A Robust Inversion Algorithm for Surface Leaf and Soil Temperatures Using the Vegetation Clumping Index

Abstract

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.

Remote sensing for agricultural applications: A meta-review

“…Conversely to the solar and microwave domains, the inversion of complex models based on radiative transfer or geometrically based have almost received no attention in the thermal domain, because of complexities induced by the spatiotemporal variability of land surface temperature (Lagouarde et al, 2013). Therefore, most studies have focused on inverting simple equations to retrieve end-member temperatures such as composite temperature for a homogeneous target, soil and vegetation component temperatures, or sunlit and shaded component temperatures (Jacob et al, 2004;Li et al, 2013;Bian et al, 2016;Bian et al, 2017;Jacob et al, 2017). Recent studies investigated the inversion of parametric (i.e.…”

Modeling Directional Brightness Temperature (DBT) over Crop Canopy with Effects of Intra-Row Heterogeneity

“…Thermal radiance varies with view angle because of the differences in component emissivity, component temperature, and proportion of the components Equation (30). The principle of CT inversion is to solve linear equations of CT, component directional emissivity, and directional radiance [45].…”

Addendum: Bian, Z. et al. A Robust Inversion Algorithm for Surface Leaf and Soil Temperatures Using the Vegetation Clumping Index. Remote Sens. 2017, 9, 780

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After publication of the research paper [1], it was found that funding information was missing from the Acknowledgment part. We subsequently addressed this point by a revision, as shown below.

The authors would like to apologize for any inconvenience caused.

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