in vegetation remote sensing.  ISPRS Journal of Photogrammetry and Remote Sensing 173, 24--49.  doi:10.1016/j.isprsjprs.2020.12.010.
  * Kerr et al. (2012) Kerr, Y.H., Waldteufel, P., Richaume, P., Wigneron, J.P., Ferrazzoli, P., Mahmoodi, A., Al Bitar, A., Cabot, F., Gruhier, C., Juglea, S.E., Leroux, D., Mialon, A., Delwart, S., 2012.  The SMOS Soil Moisture Retrieval Algorithm.  IEEE Transactions on Geoscience and Remote Sensing 50, 1384--1403.  doi:10.1109/TGRS.2012.2184548.
  * Khaki et al. (2021) Khaki, S., Pham, H., Wang, L., 2021.  Simultaneous corn and soybean yield prediction from remote sensing data using deep transfer learning.  SCIENTIFIC REPORTS 11.  doi:10.1038/s41598-021-89779-z.
  * Khan et al. (2020) Khan, M.S., Semwal, M., Sharma, A., Verma, R.K., 2020\.  An artificial neural network model for estimating Mentha crop biomass yield using Landsat 8 OLI.  PRECISION AGRICULTURE 21, 18--33.  doi:10.1007/s11119-019-09655-9.
  * Khatami et al. (2016) Khatami, R., Mountrakis, G., Stehman, S.V., 2016.  A meta-analysis of remote sensing research on supervised pixel-based land-cover image classification processes: General guidelines for practitioners and future research.  Remote Sensing of Environment 177, 89--100.  doi:10.1016/j.rse.2016.02.028.
  * Kira et al. (2017) Kira, O., Nguy-Robertson, A.L., Arkebauer, T.J., Linker, R., Gitelson, A.A., 2017.  Toward Generic Models for Green LAI Estimation in Maize and Soybean: Satellite Observations.  REMOTE SENSING 9.  doi:10.3390/rs9040318.
  * Kira and Sun (2020) Kira, O., Sun, Y., 2020.  Extraction of sub-pixel C3/C4 emissions of solar-induced chlorophyll fluorescence (SIF) using artificial neural network.  ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 161, 135--146.  doi:10.1016/j.isprsjprs.2020.01.017.
  * Kolassa et al. (2018) Kolassa, J., Reichle, R.H., Liu, Q., Alemohammad, S.H., Gentine, P., Aida, K., Asanuma, J., Bircher, S., Caldwell, T., Colliander, A., Cosh, M., Collins, C.H., Jackson, T.J., Martinez-Fernandez, J., McNairn, H., Pacheco, A., Thibeault, M., Walker, J.P., 2018\.  Estimating surface soil moisture from SMAP observations using a Neural Network technique.  REMOTE SENSING OF ENVIRONMENT 204, 43--59.  doi:10.1016/j.rse.2017.10.045.
  * Krizhevsky et al. (2012) Krizhevsky, A., Sutskever, I., Hinton, G.E., 2012.  ImageNet Classification with Deep Convolutional Neural Networks, in: Advances in Neural Information Processing Systems, Curran Associates, Inc.. pp. 1106--1114.
  * Krupavathi et al. (2022) Krupavathi, K., Raghubabu, M., Mani, A., Parasad, P.R.K., Edukondalu, L., 2022.  Field-Scale Estimation and Comparison of the Sugarcane Yield from Remote Sensing Data: A Machine Learning Approach.  JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING doi:10.1007/s12524-021-01448-w.
  * Kumar et al. (2019) Kumar, P., Prasad, R., Choudhary, A., Gupta, D.K., Mishra, V.N., Vishwakarma, A.K., Singh, A.K., Srivastava, P.K., 2019\.  Comprehensive evaluation of soil moisture retrieval models under different crop cover types using C-band synthetic aperture radar data.  GEOCARTO INTERNATIONAL 34, 1022--1041.  doi:10.1080/10106049.2018.1464601.
  * Kussul et al. (2017) Kussul, N., Lavreniuk, M., Skakun, S., Shelestov, A., 2017\.  Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data.  IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 14, 778--782.  doi:10.1109/LGRS.2017.2681128.
