Land surface temperature and surface drought
Land surface temperature IR retrievals from Meteosat can indicate dry anomalies in climatic aspect
Skin temperature retrieval according to the LSASAF Land surface temperature (LST) product (MLST) is based on clear-sky measurements in the thermal infrared window (IR10.8 and IR12.0) from the geostationary Meteosat satellite. LST values can be determined 96 times per day from MSG but fewer observations are available due to cloud cover. LST is a key parameter in the physics of land surface processes on regional and global scales, combining the results of all surface-atmosphere interactions being related to energy-water-carbon cycles. For more complex conditions like vegetated surfaces, the physical temperature of the Earth’s surface can refer to an average effective radiative temperature of the canopy and surface. As a basic environmental/meteorological parameter LST influences the function and variability of ecosystems, including agriculture; exercises control on surface-atmosphere exchange of energy, water, gases and aerosols. It is a primary variable of climatology and an indicator of climate change, being related to vegetation water stress.
LSASAF LST inferred from Meteosat observations as drought indicator
The score of LST satellite retrieval as a measure of dry surface state on a regional scale is revealed considering its consistency with Soil Moisture Availability (SMA) to vegetation cover. In this study SMA is inferred as an Index (SMAI) based on a SVAT model output and accounts for onset/termination of different levels of drought severity being functionally related to soil moisture deficit. Comparative analyses are performed over a long-term data set (2007–2018).
Fig.1: Negative linear regression models of the relationship between LSASAF LST and SMAI anomalies over Bulgaria, SEE (for root zone depths, June-August 2007-2018).
Pearson’s correlation coefficient is used as a measure of the correspondence between SMAI and LST anomalies. A stronger relationship between higher warm anomalies of LST and stronger dry anomalies of SMAI occur in the summer months when the most limited rain quantities are leading to a decrease in SMA, which restricts evapotranspirative cooling of the vegetated land surface. With strong insolation at the beginning of this period and the resulting cumulative effects, land surface temperature can increase significantly (above 45 oC) and SMA to be fully exhausted.
Spatial distribution and variability: The correlation between the SMAI and LST anomalies confirms a strong relationship for the whole region of Bulgaria. The Pearson’s correlation coefficient is above 0.5 for the growing season (May-October).
Fig. 2: Spatial distribution of growing season correlation between SMAI and LSASAF LST anomalies over Bulgaria.
For the summer months (July, August) the spatial distribution of the correlation between the SMAI and LSASAF LST anomalies shows a strong relationship for the whole region of Bulgaria (R2 mostly around 0.8, maximum 0.84).
Fig. 3: Monthly mean spatial-temporal distribution of correlation between the root zone SMAI and LSASAF LST anomalies for July and August over Bulgaria.
The physical mechanism of LST- Drought relations: As the moist soil surface dries out, more of the incoming solar energy is reflected, and a larger fraction of the absorbed energy is used to heat the air and soil. The heat flow into the soil increases at first, then decreases as the soil becomes very dry. This results in higher LSTs, which also influence the rate of drying and evapotranspiration. Therefore, high LST are both a cause and a result in the case of dry periods. In conformity with this universal mechanism, presented analyses show that LSASAF LST product can be used as a tool for drought analyses on climate bases, specifically in the critical period of July - August when most frequently severe dry anomalies occur over southeastern part of Europe.
More information in: Stoyanova J, Georgiev C, Neytchev P, Kulishev A (2019) Spatial-temporal variability of land surface dry anomalies in climatic aspect: biogeophysical insight by Meteosat observations and SVAT modeling. Atmosphere 10:636. https://doi.org/10.3390/atmos10100636