Enhanced Modeling of Annual Temperature Cycles with Temporally Discrete Remotely Sensed Thermal Observations

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