Technical Note: Comparing and ranking soil drought indices performance over Europe, through remote-sensing of vegetation

Abstract

Abstract. In the past years there have been many attempts to produce and improve global soil-moisture datasets and drought indices. However, comparing and validating these various datasets is not straightforward. Here, interannual variations in drought indices are compared to interannual changes in vegetation, as captured by NDVI. By comparing the correlations of the different indices with NDVI we evaluated which drought index describes most realistically the actual changes in vegetation. Strong correlation between NDVI and the drought indices were found in areas that are classified as warm temperate climate with hot or warm dry summers. In these areas we ranked the PDSI, PSDI-SC, SPI3, and NSM indices, based on the interannual correlation with NDVI, and found that NSM outperformed the rest. Using this best performing index, and the ICA (Independent Component Analysis) technique, we analyzed the response of vegetation to temperature and soil-moisture stresses over Europe.

Assessment of remotely sensed drought features in vulnerable agriculture

“…Meteorological satellites have also two types, namely geostationary such as METEOSAT and geosynchronous such as NOAA/AVHRR, and can contribute to operational monitoring and assessment of drought (Caccamo et al, 2011;Zhou et al, 2012). Similarly, environmental satellites such as LAND-SAT, SPOT and recently IKONOS, WV2 with high to very high resolution, but low frequency of coverage, can contribute to land-use classification and qualitative features of drought and less to quantitative assessments (Peled et al, 2010). Table 1 presents an indicative list of internationally used remotely sensed drought indices.…”

Agricultural Drought Assessment of Post Monsoon Season of Vaijapur Taluka Using Landsat 8

“…The drought indices can be compute from low spatial resolution data of different sensors like the Moderate Resolution Imaging Spetroradiometer (MODIS) or Advanced Very High Resolution Radiometer (AVHRR). The low spatial resolution is a difficult to recognize drought severity level in village, block and taluka level [22]. Therefore, for a small regional analysis can be done with According to the author, the meteorological data and remote sensing data are important for accurate assessment of drought disaster [26].…”

On the utility of land surface models for agricultural drought monitoring

“…Recently, Peled et al (2010) proposed a novel approach for evaluating LSM soil moisture predictions by examining the cross-correlation between model-estimated root-zone soil moisture anomalies and spatially concurrent anomalies in vegetation indices derived from visible/near-infrared (VIS/NIR) remote sensing. The use of VIS/NIR vegetation indices (VI) like the enhanced vegetation index (EVI) and the normalized difference vegetation index (NDVI) is well-established for monitoring the extent and severity of agricultural drought (Kogan, 1995;Peters et al, 2002;Ji and Peters, 2003).…”

“…That is, under waterlimited conditions, a negative soil moisture anomaly should temporally precede a detectable impact on vegetation health and biomass. The analysis in Peled et al (2010) is based on the assumption that the strength of soil moisture/VI crosscorrelation can be used as a large-scale proxy for the accuracy of a model-based, root-zone soil moisture product.…”

“…Here we expand the geographic scope of Peled et al (2010) (from the European continent to all global land between 60 • S and 60 • N) and evaluate a wider range of potential land surface modeling strategies. In particular, this analysis will employ various global LSMs, ranging from complex, modern LSMs to a simple antecedent precipitation index to sample lagged rank-correlations between model-estimated soil moisture and remotely sensed VI products.…”