Automated cropland mapping of continental Africa using Google Earth Engine cloud computing
Jun Xiong,Prasad S. Thenkabail,Murali Krishna Gumma,P Teluguntla,Justin Poehnelt,Russell G. Congalton,Kamini Yadav,David Thau +7 more
TLDR
In this article, an automated cropland mapping algorithm (ACMA) was used to capture extensive knowledge on the croplands of Africa available through ground-based training samples, very high (sub-meter to five-meter) resolution imagery (VHRI), and local knowledge captured during field visits and/or sourced from country reports and literature.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.read more
Citations
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Remote sensing for agricultural applications: A meta-review
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Sentinel-2 cropland mapping using pixel-based and object-based time-weighted dynamic time warping analysis
TL;DR: Object-based time-weighted dynamic time warping (TWDTW) method achieved comparable classification results to RF in Romania and Italy, but RF achieved better results in the USA, where the classified crops present high intra-class spectral variability.
Journal ArticleDOI
Google Earth Engine for geo-big data applications: A meta-analysis and systematic review
Haifa Tamiminia,Bahram Salehi,Masoud Mahdianpari,Lindi J. Quackenbush,Sarina Adeli,Brian Brisco +5 more
TL;DR: A meta-analysis investigation of recent peer-reviewed GEE articles focusing on several features, including data, sensor type, study area, spatial resolution, application, strategy, and analytical methods confirmed that GEE has and continues to make substantive progress on global challenges involving process of geo-big data.
Journal ArticleDOI
Google Earth Engine Applications Since Inception: Usage, Trends, and Potential
Lalit Kumar,Onisimo Mutanga +1 more
TL;DR: Analysis of published literature showed that a total of 300 journal papers were published between 2011 and June 2017 that used GEE in their research, spread across 158 journals; Landsat was the most widely used dataset; it is the biggest component of the GEE data portal.
Journal ArticleDOI
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
P Teluguntla,P Teluguntla,Prasad S. Thenkabail,Adam J. Oliphant,Jun Xiong,Murali Krishna Gumma,Russell G. Congalton,Kamini Yadav,Alfredo Huete +8 more
TL;DR: In this paper, a pixel-based supervised random forest (RF) machine learning algorithm (MLA) was used on the Google Earth Engine (GEE) cloud computing platform.
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