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Int J Appl Earth Obs Geoinformation

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Assessing factors impacting the spatial discrepancy of remote sensing basedcropland products: A case study in AfricaMohsen Nabila,b,c, Miao Zhanga,*, José Bofanaa,b,d, Bingfang Wua,b, Alfred Steine, Taifeng Dongf,Hongwei Zenga, Jiali Shangfa State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinabUniversity of Chinese Academy of Sciences, Beijing 100049, Chinac Division of Agriculture Applications, Soils, and Marine (AASMD), National Authority for Remote Sensing & Space Sciences, Egyptd Faculty of Agriculture-Catholic University of Mozambique-Cuamba, Mozambiquee International Institute for Geo-information Science and Earth Observation (ITC), P.O. Box 6, 7500 AA Enschede, the Netherlandsf Science and Technology Branch, Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C6, Canada

A R T I C L E I N F O

Keywords:Cropland mappingLand coverRemote sensingAfricaSpatial agreementLimiting factors

A B S T R A C T

Many African countries are facing increasing risks of food insecurity due to rising populations. Accurate andtimely information on the spatial distribution of cropland is critical for the effective management of crop pro-duction and yield forecast. Most recent cropland products (2015 and 2016) derived from multi-source remotesensing data are available for public use. However, discrepancies exist among these cropland products, and thelevel of discrepancy is particularly high in several Africa regions. The overall goal of this study was to identifyand assess the driving factors contributing to the spatial discrepancies among four cropland products derivedfrom remotely sensed data. A novel approach was proposed to evaluate the spatial agreement of these croplandproducts and assess the impact of environmental factors such as elevation dispersion, field size, land-coverrichness and frequency of cloud cover on these spatial differences. Results from this study show that the overallaccuracies of the four cropland products are below 65%. In particular, large disagreements are seen on datasetscovering Sahel zone and along the West African coasts. This study has identified land-cover richness as thedriving factor with the largest contribution to the spatial disagreement among cropland products over Africa,followed by the high frequency of cloud cover, small and fragmented field size, and elevation complexity. Toimprove the accuracy of future cropland products for African regions, the data producers are encouraged to takea multi-classification approach and incorporate multi-sensors into their cropland mapping processes.

1. Introduction

Given the overall increase in food production at the global scale,many parts of the world continue to face food insecurities. Of the 86countries that are defined as low-income and food-deficient, 43 are inAfrica (http://www.fao.org/3/w9290e/w9290e01.htm). According tothe Food and Agriculture Organization of the United Nations (UN), theprevalence of hunger in recent years has been on the rise in Africa, aftermany years of decline. Among the many causes, the high populationgrowth rate has contributed to Africa’s high risk of food insecurity (FAOet al., 2015). By 2050, the continent will occupy more than half of theworld’s population (FAO and ECA, 2018). To fulfil food requirements,African countries need to achieve a 60–70% increase in annual agri-cultural output by 2050 (Organization, 2009; Alexandratos and

Bruinsma, 2012; See et al., 2015). Compounded by climate change,plans and investments for agricultural land expansion and crop pro-ductivity may be hampered by the lack of accurate cropland informa-tion at national and local levels (See et al., 2015; Fritz et al., 2013). Atthe national scale, accurate information on cropland acreage, spatialdistribution and yield forecasts can support well-informed decisionmaking on food import and export. At the local scale, detailed mappingcan help guide the beneficial management practices (BMPs) to improveyield (Funk and Brown, 2009; Matton et al., 2015). While traditionalmethods, such as field surveys, can be reliable in gathering croplandinformation, they are time-consuming and costly. Remote sensingtechnology offers an efficient alternative for cropland information col-lection. Especially with the recent advancement in increased spatialresolute and reduced data cost, remote sensing based land-cover

https://doi.org/10.1016/j.jag.2019.102010Received 28 July 2019; Received in revised form 9 October 2019; Accepted 3 November 2019

⁎ Corresponding author.E-mail addresses: mohsen2017@radi.ac.cn (M. Nabil), zhangmiao@radi.ac.cn (M. Zhang).

Int J Appl  Earth Obs Geoinformation 85 (2020) 102010

Available online 19 November 20190303-2434/ © 2019 Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/).

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products have been routinely used in large-scale studies (KaptuéTchuenté et al., 2011; Geng et al., 2013; Buerkert and Hiernaux, 1998;Xiong et al., 2017).

In the past two decades, different land-cover products, as listed inTable 1, have been developed using multi-satellite sensors (e.g., MODIS,Landsat series, Proba-V and Sentinel-2) for public use. Despite the up-to-date information provided by these products over wide geographicalareas at almost no cost, mapping cropland from remote sensing remainsto be a challenge. Large uncertainties and discrepancies among dif-ferent cropland/land-cover products have been found particularly overAfrica (Fritz et al., 2011; Giri et al., 2005; Vancutsem et al., 2012; Xuet al., 2019; Fritz et al., 2013; FAO, 2010). Hence, great efforts areneeded to investigate the causes of discrepancies and develop meth-odologies to reduce them.

Many previous studies have focused on assessing the accuracy ofindividual cropland products over Africa. For example, Liu et al. (2018)made a comparison of cropland areas derived from the annual updatedESA CCI land cover maps and the official statistics at the national level.Their results showed an overestimation of cropland area in mostcountries (Liu et al., 2018). Validation of the three landcover datasets(CGLS-LC100, ESA-S2-LC20 and FROM-GLC-Africa30) revealed anoverall accuracy of above 60% for all three datasets, while the CGLS-LC100 was the most accurate in mapping cropland in Africa whencompared with FAO statistics (Xu et al., 2019). The overall accuracy ofESACCI-LC_S2_Prototype map was determined as approximately 65%with an overestimation of cropland areas in Chad and Sudan, andmissing croplands in Morocco and Algeria (Lesiv et al., 2017). Studiesalso found large spatial discrepancies among different cropland pro-ducts due to high omission of croplands in Africa, especially overTanzania, Kenya and Somalia (Fritz et al., 2010; Vancutsem et al.,2012). The comparison among the three datasets (CGLS-LC100, ESA-S2-LC20 and FROM-GLC-Africa30) indicated a great disagreement (43%)in mapping vegetation types (forest, shrubland, grassland and cropland)over mountainous mining regions and the Sahel zone in Africa (Xuet al., 2019). However, limited research has explored the potentialcauses of these differences, especially in mapping African’s cropland.

