Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China.
College of Land Science and Technology, China Agricultural University, Beijing, 100193, China.
Sci Data. 2019 Apr 11;6(1):26. doi: 10.1038/s41597-019-0036-3.
Knowledge of where, when, and how much paddy rice is planted is crucial information for understating of regional food security, freshwater use, climate change, and transmission of avian influenza virus. We developed seasonal paddy rice maps at high resolution (10 m) for Bangladesh and Northeast India, typical cloud-prone regions in South Asia, using cloud-free Synthetic Aperture Radar (SAR) images from Sentinel-1 satellite, the Random Forest classifier, and the Google Earth Engine (GEE) cloud computing platform. The maps were provided for all the three distinct rice growing seasons of the region: Boro, Aus and Aman. The paddy rice maps were evaluated against the independent validation samples, and compared with the existing products from the International Rice Research Institute (IRRI) and the analysis of Moderate Resolution Imaging Spectroradiometer (MODIS) data. The generated paddy rice maps were spatially consistent with the compared maps and had a satisfactory accuracy over 90%. This study showed the potential of Sentinel-1 data and GEE on large scale paddy rice mapping in cloud-prone regions like tropical Asia.
了解水稻种植的地点、时间和数量对于了解区域粮食安全、淡水资源利用、气候变化以及禽流感病毒传播至关重要。我们使用来自 Sentinel-1 卫星的无云合成孔径雷达 (SAR) 图像、随机森林分类器和 Google Earth Engine (GEE) 云计算平台，为南亚典型多云地区孟加拉国和印度东北部开发了高分辨率（10m）的季节性水稻图，用于该地区三个不同的水稻生长季节：Boro、Aus 和 Aman。我们根据独立验证样本对水稻图进行了评估，并与国际水稻研究所（IRRI）的现有产品以及中分辨率成像光谱仪（MODIS）数据进行了比较。生成的水稻图与比较图在空间上是一致的，其精度超过 90%，令人满意。这项研究表明，Sentinel-1 数据和 GEE 具有在热带亚洲等多云地区进行大规模水稻制图的潜力。
