# Satellite-based high-resolution maps of cocoa planted area for Côte d’Ivoire
and Ghana

Nikolai Kalischek  EcoVision Lab, Photogrammetry and Remote Sensing, ETH
Zurich, 8092, Zurich, Switzerland<EMAIL_ADDRESS>Nico
Lang  EcoVision Lab, Photogrammetry and Remote Sensing, ETH Zurich, 8092,
Zurich, Switzerland  Cécile Renier  Earth and Life Institute, UC Louvain, 1348
Louvain-la-Neuve, Belgium  Rodrigo Caye Daudt  EcoVision Lab, Photogrammetry
and Remote Sensing, ETH Zurich, 8092, Zurich, Switzerland  Thomas Addoah
Environmental Policy Lab, Department of Humanities, Social and Political
Sciences, ETH Zurich, 8092, Zurich, Switzerland  William Thompson  Department
of Biology, University of Oxford, Oxford, OX1 2JD, Great Britain  Wilma J.
Blaser-Hart  School of Biological Sciences, The University of Queensland, St
Lucia, Brisbane, QLD 4072, Australia  Rachael Garrett  Environmental Policy
Lab, Department of Humanities, Social and Political Sciences, ETH Zurich,
8092, Zurich, Switzerland  Konrad Schindler  EcoVision Lab, Photogrammetry and
Remote Sensing, ETH Zurich, 8092, Zurich, Switzerland  Jan D. Wegner
EcoVision Lab, Photogrammetry and Remote Sensing, ETH Zurich, 8092, Zurich,
Switzerland  Data Science for Sciences, Institute for Computational Science,
University of Zurich, 8092, Zurich, Switzerland

###### Abstract

Côte d’Ivoire and Ghana, the world’s largest producers of cocoa, account for
two thirds of the global cocoa production. In both countries, cocoa is the
primary perennial crop, providing income to almost two million farmers. Yet
precise maps of cocoa planted area are missing, hindering accurate
quantification of expansion in protected areas, production and yields, and
limiting information available for improved sustainability governance. Here,
we combine cocoa plantation data with publicly available satellite imagery in
a deep learning framework and create high-resolution maps of cocoa plantations
for both countries, validated in situ. Our results suggest that cocoa
cultivation is an underlying driver of over 37 % and 13 % of forest loss in
protected areas in Côte d’Ivoire and Ghana, respectively, and that official
reports substantially underestimate the planted area, up to 40 % in Ghana.
These maps serve as a crucial building block to advance understanding of
conservation and economic development in cocoa producing regions.

Cocoa is grown by an estimated 2 million farmers in West Africa, who supply a
complex network of middle men, private and public companies, which renders the
supply chain rather opaque [1, 2, 3]. With an average farm size of three to
five hectares [4, 5] and an estimated income of less than one dollar per day,
nearly all cocoa farmers live under the poverty line [6]. In this context,
deforestation in West African Upper Guinean forests, a biodiversity hotspot
[7], has occurred in waves across the 20th and 21st centuries [8]. Cocoa-
driven deforestation has played a significant role in this and has been
catalyzed by migration from Savannah regions, land availability and tenure
constraints for residents of existing cocoa production areas, as well as the
higher productive potential of recently cleared land [1, 9].

In recent years, corporate sustainability efforts have been initiated to
reduce cocoa-driven deforestation, improve cocoa yields, including by
promoting agroforestry [10]. While cocoa certification programs have improved
farm productivity and income, there is no conclusive evidence of their impact
on agroforestry or deforestation. Furthermore, more ambitious supply chain
targets to eliminate sourcing of deforestation-linked cocoa face major
implementation challenges due to difficulty in monitoring and tracking cocoa
expansion into forests [11, 12]. Among mining, selective logging and other
crops, cocoa has been the primary driver of deforestation in these countries
[9, 13, 2]. However the extent to which cocoa has directly and indirectly
replaced forest, until now, is uncertain.

Current map products are either derived from small reference data sets and of
low precision, or rely on manual geo-referencing and are costly to update [12,
14]. The production of accurate, high-resolution maps of cocoa growing areas
is currently missing. Up-to-date maps could greatly enhance efforts to halt
deforestation by highlighting high-deforestation risk sourcing areas for
cocoa, for verifying production quantities, and estimating on-farm versus off-
reserve production area. Beyond deforestation, the spatial extent of cocoa
production could be linked with more readily available data on production
quantities to inform more targeted extension activities.

Here, we present a large-scale, high-resolution cocoa growing map spanning
Côte d’Ivoire and Ghana, generated by satellite image analysis with a deep
neural network. Deep learning has matured and surpassed traditional hand-
crafted feature detectors in countless remote sensing tasks including
vegetation height mapping [15], localizing fires [16], predicting photovoltaic
solar facilities [17] and crop identification [18, 19]. When trained on large
reference corpora, deep models offer an unprecedented ability to recognize
visual patterns in unseen data.

For this work, we have trained a neural network on a dataset of >100,000 geo-
referenced cocoa farms to map cocoa plantations at country scale. We leverage
publicly available optical satellite imagery as input in a twofold way. First,
we train a neural network to predict canopy height in the sub-Saharan region
utilizing ground truth acquired from the GEDI mission [20]. Second, we train a
deep neural network on the same satellite imagery and a large corpus of
polygons delineating cocoa farms, using the canopy height map as an additional
input for the network, introducing an explicit prior on the plant height. With
the help of a team in Côte d’Ivoire, we validate our map in situ in a three
month long campaign, accompanied by further verification with a partially
hand-labelled test set in Ghana. Instead of a single binary cocoa map, we
create a probability map by aggregating predictions from a model ensemble and
from repeated observations of the same location, as model ensembles have been
shown to yield more reliable uncertainty calibration, and often also improved
predictive skill [21]. The final map, along side two examples of cocoa grown
in different protected areas, is depicted in Figure 1.

We illustrate the utility of our map, not only by (1) analyzing planted area,
but also on (2) farming practices and sustainability efforts on reducing
deforestation, highlighting the need for land cover mapping independent of
farmers, industry and governments, and (3) identify regional areas that are
exposed to poor growing conditions.

