McNally Amy, Shukla Shraddhanand, Arsenault Kristi R, Wang Shugong, Peters-Lidard Christa D, Verdin James P
University of Maryland Earth Systems Science Interdisciplinary Center and Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA.
University of California, Santa Barbara, CA.
Int J Appl Earth Obs Geoinf. 2016 Jun;48:96-109. doi: 10.1016/j.jag.2016.01.001. Epub 2016 Jan 21.
To assess growing season conditions where ground based observations are limited or unavailable, food security and agricultural drought monitoring analysts rely on publicly available remotely sensed rainfall and vegetation greenness. There are also remotely sensed soil moisture observations from missions like the European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) and NASA's Soil Moisture Active Passive (SMAP), however these time series are still too short to conduct studies that demonstrate the utility of these data for operational applications, or to provide historical context for extreme wet or dry events. To promote the use of remotely sensed soil moisture in agricultural drought and food security monitoring, we use East Africa as a case study to evaluate the quality of a 30+ year time series of merged active-passive microwave soil moisture from the ESA Climate Change Initiative (CCI-SM). Compared to the Normalized Difference Vegetation index (NDVI) and modeled soil moisture products, we found substantial spatial and temporal gaps in the early part of the CCI-SM record, with adequate data coverage beginning in 1992. From this point forward, growing season CCI-SM anomalies were well correlated (R>0.5) with modeled, seasonal soil moisture, and in some regions, NDVI. We use correlation analysis and qualitative comparisons at seasonal time scales to show that remotely sensed soil moisture can add information to a convergence of evidence framework that traditionally relies on rainfall and NDVI in moderately vegetated regions.
为了评估地面观测数据有限或无法获取地区的生长季状况,粮食安全和农业干旱监测分析人员依赖公开可用的遥感降雨和植被绿度数据。此外,还有来自欧洲航天局(ESA)的土壤湿度和海洋盐度(SMOS)任务以及美国国家航空航天局(NASA)的土壤湿度主动被动探测(SMAP)等任务的遥感土壤湿度观测数据,然而,这些时间序列仍然太短,无法开展研究来证明这些数据在实际应用中的效用,也无法为极端湿润或干旱事件提供历史背景信息。为了推动在农业干旱和粮食安全监测中使用遥感土壤湿度数据,我们以东非为例,评估了来自ESA气候变化倡议(CCI-SM)的30多年有源-无源微波土壤湿度合并时间序列的质量。与归一化植被指数(NDVI)和模拟土壤湿度产品相比,我们发现CCI-SM记录早期存在大量时空空白,1992年开始有足够的数据覆盖。从那时起,生长季CCI-SM异常与模拟的季节性土壤湿度以及某些地区的NDVI具有良好的相关性(R>0.5)。我们在季节时间尺度上使用相关性分析和定性比较,以表明在植被适中的地区,遥感土壤湿度能够为传统上依赖降雨和NDVI的证据汇聚框架增添信息。