Evaluation of a Linear Mixing Model to Retrieve Soil and Vegetation Temperatures of Land Targets

35th International Symposium on Remote Sensing of Environment

地球科学;生态环境科学

Yang, Jinxin^1 ; Jia, Li^1,2 ; Cui, Yaokui^1 ; Zhou, Jie^1 ; Menenti, Massimo^1,3

State Key Laboratory of Remote Sensing Science, Chinese Academy of Sciences, Beijing Normal University, China^1

Alterra, Wageningen University and Research Centre, Wageningen, Netherlands^2

Delft University of Technology, Delft, Netherlands^3

关键词: Brightness temperatures;    Component temperatures;    Heterogeneous soils;    Linear mixing models;    Retrieval accuracy;    Thermal infra red;    Two component systems;    Vegetation temperature;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/17/1/012272/pdf
DOI  :  10.1088/1755-1315/17/1/012272

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

A simple linear mixing model of heterogeneous soil-vegetation system and retrieval of component temperatures from directional remote sensing measurements by inverting this model is evaluated in this paper using observations by a thermal camera. The thermal camera was used to obtain multi-angular TIR (Thermal Infra-Red) images over vegetable and orchard canopies. A whole thermal camera image was treated as a pixel of a satellite image to evaluate the model with the two-component system, i.e. soil and vegetation. The evaluation included two parts: evaluation of the linear mixing model and evaluation of the inversion of the model to retrieve component temperatures. For evaluation of the linear mixing model, the RMSE is 0.2 K between the observed and modelled brightness temperatures, which indicates that the linear mixing model works well under most conditions. For evaluation of the model inversion, the RMSE between the model retrieved and the observed vegetation temperatures is 1.6K, correspondingly, the RMSE between the observed and retrieved soil temperatures is 2.0K. According to the evaluation of the sensitivity of retrieved component temperatures on fractional cover, the linear mixing model gives more accurate retrieval accuracies for both soil and vegetation temperatures under intermediate fractional cover conditions.