Spatial analysis of remote sensing and meteorological indices in a drought event in southwestern Spain

Abstract

The effects of global warming and climate change are being felt through more extreme and prolonged periods of drought. Multiple meteorological indices are used to measure drought, but they require hydrometeorological data; however, other indices measured by remote sensing and used to quantify vegetation vigor can be correlated with the former. This study investigated the correlation between both index types by vegetation type and season. The correlations were also spatially modeled in a drought event in southwestern Spain. In addition, three maps with different levels of detail in terms of vegetation categorization were compared. The results generally showed that grassland was the most well correlated category between the SPEI and the FAPAR, LAI, and NDVI. This correlation was more pronounced in autumn and spring, which is when most changes in vegetation senescence and growth occur. The spatiotemporal analysis indicated a very similar behavior for grasslands grouped in an area indicated by the climate change adaptation maps as having a high evapotranspiration forecast. Finally, in a forest-based forecast analysis, the indices that best explained the performance of the SPEI were again FAPAR, LAI, and NDVI, with a lag of up to 20 days. Therefore, the results showed that remotely sensed indices are good indicators of drought status and can be variably explanatory of traditional drought indicators. Moreover, complementing the study with spatiotemporal analysis made it possible to detect areas particularly vulnerable to climate change.