The overall goal of this study was to assess the spatial differencesamong four recent land-cover products over Africa that are threelandcover datasets (ESA Climate Change Initiative – global Land Coverdataset for the year 2015 (ESA CCI-LC-2015, 300m), Copernicus globalland operations (CGLS) land-cover product of Africa (CGLS-LC100,100m), the ESACCI-LC_S2_Prototype map for Africa (ESACCI-LC_S2_Prototype, 20m)), and one cropland dataset (Cropland ExtentProduct (GFSAD30AFCE, 30m) over Africa (Xiong et al., 2017)). Spe-cific objectives include 1) assess the accuracy and differences in thespatial distribution of croplands among the four recent land-cover andcropland datasets over Africa; 2) identify the regions with low spatialagreement among datasets where the accuracy of mapping croplandneeds to be improved; 3) identify the driving factor within the fourrecognized impacting factors (elevation dispersion, field size, land-cover richness, and frequency of cloud cover over the low agreementareas.

2. Data collections

2.1. Satellite-derived land-cover datasets

Four land-cover datasets have been selected for this study. The ESACCI-LC dataset of 300m spatial resolution was produced by the ESAClimate Change Initiative - Land Cover project 2017. The satellite dataintegrated the entire MERIS FR and RR archive from 2003 to 2012,AVHRR time series between 1992 and 1999, SPOT-VGT time seriesbetween 1999 and 2013, and PROBA-V data for the years of 2013, 2014and 2015. Using both unsupervised classification and machine learningalgorithms advantage has been taken of both the spectral and temporalrichness of the time series satellite images. The classification schemewas designed following the US Land Cover Classification System (LCCS)with 22 landcover classes. Cropland was represented with four classes;two pure (rain-fed and irrigated) and two mixed cropland classes. Thefull ESA CCI-LC dataset consists of 24 annual LC maps (from 1992 to2015). In this study, the land cover map for 2015 was used. The ac-curacy of this map was previously reported as 71.5% (CCI-LC-PUGV2,2017).

The Copernicus global land operations (CGLS) land cover product ofAfrica (CGLS-LC100) was the first Land Cover map produced by CGLSfor the year 2015 over the Continent of African. The map (https://land.copernicus.eu/global/products/lc) was generated from both PROBA-V300m and PROBA-V 100m time-series using the random forest algo-rithm trained and validated by 10m resolution samples gathered fromhigh-resolution satellite image interpretation using the Geo-wiki tool(https://geo-wiki.org/). The final product was one discrete landcovermap with 13 landcover classes defined according to the UN Land CoverClassification System (LCCS), as well as a set of four vegetation coverproportion maps. In our study, we used the discrete landcover map withan overall accuracy of 74.3%. The map has one integrated croplandclass that has a user’s accuracy at 66.5% and producer’s accuracy at67% (CGLOPS-1, 2018).

The 30m Cropland Extent Product (GFSAD30AFCE) dataset wasdeveloped by (Xiong et al., 2017) to map the extent of Africa’s croplandfor the growing season (July 2015 to June 2016). Satellite images fromboth Sentinel-2 MSI and Landsat-8 OLI were used to obtain two half-yearly cloud-free mosaics (Period 1: January – June 2016 and Period 2:July – December 2015) over the continent. The four correspondingspectral bands between Sentinel-2 MSI and Landsat-8 OLI, the calcu-lated NDVI from each sensor and the slope layers were used as inputs tothe pixel-based (random forest) and object-based classifiers. The re-sultant map has three categories (cropland, non-cropland, and waterbodies) with an overall accuracy of 94.5%. The cropland class has85.9% producer’s accuracy and 68.5% user’s accuracy (Xiong et al.,2017).

The ESACCI-LC_S2_Prototype map was built by ESA’s ClimateChange Initiative Land Cover project 2017 using more than 30,000Sentinel-2A L1C images (1 year of Sentinel-2A observations from Dec.2015 to Dec. 2016). Two classification algorithms, the RF and MachineLearning (ML), were used to map landcover over the whole of Africa.

Table 1List of several remote sensing land-cover products produced in the last two decades.

Dataset Spatial Resolution Year(s) Producer

MODIS Terra+Aqua Combined Land Cover product (MCD12Q1) 500 m 2001-2018 (Friedl et al., 2002)Global Land Cover 2000 (GLC2000) 1 km 2000 (Bartholomé and Belward, 2007)MERIS derived GlobCover datasets 300m 2005 & 2009 (Defourny et al., 2006)Finer resolution observation and monitoring of global land cover (FROM-C) 30m 2015 (Geng et al., 2013)Landsat-derived GLOBELAND30 (GLC30) dataset 30m 2000 & 2010 (Chen, 2015)ESA Climate Change Initiative – global Land Cover dataset (ESA CCI-LC) 300m 1992 - 2015 ESA Climate Change Initiative - Land Cover project 2017.Copernicus global land operations (CGLS) land cover product of Africa

(CGLS-LC100)100m 2015 Copernicus Global Land Service (CGLS)

ESACCI-LC_S2_Prototype map for Africa 20m 2016 ESA Climate Change Initiative - Land Cover project 2017.Cropland Extent Product (GFSAD30AFCE,) dataset over Africa 30m 2015 (Xiong et al., 2017)

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Fig. 1. A schematic shows how binary cropland maps were converted into cropland percentages and categories (green colour represents crop pixels, and grey colourrepresents non-crop pixels) at the same 300×300m reference location (black rectangle). (For interpretation of the references to colour in this figure legend, thereader is referred to the web version of this article.)

