Algorithm for estimating crop canopy temperature using EOS-MODIS data

Land Surface Temperature(LST) observed from satellite-platform is mixed temperature by various objects. How to estimate component temperature of vegetation in a pixel has become a hot issue all over the world. In this article, a new approach to extracting the component temperature of vegetation based on linear mixture model was developed. In this algorithm, firstly, land surface temperature and Normal Difference Vegetation Index(NDVI) were retrieved from EOS-MODIS Data. By comparison, the retrieved LST is almost consistent with the NASA MODIS LST product. Then, the landscape was simplified as a mixture of vegetation and bare soil. The component temperature of bare soil was estimated by using VI-Ts method and component temperature of vegetation was obtained from the linear mixture model. In order to check the performance of the algorithm, the retrieved component temperature of vegetation was compared with validation data observed at a few test sites in Hebei Province and the accuracy of the algorithm is within ±1.5℃. The research results show that the algorithm for estimating crop canopy temperature presented in this paper has higher accuracy, which can meet the accuracy requirements of related crop growth models and soil moisture models.