ABSTRACT Sustainable intensification of existing cropland is one of the most viable options for meeting the escalating food demands of the ever-increasing population in the world. Accurate geospatial data about the potential single-crop (rice-fallows) areas is vital for policymakers to target the agro-technologies for enhancing crop productivity and intensification. Therefore, the study aimed to evaluate and understand the dynamics of rice-fallows in the Odisha state of India, using SAR (Sentinel-1) and Optical (Landsat OLI) datasets. This study utilized a decision-tree approach and Principal component analysis (PCA) for the segmentation and separation of different vegetation classes. The estimated overall accuracy of extracted rice-fallow maps was in the range of 84 to 85 percent. The study identified about 2.2, 2.0 and 2.1mha of Rice-Fallows in the years 2015–16, 2016–17, and 2017–18, respectively. The combined analysis (all three years) of rice-fallow maps identified about 1.34mha of permanent rice-fallows, whereas the remaining 0.6–0.8mha area was under the current-fallow category. About 50% of the total permanent rice-fallows were detected in the rainfed areas of Mayurbhanj, Bhadrak, Bolangir, Sundargarh, Keonjhar, Baleswar, Nawarangpur and Bargarh districts. The study also illustrated the time-series profiles of SMAP (soil moisture) datasets for the ten agroclimatic zones of the Odisha, which can be utilized (along with rice-fallow maps) for the selection of crop and cultivars (e.g. short or medium duration pulses or oilseeds) to target the rice fallows. The approach utilized in the current study can be scaled up in similar areas of South and South-east Asia and Africa to identify single-crop areas for targeting improved technologies for enhanced crop productivity and intensification.

中文翻译：

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使用多时相 Landsat-8 OLI 和 Sentinel-1 数据分析奥里萨邦多年的水稻休耕动态

摘要 现有农田的可持续集约化是满足世界上不断增长的人口不断增长的粮食需求的最可行的选择之一。有关潜在单一作物（水稻休耕）地区的准确地理空间数据对于决策者将农业技术作为提高作物生产力和集约化的目标至关重要。因此，该研究旨在使用 SAR (Sentinel-1) 和光学 (Landsat OLI) 数据集评估和了解印度奥里萨邦的水稻休耕动态。本研究利用决策树方法和主成分分析 (PCA) 对不同植被类别进行分割和分离。提取的水稻休耕地图的估计总体准确度在 84% 到 85% 的范围内。该研究确定了大约 2.2、2.0 和 2。2015-16 年、2016-17 年和 2017-18 年的水稻休耕面积分别为 1mha。对水稻休耕地图的综合分析（所有三年）确定了大约 1.34 公顷的永久性水稻休耕，而其余 0.6-0.8 公顷的面积属于当前的休耕类别。在 Mayurbhanj、Bhadrak、Bolangir、Sundargarh、Keonjhar、Baleswar、Nawarangpur 和 Bargarh 地区的雨养区发现了大约 50% 的永久性稻谷休耕。该研究还说明了奥里萨邦 10 个农业气候带的 SMAP（土壤水分）数据集的时间序列概况，这些数据集可（与稻谷地图一起）用于选择作物和栽培品种（例如短期或中期持续时间）豆类或油籽）以针对水稻休耕。