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The two maps produced by the two aforementioned classifiers werethen integrated to generate the final landcover map with nine landcoverclasses (Fabrizio et al., 2018). The resultant map has an overall accu-racy of 65%, and the cropland class has the user’s and producer’s ac-curacies of 46% and 71%, respectively (Lesiv et al., 2017).

Other information on spatial resolutions, satellite data, classifica-tion systems and classification algorithms of each of the four productsare summarized in Appendix Table A1.

2.2. The validation dataset

The validation dataset for the 2015–2016 growing season was de-rived from the Geo-Wiki global reference cropland database (LasoBayas et al., 2017). This global database was produced through acrowdsourcing campaign with participation from trained students andexperts. Overall, almost 36,000 possible cropland locations around theworld were reviewed through visually interpreting high-resolutionimagery (Google and Bing) and analyzing NDVI time-series profilesfrom different sensors (e.g. Landsat 7 ETM+, Landsat 8 OLI, andMODIS Terra) at each of the 300×300m sample sites. The percentageof cropland was determined by more than one participant at each site.In our study, we derived our validation dataset by averaging croplandpercentages determined by different users at 7311 sample locationsthroughout Africa. The derived validation dataset was then used toassess the mapping accuracy of the aforementioned four cropland da-tasets over Africa.

2.3. Ancillary data

In this study, four ancillary datasets used include the Global Agro-Environmental Stratification (GAES) (Mücher et al., 2016), theGTOPO30 (Global Topography in 30 arc-sec) dataset, the Global FieldSize (GFS) map (Fritz et al., 2015), and the MODIS derived Global 1 kmCloud Cover (GCC1 km) dataset (Wilson and Jetz, 2016).

The stratification of GAES was based on the region’s agro-environ-mental characteristics including climatic regimes, soil, terrain, eleva-tion conditions, water availability and land cover proprieties. Theproduct includes four different levels of details, and the fourth level isthe most detailed. Over Africa, a total number of 238 strata at Level 4were clipped from the global dataset to be used to highlight the areashaving low spatial agreement among the four cropland datasets.

The GTOPO30 is a global digital elevation dataset for the worldgenerated by the United States Geological Survey (USGS) with a spatialresolution of 30-arc seconds (approximately 1 km) (Gesch andGreenlee, 1996). It was used in this study to produce the elevationdispersion layer over Africa.

The global field size map was produced at 1 km resolution based onfield-size data collected via a Geo-Wiki crowdsourcing campaign (Fritzet al., 2015). The GFS map ranks the sizes of the global agriculturalfields from very small to large. In our study, the GFS map was used toderive the average size of agriculture fields over Africa.

The GCC1 km dataset was produced by (Wilson and Jetz, 2016) withhigh agreement (R2=0.74, n=53,678, p < 0.001) with weatherstation observations. We used this dataset to generate the average an-nual cloud frequency over Africa. To further investigate the cloud

effect, average cloud frequency for each of the three periods (October –January, February – May, and June – September) was also generated.

3. Methodology

3.1. Dataset harmonization, cropland layer generation and accuracyassessment

Data harmonization is a key prerequisite to obtain meaningful in-formation from multiple spatial datasets (Fichtinger et al., 2011;Pollard et al., 2019). In this study, data harmonization was performedbefore spatial comparison to ensure the comparability among datasets.To achieve this, all four datasets were first reprojected to the samegeographic coordinate system ([GCS-WGS-1984], DATUM: [D-WGS-1984]). A binary map was then generated using each of the four re-projected products. The resultant map contains only two landcovertypes, cropland and non-cropland. Three of the four datasets (CGLS-LC100, GFSAD30AFCE and ESACCI-LC_S2_Prototype map) have onepure cropland category. The ESA CCI-LC 2015 dataset has two types ofcropland classes, pure cropland and mosaic cropland. The pure crop-land class refers to irrigated and rainfed cropland. The mosaic classcontains a range of cropland cover fractions. Hence, this dataset wasfurther reclassified into four classes (1: 100% cropland, 2:> 50%cropland, 3:< 50% cropland and 4: non-cropland class includes allother landcover classes). Finally, four cropland datasets (20m, 30m,100m and 300m) were derived.

The reference dataset used in this study consists of percentagecropland cover estimated form 300×300m frames over Africa (LasoBayas et al., 2017). To validate the cropland layers, the percentages ofcropland cover at the same reference locations were also estimated bycounting the cropland pixels to the total number of pixels containedwithin the 300m×300m area for the cropland layers derived fromeach of the three datasets (GFSAD30AFCE, CGLS-LC100, and ESACCI-LC_S2_Prototype) (Fig. 1). These derived cropland percentage coverwere then compared against the reference dataset by exploring theirlinear regression, the Pearson coefficient r and the root-mean-square-error (RMSE). The cropland layer derived from ESACCI-LC_2015 da-taset was excluded from regression analysis as it has categorical crop-land classes (100%,< 50%, and> 50% cropland) rather than con-tinues cropland percentage (from 0 to 100% cropland coverage).Therefore, in order to compare the four datasets, all cropland percen-tage maps and the validation dataset were converted to the same ca-tegorical cropland classes as in ESA-LC_2015 dataset (1: noncropland,2: cropland<50%, 3: cropland> 50%, and 4: pure cropland 100%) asshown in Fig. 1. Then, the accuracies of all four cropland datasets werecompared using descriptive statistics (overall, producer’s and user’saccuracy, and Kappa coefficient).