  * Kuznetsova et al. (2020) Kuznetsova, A., Rom, H., Alldrin, N., Uijlings, J., Krasin, I., Pont-Tuset, J., Kamali, S., Popov, S., Malloci, M., Kolesnikov, A., Duerig, T., Ferrari, V., 2020\.  The Open Images Dataset V4: Unified Image Classification, Object Detection, and Visual Relationship Detection at Scale.  International Journal of Computer Vision 128, 1956--1981.  doi:10.1007/s11263-020-01316-z.
  * Lei et al. (2022) Lei, T.C., Wan, S., Wu, Y.C., Wang, H.P., Hsieh, C.W., 2022\.  Multi-temporal data fusion in MS and SAR images using the dynamic time warping method for paddy rice classification.  AGRICULTURE-BASEL 12.  doi:10.3390/agriculture12010077.
  * Li et al. (2007) Li, A., Liang, S., Wang, A., Qin, J., 2007.  Estimating crop yield from multi-temporal satellite data using multivariate regression and neural network techniques.  PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING 73, 1149--1157.  doi:10.14358/PERS.73.10.1149.
  * Li et al. (2016) Li, F., Zheng, J., Wang, H., Luo, J., Zhao, Y., Zhao, R., 2016.  Mapping grazing intensity using remote sensing in the Xilingol steppe region, Inner Mongolia, China.  REMOTE SENSING LETTERS 7, 328--337.  doi:10.1080/2150704X.2015.1137987.
  * Li et al. (2022a) Li, H., Lu, J., Tian, G., Yang, H., Zhao, J., Li, N., 2022a.  Crop classification based on GDSSM-CNN using multi-temporal RADARSAT-2 SAR with limited labeled data.  REMOTE SENSING 14.  doi:10.3390/rs14163889.
  * Li et al. (2021) Li, J., Shen, Y., Yang, C., 2021.  An Adversarial Generative Network for Crop Classification from Remote Sensing Timeseries Images.  REMOTE SENSING 13.  doi:10.3390/rs13010065.
  * Li et al. (2020) Li, M., Zhang, Z., Lei, L., Wang, X., Guo, X., 2020\.  Agricultural Greenhouses Detection in High-Resolution Satellite Images Based on Convolutional Neural Networks: Comparison of Faster R-CNN, YOLO v3 and SSD.  SENSORS 20.  doi:10.3390/s20174938.
  * Li et al. (2022b) Li, T., Johansen, K., McCabe, M.F., 2022b.  A machine learning approach for identifying and delineating agricultural fields and their multi-temporal dynamics using three decades of Landsat data.  ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 186, 83--101.  doi:10.1016/j.isprsjprs.2022.02.002.
  * Li et al. (2019) Li, W., Dong, R., Fu, H., Yu, L., 2019.  Large-Scale Oil Palm Tree Detection from High-Resolution Satellite Images Using Two-Stage Convolutional Neural Networks.  REMOTE SENSING 11.  doi:10.3390/rs11010011.
  * Li et al. (2022c) Li, Z., Chen, S., Meng, X., Zhu, R., Lu, J., Cao, L., Lu, P., 2022c.  Full convolution neural network combined with contextual feature representation for cropland extraction from high-resolution remote sensing images.  REMOTE SENSING 14.  doi:10.3390/rs14092157.
  * Lin et al. (2021) Lin, C., Jin, Z., Mulla, D., Ghosh, R., Guan, K., Kumar, V., Cai, Y., 2021.  Toward Large-Scale Mapping of Tree Crops with High-Resolution Satellite Imagery and Deep Learning Algorithms: A Case Study of Olive Orchards in Morocco.  REMOTE SENSING 13.  doi:10.3390/rs13091740.
  * Lin et al. (2014) Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L., 2014\.  Microsoft COCO: Common Objects in Context, in: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (Eds.), Computer Vision - ECCV 2014, Springer International Publishing, Cham. pp. 740--755.
  * Liu et al. (2022) Liu, Y., Wang, S., Wang, X., Chen, B., Chen, J., Wang, J., Huang, M., Wang, Z., Ma, L., Wang, P., Amir, M., Zhu, K., 2022\.  Exploring the superiority of solar-induced chlorophyll fluorescence data in predicting wheat yield using machine learning and deep learning methods.  COMPUTERS AND ELECTRONICS IN AGRICULTURE 192\.  doi:10.1016/j.compag.2021.106612.
  * Long et al. (2022) Long, J., Li, M., Wang, X., Stein, A., 2022.  Delineation of agricultural fields using multi-task BsiNet from high-resolution satellite images.  INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 112.  doi:10.1016/j.jag.2022.102871.
