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Addendum published on 11 October 2017, see Remote Sens. 2017, 9(10), 1039.

Open AccessArticle
Remote Sens. 2017, 9(8), 780; doi:10.3390/rs9080780

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

1,2
,
1,†
,
1,†
,
1,* , 3
,
4
,
1,†
and
1,*
1
State Key Laboratory of Remote Sensing, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 10010, China
2
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
3
Chongqing Key Laboratory of Karst Environment School of Geographical Sciences, Southwest University, Chongqing 400712, China
4
Institute of RS and GIS, Peking University, Beijing 100871, China
These authors contributed equally to this work.
*
Authors to whom correspondence should be addressed.
Received: 12 June 2017 / Revised: 13 July 2017 / Accepted: 28 July 2017 / Published: 30 July 2017
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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. View Full-Text
Keywords: land surface component temperature; directional remote sensing; vegetation clumping index land surface component temperature; directional remote sensing; vegetation clumping index
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Bian, Z.; Cao, B.; Li, H.; Du, Y.; Song, L.; Fan, W.; Xiao, Q.; Liu, Q. A Robust Inversion Algorithm for Surface Leaf and Soil Temperatures Using the Vegetation Clumping Index. Remote Sens. 2017, 9, 780.

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