3.2. Spatial-agreement assessment of cropland datasets

The four cropland layers extracted from the aforementioned fourdatasets have different spatial resolutions (20m, 30m, 100m, and300m). To unify the spatial resolution and perform the per-pixelcomparison of the four cropland datasets, they were converted intocropland coverage percentage layers following the same methodologyproposed in (Fritz et al., 2011, 2010). Cropland coverage was defined asthe percentage of cropland pixels to the total number of pixels within a10 km×10 km cell. For the ESA CCI LC 2015, the percentage for purecropland pixels (in Class1) was considered as 100%, while the mixedcropland pixels (Class2 > 50% cropland and Class3 < 50% cropland)were considered as 75% and 25% cropland coverage, respectively. Fi-nally, four cropland coverage percentage layers were produced in10 km cell size. The agreement among the cropland layers over Africawas measured by estimating the per-pixel standard deviation (STdv).The low STdv value indicates that the cropland coverage percentagesmeasured by the four datasets are close (high agreement), while the

Table 2Linear regression analysis between each of the cropland datasets and the re-ference dataset.

ESACCI-LC_S2_Prototype

GFSAD30AFCE CGLS-LC100

R2 0.44 0.45 0.47RMSE 37.6 37.3 36.6N. of validation locations 7311 7311 7311

Regression constant= 0, Confidence level= 95%.

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Fig. 2. The 10 km spatial agreement maps derived based on the standard deviation (STdv) between two datasets ((a) between ESACCI-LC_S2_Prototype map andGFSAD30AFCE (b) between ESACCI-LC_S2_Prototype map and CGLS-LC100 (c) between GFSAD30AFCE and CGLS-LC100), among three datasets (d) (ESACCI-LC_S2_Prototype map, GFSAD30AFCE and CGLS-LC100), and among the four datasets (e)).

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high STdv means high variation in mapping cropland coverage amongdatasets (low agreement).

By overlaying the African Agro environmental strata (AES) with the10 km map that shows the spatial agreement among all four datasets,the zones with the lowest spatial agreement were identified. The cor-relation between several factors and spatial disagreement among da-tasets in these regions were further investigated.

3.3. Factors affecting cropland mapping accuracies

The accuracies of cropland map products are the combined resultsof many factors. The use of different classification schemes and theabsence of a universal landcover system had a negative influence on thespatial consistency among landcover datasets (Herold et al., 2008; Wuet al., 2008; Gao and Jia, 2012). The overlap in the definition of dif-ferent classes among classification systems (Giri et al., 2005), the use ofthresholds for separating the classes (Fritz and See, 2005) and the ab-sence of some classes in one of the classification systems (McCallumet al., 2006) can also lead to large spatial discrepancies. In addition, thepixel-based classification methods used (Wu et al., 2008), datasets de-rived from different data sources (Giri et al., 2005), and when the da-tasets being compared representing different time frames (Pérez-Hoyoset al., 2017), reduced spatial consistency between landcover datasetscan occur. Furthermore, environmental factors such as complex topo-graphy featured by high elevation and slope usually lead to smallerfield sizes for cultivation. The highly fragmented landscape tends tocause large spectral confusions among different landcover types;whereas the use of coarse-resolution landcover datasets in these regionscan introduce highly uncertainties in the final map projects (Wu et al.,2008; Pérez-Hoyos et al., 2017).

Previous studies have assessed the impacts of elevation, slope,temperature and precipitation on the discrepancies among differentlandcover datasets and the impact of cloud frequency on croplandmapping accuracy (Xu et al., 2019). In our study, we focused on theinfluences of four factors including elevation dispersion, agriculturefield size, landcover richness and frequency of cloud occurrence on theaccuracy and spatial consistency of the cropland layer derived from thefour datasets in Africa. Comparing with the impact factors evaluated inprevious studies, the factors considered in our study have more complexnatures. Instead of using the simple elevation on its own, we usedelevation dispersion which describes the variation in the elevations of agiven area as an indicator of the surface roughness. It was measured asthe standard deviation between each of the 10× 10 neighbour pixels ofthe 1 km GTOPO30 elevation dataset and assigned the value to the new10 km grid cells. The agriculture field-size layer over Africa was derivedfrom the 1 km global field-size map by calculating the average of each

10× 10 pixel window and assigning the value to the new 10 km gridcells. Cloud frequency is expressed as a percentage of cloud occurrence(0% means no clouds during the period, and 100% means that theclouds were present during the whole period). The annual cloud fre-quency was retrieved from the MODIS derived Global 1-km CloudCover dataset by averaging every 10×10 pixels and assigning thevalue to the new 10 km grid cells over Africa. The same method wasused to produce the average cloud frequency during each of the three 4-month periods (October – January, February – May, and June – Sep-tember) at 10 km grid cells. These three periods could are considered asthe start, mid and end of the growing season for most of the Africanregions.

Measurement of landscape heterogeneity was made in previousstudies (Fritz et al., 2011; Herold et al., 2008; McCallum et al., 2006) bycounting the number of patches (contiguous areas of the same land-cover class) or by determining the proportion of pixels located in bothhomogeneous and heterogeneous areas. Then, the relation between thelandcover dataset’s heterogeneity and mapping accuracy was in-vestigated. In our study, landcover richness was determined bycounting how many different thematic classes are present in each targetneighbourhood (10× 10 km), and the average value from the threedatasets (ESA CCI-LC 2015, CGLS-LC100, and ESACCI-LC_S2_Prototype)was used to describe the richness of the landscape. An area is con-sidered homogenous if only one class is present in the area. Beforedetermining the landcover richness, the legends of the three landcoverdatasets were reconciled into a more general legend with 8 mainlandcover classes (1: Cropland, 2: Trees cover areas, 3: Shrubs coverareas, 4: Herbaceous vegetation, 5: Bare/sparse vegetation, 6: Herbac-eous wetland, 7: Built-up areas and 8: Water).

The effects of the four limiting factors at low spatial-agreementregions, identified based on the methodology described in Section 3.2,were assessed using the Pearson correlation coefficient r calculatedbetween each limiting factor and the degree of spatial agreement. Basedon r for each factor, the map revealing the key limiting factor (high r, p-value< 0.05) for each region was produced. When no significant re-lationship for any specific factor over a region, we consider that allfactors have an equal impact on the accuracy of cropland mapping.

