Recently, graduate student Xiao Xiongxin and his supvisor professor Zhang Tingjun (communication author) and other collaborators published an article entitled 'Support vector regression snow-depth retrieval algorithm using passive microwave remote sensing data ' on the international top journal Remote Sensing of Environment.
In this study, a combination of passive microwave remote sensing data and ground measured data was used to carry out the research on snow depth inversion, and a new snow depth inversion method-Support Vector Regression (Support Vector Regression) was established. The modified model is suitable for snow depth inversion with different land cover types and different snow periods. The algorithm has the highest precision and the smallest error, and can estimate the snow depth of Eurasia and Northern Hemisphere more accurately.
Fig.1 Using the five snow depth inversion algorithms to obtain the scatter density map of the snow depth estimation and the ground measured value, the data is the snow ii period (2007-2012), and the underlying vegetation type is shrub.
