Xu Yaping, Wang Lei, Ross Kenton W, Liu Cuiling, Berry Kimberly
Department of Geography and Anthropology, Louisiana State University, Baton Rouge, LA 70803, USA.
NASA DEVELOP National Program, NASA Langley Research Center, MS 307, Hampton, Virginia 23681.
Remote Sens (Basel). 2018 Feb;10(2). doi: 10.3390/rs10020301. Epub 2018 Feb 15.
Droughts can severely reduce the productivity of agricultural lands and forests. The United States Department of Agriculture (USDA) Southeast Regional Climate Hub (SERCH) has launched the Lately Identified Geospecific Heightened Threat System (LIGHTS) to inform its users of potential water deficiency threats. The system identifies droughts and other climate anomalies such as extreme precipitation and heat stress. However, the LIGHTS model lacks input from soil moisture observations. This research aims to develop a simple and easy-to-interpret soil moisture and drought warning index - Standardized Soil Moisture Index (SSI) - by fusing the space-borne Soil Moisture Active Passive (SMAP) soil moisture data with the NLDAS climate index. Ground truth soil moisture data from the Soil Climate Analysis Network (SCAN) were collected for validation. As a result, the accuracy of using SMAP to monitor soil moisture content generally displayed a good statistical correlation with the SCAN data. The validation through the Palmer Drought Severity Index (PDSI) and Normalized Difference Water Index (NDWI) suggested that SSI was effective and sensitive for short-term drought monitoring across large areas.
干旱会严重降低农田和森林的生产力。美国农业部（USDA）东南部区域气候中心（SERCH）推出了最新确定的地理特定强化威胁系统（LIGHTS），以告知用户潜在的缺水威胁。该系统可识别干旱及其他气候异常情况，如极端降水和热应激。然而，LIGHTS模型缺乏土壤湿度观测数据的输入。本研究旨在通过将星载土壤湿度主动被动（SMAP）土壤湿度数据与NLDAS气候指数相结合，开发一种简单且易于解释的土壤湿度和干旱预警指数——标准化土壤湿度指数（SSI）。收集了来自土壤气候分析网络（SCAN）的地面实测土壤湿度数据进行验证。结果表明，利用SMAP监测土壤湿度含量的准确性与SCAN数据总体上呈现出良好的统计相关性。通过帕尔默干旱严重度指数（PDSI）和归一化差异水指数（NDWI）进行的验证表明，SSI对于大面积短期干旱监测是有效且敏感的。
