Method of Calculating Land Surface Temperatures Based on the Low-altitude UAV Thermal Infrared Remote Sensing Data and the Near-ground Meteorological Data

2021 ◽  
pp. 103615
Author(s):  
Yafei Wu ◽  
Yao Shan ◽  
Yuanming Lai ◽  
Shunhua Zhou
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Meteorological Data ◽  
Remote Sensing Data ◽  
Thermal Infrared ◽  
Thermal Infrared Remote Sensing ◽  
Sensing Data ◽  
Infrared Remote Sensing ◽  
Land Surface Temperatures ◽  
Low Altitude

scholarly journals Contribution of Thermal Infrared Remote Sensing Data in Multiobjective Calibration of a Dual-Source SVAT Model

2006 ◽  
Vol 7 (3) ◽  
pp. 404-420 ◽  
Author(s):  
Benoit Coudert ◽  
Catherine Ottlé ◽  
Brice Boudevillain ◽  
Jérôme Demarty ◽  
Pierre Guillevic
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Model Calibration ◽  
Initial Conditions ◽  
Remote Sensing Data ◽  
Thermal Infrared ◽  
Model Parameters ◽  
Thermal Infrared Remote Sensing ◽  
Sensing Data ◽  
Dual Source

Abstract This study fits within the overall research on the usage of space remote sensing data to constrain land surface models (LSMs) (also called SVAT models for soil–vegetation–atmosphere transfer). The goal of this paper is to analyze the potential of using thermal infrared (TIR) remote sensing data for LSM calibration. LSMs are characterized by a large number of parameters and initial conditions that have to be specified. This model calibration is generally performed at a local scale by minimization between measurements and time series difference. Recent studies have shed light on the use of multiobjective approaches for performing calibration and for analyzing the model’s sensitivity to input parameters. Such an approach has been implemented in the SEtHyS LSM (for “Suivi de l’Etat Hydrique des Sols,” the French acronym for soil moisture monitoring) with the objective of assessing the information contributed by having knowledge of the remote sensing surface brightness temperature. For this purpose, the model calibration was performed in three different cases at field scale corresponding to different calibration design. The analysis of these numerical experiments permits the authors to show the contribution and the limits of TIR remote sensing data for LSM calibration, in various environmental conditions. The perspectives underline the potential of using a dynamic calibration methodology, taking advantage of the time-varying model parameters’ influence.

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