Figure 1: Cocoa map for Côte d’Ivoire and Ghana. Probability map with
10$\times$10 m ground sampling distance. The map indicates detection
confidence on a range $[0\ldots 1]$, i.e., values near 1 indicate that model
predictions across most time steps agree on the presence of cocoa, values near
0 agree on the absence of cocoa. (A and B) Two forested regions in Ghana where
our model has detected cocoa farming (satellite images and confidence maps).
The location of these forest regions are shown with red dots in the cocoa map.

## Results

### Evaluation

First, we demonstrate the reliability of our ensemble model with four standard
accuracy metrics. Precision (user’s accuracy) measures the proportion of
correctly classified pixels among all pixels assigned to a class. Recall
(producer’s accuracy) is the proportion of correctly classified pixels among
all pixels that truly belong to a class. As summary statistics, we
additionally report accuracy, i.e. overall fraction of correctly classified
pixels, and $F_{1}$-score, defined as the harmonic mean of precision and
recall for a class $c$:

$F_{1}^{c}=2\cdot\frac{\text{recall}\cdot\text{precision}}{\text{recall}+\text{precision}}.$

To avoid evaluation bias [22], we present all metrics for both classes, cocoa
and background, in Figure 2(b) and showcase one examples of our in situ test
set in Figure 2(a).

(a)

(b) Class |  Precision (%) |  Recall (%) |  F1 (%) |  Accuracy (%)
---|---|---|---|---
Cocoa |  88.5 |  87.2 |  87.3 |  85.9
Non-cocoa |  83.9 |  84.3 |  84.1

Figure 2: In situ evaluation. (a) Sites from our in situ test set around Divo,
Côte d’Ivoire. Left to right: a satellite image with reference data,
confidence predictions and binary cocoa maps at a confidence threshold of
0.65. The green sites are mapped cocoa farms, the blue sites verified non-
cocoa sites. In the binary map each value is either 0 or 1, i.e. each area is
classified as either non-cocoa (0) or cocoa (1), whereas in the probability
map values can be in between. (b) Quantitative performance of cocoa detection
(confidence threshold 0.65). Contains modified Copernicus Sentinel data
[2020].

A confidence map brings several benefits, one notable advantage is that one
can calibrate the optimal threshold for binary cocoa presence/absence mapping
with an additional, much smaller validation set for specific regions or
applications. We do this in order to evaluate our cocoa map against two
independent, binary validation datasets. In Côte d’Ivoire, we manually labeled
over 2,000 polygons and verified these estimates with on-farm visits.
Additional information on this in situ dataset is given in Methods. For Ghana,
we acquire geo-referenced cocoa polygons from an independent commercial data
provider. While our in situ test set covers various regions in both countries,
it is restricted to the areas around major cities in Côte d’Ivoire, since it
was infeasible to collect a statistically rigorous (stratified) random sample
[23] over a region of this scale.

Compared to previous mapping efforts, our approach offers several advantages.
First, by using an end-to-end trainable framework, feature selection is
automated. Our approach boosts all metrics by large margins compared to the
only other large-scale cocoa map we are aware of [14], improving precision and
recall by more than 30 % and 8 %, respectively. In terms of mapping effort,
existing accountability maps [24, 25] rely on extensive collaborations with
cocoa cooperatives to create, update and maintain databases of cocoa farms,
which impedes their extension to underrepresented regions, both within a
country and beyond. In contrast, our mapping system is naturally expandable to
areas that have not previously been mapped. Our mapping system can be adjusted
with a small amount of local reference data when expanded to a new area that
enclose similar plant characteristics. For example, our map detects cocoa
plantations in regions that, so far, have been ignored in official figures
[26], e.g. the Volta region in Ghana.

### Planted area

With our cocoa map, we are able to calculate the total planted area in Côte
d’Ivoire and in Ghana, and to compare it to official figures. We compute the
best threshold at 0.65 according to a held-out validation set, i.e. all values
above 65 % in the probability map are classified as cocoa and all others as
non-cocoa areas. Importantly, the probabilistic mapping approach makes it
possible to select the threshold that maximizes the F1 score over the
validation set, thus balancing the expected precision and recall on unseen
data.We empirically found maximizing the F1-score to be more robust than
direct matching of false negative and false positive counts: under practical
conditions, where the training and test data are not perfect random samples
from the underlying distribution, it leads to a lower area bias.The
corresponding curve, where F1 score is plotted against different thresholds,
can be found in the supplementary information.

To estimate planting areas, the uncertainty quantification via our model
ensemble approach has several advantages. It has been argued that plain pixel
counting in a binary classification map can be problematic for area estimation
[23]. However, our final map consists of continuous probability values,
similar to model-based area estimation [27], that can be thresholded in a
post-processing step to minimize the bias of the area estimates. The
theoretical scale factor between the true and estimated areas is
$\frac{\text{precision}}{\text{recall}}$. For our test results that factor
amounts to 1.02, i.e., a difference of less than 2%. Moreover, each ensemble
member produces its own (continuous) cocoa map and can be thresholded
separately, thus drawing multiple samples from the distribution of area
estimates. Contrary to naive pixel counting, we can thus characterize the
uncertainty of the planted area estimate, by computing a mean area with an
associated standard deviation and confidence intervals, assuming an underlying
$t$-distribution. The alternative, computing area estimates via optimally
selected, stratified samples [23, 28] is difficult for large-scale mapping
efforts, particularly when the map resolution is high (in our case 10 m). To
obtain such samples one would either need access to images of even higher
resolution anywhere in the region of interest, so as to perform photo-
interpretation. Or one must collect in situ data across entire countries,
which is often not possible due to the difficulties of accessing randomly
sampled locations that may lie in difficult terrain, lack infrastructure, be
subject to land rights, etc.

In 2021, the mean total area under cultivation amounts to 4.45 Mha (CI 95 %:
3.95 – 4.95 Mha) in Côte d’Ivoire and 2.71 Mha (CI 95 %: 2.21 – 2.89 Mha) in
Ghana, corresponding to 13.8 % and 11.4 % of Côte d’Ivoire’s and Ghana’s land
area respectively. The detected cocoa plantings align well with climatically
suitable growing regions in both countries [29], although we have not
restricted the detection to those areas a priori, as in previous mapping
projects [14]. Compared to the official FAOSTAT figures [30], our result
deviates only marginally from the harvested area (average 2017–2020) in Côte
d’Ivoire, but drastically differs for Ghana’s total harvested area. FAOSTAT
reports 4.47 Mha harvested area in Côte d’Ivoire (i.e. 0.5 % more than our
estimate) and only 1.63 Mha in Ghana, i.e. 39.8 % less than our estimate.

