Timely and accurate regional rice paddy monitoring plays a significant role in maintaining the sustainable rice production, food security, and agricultural development. This study proposes an operational automatic approach to mapping rice paddies using time-series SAR data. The proposed method integrates time-series Sentinel-1 data, auxiliary data of global surface water, and rice phenological characteristics with Google Earth Engine cloud computing platform. A total of 402 Sentinel-1 scenes from 2017 were used for mapping rice paddies extent in the Mun River basin. First, the calculated minimum and maximum values of the backscattering coefficient of permanent water (a classification type within global surface water data) in a year was used as the threshold range for extracting the potential extent. Then, three rice phenological characteristics were extracted based on the time-series curve of each pixel, namely the date of the beginning of the season (DBS), date of maximum backscatter during the peak growing season (DMP), and length of the vegetative stage (LVS). After setting a threshold for each phenological parameter, the final rice paddy extent was identified. Rice paddy map produced in this study was highly accurate and agreed well with field plot data and rice map products from the International Rice Research Institute (IRRI). The results had a total accuracy of 89.52% and an F1 score of 0.91, showing that the spatiotemporal pattern of extracted rice cover was consistent with ground truth samples in the Mun River basin. This approach could be expanded to other rice-growing regions at the national scale, or even the entire Indochina Peninsula and Southeast Asia.

中文翻译：

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基于时间序列Sentinel-1 SAR数据的泰国芒河流域高分辨率稻作图方法

及时准确的区域稻田监测在维持稻米可持续生产，粮食安全和农业发展方面发挥着重要作用。这项研究提出了一种使用时间序列SAR数据绘制稻田作图的自动操作方法。该方法将时间序列Sentinel-1数据，全球地表水的辅助数据以及水稻物候特征与Google Earth Engine云计算平台集成在一起。自2017年起，共使用402个Sentinel-1场景绘制了Mun河流域的稻田范围图。首先，将一年中计算出的永久水（全球地表水数据中的分类类型）的反向散射系数的最小值和最大值用作提取潜在程度的阈值范围。然后，根据每个像素的时间序列曲线提取了三个水稻物候特征，分别是季节开始的日期（DBS），高峰生长期（DMP）的最大反向散射日期和营养期的长度（ LVS）。在为每个物候参数设置阈值后，确定最终的稻田范围。这项研究中制作的稻田地图非常准确，并且与国际稻米研究所（IRRI）的田间田地数据和稻田地图产品非常吻合。结果的总准确度为89.52％，F1分数为0.91，表明提取的水稻覆盖量的时空分布与Mun河流域的地面真相样本一致。这种方法可以扩展到全国范围内的其他水稻种植地区，