  * Lu and Weng (2007) Lu, D., Weng, Q., 2007.  A survey of image classification methods and techniques for improving classification performance.  International Journal of Remote Sensing 28, 823--870.  doi:10.1080/01431160600746456.
  * Lu et al. (2022) Lu, T., Brandt, M., Tong, X., Hiernaux, P., Leroux, L., Ndao, B., Fensholt, R., 2022\.  Mapping the Abundance of Multipurpose Agroforestry Faidherbia albida Trees in Senegal.  REMOTE SENSING 14.  doi:10.3390/rs14030662.
  * Luo et al. (2022) Luo, Y., Zhang, Z., Cao, J., Zhang, L., Zhang, J., Han, J., Zhuang, H., Cheng, F., Tao, F., 2022.  Accurately mapping global wheat production system using deep learning algorithms.  INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 110.  doi:10.1016/j.jag.2022.102823.
  * Ma et al. (2019) Ma, H., Huang, W., Jing, Y., Yang, C., Han, L., Dong, Y., Ye, H., Shi, Y., Zheng, Q., Liu, L., Ruan, C., 2019.  Integrating Growth and Environmental Parameters to Discriminate Powdery Mildew and Aphid of Winter Wheat Using Bi-Temporal Landsat-8 Imagery.  REMOTE SENSING 11.  doi:10.3390/rs11070846.
  * Ma et al. (2021) Ma, Y., Zhang, Z., Kang, Y., Ozdogan, M., 2021.  Corn yield prediction and uncertainty analysis based on remotely sensed variables using a Bayesian neural network approach.  REMOTE SENSING OF ENVIRONMENT 259\.  doi:10.1016/j.rse.2021.112408.
  * Ma et al. (2022) Ma, Y., Zhou, Z., She, X., Zhou, L., Ren, T., Liu, S., Lu, J., 2022.  Identifying Dike-Pond System Using an Improved Cascade R-CNN Model and High-Resolution Satellite Images.  REMOTE SENSING 14.  doi:10.3390/rs14030717.
  * Ma and Mei (2021) Ma, Z., Mei, G., 2021.  Deep learning for geological hazards analysis: Data, models, applications, and opportunities.  Earth-Science Reviews 223, 103858\.  doi:10.1016/j.earscirev.2021.103858.
  * MacDonald and Hall (1980) MacDonald, R.B., Hall, F.G., 1980\.  Global Crop Forecasting.  Science 208, 670--679.  doi:10.1126/science.208.4445.670.
  * Malerba et al. (2021) Malerba, M.E., Wright, N., Macreadie, P.I., 2021.  A Continental-Scale Assessment of Density, Size, Distribution and Historical Trends of Farm Dams Using Deep Learning Convolutional Neural Networks.  REMOTE SENSING 13.  doi:10.3390/rs13020319.
  * Malerba et al. (2022) Malerba, M.E., Wright, N., Macreadie, P.I., 2022.  Australian farm dams are becoming less reliable water sources under climate change.  SCIENCE OF THE TOTAL ENVIRONMENT 829\.  doi:10.1016/j.scitotenv.2022.154360.
  * Masoud et al. (2020) Masoud, K.M., Persello, C., Tolpekin, V.A., 2020.  Delineation of Agricultural Field Boundaries from Sentinel-2 Images Using a Novel Super-Resolution Contour Detector Based on Fully Convolutional Networks.  REMOTE SENSING 12.  doi:10.3390/rs12010059.
  * McNairn et al. (2009) McNairn, H., Champagne, C., Shang, J., Holmstrom, D., Reichert, G., 2009.  Integration of optical and Synthetic Aperture Radar (SAR) imagery for delivering operational annual crop inventories.  ISPRS Journal of Photogrammetry and Remote Sensing 64, 434--449.  doi:10.1016/j.isprsjprs.2008.07.006.
  * Mei et al. (2022) Mei, W., Wang, H., Fouhey, D., Zhou, W., Hinks, I., Gray, J.M., Van Berkel, D., Jain, M., 2022.  Using deep learning and very-high-resolution imagery to map smallholder field boundaries.  REMOTE SENSING 14.  doi:10.3390/rs14133046.
  * Metternicht and Zinck (2003) Metternicht, G.I., Zinck, J.A., 2003\.  Remote sensing of soil salinity: Potentials and constraints.  Remote Sensing of Environment 85, 1--20.  doi:10.1016/S0034-4257(02)00188-8.
