Spatial-Temporal Variability of Land Surface Dry Anomalies in Climatic Aspect: Biogeophysical Insight by Meteosat Observations and SVAT Modeling

• Main Authors: Julia Stoyanova, Christo Georgiev, Plamen Neytchev, Andrey Kulishev
• Format: Article
• Language: English
• Published: MDPI AG 2019-10-01
• Series: Atmosphere
• Subjects: dry land surface anomalies; soil moisture availability index; svat modeling; land surface temperature; satellite observations; regional climate; biomass burning
• Online Access: https://www.mdpi.com/2073-4433/10/10/636

The spatial-temporal variability of drought occurrence over Bulgaria is characterized based on long-term records (2007−2018) of Meteosat information and the SVAT model-derived soil moisture availability index (referred to root zone depth, SMAI). Land surface temperature according to the satellite-derived Land Surface Analysis Satellite Application Facility Land Surface Temperature (LSASAF LST) product and SMAI were used to designate land surface state dry anomalies. The utility of LST for drought assessment is tested by statistical comparative analyses, applying two approaches, site-scale quantitative comparison, and evaluation of spatial-temporal consistency between SMAI and LST variability. Pearson correlation and regression modeling techniques were applied. The main results indicate for a synchronized behavior between SMAI and LST during dry spells, as follows: opposite mean seasonal course (March−October); high to strong negative monthly correlation for different microclimate regimes. Negative linear regressions between the anomalies of SMAI and LST (monthly mean), with a strong correlation in their spatial-temporal variability. Qualitative evaluation of spatial-temporal variability dynamics is analyzed using color maps. Drought-prone areas were identified on the bases of LST maps (monthly mean), and it is illustrated they are more vulnerable to vegetation burning as detected by the Meteosat FRP-PIXEL product. The current study provides an advanced framework for using LST retrievals based on IR satellite observations from the geostationary MSG satellite as an alternative tool to SMAI, whose calculation requires the input of many parameters that are not always available.