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Free keywords:
thermal remote sensing; land surface temperature; annual temperature cycle; LST dynamics; MODIS
Abstract:
Abstract: Satellite thermal remote sensing provides land surface temperatures (LST) over extensive
areas that are vital in various applications, but this technique suffers from its sampling style and the
impenetrability of clouds, which frequently generates data gaps. Annual temperature cycle (ATC)
models can fill these gaps and estimate continuous daily LST dynamics from a number of thermal
observations. However, the standard ATC model (termed ATCS) remains incapable of quantifying
the short-term LST variations caused by synoptic conditions. By incorporating in-situ surface air
temperatures (SATs) and satellite-derived normalized difference vegetation indexes (NDVIs), here we
proposed an enhanced ATC model (ATCE) to describe the daily LST fluctuations. With Aqua/MODIS
LST products as validation data, we implemented and tested the ATCE over the Yangtze River
Delta region of China. The results demonstrate that, when compared with the ATCS, the overall
root mean square errors of the ATCE decrease by 1.0 and 0.8 K for the day and night, respectively.
The accuracy improvements vary with land cover types with greater improvements over the forest,
grassland, and built-up areas than over cropland and wetland. The assessments at different time
scales further confirm that LST fluctuations can be better described by the ATCE. Though with
limitations, we consider this new model and its associated parameters hold great potentials in
various applications.
