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Cropland extent of South Asia @ 30 m resolution using time series Landsat data and machine learning algorithm through Google Earth Engine
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Cropland extent of South Asia @ 30 m resolution using time series Landsat data and machine learning algorithm through Google Earth Engine

  1. About ICRISAT: www.icrisat.org ICRISAT’s scientific information: http://EXPLOREit.icrisat.org Feb 2019 Cropland extent of South Asia @ 30 m resolution using time series Landsat data and machine learning algorithm through Google Earth Engine Introduction Though South Asia has one of the largest cropland area as well as irrigated area, the region runs the risk of becoming food insecure because of its rapidly growing population. Information on the spatial distribution of agriculture and irrigation is crucial for policy making affecting sustainable agriculture and food security. Current low resolution crop extent datasets are inadequate in providing a complete and accurate picture of cropland. Detailed spatial distribution of croplands and agricultural water use at high spatial resolution are critical baseline data to understand crop and water dynamics, and to generate higher level products like crop type maps and biomass and yield assessments, which will aid decision making and planning for food security. Project objective To produce a precise and accurate cropland extent product of South Asia using Landsat 30 m data, and machine learning algorithms (MLAs) on Google Earth Engine (GEE) platform. Methods and approaches The study used Random Forest MLA because of its effectiveness and resistance to over-fitting. A 31-band mega-file data-cube, consisting of 10 time-composited bands of three time periods of Landsat 8 and 7, 30 m, 8-day time series data over two years (2014 and 2015), along with an ASTER GDEM-derived slope map were also used. South Asia, consisting of six countries, was divided into five refined agro- ecological zones. Extensive set of ground reference data for each of the zones was gathered through field campaigns and sub-meter and 5-meter very high resolution imagery (VHRI). The reference data was used to identify two major classes – cropland and non-cropland. Irrigated and rainfed cropland classes were identified from the cropland reference data. This data was used to train the Random Forest model to generate cropland extent maps. The methodology used to generate cropland extent maps for South Asia is shown in Figure 1 . MK Gumma1 *, PS Thenkabail2 , P Teluguntla2 , A Oliphant2 , J Xiong2 , V Pyla1 , K Tummala1 , B Pinjarla1 , S Dixit1 and A Whitbread1 1 International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru 502324, India 2 US Geological Survey, Flagstaff * Corresponding author email: m.gumma@cgiar.org Figure 1. Methodology for mapping 30 m cropland extent maps. Figure 2. Irrigated versus rainfed croplands captured using random Forest Machine Learning Algorithm. Figure 3. A comparison of district-wise cropland areas in India using 30 m Landsat derived and national area statistics. Results The study generated a 30 m map of cropland extent and a 30 m map of irrigated versus rainfed cropland maps of South Asia. The extent of irrigated and rainfed croplands is shown in Figure 2. Conclusion The cropland extent maps for South Asia found to be highly accurate, with a weighted overall accuracy of 84.5%, producer’s accuracy of 82.5% and user’s accuracy of 83.9%. It correlated satisfactorily with national statistics (Fig. 3). In South Asia comprising of India, Pakistan, Bangladesh, Nepal, Sri Lanka and Bhutan, croplands occupy 220 M ha, 49.55% of the total geographical area of the region.
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