While the country-wide harvesting numbers are impressive by themselves for a
single agricultural commodity, zooming in on a regional level further reveals
the massive impact of cocoa cultivation on the two countries. As shown in
Figure 3(a), the highest proportions for a single region are 43.0 % (Bas-
Sassandra CI 95 %: 1.16 – 1.22 Mha) and 44.6 % (Western CI 95 %: 1.06 – 1.11
Mha) for Côte d’Ivoire and Ghana, respectively, leaving little to no forested
area in agricultural regions. This reveals the extent to which cocoa
production has replaced native forests, which is associated with biodiversity
loss, local and global climate impacts and the loss of multiple resources
supporting food security and livelihoods.

### Production

Using production data obtained from the Ghana Cocoa Board (COCOBOD) [31] and
averaged over the years 2017 to 2020, we compare planted area with production
data on a regional level in Ghana (production data at subnational level was
not available for Côte d’Ivoire). By dividing production by planted area in
every region, we obtain local mean yield estimates, important for farming
practices, sustainability and regeneration efforts. Mean annual yields range
from 250 kg/ha in Ashanti region to over 380 kg/ha in the Central region. The
mean yields for the Volta region (120 kg/ha) need to be treated with care in
our analysis, because production is reported only for one out of four cocoa
growing districts. In Figure 3(b), we highlight significant differences in
farming productivity within the cocoa growing regions in Ghana. As an example,
even though the Western region contributes >40% of the total growing area
within the COCOBOD boundaries, the average yield is lower compared to the
Central region, suggesting potential to improve farming practices. Even more
extreme is the Ashanti region, where the annual yield is as low as 250 kg/ha.
On average, we obtain a mean annual yield of 320 kg/ha for Ghana. Earlier
studies have reported average cocoa yields in the range of 400 to 530 kg/ha
[32, 33, 34, 5], which is significantly higher compared to our estimate. These
earlier numbers are based on small sets of field samples, which may be biased
towards farms with above-average productivity [5]. Our yield estimates may
also be slightly lower due to young planted farms detected by our map that may
not be productive yet, therefore bringing down the average yield per unit
area.

(a)

(b)

Figure 3: Planted area, mean annual yield and correlation between growing area
and production volume per region. a, Comparison of planted area and total area
per region. Top figure depicts regions in Côte d’Ivoire, bottom figure in
Ghana. The grey bars represent the total area of a region, while the red
shaded bars show the absolute cocoa planted area. The percentages indicate the
relative amount of cocoa planted area to the total area per region. b,
Regional yield differences measured in kilogram per hectare in Ghana. In case
of the Volta region, we only obtained production data for a single
subdivision, hence the low average yield.

### Protected areas

(a)  Côte d’Ivoire | Ghana
---|---
Protected area |  Cocoa (ha) |  Land cover (%) |  Deforestation (%) |  Protected area |  Cocoa (ha) |  Land cover (%) |  Deforestation (%)
Niegre (CF) |  108,256 |  81.8 |  86.6 |  Tano Ehuro (FR) |  16,275 |  77.6 |  82.2
Scio (CF) |  90,418 |  68.2 |  78.3 |  Manzan (FR) |  15,512 |  56.1 |  48.6
Mt. Sassandra (CF) |  54,946 |  49.0 |  55.8 |  Upper Wassaw (FR) |  3,198 |  23.6 |  14.6
Mt. Péko (NP) |  6,479 |  21.5 |  19.3 |  Sui River (FR) |  3,497 |  9.84 |  39.3
Marahoué (NP) |  2,789 |  18.0 |  13.2 |  Kakum (NP) |  256 |  1.0 |  21.0

(b)

Figure 4: Cocoa encroachment into protected areas. (a) List of selected
protected areas. Land cover is the proportion of cocoa within the protected
area. Deforestation indicates the proportion of cocoa grown on deforested
areas. CF is short for classified forest, FR is short for forest reserve and
NP short for national park. Forest reserves are categorized as protected areas
with sustainable use of natural resources. Full list can be found in Table 2
in the Appendix. (b) Maps of selected protected areas.

Côte d’Ivoire and Ghana continue to experience high forest loss. Côte d’Ivoire
is estimated to have lost more than 90 % of its forest cover since 1950, while
Ghana has incurred forest losses of more than 65 % [12]. Forest clearance
rates reached a high in 2018, increasing by 60 % over 2017 in Ghana and by 26
% in Côte d’Ivoire, the two highest increases in annual deforestation rates
worldwide [35]. Though deforestation rates have fluctuated since 2018.

Here, we examine to what extent cocoa replaced native forest. Our map enables
an accurate assessment of cocoa related deforestation within protected areas
mapped in the World Database on Protected Areas (WDPA) [36]. The WDPA includes
various types of protected areas, including strict nature reserves, national
parks and protected landscapes. Across all management categories, there are
242 and 286 protected areas in Côte d’Ivoire and in Ghana, respectively.
Across these areas, we find a total cocoa planted area of >1.5 Mha located in
protected areas: 1.3 Mha in Côte d’Ivoire, corresponding to 30 % of the total
cocoa area of the country, and 0.2 Mha in Ghana (i.e. 7 % of the total cocoa
area). These numbers correspond to >13.6 % of the overall protected area in
Côte d’Ivoire (9.8 Mha), and >4.5 % in Ghana (3.7 Mha). Using the annually
updated forest cover loss [37], we can directly relate forest loss of over
360,000 hectares in protected areas (including classified forests) to cocoa
cultivation in Côte d’Ivoire from 2000 to 2020. Given an overall forest loss
of 962,000 hectares since 2000, cocoa is directly or indirectly responsible
for almost 37.4 % of forest loss in protected areas. Similarly, we can trace
back 26,000 hectares of cocoa driven deforestation in protected areas in
Ghana, corresponding to 13.5 % of the total forest lost in protected areas
(193,000 ha) since 2000.

We further break down the numbers to individual protected areas. Figure 4(a)
lists the total cocoa area and the corresponding relative land cover and
deforestation percentage for five selected areas per country. Figure 4(b)
visually shows the encroachment. The results reveals that for certain
protected areas in the WDPA up to 80 % of the surface are covered by cocoa
plantations. They also show a large difference in deforestation across
protected area types that requires significantly more investigation.

