Agricultural drought monitoring using European Space Agency Sentinel 3A land surface temperature and normalized difference vegetation index imageries

Agricultural drought is one of most damaging agricultural hazards worldwide that can bring significant agricultural losses and water scarcity. The use of satellite images for monitoring agricultural drought has received increasing research attention and has also been applied at both the regional and global scales. In this paper, the land surface temperature (LST) and radiance products of the new Sentinel-3A SLSTR (sea and land surface temperature radiometer) launched by European Space Agency (ESA) are used for the first time for estimating the vegetation temperature condition index (VTCI), which in turn is used for monitoring agricultural drought in the Hetao Plain of Inner Mongolia, China. This paper initially analyzes the correlation between LST and normalized difference vegetation index (NDVI) by using time series time MODIS LST and NDVI products under different vegetation growth conditions. The findings reveal that VTCI can only be used in warm seasons (late spring and summer periods) when negative correlations between LST and NDVI are observed. Therefore, VTCI images are captured in the study area between July and August 2017 by using Sentinel-3A SLSTR LST and NDVI and are utilized for drought investigation. These images reveal that the average VTCI of the cultivated land pixels in the study area has increased from 0.4511 on July 28 to 0.5229 on August 12 before declining to 0.4710 on August 18 due to the rainfall in the first period, thereby indicating that VTCI has a timely response to rainfall. Meanwhile, cross-comparison of VTCI values from Sentinel-3A SLSTR shows high consistency in terms of spatial distribution with that estimated from EOS MODIS products. The difference between these indices ranged from -0.1 to 0.1 for most points, especially in the cultivated land cover. Overall, the findings support the use of the LST and NDVI products of Sentinel-3A SLSTR in monitoring agricultural drought.