Details:

Journal Title:
Remote Sensing
Personal Author:
Sun, Donglian ; Li, Yu ; Zhan, Xiwu ; Houser, Paul ; Yang, Chaowei ; ... More +
Sun, Donglian ; Li, Yu ; Zhan, Xiwu ; Houser, Paul ; Yang, Chaowei ; Chiu, Long ; Yang, Ruixin Less -
NOAA Program & Office:
NESDIS (National Environmental Satellite, Data, and Information Service) ; STAR (Center for Satellite Applications and Research)
NESDIS (National Environmental Satellite, Data, and Information Service) ; STAR (Center for Satellite Applications and Research) Less -
Description:
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.
Keywords:
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Droughts Remote Sensing AMSR-2 AMSR-E GOES LST MODIS
Source:
Remote Sens. 2019, 11(14), 1704
DOI:
https://doi.org/10.3390/rs11141704
Document Type:
Journal Article
Rights Information:
CC BY
Compliance:
Submitted
Main Document Checksum:
[+]
urn:sha256:ee71d1fd67b773395e284c8ab74fe44a5adfc02ee0ed4e1bc5ebd6152c182b0d
Download URL:
https://repository.library.noaa.gov/view/noaa/45238/noaa_45238_DS1.pdf
File Type:

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