The most deforested protected areas in Côte d’Ivoire are the classified
forests of Niegre, Scio and Mt. Sassandra, with 81.8 %, 68.2 % and 49.0 % of
their area under cocoa cultivation, respectively. All three of them have been
exposed to illegal farming for decades [13, 38]. Similarly, forest reserves
such as Tano Ehuro, Manzan and Upper Wassaw in Ghana have severe forest
clearing [39, 40] with cocoa expansion occurring within 23 % to 77 % of their
surface. These high levels of deforestation in protected areas confirm and
extend what has been found in Abu et al. for a very small subset of protected
areas. For protected areas "of highest protection" in Ghana (e.g. Kakum
National Park) our map detects almost no illegal cocoa plantations (1.0 %).
However, some of the national parks in Côte d’Ivoire are highly affected by
illegal cocoa farming. In line with recent literature [6, 41, 42], we are able
to quantify the spatial extent of cocoa plantations within the protected areas
such as over 6,400 ha and 2,700 ha in Mt. Péko National Park and Marahoué
National Park, respectively. Yet, Tai National Park, a World Heritage Site and
one of the largest protected areas in Côte d’Ivoire with has experienced very
little deforestation for cocoa.

Part of the reason for the high overall deforestation rate - aside the many
underlying drivers outlined in the discussion - is that some of the protected
areas in the WDPA had already been degazetted early in the study period,
thereby allowing cocoa production. In other regions particularly in Ghana,
some villages and farms known as "admitted communities and/or farms" are
legally allowed to remain in the forest reserves and to farm within delineated
boundaries. However, it is known that these rights have been misused to
further expand into remaining forests [43].

(a)

(b)

Figure 5: Vegetation health of cocoa, measured by normalized difference
vegetation index (NDVI). (a) Computing NDVI per district using only locations
with actual cocoa plantings diminishes biases due to other crop types of
vegetation and (b) allows targeted actions up to farm level when comparing at
the native map resolution. The red dot in (a) indicates the approximate
location in (b).

Our map makes it possible to compute further vegetation parameters
specifically for regions where cocoa is grown, while excluding other
vegetation, i.e. plants that are not mapped as cocoa by our model. Naturally,
the computed values can be influenced by shade trees and other vegetation
within agroforestry systems. This segregation of cocoa and other vegetation
allows us to use normalized difference vegetation index (NDVI) to monitor
cocoa health on a large scale, with analyses at either pixel or district
level, and to find regions where resources could be best used to improve the
conditions of cocoa plantations. To demonstrate this, we measure vegetation
health in terms of the NDVI. That index is based directly on the absorption of
photosynthetically active radiation by leaves, and the re-emission of near-
infrared radiation with too low photon energy. The index is defined as

$\text{NDVI}=\frac{\text{NIR}-\text{RED}}{\text{NIR}+\text{RED}},$ (1)

where NIR and RED are the spectral reflectances in the near-infrared and red
spectral bands, respectively. Higher NDVI at similar leaf area corresponds to
better health. We showcase a fine-grained analysis at the district level for
Côte d’Ivoire and Ghana, with the potential to identify local farming
practices that promote the health of cocoa plants. In Figure 5(a), we depict
the average NDVI per district for the time interval October 2018 to December
2021. The values range from slightly below 0.34 to slightly above 0.41, with
an average of 0.38. There are clear regional differences. Particularly the
border region between the Northeast of Côte d’Ivoire and the West of Ghana
exhibits a cluster of lower NDVI values. We note that in Ghana the Western
region is the principal cocoa growing area, with over one million hectares of
cocoa. Yet, the low NDVI suggests worse plant health and lower productivity
compared to, for instance, the Ashanti and Central regions. This could be
further investigated, as the NDVI values could also be influenced by shade
trees within agroforestry systems or local climatic characteristics. While
NDVI at the district level is appealing in order to quickly identify larger
areas that may have to be further investigated, it simultaneously becomes less
expressive due to averaging effects over large areas. However, our map allows
for fine-grained analysis up to farm or even sub-hectare level. As seen in
Figure 5(b), we can compute vegetation health indices per pixel and year as an
average over three months to reduce measurement noise in the corresponding
Sentinel-2 bands. These maps can be used individually to compare the indices
of neighboring areas or combined to produce annual difference maps. The former
provides important information that may be directly relevant for farmers to
improve the plants’ health during the year and to monitor the impact of
different weather patterns; while the latter may be an indicator of long-term
developments, e.g. depleted soil.

## Discussion

In the following, we discuss benefits, implications and applications of our
end-to-end framework and its product, concluding with the potential of our map
to increase sustainability along the cocoa supply chain.

Compared to existing mapping efforts, our framework promises a number of
advantages. First, utilizing model ensembles in combination with aggregating
over multiple satellite images of the same location allows for a confidence
map in contrast to binary predictions. Consequently, an end-user gains an
additional degree of freedom when using our map. Depending on the concrete
tasks, one can adapt the threshold of classifying cocoa according to their
needs, e.g. to fine-tune on a local region. Furthermore, the confidence map
serves as guidance for measurements and improving predictions. While scores on
the lower and upper range can be used to accurately and confidently take
plant-specific measurements by decreasing the bias of false-positives,
uncertain predictions can be preferably checked on the ground to improve the
model performance. Hence, non-profit organizations, initiatives and
governments can drastically reduce human resources for on ground surveys and
mapping efforts. Mapping and protection efforts can be concentrated around and
within protected areas. In case of reduced manpower, inspections on the ground
can be focused around highly certain predictions. Additional to short-term
forest clearance, it is possible to correlate long-term primary forest loss
with cocoa encroachment in a highly accurate manner. We additionally tuned the
confidence threshold on country-wide validation data. As demonstrated in the
previous section, this results in a highly accurate binary cocoa map that can
be used in various downstream tasks, e.g., to specifically mask out vegetation
areas not used for cocoa production, in order to compute cocoa-specific
vegetation indices such as the NDVI at local community level, thanks to the
map’s high resolution of 10 m.

