Automated cropland mapping of continental Africa using Google Earth Engine cloud computing
By Jun Xiong, Prasad S. Thenkabail, M. Gumma, Pardhasaradhi Teluguntla, Justin Poehnelt, Russ Congalton, Kamini Yadav, D. Thau. 2017
434 citations recorded by the source corpus.
Abstract 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.
Subfields: Ecology, Evolution, Behavior and Systematics, Management, Monitoring, Policy and Law, Plant Science
View Paper: https://www.semanticscholar.org/paper/65d8b387528bb8eb18bd4f5e330b801a55e8c652
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In Management, Monitoring, Policy and Law
In Plant Science
Citations in the corpus
- Nominal 30-m Cropland Extent Map of Continental Africa by Integrating Pixel-Based and Object-Based Algorithms Using Sentinel-2 and Landsat-8 Data on Google Earth Engine (2017, methodology): “There are 12 countries (DR Congo, Mali, Zimbabwe, Kenya, Morocco, Algeria, Niger, Zambia, Uganda, Mozambique, Burkina Faso, Chad) which have above 2% but below 5% of Africa’s TNCA of 313 Mha.”
- Sentinel-2 cropland mapping using pixel-based and object-based time-weighted dynamic time warping analysis (2018, background): “There are various studies, using supervised or unsupervised algorithms, dedicated to cropland mapping from time series or single-date remote sensing images (Petitjean et al., 2012b; Xiong et al., 2017; Yan and Roy, 2015).”
- Google Earth Engine Applications Since Inception: Usage, Trends, and Potential (2018, methodology): “[20], undertaken to automate cropland mapping of continental Africa using GEE, shows the potential of the platforms application for the African continent.”
- GLC_FCS30: global land-cover product with fine classification system at 30 m using time-series Landsat imagery (2020, methodology): “(2018) and Yang et al. (2017b) found that the CCI_LC performed better over homogeneous areas; therefore, a larger local 5×5 window was applied to the CCI_LC land-cover product to refine and label each spectrally homogeneous MODIS-Landsat pixel.”
- A 30-m landsat-derived cropland extent product of Australia and China using random forest machine learning algorithm on Google Earth Engine cloud computing platform (2018, positive): “For example, in a recent publication on Africa, Xiong et al. (2017a) reported a weighted overall accuracy of 94%, with a producer ’ s accuracy of 85.9% (or omission error of 14.1%), and user ’ s accuracy of 68.5% (commission error of 31.5”