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• W2885406917 abstract "Mapping high resolution (30-m or better) cropland extent over very large areas such as continents or large countries or regions accurately, precisely, repeatedly, and rapidly is of great importance for addressing the global food and water security challenges. Such cropland extent products capture individual farm fields, small or large, and are crucial for developing accurate higher-level cropland products such as cropping intensities, crop types, crop watering methods (irrigated or rainfed), crop productivity, and crop water productivity. It also brings many challenges that include handling massively large data volumes, computing power, and collecting resource intensive reference training and validation data over complex geographic and political boundaries. Thereby, this study developed a precise and accurate Landsat 30-m derived cropland extent product for two very important, distinct, diverse, and large countries: Australia and China. The study used of eight bands (blue, green, red, NIR, SWIR1, SWIR2, TIR1, and NDVI) of Landsat-8 every 16-day Operational Land Imager (OLI) data for the years 2013–2015. The classification was performed by using a pixel-based supervised random forest (RF) machine learning algorithm (MLA) executed on the Google Earth Engine (GEE) cloud computing platform. Each band was time-composited over 4–6 time-periods over a year using median value for various agro-ecological zones (AEZs) of Australia and China. This resulted in a 32–48-layer mega-file data-cube (MFDC) for each of the AEZs. Reference training and validation data were gathered from: (a) field visits, (b) sub-meter to 5-m very high spatial resolution imagery (VHRI) data, and (c) ancillary sources such as from the National agriculture bureaus. Croplands versus non-croplands knowledge base for training the RF algorithm were derived from MFDC using 958 reference-training samples for Australia and 2130 reference-training samples for China. The resulting 30-m cropland extent product was assessed for accuracies using independent validation samples: 900 for Australia and 1972 for China. The 30-m cropland extent product of Australia showed an overall accuracy of 97.6% with a producer’s accuracy of 98.8% (errors of omissions = 1.2%), and user’s accuracy of 79% (errors of commissions = 21%) for the cropland class. For China, overall accuracies were 94% with a producer’s accuracy of 80% (errors of omissions = 20%), and user’s accuracy of 84.2% (errors of commissions = 15.8%) for cropland class. Total cropland areas of Australia were estimated as 35.1 million hectares and 165.2 million hectares for China. These estimates were higher by 8.6% for Australia and 3.9% for China when compared with the traditionally derived national statistics. The cropland extent product further demonstrated the ability to estimate sub-national cropland areas accurately by providing an R2 value of 0.85 when compared with province-wise cropland areas of China. The study provides a paradigm-shift on how cropland maps are produced using multi-date remote sensing. These products can be browsed at www.croplands.org and made available for download at NASA’s Land Processes Distributed Active Archive Center (LP DAAC) https://www.lpdaac.usgs.gov/node/1282." @default.
• W2885406917 created "2018-08-22" @default.
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• W2885406917 date "2018-10-01" @default.
• W2885406917 modified "2023-10-17" @default.
• W2885406917 title "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" @default.
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• W2885406917 cites W1968955602 @default.
• W2885406917 cites W1970687962 @default.
• W2885406917 cites W1971364019 @default.
• W2885406917 cites W1971683018 @default.
• W2885406917 cites W1979568597 @default.
• W2885406917 cites W1980225565 @default.
• W2885406917 cites W1981213426 @default.
• W2885406917 cites W1986738039 @default.
• W2885406917 cites W1990653740 @default.
• W2885406917 cites W1993585210 @default.
• W2885406917 cites W1994050572 @default.
• W2885406917 cites W2001510610 @default.
• W2885406917 cites W2001728294 @default.
• W2885406917 cites W2006929658 @default.
• W2885406917 cites W2008085934 @default.
• W2885406917 cites W2024339093 @default.
• W2885406917 cites W2027475314 @default.
• W2885406917 cites W2031775731 @default.
• W2885406917 cites W2031841848 @default.
• W2885406917 cites W2035222601 @default.
• W2885406917 cites W2037455286 @default.
• W2885406917 cites W2042386716 @default.
• W2885406917 cites W2042692910 @default.
• W2885406917 cites W2046033369 @default.
• W2885406917 cites W2049427966 @default.
• W2885406917 cites W2055248879 @default.
• W2885406917 cites W2058208710 @default.
• W2885406917 cites W2063907334 @default.
• W2885406917 cites W2072305677 @default.
• W2885406917 cites W2072465375 @default.
• W2885406917 cites W2076186394 @default.
• W2885406917 cites W2081562328 @default.
• W2885406917 cites W2082081125 @default.
• W2885406917 cites W2103614420 @default.
• W2885406917 cites W2117706739 @default.
• W2885406917 cites W2123733695 @default.
• W2885406917 cites W2126902408 @default.
• W2885406917 cites W2127559745 @default.
• W2885406917 cites W2133941557 @default.
• W2885406917 cites W2138973222 @default.
• W2885406917 cites W2139709933 @default.
• W2885406917 cites W2142231247 @default.
• W2885406917 cites W2145862305 @default.
• W2885406917 cites W2146497894 @default.
• W2885406917 cites W2151456308 @default.
• W2885406917 cites W2155289042 @default.
• W2885406917 cites W2156382101 @default.
• W2885406917 cites W2160861680 @default.
• W2885406917 cites W2166307050 @default.
• W2885406917 cites W2173562516 @default.
• W2885406917 cites W2176432844 @default.
• W2885406917 cites W2178470810 @default.
• W2885406917 cites W2180682969 @default.
• W2885406917 cites W2200121095 @default.
• W2885406917 cites W2261059368 @default.
• W2885406917 cites W2278948991 @default.
• W2885406917 cites W2290682182 @default.
• W2885406917 cites W2297019642 @default.
• W2885406917 cites W2347192404 @default.
• W2885406917 cites W2438450043 @default.
• W2885406917 cites W2485245346 @default.
• W2885406917 cites W2531168480 @default.
• W2885406917 cites W2558892021 @default.
• W2885406917 cites W2584743460 @default.
• W2885406917 cites W2592712793 @default.
• W2885406917 cites W2725897987 @default.
• W2885406917 cites W2766727660 @default.
• W2885406917 cites W2781971575 @default.
• W2885406917 cites W2800354856 @default.
• W2885406917 cites W2805112544 @default.
• W2885406917 cites W2883890464 @default.
• W2885406917 cites W2911964244 @default.
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• W2885406917 doi "https://doi.org/10.1016/j.isprsjprs.2018.07.017" @default.
• W2885406917 hasPublicationYear "2018" @default.
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