4. Results & discussion

4.1. Cropland mapping accuracy assessment

Table 2 reveals that the three datasets (ESACCI-LC_S2_ Prototype,GFSAD30AFCE and CGLS-LC100) had moderate correlations with thereference dataset. The average R2=0.45 and average RMSE=37 ±0.5% for the cropland class for the three datasets combined.

Fig. 3. (a) the area of different agreement levels estimates as a percentage of the total area of agreement, and (b) the RMSE in estimating the actual cropland coveragepercentage at each level of agreement.

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Individually, the CGLS-LC100 dataset achieved the highest correlation(R2=0.47) and lowest error (RMSE=36.6% for cropland).

Error matrices were used in comparing the four datasets after con-verting all cropland layers using the same categories of ESA-LC_2015cropland layer. As shown in Appendix Table A2, GFSAD30AFCE had thehighest accuracy, followed by CGLS-LC100, ESA CCI-LC 2015, andESACCI-LC_S2_Prototype map. In general, the overall accuracies of allfour datasets were below 65%. The fully-cropped areas (100% croplandcoverage) were mapped with higher producer’s accuracy, but with thelowest user’s accuracy, compared with areas having less than 100%cropland coverage. This suggests that cropland areas were over-estimated in all four datasets cropland datasets, which is consistentwith the conclusion from other studies (Liu et al., 2018; Xu et al., 2019)

4.2. Regions of low spatial agreement

The spatial agreements between datasets are shown in Fig. 2. Ingeneral, the standard deviations among all four cropland datasets varyfrom 0 to 50% cropland coverage over Africa. The Sahel is the regionshowing the highest spatial disagreement (Fig. 2e), which reflects thehigh uncertainty of the actual cropland distribution from the four da-tasets. A large spatial difference between ESACCI-LC_S2_Prototype mapand GFSAD30AFCE cropland datasets was also found in some regions inthe northwest part of Africa, especially in Morocco and Algeria(Fig. 2a). Large portions of croplands were missing from ESACCI-LC_S2_Prototype map in Morocco and Algeria, as reported by (Lesivet al., 2017). The spatial difference over this region is lower between

Fig. 4. Low agreement regions (STdv> 10%) among the four cropland datasets over Africa.

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GFSAD30AFCE and CGLS-LC100 as shown in Fig. 2c. A large spatialdifference also appeared in West African coast when comparingGFSAD30AFCE and CGLS-LC100 (Fig. 2c) and also among the fourproducts (Fig. 2e). In particular, the GFSAD30AFCE underestimatedcropland area for both Cote d'Ivoire and Ghana in West Africa whencompared with statistical area from UN FAO (Xiong et al., 2017). Thelow agreement in coastal regions of Somali is apparent when comparingCGLS-LC100 with other cropland datasets (Fig. 2b & c). This region isamong other areas where CGLS-LC100 showed a low mapping accuracyas reported by (CGLOPS-1, 2018). The areas with low agreement amongdatasets increased when adding the ESA-LC_2015, especially in Sahel

and West African coast.Fig. 2e shows the agreement among the four cropland datasets re-

presented by ten categories of agreement. The area of each category inis presented in Fig. 3a as a percentage of the total area of agreement(areas with STdv>0). Fig. 3a also shows that 63.8% of the total area ofcropland has STdv less than 10%. Fig. 3b demonstrates that the accu-racy of cropland map decreases with reduced spatial agreement amongthe four datasets (RMSE in mapping cropland coverage increases withthe increase of STdv), with a relatively strong relationship (r=0.73).The average accuracy of cropland map over areas with STdv<10%achieved 70% for all four datasets, while average accuracy over the

Fig. 5. Maps showing key limiting factors for each of the low-agreement areas over the Continent of Africa, with four regions highlighted: a) West African coast, themostly impacted region by cloud frequency, b) the area between Mauritania and Mali is mostly impacted by small field size, c) the area between Sudan and Chad ismostly influenced by landcover richness, and d) the region in south Somalia (East Africa) is largely influenced by elevation dispersion.

Fig. 6. The contribution of each factor on the spatial difference.

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areas with STdv>10% (about 36.2% of the total area of agreement)was only 40% for the four datasets, as shown in the error matrices inAppendix Table A3. This made the 10% threshold of STdv a benchmarkpoint separating the regions of accurate cropland mapping from lowspatial consistency among datasets. The average STdv was then esti-mated for each of the agro-Environmental strata (238 strata overAfrica), and only areas with STdv> 10% were considered as low-agreement areas and highlighted in Fig. 4. Effects of limiting factors onthe spatial agreement among datasets were only assessed for the areaswith the low spatial agreement (Fig. 4). In terms of environmentalconditions, the areas with extremely hot and moist hills located in WestAfrica and the areas of extremely hot and xeric hills situated mostly inSahel region were the areas with the largest spatial disagreements inAfrica.

4.3. Driving factors for spatial disagreement

Fig. 5 depicts the factors contributing to spatial disagreementamong the four datasets. On the map, each of the low-agreement re-gions is identified and the key limiting factors (having the highest rvalue with the spatial agreement, at p-value<0.05) colour coded.Based on Fig. 5, the percentage area affected by each of the four factorswas calculated using the total area of the low agreement regions (areaswith STdv> 10%) as the base value (Fig. 6).

As shown in Figs. 5 & 6, landcover richness is the most influentialfactor, affecting 21.6% of the total area. The curvilinear relationship

between landcover richness (an indicator of landscape heterogeneity)and average STdv (an indicator of the agreement among dataset) isshown in Fig. 7. The disagreement among datasets increased with theincrease in landcover richness up to the level of five classes. If thelandcover richness continue to rise (containing 6 classes), the effect ofmixed pixels becomes more dominant.

