Abstract
The inversion of land surface component temperatures is an essential source of information for mapping heat fluxes and the angular normalization of thermal infrared (TIR) observations. Leaf and soil temperatures can be retrieved using multiple-view-angle TIR observations. In a satellite-scale pixel, the clumping effect of vegetation is usually present, but it is not completely considered during the inversion process. Therefore, we introduced a simple inversion procedure that uses gap frequency with a clumping index (GCI) for leaf and soil temperatures over both crop and forest canopies. Simulated datasets corresponding to turbid vegetation, regularly planted crops and randomly distributed forest were generated using a radiosity model and were used to test the proposed inversion algorithm. The results indicated that the GCI algorithm performed well for both crop and forest canopies, with root mean squared errors of less than 1.0 °C against simulated values. The proposed inversion algorithm was also validated using measured datasets over orchard, maize and wheat canopies. Similar results were achieved, demonstrating that using the clumping index can improve inversion results. In all evaluations, we recommend using the GCI algorithm as a foundation for future satellite-based applications due to its straightforward form and robust performance for both crop and forest canopies using the vegetation clumping index.
Highlights
Land surface temperature (LST) is an important forcing variable for physical processes in surface–atmosphere interactions, including the energy budget and the hydrological cycle [1,2]
Regarding the normalized difference vegetation index (NDVI) algorithm, a good agreement can be found for the scene with an leaf area index (LAI) value of 1.0, but a slight underestimation of the leaf effective emissivity and a slight overestimation of the soil effective emissivity appeared over the scene with an LAI value of 3.0
This difference is likely due to the average clumping index used in the gap frequency with a clumping index (GCI) algorithm
Summary
Land surface temperature (LST) is an important forcing variable for physical processes in surface–atmosphere interactions, including the energy budget and the hydrological cycle [1,2]. The importance of the temperature distribution in heterogeneous and non-isothermal pixels has been increasingly recognized for the full characterization of the surface temperature state. This issue arises from surface temperature differences between components of a vegetation–soil system during most of the day because of the inhomogeneity of the intrinsic structures and physical. These differences can reach up to 11.0 ◦ C, as has been reported in many in situ experiments [9,10,11] This temperature information can facilitate understanding the complicated physical processes in energy and radiative transfers. These temperature differences are a factor that leads to the directional anisotropy of measured radiance in the thermal infrared (TIR)
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