4.7 Article

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

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ELSEVIER
DOI: 10.1016/j.isprsjprs.2020.08.007

关键词

Monthly land surface temperature; Diurnal temperature cycle model; Thermal anomalies; Droughts; MODIS

资金

  1. USAID Feed the Future program [7200AA18CA00014]

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

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