A major finding of our mapping efforts is the significant difference between
the official harvested area in Ghana (1.63 Mha, average 2017–2020) and our
total planted area estimation (2.7 Mha). Various reasons can partially explain
this difference - which seems too important to only be due to young cocoa
plantations detected by our map but not yet productive, and thus not accounted
for in the harvested area. First, FAOSTAT numbers are based on imputed data,
which are not as trustworthy for permanent crops due to unreliability of
figures reported by the corresponding country, in particular for cocoa and
coffee [44]. In addition, it is known that up to 100,000 tonnes of cocoa beans
per year have been smuggled across the border between the two countries back
and forth [45, 46], resulting in skewed official production figures. Our map
also differs from the official Ghanaian maps [26], which are only partially
mapping cocoa in the country. In particular, the Volta region is ignored
completely even though official production numbers (as seen in Section
"Production") suggest cocoa farming takes place in the eastern part of the
country. Our mapping efforts predicted a total of 60,000 ha additional cocoa
plantations in the Volta region.

Our map demonstrates the massive role of cocoa in forest clearance in
protected areas. Cocoa-driven deforestation is rooted in many interrelated
factors, extensively explored in other studies [5, 13, 38]. At a basic level
farmers pursue cocoa in protected activities to meet essential livelihood
needs, including income and food production, which have been disrupted due to
declining productivity, civil unrest [13], migration and population pressure
in existing farming areas. In both countries, nearly all cocoa smallholders
live under the poverty line, with daily wages of less than one dollar a day
[6]. Average cocoa yields are low, mainly due to depleted soils, ageing and
diseased trees and low input use [47]. Concomitant to securing land rights,
clearing natural forests to establish new cocoa farms provides farmers with
temporarily fertile land and thus higher yields and income in the short-run,
compared to already cultivated sites[9]. Our findings stress the drastic need
for fairer prices and improved government and company policies to support
cocoa farmers’ adoption of improved practices. This must happen alongside
stronger law enforcement to avoid rebound effects and preserve the remaining
forests of Côte d’Ivoire and Ghana.

We have developed an end-to-end trainable framework to map cocoa in the
world’s largest cocoa producing countries, promising high accuracy and
flexibility. Nevertheless, a few limitations have to be considered. We have
demonstrated the applicability and usefulness of deep learning for automated
crop identification with optical satellite imagery. Yet, the final map relies
on multiple image acquisitions for each location to cope with atmospheric
disturbances, as optical sensors are limited by cloud cover. Due to this
limitation, while the map itself can be used to detect cocoa within protected
areas, it is not yet possible to capture new cocoa plantations on a weekly or
monthly update. Integrating radar-based observations, in particular synthetic
aperture radar (SAR) as an additional input for our framework could likely
reduce the number of images needed per location, ultimately increasing the
update rate of our map. Combining historical satellite data with our map to
detect past and current cocoa expansion rates is also an interesting future
application. The proposed approach is a generic framework not limited to a
specific region and is expected to generalize to new areas. Given reference
data from new regions of interest, the model may be fine-tuned to adapt to
local conditions and patterns. In particular, regions with similar landscape
characteristics (e.g., Cameroon or Nigeria) should only need small additional
datasets, whereas adapting to countries such as Malaysia, Indonesia or
Honduras with challenging growing practices, e.g., high shade tree density or
mixed cultivation, will likely require a lot more reference data.

We believe that our study vindicates automatic analysis of satellite imagery
as a tool for large-scale mapping of cocoa, and thereby presents a step
forward in analyzing the cocoa supply chain and its sustainability
implications. Beyond the cocoa supply chain this study also highlights the
potential of using satellite imagery to derive the spatial extent of
agricultural production in contexts with limited land documentation and
therefore opens up opportunities to inform the design and implementation of
public and private sustainability initiatives.

## Methods

### Data

As model input, we use publicly available optical satellite imagery to train
and apply our predictor. Data for both countries are acquired from the
Copernicus Sentinel-2 mission. Sentinel-2 images consist of thirteen spectral
bands, ranging from short-wave infrared to visible at a resolution of at most
ten meters. We discard bands with a resolution of 60 meters and bilinearly
upsample all 20-meter spectral bands to 10 meter resolution, for a total of
nine channels that serve as input to our neural network. To make the model
more robust towards atmospheric noise, cloudy recordings and sensor noise, we
obtain in each Sentinel-2 grid tile the ten images with the lowest cloud cover
within each 6-month period, over a total observation window of three years
between October 2018 and December 2021. Consequently, for each pixel location,
we have up to 60 valid observations. In most cases the number is smaller due
to local cloud cover in some of the images (according to the Sentinel-2 basic
cloud mask). Both during training and testing, such cloudy samples are masked
with _nodata_ values in a post-processing step.

We obtained ground truth from different data providers, mainly industrial
partners, cocoa foundations and nonprofit organizations. In total, we
collected over 100,000 GPS-tracked cocoa farms and manually labelled over
10,000 background polygons of different sizes across both Ghana and the Ivory
Coast. These negative samples, needed for supervised end-to-end learning,
consist of villages, cities, rivers, lakes, open land, shrub land, forest and
different commodities such as oil palm, rice or rubber. While cocoa has the
largest number of individual polygons, the background samples have more than
four times larger surface area due to the small size of cocoa farms (see
Section "Deep learning framework" on how we account for this class imbalance
during training). Instead of splitting our dataset into training and
validation parts at the farm level, we randomly crop out large connected
regions as validation areas, so as to avoid biases caused by spatial
correlation between nearby farms [48]. In total we hold out $\approx\,$20% as
validation data. For training, we project and rasterize every ground truth
polygon to the corresponding Sentinel-2 tile, randomly choose a patch of 320
$\times$ 320 meters (32 $\times$ 32 pixels) in which at least 10% of the
pixels are labelled, and extract the corresponding patch from a randomly
selected Sentinel-2 image. That procedure is repeated to generate hundreds of
millions of input samples. The statistical strength afforded by this massive
amount of training data is one reason for the good performance of our
framework.