  * Metzger et al. (2022) Metzger, N., Turkoglu, M.O., D’Aronco, S., Wegner, J.D., Schindler, K., 2022.  Crop Classification Under Varying Cloud Cover With Neural Ordinary Differential Equations.  IEEE Transactions on Geoscience and Remote Sensing 60, 1--12.  doi:10.1109/TGRS.2021.3101965.
  * Moreno-Revelo et al. (2021) Moreno-Revelo, M.Y., Guachi-Guachi, L., Gomez-Mendoza, J.B., Revelo-Fuelagan, J., Peluffo-Ordonez, D.H., 2021.  Enhanced Convolutional-Neural-Network Architecture for Crop Classification.  APPLIED SCIENCES-BASEL 11.  doi:10.3390/app11094292.
  * Moumni and Lahrouni (2021) Moumni, A., Lahrouni, A., 2021\.  Machine Learning-Based Classification for Crop-Type Mapping Using the Fusion of High-Resolution Satellite Imagery in a Semiarid Area.  SCIENTIFICA 2021.  doi:10.1155/2021/8810279.
  * Muradyan et al. (2020) Muradyan, V., Tepanosyan, G., Asmaryan, S., Maghakyan, N., Sahakyan, L., Saghatelyan, A., 2020\.  Estimating Mo, Cu, Ni, Cd Contents in the Crop Leaves Growing on Small Land Plots Using Satellite Data.  COMMUNICATIONS IN SOIL SCIENCE AND PLANT ANALYSIS 51, 1457--1468.  doi:10.1080/00103624.2020.1784922.
  * Ndikumana et al. (2018) Ndikumana, E., Minh, D.H.T., Baghdadi, N., Courault, D., Hossard, L., 2018.  Deep Recurrent Neural Network for Agricultural Classification using multitemporal SAR Sentinel-1 for Camargue, France.  REMOTE SENSING 10.  doi:10.3390/rs10081217.
  * Nickmilder et al. (2021) Nickmilder, C., Tedde, A., Dufrasne, I., Lessire, F., Tychon, B., Curnel, Y., Bindelle, J., Soyeurt, H., 2021\.  Development of Machine Learning Models to Predict Compressed Sward Height in Walloon Pastures Based on Sentinel-1, Sentinel-2 and Meteorological Data Using Multiple Data Transformations.  REMOTE SENSING 13.  doi:10.3390/rs13030408.
  * Nielsen et al. (2012) Nielsen, D.C., Miceli-Garcia, J.J., Lyon, D.J., 2012.  Canopy Cover and Leaf Area Index Relationships for Wheat, Triticale, and Corn.  Agronomy Journal 104, 1569--1573.  doi:10.2134/agronj2012.0107n.
  * Njoku et al. (2003) Njoku, E., Jackson, T., Lakshmi, V., Chan, T., Nghiem, S., 2003.  Soil moisture retrieval from AMSR-E.  IEEE Transactions on Geoscience and Remote Sensing 41, 215--229.  doi:10.1109/TGRS.2002.808243.
  * Nyborg et al. (2022) Nyborg, J., Pelletier, C., Lefevre, S., Assent, I., 2022\.  TimeMatch: Unsupervised cross-region adaptation by temporal shift estimation.  ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 188, 301--313.  doi:10.1016/j.isprsjprs.2022.04.018.
  * Ofori-Ampofo et al. (2021) Ofori-Ampofo, S., Pelletier, C., Lang, S., 2021.  Crop Type Mapping from Optical and Radar Time Series Using Attention-Based Deep Learning.  Remote Sensing 13, 4668\.  doi:10.3390/rs13224668.
  * Paul et al. (2022) Paul, S., Kumari, M., Murthy, C.S., Kumar, D.N., 2022\.  Generating pre-harvest crop maps by applying convolutional neural network on multi-temporal Sentinel-1 data.  INTERNATIONAL JOURNAL OF REMOTE SENSING doi:10.1080/01431161.2022.2030072.
  * Pavlovic et al. (2022) Pavlovic, M., Ilic, S., Antonic, N., Culibrk, D., 2022\.  Monitoring the impact of large transport infrastructure on land use and environment using deep learning and satellite imagery.  REMOTE SENSING 14.  doi:10.3390/rs14102494.