Landcover richness also has a large influence on the spatial differ-ence between products, especially for regions dominated by herbaceousvegetation (includes grassland). This is consistent with (Herold et al.,2008; McCallum et al., 2006; Xiong et al., 2017; Fritz et al., 2013;Pérez-Hoyos et al., 2017) that the spectral similarity with grassland andother herbaceous vegetation limits accurate mapping of African crop-land, especially in arid and semi-arid areas where the rainfall regimecauses synchronized phenology between crops and natural vegetationcausing difficulty to separate crops from mother natural vegetation dueto high spectral similarities (Vintrou et al., 2012). The main area af-fected by the landcover richness factor is the Sahel region, which is thetransitional zone of eco-climatic and biogeographical significance withSahara to the north and the Sudanian Savanna to the south (https://en.wikipedia.org/wiki/Sahel). In particular, the area bordering Niger andNigeria and the southwestern part of South Sudan (Fig. 5) is largelyaffected. The seminar-arid environment and heavily active agricultureactivities have all contributed to the highly diversified and segmentedlandcover types.

In addition to landcover richness, contributions from other im-pacting factors are also evident. For example, even at locations domi-nated by cropland (Fig. 8, orange colour bar), the disagreement amongdatasets is still high (large STdv) suggesting strong influences fromfactors other than landcover richness.

Cloud frequency was the second most influential factor, account for20.4% of the total influenced area (Fig. 6), particularly over the WestAfrican coast (Fig. 5a) and some regions in North Africa (south Algeria,south Tunisia, and east Morocco). The correlation between annualcloud frequency and average STdv is strong (R2= 0.95, Fig. 9a) overWest Africa. Fig. 9 reveals a weak spatial consistency among croplanddatasets corresponding to high cloud frequency. However, when cloudfrequency reaches around 75% (Fig. 9b and c), the disagreement be-tween datasets starts to decrease. This could be explained by the remotesensing datasets may wrongly agree on the absence of cropland at re-gions with very high cloud frequency (e.g. 75%) where the number ofhigh-quality satellite images became rare. During the early and mid-agricultural growing season, the presence of clouds is particularlyhampering the ability of optical remote sensing satellites from accuratecrop-type identification (Becker-Reshef et al., 2010; Whitcraft et al.,

Fig. 7. The relationship between landcover and the spatial agreement (re-presented as average STdv) among the four datasets evaluated.

Fig. 8. The average agreement among cropland datasets at locations dominated by cropland (orange), vegetation classes (green), and non-vegetation classes (red andblue). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

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2015b) and crop growth condition monitoring (Duveiller et al., 2012).In the West African coast, especially during the mid-growing season(June – Sept.), cloud frequency had the highest negative effect(R2= 0.91, Fig. 9d) on the agreement among datasets when comparedwith the effect of the cloud frequency at the other two growth periodsOct. – Jan. and Feb. – May (Fig. 9b, c). This phenomenon highlights therelative importance of data availability during the mid-growing seasonto achieve high cropland discrimination in West Africa.

Our analysis reveals that field size is the third most influential factorover Africa, account for 9.4% of the total area of influence (Fig. 6),mostly located in Central Africa (Fig. 5b, the boundary betweenMauritania and Mali) and the eastern coast of Madagascar. At theseregions, the relationship between field size and the agreement amongdatasets is strongly correlated (R2=0.7, Fig. 10). The large size of the

agricultural fields corresponds with higher spatial agreements amongthe four datasets tested.

Elevation dispersion is the factor with the lowest impact on thespatial consistency of cropland datasets compared with the other threelimiting factors with the smallest area of influence (0.7%) over Africa(Fig. 5d). Over its impacted regions, such as south Somalia, the corre-lation between elevation dispersion and dataset agreement is char-acterized by a weak positive linear relationship (R2= 0.29, Fig. 11).The high elevation dispersion (high fragmentation and rough topo-graphy) corresponds to a high disagreement among the cropland da-tasets. The wide dynamic range of elevations in these regions alsocaused reduced accuracy of thematic maps. The irregular topographyand the associated shadows can cause illuminative variations of thetargets and introduce spectral variations to the same landcover class

Fig. 9. The relation between cloud frequency and the agreement among datasets (STdv as % cropland coverage) over West African coast: (a) The annual cloudfrequency; (b) during the period (October – January); (c) during the period (February – May) and (d) during the period (June - September). Cloud frequency isexpressed as a percentage from 0% (no clouds during the period) to 100% (clouds present during the whole period).

Fig. 10. The relation between Agriculture field size and the agreement amongcropland datasets.

Fig. 11. The relation of elevation dispersion with datasets agreement (averageSTdv).

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(Rosen, 2005). Hence, topographic correction is one of the pre-pro-cessing steps to be applied to remote sensing images before classifica-tion (Vanonckelen et al., 2015; Goslee, 2012; Reddy and Blah, 2009).

4.4. Challenges of cropland mapping at the continental scale using remotesensing data

All four datasets tested in this study have a tendency to overestimatethe cropland area. This finding confirms the results reported by otherstudies (Xu et al., 2019; Lesiv et al., 2017). Due to high landscapeheterogeneity (high landcover richness) and spectral confusion amongdifferent landcover classes, especially grasslands, highly accuratecropland mapping at the continental scale using remote sensing data isvery difficult to achieve (Lesiv et al., 2017). Increasing the source dataresolution did not seem to be able to solve this issue. For incidence, the20m finer resolution ESA CCI_LC_S2_Prototype map did not achieve thehighest accuracy of cropland mapping in Africa (Tsendbazar et al.,2015). In future mapping activities, landcover heterogeneity should bein conjunction with pixel sizes for better cropland mapping. In addition,classification methods capable of decomposing mixed pixels should beadopted to produce more accurate cropland maps over vast and dy-namic regions of Africa.