### In situ data

Independent from training and validation data, we create a unique evaluation
protocol by gathering additional test data on the ground. Together with our
partners from industry, we designed a verification campaign by choosing over
2,000 random locations around ten different cities in Côte d’Ivoire, in such a
way that they overlap neither with the training nor with the validation set.
Each location was defined by a center coordinate and an area around that
center point, which may vary in size and shape. Several teams were sent out to
visit these pre-defined sites. Whenever possible, they were instructed to walk
around the boundary of the area and to report back the estimated percentage of
cocoa grown on the site. Furthermore, they were asked to note down any other
commodity grown within the area, i.e., an exemplary feedback would be that the
area was occupied by "60 % cocoa, 20 % natural forest, 10 % manioc, 10 % palm
trees". If the majority (>50 % of the total area) was cocoa, the location was
considered as a positive cocoa sample, otherwise a negative (non-cocoa) one.
The on-site verification lasted for more than six months, beginning in March
2021. In that dataset the actual cocoa plantings are not geo-located within
the polygons (smallholders in general grow multiple crops on their territory),
thus we evaluate our map on the farm level and count a polygon as cocoa if a
majority of its pixels falls into that class.

### Deep learning framework

With the aforementioned database, we were able to train end-to-end from raw
spectral values, bypassing the manual design of predictive features. Instead,
feature extraction is learned automatically. To that end, we employ a fully
convolutional neural network based on the architecture proposed by Lang et al.
[15]. The entry block of our model receives an image patch with nine
Sentinel-2 bands and passes it through three consecutive residual blocks [49]
with learnable 1 $\times$ 1 convolutional filters. The output of that purely
spectral, per-pixel analysis is then fed into a series of six residual blocks
with 3 $\times$ 3 depth-wise separable convolutional layers [50], which enable
the network to exploit textural features (i.e., spatial correlations between
pixels). Next the (normalized) vegetation height map is included, by simply
adding it to every channel of the intermediate feature map, and the result is
fed through two further separable residual blocks to obtain the final feature
representation. From it, the final output is computed with a single
convolutional layer with two 1 $\times$ 1 filters, whose 2-channel output is
passed through a Sigmoid transformation. This yields, at every 10 $\times$ 10
m pixel, two positive output values that sum to 1 and can be interpreted as
the probabilities for the presence, respectively absence of cocoa. Since there
are no downsampling operations and padding is applied in all residual blocks,
the input resolution is retained and one can directly compare the output to
the ground truth map. Additionally, as the network architecture is fully
convolutional, it is not fixed to a specific spatial input size and can
process image tiles of any size (subject to computing memory) during
inference, reducing computation time during deployment.

We optimize the neural network’s weights by minimizing the Dice coefficient,
also called the overlap index. The dice coefficient loss is defined as

$\mathcal{L}=\sum\limits_{c}\bigg{(}1-\frac{2\sum\limits_{i}p_{ci}g_{ci}+\epsilon}{\sum\limits_{i}p_{ci}+\sum\limits_{i}g_{ci}+\epsilon}\bigg{)},$
(2)

where $c$ is the number of classes, $i$ the pixel index, $p$ and $g$ the
prediction and ground truth, respectively. For numerical stability, a small
$\epsilon$ is added to the numerator and denominator. The Dice loss is a
common loss function used in medical image segmentation, as it is more robust
under data imbalance compared to loss functions based on standard cross-
entropy [51, 52]. As our training data is sparse within patches (a training
patch only needs to have ground truth at >10 % of its pixels), we further mask
out all pixels without a ground truth label and compute the Dice loss
selectively only for the labelled part. Patches are combined into batches of
size 32. The network is trained for 500 epochs, with each epoch consisting of
40,000 iterations, using the Adam optimizer [53] with a base learning rate of
$10^{-5}$. On our high-performance computing infrastructure, one training run
took slightly more than 5 days.

Confidence (respectively, uncertainty) estimates from individual deep neural
networks are known to be poorly calibrated [54]. For better uncertainty
calibration we employ a model ensemble [21]. Ten replicas of the neural
network just described are trained independently on the same dataset, with
different random initializations and different (random) batches. Additionally,
averaging estimates over multiple observations diminishes the influence of
faulty classifications due to noisy observations.

### Vegetation height map

Besides the nine Sentinel-2 optical bands, our network ingests a dense
vegetation height map as an auxiliary input channel (see previous section).
The per-pixel vegetation heights have been derived from Sentinel-2 optical
images with a deep learning method originally developed and tested for
Southeast Asia [55]. That method also employs a fully convolutional neural
network, but which is trained to regress canopy height from Sentinel-2
imagery, using as training target sparse canopy height samples extracted from
NASA’s GEDI mission [56]. Due to the sparsity of GEDI’s LiDAR footprints, we
train this model not only on samples from Côte d’Ivoire and Ghana, but on an
extended training area covering entire West Africa. Despite being trained on
sparse data, the model outputs a dense canopy height map with 10 m ground
sampling distance. Although the vegetation height map is also derived from
Sentinel-2, and therefore arguably just another feature that could be learned
from the input imagery, there are two reasons to directly incorporate it as an
input channel. On the one hand, cocoa trees are known to only grow to a
maximum height of $\approx$8 meters [57, 58] (sometimes under higher shade
trees, but these scattered trees protruding from the lower cocoa plants also
provide a distinctive height pattern). Hence, vegetation height is an obvious
predictive feature simply for its ability to identify high vegetation as not
being cocoa. It therefore seems reasonable to simplify the learning process
and save model capacity, by supplying it directly. On the other hand, there is
a more essential reason why we expect the separate tree height estimator to
improve the estimates, namely that it brings in additional information. While
the vegetation height map is indeed based on the same _input_ , it has not
been learned from the same _output_. Rather, the cocoa mapping pipeline
benefits from the additional, strong supervision signal of the GEDI LiDAR
measurements, which is baked into the canopy height map.

## Data availability

The cocoa probability map and its thresholded version will be released for
download and will be available in the Google Earth Engine. Both maps can be
explored interactively in the following Google Earth Engine application:

https://nk.users.earthengine.app/view/cocoa-map.

## Code availability

Code is available at https://github.com/D1noFuzi/cocoamapping/.

## Acknowledgements

The project received funding from Barry Callebaut Sourcing AG, as part of a
Research Project Agreement. In particular, we thank Barry Callebaut Sourcing
AG for realizing the ground campaign together. This research was funded
through the 2019-2020 BiodivERsA joint call for research proposals, under the
BiodivClim ERA-Net COFUND programme, and with the funding organisations Swiss
National Science Foundation. We greatly appreciate the open data policies of
the ESA Copernicus program.

