Improved estimates of monthly land surface temperature from MODIS using a diurnal temperature cycle (DTC) model

摘要: Abstract Satellite-derived land surface temperature (LST) has been widely used to understand energy exchange and radiation budget is a critical input of earth system models for carbon water cycles studies at regional global scales. Spatially temporally consistent thermal measurements with long historical records are in high demand the biophysical changes Earth surfaces. The current monthly LST products’ spatial resolutions relatively coarse temporal consistency could be interrupted by presence clouds satellite orbit limitations. This study develops diurnal cycle (DTC) model-based approach from MODIS observations that suitable constructing long-term near-global coverage LSTs 1 km fill these data gaps. new allows us estimate representative 24-hr mean maximum temperatures each month. We performed an inter-comparison among satellite-based data, including DTC-based estimates, simple composite four overpasses, hourly geostationary (GEO), also assessed against in-situ FLUXNET globally. Our proposed outperformed other two estimates GEO, showing difference 0.3 °C RMSE 2.2 °C relative measurements. Moreover, we illustrate application explores relationship between soil moisture anomalies across United States. estimated DTC scheme show higher sensitivity droughts than mean. In sum, improved dataset using can enhance our understanding dynamics resulting land–atmosphere interaction local, regional,