The GFSAD30AFCE dataset achieved relatively higher mappingaccuracy because it took into consideration of the phenological in-formation crop growth derived from remote sensing data (Xiong et al.,2017). As stated by (Xu et al., 2019), the single-type landcover mappingwill potentially achieve better results considering the less variabilitywithin a single targeted type and diverse contrast with other landcovertypes. However, cropland area estimated from GFSAD30AFCE datasetwas 35% higher than the FAO national statistics (Xiong et al., 2017).The dataset’s producers attributed the high discrepancies betweenGFSAD30AFCE and national statistics to the presence of thick cloudsover particular regions which limited availability of seasonal cloud-freeor near-cloud-free mosaics from Landsat-8 and Sentinel-2 satellites overAfrica. It was estimated by (Whitcraft et al., 2015b) that about one-fifthof Africa’s cropland was dominantly impeded by cloud contamination.In our study, cloud frequency was ranked as the second most influentialfactor (20.4% of agreement area) especially over Western African coast,North Africa (e.g. south Algeria, south Tunisia, and East Morocco) andCentral Africa (the middle part of the Democratic Republic of Congo).The presence of intensive clouds, especially during the mid-growingseason (June – Sept.), has the highest negative impact on croplandmapping accuracy over these regions. To achieve higher mapping ac-curacy, alternatives such as the inclusion of synthetic aperture radar(SAR) should be considered to increase the availability of croplandinformation from different growth stages which can lead to better se-paration of cropland from other vegetation types.

The majority of Africa’s agricultural land falls within small(< 1.5 ha) and very small (< 0.15 ha) parcel sizes (Pérez-Hoyos et al.,2017). The use of 30m resolution satellite images is not sufficient tomap Africa’s cropland with high accuracy (Xiong et al., 2017). Thespatial resolution required for agriculture monitoring is 5–10m forsmall fields size (< 1.5 ha) and less than 5m for very small field size(< 0.15 ha) in Africa (Pérez-Hoyos et al., 2017; Whitcraft et al., 2015a).According to our analysis, field size is ranked as the third most influ-ential factor that negatively impacts the cropland mapping accuracyand spatial consistency among datasets, account for over 9.4% of thelow agreement areas, mainly distributed over West Africa, Centre Africa(e.g. south Chad), and the eastern coast of Madagascar. Although ouranalysis identified the critical factors for 52% of the problematic (low-agreement) areas, the limiting factors for the rest of the area (48%) arestill unclear. Areas including the northern part of Tunisia, Algeria andMorocco, and in countries like Burkina Faso, Kenia and Tanzania will

require further investigation to identify the major factors impacting thespatial discrepancies among cropland datasets.

5. Conclusions and recommendations

Despite the improvement in the spatial resolution of recent land-cover datasets due to the use of Sentinel-2 imageries, the accuracy ofAfrica’s existing cropland mapping products derived from remote sen-sing data is still limited. Sahel region and West Africa are the areas withthe largest spatial disagreements among four land-cover and croplanddatasets. High landscape heterogeneity, frequent cloud cover, and smallfield size ranked as the top three factors impacting the spatial dis-crepancy of remote sensing-based cropland products over some Africanregions. To overcome this limitation, an integrated mapping approachshould be considered. It is anticipated that improved accuracy can beachieved by applying environmental stratification, expanding inputdata sources, and optimizing the classification method used for crop-land mapping in Africa. Spatial stratification can help solve issuescaused by intra-class variability and isolate special classes that areunique only to specific regions (Inglada et al., 2017). The inclusion ofmicrowave sensors such as the Sentinel-1 synthetic aperture radar(SAR) data will dramatically increase data availability due to SAR’s all-weather and day-and-night capability; hence, missing data due to cloudcover at key crop growth stages won’t be an issue anymore. To meet therequirement of small field size, the synergy generated through the in-tegration of high-resolution optical data with SAR data will offer boththe spatial details and the temporal frequency needed for improvedcropland classification. Selecting the appropriate classification methodsis also a key to improve classification accuracy. One of the approachesto solve the issues inherent in mixed pixels is using a soft classificationapproach such as fuzzy logic (Lu and Weng, 2007). In future croplandmapping activates, it is recommended the adoption of an integratedapproach that would include a suite of classification methodologiessuitable for individual needs from different landscape and croppingsystems.

Author contributions

Mohsen Nabil, Miao Zhang and Bingfang Wu were responsible forresearch conceptualization and design, Mohsen Nabil was responsiblefor data collection, data processing and manuscript preparation. MiaoZhang was responsible for preparing and revising the manuscript.Mohsen Nabil and José Bofana were accountable for validation,Bingfang Wu, Alfred Stein, Taifeng Dong, Hongwei Zeng and JialiShang contributed to the revision & editing of the paper.

Funding

This research was funded by the National Key R＆D Program ofChina (2016YFA0600302) and National Natural Science Foundation ofChina (41561144013, 41861144019 and 41701496).

Declaration of Competing Interest

The authors declare no conflict of interest and the founding spon-sors had no role in the design of the study; in the collection, analyses, orinterpretation of data; in the writing of the manuscript, and in the de-cision to publish the results.

Acknowledgements

The first author, Mohsen Nabil, acknowledges the Chinese Academyof Sciences (CAS) for supporting him to carry out the research.

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Appendix A

Table A1The general characteristics of the four recent remote sensing-based datasets investigated in this study.

ESA CCI-LC 2015 CGLS-LC100 GFSAD30AFCE ESACCI-LC_S2_Prototype

Dataset Producer ESA Climate Change Initiative -Land Cover project 2017.

Copernicus Global Land Service(CGLS)

(Xiong et al., 2017) ESA Climate Change Initiative -Land Cover project 2017.