  * Pedrayes et al. (2021) Pedrayes, O.D., Lema, D.G., Garcia, D.F., Usamentiaga, R., Alonso, A., 2021.  Evaluation of Semantic Segmentation Methods for Land Use with Spectral Imaging Using Sentinel-2 and PNOA Imagery.  REMOTE SENSING 13.  doi:10.3390/rs13122292.
  * Peng et al. (2022) Peng, Y., Liu, Z., Lin, C., Hu, Y., Zhao, L., Zou, R., Wen, Y., Mao, X., 2022.  A new method for estimating soil fertility using extreme gradient boosting and a backpropagation neural network.  REMOTE SENSING 14.  doi:10.3390/rs14143311.
  * Persello et al. (2019) Persello, C., Tolpekin, V.A., Bergado, J.R., de By, R.A., 2019\.  Delineation of agricultural fields in smallholder farms from satellite images using fully convolutional networks and combinatorial grouping.  REMOTE SENSING OF ENVIRONMENT 231\.  doi:10.1016/j.rse.2019.111253.
  * Pignatti et al. (2021) Pignatti, S., Casa, R., Laneve, G., Li, Z., Liu, L., Marzialetti, P., Mzid, N., Pascucci, S., Silvestro, P.C., Tolomio, M., Upreti, D., Yang, H., Yang, G., Huang, W., 2021.  Sino-EU Earth Observation Data to Support the Monitoring and Management of Agricultural Resources.  REMOTE SENSING 13.  doi:10.3390/rs13152889.
  * Qi et al. (2020) Qi, G., Zhao, G., Xi, X., 2020\.  Soil Salinity Inversion of Winter Wheat Areas Based on Satellite-Unmanned Aerial Vehicle-Ground Collaborative System in Coastal of the Yellow River Delta.  SENSORS 20.  doi:10.3390/s20226521.
  * Qiao et al. (2021) Qiao, M., He, X., Cheng, X., Li, P., Luo, H., Tian, Z., Guo, H., 2021.  Exploiting Hierarchical Features for Crop Yield Prediction Based on 3-D Convolutional Neural Networks and Multikernel Gaussian Process.  IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 14, 4476--4489.  doi:10.1109/JSTARS.2021.3073149.
  * Rabiei et al. (2021) Rabiei, S., Jalilvand, E., Tajrishy, M., 2021.  A Method to Estimate Surface Soil Moisture and Map the Irrigated Cropland Area Using Sentinel-1 and Sentinel-2 Data.  Sustainability 13, 11355\.  doi:10.3390/su132011355.
  * Rahimi-Ajdadi and Khani (2021) Rahimi-Ajdadi, F., Khani, M., 2021\.  Remote sensing-based detection of tea land losses: The case of Lahijan, Iran.  REMOTE SENSING APPLICATIONS-SOCIETY AND ENVIRONMENT 23.  doi:10.1016/j.rsase.2021.100568.
  * Rahman et al. (2018) Rahman, M.M., Robson, A., Bristow, M., 2018.  Exploring the Potential of High Resolution WorldView-3 Imagery for Estimating Yield of Mango.  REMOTE SENSING 10.  doi:10.3390/rs10121866.
  * Rauf et al. (2022) Rauf, U., Qureshi, W.S., Jabbar, H., Zeb, A., Mirza, A., Alanazi, E., Khan, U.S., Rashid, N., 2022\.  A new method for pixel classification for rice variety identification using spectral and time series data from Sentinel-2 satellite imagery.  COMPUTERS AND ELECTRONICS IN AGRICULTURE 193\.  doi:10.1016/j.compag.2022.106731.
  * Rawat et al. (2021) Rawat, A., Kumar, A., Upadhyay, P., Kumar, S., 2021\.  Deep learning-based models for temporal satellite data processing: Classification of paddy transplanted fields.  ECOLOGICAL INFORMATICS 61.  doi:10.1016/j.ecoinf.2021.101214.
  * Rawat et al. (2022) Rawat, A., Kumar, A., Upadhyay, P., Kumar, S., 2022\.  A Comparative Study of 1D-Convolutional Neural Networks with Modified Possibilistic c-Mean Algorithm for Mapping Transplanted Paddy Fields Using Temporal Data.  JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING doi:10.1007/s12524-020-01303-4.
  * Richter et al. (2009) Richter, K., Atzberger, C., Vuolo, F., Weihs, P., D’Urso, G., 2009.  Experimental assessment of the Sentinel-2 band setting for RTM-based LAI retrieval of sugar beet and maize.  CANADIAN JOURNAL OF REMOTE SENSING 35, 230--247.  doi:10.5589/m09-010.
