An agricultural drought severity index using quasi-climatological anomalies of remotely sensed data

Obtaining an agricultural drought index using solely remotely sensed products has numerous benefits over their in situ counterparts such as if a country does not have the resources to implement an in situ ground network. One such index, created by Rhee et al. (2010), uses a combination of precipitation data from the Tropical Rainfall Measuring Mission (TRMM), with land-surface temperature (LST) data and vegetation indices (VIs) using data from the Moderate Resolution Imaging Spectroradiometer (MODIS) to assess drought conditions. With TRMM data becoming no longer available (as of mid-2015), this study sought to test precipitation data from the Climate Prediction Center (CPC) Morphing (CMORPH) Technique over the study period of January 2003–September 2014, in order to take the place of the TRMM data set in a drought severity index (DSI). This study also attempted to refine the methodology using the quasi-climatological anomalies (short-term climatological anomalies) of each parameter within the DSI. We validated the results of the DSI against in situ percentage available water (PAW) data from a soil water balance (SWB) model over the country of Uruguay. The results of the DSI correlated well with the PAW over the warmer months (October–March) of the year with average r -values ranging from 0.74 to 0.81, but underperformed during the colder months (April–September) with average r -values ranging from 0.38 to 0.50. This underperformance is due to the fact that precipitation during this season continues to have high variability, whereas PAW stays relatively constant. Spatially the DSI correlates well over the majority of the country with the possible exception of underperformance near the coastal area in the southeastern portion of the country. Ultimately, this research has the ability to aid Uruguay in better drought monitoring and mitigation practices as well as emergency aid resource allocation.