SMOS data as a source of the agricultural drought information: Case study of the Vistula catchment, Poland

- We calculated Soil Moisture Index (SMI) and improved SMI (SMIm) using SMOS data.
- SMOS data allows us to observe drought at better scale than robust SPEI index.
- SMIm histograms had better structure and are more normal than SMI and SPEI.

So far numerous drought indices have been developed to quantitatively describe the drought phenomenon. Most of the commonly used indices apply diverse meteorological information, and use nontrivial modeling schemes. Relatively smaller number of drought indices uses information about soil moisture (SM) content that is directly related to water available for the plants. This could be explained by the limitations associated with high costs of the in-situ SM measurements especially when they are carried out over large areas. The aim of this study was to demonstrate that SMOS satellite mission is an important source of information not about SM only, but also about agricultural drought conditions on a regional scale.To do so, we compared over the Vistula river catchment, Soil Moisture Index (SMI) calculated using original formula and exclusively SMOS data with more complicated and computationally demanding Standardized Precipitation Evapotranspiration Index (SPEI). Our analysis involved almost a four-year period 2010-2014. We also proposed new Modified Soil Moisture Index (SMIm) to be useful specifically for SMOS data application. It was shown, that spatial distributions of SMI and particularly those of SMIm, calculated using only SMOS SM data, reveal many details about drought conditions, often more than spatial distributions of SPEI. In particular, comparisons made for a severe drought which occurred in Poland in 2013, revealed that SMI and SMIm are suitable to assess agricultural drought risk. Thus our results show that SMIm could be a cheap supplementary tool for drought monitoring in large areas.