  * Rodriguez et al. (2021) Rodriguez, A.C., Daudt, R.C., D’Aronco, S., Schindler, K., Wegner, J.D., 2021.  Robust Damage Estimation of Typhoon Goni on Coconut Crops with Sentinel-2 Imagery.  REMOTE SENSING 13.  doi:10.3390/rs13214302.
  * Ronneberger et al. (2015) Ronneberger, O., Fischer, P., Brox, T., 2015.  U-Net: Convolutional Networks for Biomedical Image Segmentation, in: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (Eds.), Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015, Springer International Publishing, Cham. pp. 234--241.  doi:10.1007/978-3-319-24574-4_28.
  * Roughgarden et al. (1991) Roughgarden, J., Running, S.W., Matson, P.A., 1991.  What Does Remote Sensing Do For Ecology?  Ecology 72, 1918--1922.  doi:10.2307/1941546.
  * Roy et al. (2014) Roy, D.P., Wulder, M.A., Loveland, T.R., C.e., W., Allen, R.G., Anderson, M.C., Helder, D., Irons, J.R., Johnson, D.M., Kennedy, R., Scambos, T.A., Schaaf, C.B., Schott, J.R., Sheng, Y., Vermote, E.F., Belward, A.S., Bindschadler, R., Cohen, W.B., Gao, F., Hipple, J.D., Hostert, P., Huntington, J., Justice, C.O., Kilic, A., Kovalskyy, V., Lee, Z.P., Lymburner, L., Masek, J.G., McCorkel, J., Shuai, Y., Trezza, R., Vogelmann, J., Wynne, R.H., Zhu, Z., 2014\.  Landsat-8: Science and product vision for terrestrial global change research.  Remote Sensing of Environment 145, 154--172.  doi:10.1016/j.rse.2014.02.001.
  * Ruan et al. (2021) Ruan, C., Dong, Y., Huang, W., Huang, L., Ye, H., Ma, H., Guo, A., Ren, Y., 2021.  Prediction of Wheat Stripe Rust Occurrence with Time Series Sentinel-2 Images.  AGRICULTURE-BASEL 11.  doi:10.3390/agriculture11111079.
  * Rußwurm and Koerner (2020) Rußwurm, M., Koerner, M., 2020\.  Self-attention for raw optical Satellite Time Series Classification.  ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 169, 421--435.  doi:10.1016/j.isprsjprs.2020.06.006.
  * Rußwurm and Körner (2018) Rußwurm, M., Körner, M., 2018\.  Multi-Temporal Land Cover Classification with Sequential Recurrent Encoders.  ISPRS International Journal of Geo-Information 7, 129.  doi:10.3390/ijgi7040129.
  * Rußwurm et al. (2020) Rußwurm, M., Pelletier, C., Zollner, M., Lefèvre, S., Körner, M., 2020.  BreizhCrops: A time series dataset for crop type mapping.  International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ISPRS (2020) .
  * Sagan et al. (2021) Sagan, V., Maimaitijiang, M., Bhadra, S., Maimaitiyiming, M., Brown, D.R., Sidike, P., Fritschi, F.B., 2021.  Field-scale crop yield prediction using multi-temporal WorldView-3 and PlanetScope satellite data and deep learning.  ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 174, 265--281.  doi:10.1016/j.isprsjprs.2021.02.008.
  * Santi et al. (2013) Santi, E., Paloscia, S., Pettinato, S., Notarnicola, C., Pasolli, L., Pistocchi, A., 2013\.  Comparison between SAR Soil Moisture Estimates and Hydrological Model Simulations over the Scrivia Test Site.  REMOTE SENSING 5, 4961--4976.  doi:10.3390/rs5104961.
  * Saraiva et al. (2020) Saraiva, M., Protas, E., Salgado, M., Souza Jr, C., 2020\.  Automatic Mapping of Center Pivot Irrigation Systems from Satellite Images Using Deep Learning.  REMOTE SENSING 12.  doi:10.3390/rs12030558.
  * Saralioglu and Gungor (2022) Saralioglu, E., Gungor, O., 2022\.  Semantic segmentation of land cover from high resolution multispectral satellite images by spectral-spatial convolutional neural network.  GEOCARTO INTERNATIONAL 37, 657--677.  doi:10.1080/10106049.2020.1734871.