Satellite sensor MERIS, AVHRR, SPOT-VGT &PROBA-V

PROBA-V 300m &PROBA-V 100 Sentinel-2 & Landsat 8 Sentinel-2A

Spatial Resolution 300meters 100meters 30meters 20metersScale Global Africa Africa AfricaPeriod of Data

Acquisition2015 2015 2015/2016 2015/2016

Classification Method Unsupervised classification andmachine learning

Random Forest regression Pixel-Based (Random forest & Supportvector machine) and Object-BasedAlgorithms

Random Forest (RF) and MachineLearning (ML),

Overall Accuracy 71.45% -75.4% (reported by thedataset producer)

74.3 +/-1.8% (reported by thedataset producer)

94.5% (Reported by the datasetproducer)

65% (Reported by (Lesiv et al.,2017))

Cropland class(es): - Cropland, rainfed - Cropland - Cropland - Cropland- Cropland irrigated or post flooding- Mosaic cropland (> 50%) /natural vegetation (tree, shrub,herbaceous cover) (< 50%)

Cropland class Accuracy rainfed cropland: 89-92%, andirrigated cropland; 89-83%.(Reported by the dataset producer)

Croplands user accuracy was 66.5+/- 4.6%, and the produceraccuracy was 67.0 +/- 5.2%(Reported by the dataset producer)

Producer’s accuracy of 85.9% anduser’s accuracy of 68.5% for thecropland class. (Reported by thedataset producer)

Users (46 % and 50.4 %) andproducers (71% and 63%) (Reportedby (Lesiv et al., 2017)).

The source of table info (CCI-LC-PUGV2, 2017) (CGLOPS-1, 2018) (Xiong et al., 2017) (Fabrizio et al., 2018; Lesiv et al.,2017)

Table A2The confusion matrices and accuracy reports of the four datasets.

Geo-Wiki Cropland Percentage Total User’s Acc.

0 <50% >50% 100%

0 2254 324 100 223 2901 0.78< 50% 1373 490 266 347 2476 0.20> 50% 454 234 289 487 1464 0.20100% 77 41 76 276 470 0.59Total 4158 1089 731 1333 7311Producer’s Acc. 0.54 0.45 0.40 0.21 Overall Acc. 0.45ESACCI-LC_S2_Prototype Kappa Coeff. 0.210 3644 627 262 445 4978 0.73< 50% 275 227 102 147 751 0.30> 50% 127 133 178 177 615 0.29100% 112 102 189 564 967 0.58Total 4158 1089 731 1333 7311Producer’s Acc. 0.88 0.21 0.24 0.42 Overall Acc. 0.63GFSAD30AFCE Kappa Coeff. 0.440 3533 596 240 406 4775 0.74< 50% 341 246 119 153 859 0.29> 50% 158 124 178 177 637 0.28100% 126 123 200 584 1033 0.57Total 4158 1089 737 1320 7304Producer’s Acc. 0.85 0.23 0.24 0.44 Overall Acc. 0.62CGLS-LC100 Kappa Coeff. 0.420 3075 568 297 493 4433 0.69< 50% 186 84 47 81 398 0.21> 50% 211 79 55 88 433 0.13100% 686 358 332 671 2047 0.33Total 4158 1089 731 1333 7311Producer’s Acc. 0.74 0.08 0.08 0.50 Overall Acc. 0.53ESA CCI-LC 2015 Kappa Coeff. 0.41

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Table A3Confusion matrices for regions with less than 10% STdv (left) and regions with more than 10% STdv (right).

Geo-Wiki Cropland Percentage Total User Acc. Geo-Wiki Cropland Percentage Total User Acc.

0 < 50% >50% 100% 0 <50% >50% 100%

0 1900 237 72 160 2369 0.80 0 290 69 23 58 440 0.66< 50% 636 153 65 81 935 0.16 < 50% 674 323 193 256 1446 0.22> 50% 17 38 81 152 288 0.28 > 50% 433 192 197 317 1139 0.17100% 23 27 49 178 277 0.64 100% 46 13 19 78 156 0.50Total 2576 455 267 571 3869 Total 1443 597 432 709 3181Producer Acc. 0.74 0.34 0.30 0.31 Overall Acc. 0.60 Producer Acc. 0.20 0.54 0.46 0.11 Overall Acc. 0.28ESACCI-LC_S2_Prototype Kappa Coeff. 0.27 ESACCI-LC_S2_Prototype Kappa Coeff. 0.090 2446 337 114 214 3111 0.79 0 1079 267 138 219 1703 0.63< 50% 90 53 22 27 192 0.28 < 50% 177 166 77 118 538 0.31> 50% 13 18 42 42 115 0.37 > 50% 113 114 131 130 488 0.27100% 27 47 89 288 451 0.64 100% 74 50 86 242 452 0.54Total 2576 455 267 571 3869 Total 1443 597 432 709 3181Producer Acc. 0.95 0.12 0.16 0.50 Over all Acc. 0.73 Producer Acc. 0.75 0.28 0.30 0.34 Over all Acc. 0.51GFSAD30AFCE Kappa coeff. 0.39 GFSAD30AFCE Kappa coeff. 0.270 2429 338 119 217 3103 0.78 0 983 233 108 178 1502 0.65< 50% 107 52 17 24 200 0.26 < 50% 228 187 102 125 642 0.29> 50% 11 14 42 42 109 0.39 > 50% 144 109 131 130 514 0.25100% 29 51 97 291 468 0.62 100% 88 68 86 258 500 0.52Total 2576 455 275 574 3880 Total 1443 597 427 691 3158Producer Acc. 0.94 0.11 0.15 0.51 Over all Acc. 0.73 Producer Acc. 0.68 0.31 0.31 0.37 Over all Acc. 0.49CGLS-LC100 Kappa Coeff. 0.38 CGLS-LC100 Kappa coeff. 0.260 2528 379 132 238 3277 0.77 0 528 180 163 254 1125 0.47< 50% 8 11 4 3 26 0.42 < 50% 70 50 32 64 216 0.23> 50% 4 7 11 7 29 0.38 > 50% 197 69 30 53 349 0.09100% 36 58 120 323 537 0.60 100% 648 298 207 338 1491 0.23Total 2576 455 267 571 3869 Total 1443 597 432 709 3181Producer Acc. 0.98 0.02 0.04 0.57 Over all Acc. 0.74 Producer Acc. 0.37 0.08 0.07 0.48 Over all Acc. 0.30ESA CCI-LC 2015 Kappa Coeff. 0.38 ESA CCI-LC 2015 Kappa coeff. 0.01Average accuracy = 0.70 Average accuracy = 0.40

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