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## Supplementary information

Country |  Region |  Derived planted area (Mha) |  Land cover (%)
---|---|---|---
Ghana |  Western region |  1.09 |  44.6
|  Eastern region |  0.29 |  15.5
|  Central region |  0.32 |  33.0
|  Volta region |  0.06 |  2.7
|  Brong Ahafro region |  0.29 |  7.3
|  Ashanti region |  0.66 |  26.7
Côte d’Ivoire |  Yamoussoukro region |  0.01 |  6.2
|  Woroba region |  0.03 |  1.0
|  Montagnes region |  0.95 |  31.2
|  Sassandra-Marahoué region |  0.54 |  22.3
|  Lacs region |  0.16 |  6.0
|  Lagunes region |  0.47 |  23.8
|  Gôh-Djiboua region |  0.60 |  38.5
|  Bas-Sassandra region |  1.18 |  42.9
|  Comoé region |  0.36 |  24.7
|  Zanzan region |  0.03 |  0.9
Table 1: Total planted area and relative land cover per region in Côte d’Ivoire and Ghana. Regions are defined following official administrative boundaries. Côte d’Ivoire | Ghana
---|---
Protected area |  Cocoa (ha) |  Land cover (%) |  Protected area |  Cocoa (ha) |  Land cover (%)
Taï National. (NP) |  1,552 |  0.3 |  Subri River (FR) |  726 |  1.2
Rapide Grah (CF) |  96,089 |  60.1 |  Bia Tawya (FR) |  41,727 |  77.1
Haute Dodo (CF) |  75,281 |  51.3 |  Tain Tributa. (FR) |  132 |  0.2
Scio (CF) |  90,418 |  68.2 |  Krokosua Hil. (FR) |  5,099 |  10.8
Niegre (CF) |  108,256 |  81.8 |  Anlo-Keta la. (RSWoII) |  0 |  0.0
Seguela (CF) |  25,564 |  22.3 |  Tano Ofin (FR) |  858 |  2.1
Mt. Sassandr. (CF) |  54,946 |  49.0 |  Ankasa (RR) |  776 |  2.1
CFNU No.72 (CF) |  51,080 |  48.7 |  Kyabobo Nati. (NP) |  6 |  0.0
Marahoue Nat. (NP) |  8,352 |  8.1 |  Kogyae (SNR) |  0 |  0.0
Mont Sangbe . (NP) |  0 |  0.0 |  Bia North (FR) |  1,010 |  2.8
Songan/Tamin (CF) |  35,763 |  39.0 |  Subuma (FR) |  997 |  2.8
Mabi/Yaya (CF) |  16,299 |  18.2 |  Sui River (FR) |  3,497 |  9.8
CFNU No.77 (CF) |  8,730 |  9.8 |  Mpameso (FR) |  741 |  2.2
CFNU No.58 (CF) |  62,359 |  72.3 |  Bia National. (NP) |  891 |  2.8
Go Bodienou (CF) |  39,275 |  47.9 |  Bia Resource. (RR) |  723 |  2.3
Yarani (CF) |  2,720 |  3.7 |  Boin River (FR) |  764 |  2.5
Issia (CF) |  18,603 |  27.9 |  Bia National. (UBR) |  18 |  0.1
Nibi Hana (CF) |  29,982 |  48.3 |  Manzan (FR) |  15,512 |  56.1
Duekoue (CF) |  18,096 |  29.7 |  Asenanyo (FR) |  2,232 |  8.6
Sangoue (CF) |  21,373 |  36.1 |  Asukese (FR) |  87 |  0.3
Bayota (CF) |  18,204 |  31.5 |  Yoyo (FR) |  270 |  1.2
Mt. Ko (CF) |  5,295 |  9.2 |  Bomfoun (FR) |  0 |  0.0
Dogodou (CF) |  21,308 |  39.9 |  Subin (FR) |  533 |  2.3
Irobo (CF) |  13,225 |  25.3 |  Draw River (FR) |  193 |  0.9
De (CF) |  16,386 |  31.5 |  Atewa Range (FR) |  210 |  1.0
Tene (CF) |  6,763 |  14.5 |  Tano Ehuro (FR) |  16,275 |  77.6
Besso (CF) |  19,732 |  44.2 |  Kakum (NP) |  256 |  1.2
Beki Bosse M. (CF) |  12,096 |  28.8 |  Tano Nimri (FR) |  669 |  3.3
Diambarakrou (CF) |  26,581 |  64.2 |  Pamu Berekum (FR) |  89 |  0.5
CFNU No.43 (CF) |  12,993 |  32.1 |  Bia Tano (FR) |  195 |  1.0
CFNU No.66 (CF) |  1,141 |  3.0 |  Afram Headwa. (FR) |  60 |  0.3
Plaine des E. (CF) |  7,489 |  20.2 |  Bodi (FR) |  12,947 |  70.0
Kamesso (CF) |  10 |  0.0 |  Chai River (FR) |  22 |  0.1
Abeanou (CF) |  4,550 |  13.6 |  Bandai Hills. (FR) |  0 |  0.0
Tanoe (CF) |  18,332 |  55.7 |  Fure Headwat. (FR) |  669 |  4.0
Monogaga (CF) |  20,937 |  65.3 |  Oda River (FR) |  306 |  1.8
Gouin (CF) |  19,099 |  61.2 |  Bonsa Ben (FR) |  178 |  1.1
Mont Peko Na. (NP) |  6,479 |  21.5 |  Bonsa River (FR) |  1,059 |  6.5
Seguie (CF) |  8,850 |  29.9 |  Worobong Nor. (FR) |  1,852 |  11.3
CFNU No.50 (CF) |  2,862 |  9.7 |  Dadieso (FR) |  303 |  1.9
CFNU No.30 (CF) |  3,552 |  12.2 |  Fure River (FR) |  161 |  1.0
Yalo (CF) |  2,941 |  10.1 |  Assin Attand. (RR) |  387 |  2.5
CFNU No.40 (CF) |  2,760 |  9.8 |  Desiri (FR) |  9,383 |  60.2
N’Zo Fauna R. (PFR) |  13 |  0.0 |  Boabeng-Fiem. (WS) |  0 |  0.0
