Automated cropland mapping of continental Africa using Google Earth Engine cloud computing

Xiong, J and Thenkabail, P S and Gumma, M K and Teluguntla, P and Poehnelt, J and Congalton, R G and Yadav, K and Thau, D (2017) Automated cropland mapping of continental Africa using Google Earth Engine cloud computing. ISPRS Journal of Photogrammetry and Remote Sensing, 126. pp. 225-244. ISSN 0924-2716

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The automation of agricultural mapping using satellite-derived remotely sensed data remains a challenge in Africa because of the heterogeneous and fragmental landscape, complex crop cycles, and limited access to local knowledge. Currently, consistent, continent-wide routine cropland mapping of Africa does not exist, with most studies focused either on certain portions of the continent or at most a one-time effort at mapping the continent at coarse resolution remote sensing. In this research, we addressed these limitations by applying an automated cropland mapping algorithm (ACMA) that captures extensive knowledge on the croplands of Africa available through: (a) ground-based training samples, (b) very high (sub-meter to five-meter) resolution imagery (VHRI), and (c) local knowledge captured during field visits and/or sourced from country reports and literature. The study used 16-day time-series of Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) composited data at 250-m resolution for the entire African continent. Based on these data, the study first produced accurate reference cropland layers or RCLs (cropland extent/areas, irrigation versus rainfed, cropping intensities, crop dominance, and croplands versus cropland fallows) for the year 2014 that provided an overall accuracy of around 90% for crop extent in different agro-ecological zones (AEZs). The RCLs for the year 2014 (RCL2014) were then used in the development of the ACMA algorithm to create ACMA-derived cropland layers for 2014 (ACL2014). ACL2014 when compared pixel-by-pixel with the RCL2014 had an overall similarity greater than 95%. Based on the ACL2014, the African continent had 296 Mha of net cropland areas (260 Mha cultivated plus 36 Mha fallows) and 330 Mha of gross cropland areas. Of the 260 Mha of net cropland areas cultivated during 2014, 90.6% (236 Mha) was rainfed and just 9.4% (24 Mha) was irrigated. Africa has about 15% of the world’s population, but only about 6% of world’s irrigation. Net cropland area distribution was 95 Mha during season 1, 117 Mha during season 2, and 84 Mha continuous. About 58% of the rainfed and 39% of the irrigated were single crops (net cropland area without cropland fallows) cropped during either season 1 (January-May) or season 2 (June-September). The ACMA algorithm was deployed on Google Earth Engine (GEE) cloud computing platform and applied on MODIS time-series data from 2003 through 2014 to obtain ACMA-derived cropland layers for these years (ACL2003 to ACL2014). The results indicated that over these twelve years, on average: (a) croplands increased by 1 Mha/yr, and (b) cropland fallows decreased by 1 Mha/year. Cropland areas computed from ACL2014 for the 55 African countries were largely underestimated when compared with an independent source of census-based cropland data, with a root-mean-square error (RMSE) of 3.5 Mha. ACMA demonstrated the ability to hind-cast (past years), now-cast (present year), and forecast (future years) cropland products using MODIS 250-m time-series data rapidly, but currently, insufficient reference data exist to rigorously report trends from these results.

Item Type: Article
Divisions: Research Program : Innovation Systems for the Drylands (ISD)
Uncontrolled Keywords: Cropland mapping; Classification; MODIS; Remote sensing products; Google Earth Engine; Africa; Automated cropland mapping algorithm
Subjects: Others > GIS Techniques/Remote Sensing
Others > Information Technology
Others > African Agriculture
Depositing User: Mr Ramesh K
Date Deposited: 14 Mar 2017 06:54
Last Modified: 14 Mar 2017 06:54
Official URL:
Acknowledgement: The authors would like to thank following persons for their support: Dr. Felix T. Portman and Dr. Stefan Siebert for providing statistics of MIRCA2000 ((Portmann et al., 2010), also latest statistics through personal communication between Dr. Stefan Siebert and Prasad S. Thenkabail); Dr. Peng Gong for sharing of FROMGLC Validation Dataset (Zhao et al., 2014); Dr. Ryutaro Tateishi for sharing of CEReS Gaia validation data Tateishi et al. (2014), and Dr. Friedl Mark for sharing GRIPC500 dataset for inter-comparison. Special thanks to Dr. Fabio Grita and Dr. Michela Marinelli’s help of FAO/CountrySTAT team. Jennifer Dungan (NASA Ames Research Center) and Mutlu Ozdogan (University of Wisconsin-Madison) area acknowledged for reviewing an earlier version of the manuscript. This study was supported by the NASA MEaSUREs (Making Earth System Data Records for Use in Research Environments). The project is funded by NASA MEaSUREs (NNH13AV82I) and the USGS Sales Order number is 29039. We gratefully acknowledge this support. The United States Geological Survey (USGS) provided supplemental funding as well as numerous other direct and indirect support through its Land Change Science (LCS), and Land Remote Sensing (LRS) programs, as well as support from USGS Climate and Land Use Change Mission Area. This research was also supported by Africa Research in Sustainable Intensification for the Next Generation (Africa RISING) program. We would like to thank Dr. Moses Siambi, Research Program Director – Eastern & Southern Africa; Dr. Birhanu Zemadim, Scientist Dr. Ramadjita Tabo, Research Program Director West & Central Africa and Dr. Anthony Whitbread, Research Program Director – Innovation Systems for the Drylands for supporting ground data collection.
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