Performance of SMAP, AMSR-E and LAI for weekly agricultural drought forecasting over continental United States

Abstract

This study applied support vector machines (SVMs) and data assimilation (DA) methods to investigate the performance of in-situ and remotely sensed products (i.e., leaf area index (LAI), AMSR-E and SMAP soil moisture retrievals) for near-real time agricultural drought forecasting for in-situ stations located in continental United States (CONUS). The agricultural drought was quantified using soil water deficit index (SWDI) derived based on available soil moisture and basic soil water parameters. It is observed that SVMs or SVM-DA with limited meteorological variables as inputs able to forecast SWDI at most of the in-situ stations up to 1–2-week lead time. Addition of remotely sensed products (i.e., LAI, AMSR-E, SMAP) either individually or simultaneously as inputs to SVMs can able to improve SWDI forecast at most of the stations where the strong relationship exists between LAI (and/or AMSR-E, SMAP) with SWDI. Such improvement can persist up to 2–4-week lead time at some of the stations. But the efficiency tends to decrease with the increase in lead time. The addition of both LAI and SMAP (AMSR-E) performs better than the independent addition of LAI or SMAP (AMSR-E). Typically, the performance of drought prediction varies at local scales; therefore it is difficult to generalize our findings at regional scale.