Rice growth requires a large amount of water, and planting rice will increase the contradiction between supply and demand of water resources. Paddy field fallowing is important for the sustainable development of an agricultural region, but it remains a great challenge to accurately and quickly monitor the extent and area of fallowed paddy fields. Paddy fields have unique physical features associated with paddy rice during the flooding and transplanting phases. By comparing the differences in phenology before and after paddy field fallowing, we proposed a phenology-based fallowed paddy field mapping algorithm. We used the Google Earth Engine (GEE) cloud computing platform and Landsat 8 images to extract the fallowed paddy field area on Sanjiang Plain of China in 2018. The results indicated that the Landsat8, GEE, and phenology-based fallowed paddy field mapping algorithm can effectively support the mapping of fallowed paddy fields on Sanjiang Plain of China. Based on remote sensing monitoring, the total fallowed paddy field area of Sanjiang Plain is 91 543 ha. The resultant fallowed paddy field map is of high accuracy, with a producer (user) accuracy of 83% (81%), based on validation using ground-truth samples. The Landsat-based map also exhibits high consistency with the agricultural statistical data. We estimated that paddy field fallowing reduced irrigation water by 384-521 million cubic meters on Sanjiang Plain in 2018. The research results can support subsidization grants for fallowed paddy fields, the evaluation of fallowed paddy field effects and improvement in subsequent fallowed paddy field policy in the future.

Rice growth requires a large amount of water, and planting rice will increase the contradiction between supply and demand of water resources. Paddy field fallowing is important for the sustainable development of an agricultural region, but it remains a great challenge to accurately and quickly monitor the extent and area of fallowed paddy fields. Paddy fields have unique physical features associated with paddy rice during the flooding and transplanting phases. By comparing the differences in phenology before and after paddy field fallowing, we proposed a phenology-based fallowed paddy field mapping algorithm. We used the Google Earth Engine (GEE) cloud computing platform and Landsat 8 images to extract the fallowed paddy field area on Sanjiang Plain of China in 2018. The results indicated that the Landsat8, GEE, and phenology-based fallowed paddy field mapping algorithm can effectively support the mapping of fallowed paddy fields on Sanjiang Plain of China. Based on remote sensing monitoring, the total fallowed paddy field area of Sanjiang Plain is 91 543 ha. The resultant fallowed paddy field map is of high accuracy, with a producer (user) accuracy of 83% (81%), based on validation using ground-truth samples. The Landsat-based map also exhibits high consistency with the agricultural statistical data. We estimated that paddy field fallowing reduced irrigation water by 384-521 million cubic meters on Sanjiang Plain in 2018. The research results can support subsidization grants for fallowed paddy fields, the evaluation of fallowed paddy field effects and improvement in subsequent fallowed paddy field policy in the future.