National Cropland Classification with Agriculture Census Information and EO Datasets (Papers Track)

Junshi Xia (RIKEN); Naoto Yokoya (The University of Tokyo); Bruno Adriano (RIKEN Center for Advanced Intelligence Project (AIP))

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Agriculture & Food Computer Vision & Remote Sensing


National cropland classification is critical to monitor food security, comprehend environmental circumstances and climate change, and participate in agricultural policy development. The increasing earth observation datasets, especially the free available Sentinel and Landsat, open unprecedented large-scale mapping opportunities. However, most applied machine learning techniques have relied on substantial training datasets, which are not always available and may be expensive to create or collect. Focusing on Japan, this work indicates what kinds of information can be extracted from agriculture census information then used for mapping different crop types. Different classification approaches of pixel-based and parcel-based are compared. Then, the efficient method is used to generate Japan's first national cropland classification with Sentinel-1 C-band and Landsat-8 time series. For 2015, the overall accuracies for the prefectures range between 71\% and 94\%. This national cropland classification map, which particularly succeeds in extracting high-precision rice products for the whole of Japan and other classes for different prefectures, can be treated as the base map of Japan for future studies related to agriculture, environment, and climate change.

Recorded Talk (direct link)