Surface Reflectance-Derived Spectral Indices for Drought Detection: Application to the Guadalupe Valley Basin, Baja California, Mexico

Evaluating how meteorological drought affects areas covered by natural ecosystems is challenging due to the lack of ground-based climate data, historical records, and weather station observation with limited coverage. This research tests how the surface reflectance-derived indices (SRDI) may solve this problem by assessing the condition and vegetation dynamics. We use long-term, monthly surface reflectance data (26 hydrological years, 1992/93-2017/18) from Landsat 5 TM, 7 ETM+, and 8 OLI/TIRS satellites and calculated the following five SRDI: Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), Vegetation Health Index (VHI), Normalized Difference Water Index (NDWI), and Modified Soil Adjusted Vegetation Index (MSAVI). The SRDI allows us to detect, classify, and quantify the area affected by drought in the Guadalupe Valley Basin (GVB) via correlations with the Reconnaissance Drought Index (RDI) and the Standardized Precipitation Index (SPI) (weather station-based data). For particular SRDI-RDI and SRDI-SPI combinations, we find positive seasonal correlations during April-May (IS2) and for annual (AN) values (MSAVI IS2-RDI AN, R = 0.90; NDWI IS2-SPI AN, R = 0.89; VHI AN-RDI AN, R = 0.86). The drought-affected GVB area accounted for >87% during 2001/02, 2006/07, 2013/14, and 2017/18. MSAVI and NDWI are the best meteorological drought indicators in this region, and their application minimizes the dependence on the availability of climatic data series.

Automated summary

This research explores the use of surface reflectance-derived indices (SRDI) to assess the impact of meteorological drought on natural ecosystems. The study finds that SRDI can be used to detect and quantify drought-affected areas through correlations with the Reconnaissance Drought Index (RDI) and the Standardized Precipitation Index (SPI). MSAVI and NDWI are identified as the most effective meteorological drought indicators in the region.