Abstract
Land surface temperature (LST) is an important input to the Atmosphere–Land Exchange Inverse (ALEXI) model to derive the Evaporative Stress Index (ESI) for drought monitoring. Currently, LST inputs to the ALEXI model come from the Geostationary Operational Environmental Satellite (GOES) and Moderate Resolution Imaging Spectroradiometer (MODIS) products, but clouds affect them. While passive microwave (e.g., AMSR-E and AMSR-2) sensors can penetrate non-rainy clouds and observe the Earth’s surface, but usually with a coarse spatial resolution, how to utilize multiple instruments’ advantages is an important methodology in remote sensing. In this study, we developed a new five-channel algorithm to derive LST from the microwave AMSR-E and AMSR-2 measurements and calibrate to the MODIS and GOES LST products. A machine learning method is implemented to further improve its performance. The MODIS and GOES LST products still show better performance than the AMSR-E and AMSR-2 LSTs when evaluated against the ground observations. Therefore, microwave LSTs are only used to fill the gaps due to clouds in the MODIS and GOES LST products. A gap filling method is further applied to fill the remaining gaps in the merged LSTs and downscale to the same spatial resolution as the MODIS and GOES products. With the daily integrated LST at the same spatial resolution as the MODIS and GOES products and available under nearly all sky conditions, the drought index, like the ESI, can be updated on daily basis. The initial implementation results demonstrate that the daily drought map can catch the fast changes of drought conditions and capture the signals of flash drought, and make flash drought monitoring become possible. It is expected that a drought map that is available on daily basis will benefit future drought monitoring.
Highlights
Land surface temperature (LST) plays a critical role in the interaction between the Earth land surface and the atmosphere by controlling the surface upwelling radiation and affecting surface energy exchange with the atmosphere
The Moderate Resolution Imaging Spectroradiometer (MODIS) LST data were aggregated to 25 km, the same resolution as the Advanced Microwave Scanning Radiometer (AMSR)-E
The LST retrieved from the AMSR-E are compared with the MODIS LST product
Summary
Land surface temperature (LST) plays a critical role in the interaction between the Earth land surface and the atmosphere by controlling the surface upwelling radiation and affecting surface energy (sensible heat and latent heat flux) exchange with the atmosphere. LST, as a key parameter for the Earth’s surface energy balance and exchange, is significant in researching the fields of climatology, hydrology, meteorology, and ecology [1,2]. Significant efforts have been made throughout the past decades to derive LST from space and aircraft optical sensors, such as polar orbit sensors, like the Advanced Very-High-Resolution Radiometer (AVHRR) [18], the Moderate Resolution Imaging Spectroradiometer (MODIS) [19,20,21], and the Visible Infrared Imaging Radiometer Suite (VIIRS) [22]; and, geostationary satellites, like the Geostationary Operational Environmental Satellite (GOES) [23,24,25,26]. Clouds affect optical sensors, like AVHRR, MODIS, VIIRS, and GOES.
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