Mopri (CF) |  10,742 |  39.6 |  Togo Plateau (FR) |  301 |  2.0
Arrah (CF) |  7,809 |  29.6 |  Tano Anwia (FR) |  212 |  1.4
CFNU No.56 (CF) |  12,007 |  47.2 |  Tonton (FR) |  161 |  1.1
CFNU No.31 (CF) |  228 |  0.9 |  Bosomkese (FR) |  250 |  1.7
Tiapleu (CF) |  5,416 |  22.0 |  Sukusuki (FR) |  12,053 |  83.6
Mt. Tia (CF) |  14,886 |  62.0 |  Southern Sca. (FR) |  1,486 |  10.7
Ebrinenou (CF) |  12,744 |  53.6 |  Upper Wassaw (FR) |  3,198 |  23.6
Moyenne Mara. (CF) |  1,280 |  5.7 |  Bonsam Bepo (FR) |  807 |  6.1
Azagny Natio. (NP) |  249 |  1.1 |  Nini-Suhien (NP) |  665 |  5.1
Côte d’Ivoire | Ghana
---|---
Protected area |  Cocoa (ha) |  Land cover (%) |  Protected area |  Cocoa (ha) |  Land cover (%)
CFNU No.53 (CF) |  2,619 |  12.5 |  Awura (FR) |  0 |  0.0
Agbo (CF) |  12,029 |  57.8 |  Neung South (FR) |  399 |  3.1
Zuoke (CF) |  10,825 |  52.1 |  Pra Anum (FR) |  984 |  7.6
Abokouamekro. (NR) |  4 |  0.0 |  Opon Mansi (FR) |  646 |  5.0
Niouniourou (CF) |  12,375 |  64.5 |  Opro River (FR) |  19 |  0.2
Hein (CF) |  2,546 |  13.9 |  Bosomoa (FR) |  0 |  0.0
Tyemba (CF) |  0 |  0.0 |  Esukawkaw (FR) |  124 |  1.0
Zagoreta (CF) |  8,844 |  50.0 |  Southern Sca. (FR) |  277 |  2.3
Vavoua (CF) |  4,186 |  23.8 |  Anhwiaso Eas. (FR) |  1,033 |  8.5
Brassue (CF) |  5,034 |  28.8 |  Boi Tano (FR) |  210 |  1.7
Semien Flans. (CF) |  4,536 |  26.0 |  Tinte Bepo (FR) |  316 |  2.6
Elroukro (CF) |  2,661 |  15.3 |  Ayum (FR) |  134 |  1.1
Adzope (CF) |  6,500 |  38.3 |  Worobong Sou. (FR) |  104 |  0.9
Tos (CF) |  1,477 |  8.9 |  Bowiye Range (FR) |  222 |  2.0
Kavi (CF) |  9,125 |  57.8 |  Chirimfa (FR) |  3 |  0.0
Oume Doka (CF) |  4,213 |  27.0 |  Bimpong (FR) |  233 |  2.2
Marahoue (CF) |  2,789 |  18.0 |  Pra Suhyien . (FR) |  373 |  3.6
Tankesse (CF) |  2,307 |  15.3 |  Bura River (FR) |  113 |  1.1
Kravassou (CF) |  5,468 |  37.2 |  Nkrabia (FR) |  244 |  2.4
Ira (CF) |  5,871 |  41.1 |  Tano Suhyien (FR) |  407 |  4.5
Manzan (CF) |  5,596 |  39.9 |  Mankrang (FR) |  1 |  0.0
Mt. De (CF) |  1,900 |  14.0 |  Kwamisa (FR) |  234 |  2.8
Bouafle (CF) |  2,163 |  16.1 |  Fum Headwate. (FR) |  158 |  1.9
Dassieko (CF) |  4,692 |  34.8 |  Kabo River (FR) |  1,544 |  19.0
CFNU No.39 (CF) |  7,484 |  55.9 |  Dampia Range (FR) |  414 |  5.1
Kassa (CF) |  2,617 |  19.9 |  Dome River (FR) |  533 |  6.7
Offumpo (CF) |  6,637 |  51.2 |  Pra Suhyien . (FR) |  76 |  1.0
Ananguie (CF) |  7,712 |  60.5 |  Asuokoko Riv. (FR) |  157 |  2.0
Nizoro (CF) |  7,477 |  59.4 |  Afrensu Broh. (FR) |  0 |  0.0
Mt. Sainte/C. (CF) |  7,612 |  62.7 |  Tano Suraw E. (FR) |  2,216 |  28.6
Bableu (CF) |  5,249 |  43.6 |  Kalakpa (RR) |  0 |  0.0
Krozalie (CF) |  5,486 |  48.7 |  Asubima (FR) |  1 |  0.0
Mt. Bolo (CF) |  4,929 |  44.0 |  Bosumtwi Ran. (FR) |  1,089 |  14.1
CFNU No.68 (CF) |  6,683 |  59.7 |  Bomfobiri (WS) |  333 |  4.3
Mando (CF) |  2,016 |  18.7 |  North Bandai. (FR) |  0 |  0.0
CFNU No.42 (CF) |  4,387 |  41.7 |  Bonkoni (FR) |  53 |  0.7
CFNU No.34 (CF) |  4,946 |  47.2 |  Jema Asemkro. (FR) |  1,925 |  27.1
Kouadikro (CF) |  246 |  2.4 |  Ndumfri (FR) |  156 |  2.2
CFNU No.36 (CF) |  96 |  0.9 |  Apepesu Rive. (FR) |  1,084 |  16.2
Bolo (CF) |  4,676 |  47.1 |  Muro (FR) |  183 |  2.8
CFNU No.74 (CF) |  1 |  0.0 |  Sawsaw (FR) |  9 |  0.1
CFNU No.33 (CF) |  2,514 |  27.3 |  Mirasa Hills (FR) |  191 |  3.0
Goudi (CF) |  4,548 |  50.2 |  Totua Shelte. (FR) |  65 |  1.0
CFNU No.57 (CF) |  589 |  6.5 |  Northern Sca. (FR) |  0 |  0.0
CFNU No.38 (CF) |  2,684 |  30.7 |  Nsuensa (FR) |  10 |  0.2
Table 2: Extended table of the one hundred protected areas ordered according
to their total protected area in Côte d’Ivoire and Ghana (within suitable
cocoa growing area). CFNU = classified forest unknown name, CF = classified
forest, NP = national park, WS = wildlife sanctuary, FR = forest reserve, RR =
resource reserve.