Agricultural cropland extent and areas of South Asia derived using Landsat satellite 30-m time-series big-data using random forest machine learning algorithms on the Google Earth Engine cloud

Abstract

The South Asia (India, Pakistan, Bangladesh, Nepal, Sri Lanka and Bhutan) has a staggering 900 million
\npeople (~43% of the population) who face food insecurity or severe food insecurity as per United
\nNations, Food and Agriculture Organization’s (FAO) the Food Insecurity Experience Scale (FIES). The
\nexisting coarse-resolution (≥250-m) cropland maps lack precision in geo-location of individual farms
\nand have low map accuracies. This also results in uncertainties in cropland areas calculated fromsuch
\nproducts. Thereby, the overarching goal of this study was to develop a high spatial resolution (30-m
\nor better) baseline cropland extent product of South Asia for the year 2015 using Landsat satellite
\ntime-series big-data and machine learning algorithms (MLAs) on the Google Earth Engine (GEE) cloud
\ncomputing platform. To eliminate the impact of clouds, 10 time-composited Landsat bands (blue,
\ngreen, red, NIR, SWIR1, SWIR2, Thermal, EVI, NDVI, NDWI) were derived for each of the three timeperiods
\nover 12 months (monsoon: Days of the Year (DOY) 151–300; winter: DOY 301–365 plus 1–60;
\nand summer: DOY 61–150), taking the every 8-day data from Landsat-8 and 7 for the years
\n2013–2015, for a total of 30-bands plus global digital elevation model (GDEM) derived slope band.
\nThis 31-band mega-file big data-cube was composed for each of the five agro-ecological zones
\n(AEZ’s) of South Asia and formed a baseline data for image classification and analysis. Knowledgebase
\nfor the Random Forest (RF) MLAs were developed using spatially well spread-out reference
\ntraining data (N = 2179) in five AEZs. The classification was performed on GEE for each of the five AEZs
\nusing well-established knowledge-base and RF MLAs on the cloud. Map accuracies were measured
\nusing independent validation data (N = 1185). The survey showed that the South Asia cropland
\nproduct had a producer’s accuracy of 89.9% (errors of omissions of 10.1%), user’s accuracy of 95.3%
\n(errors of commission of 4.7%) and an overall accuracy of 88.7%. The National and sub-national
\n(districts) areas computed from this cropland extent product explained 80-96% variability when
\ncompared with the National statistics of the South Asian Countries. The full-resolution imagery can be
\nviewed at full-resolution, by zooming-in to any location in South Asia or the world, atwww.croplands.
\norg and the cropland products of South Asia downloaded from The Land Processes Distributed Active
\nArchive Center (LP DAAC) of National Aeronautics and Space Administration (NASA) and the United
\nStates Geological Survey (USGS): https://lpdaac.usgs.gov/products/gfsad30saafgircev001/.