Research contents:

The Innovation Team of Agricultural Remote Sensing works on the thermal infrared quantitative remote sensing inversion method for raising land surface temperature, studies the fusion method and theory of passive microwave land surface temperature products and thermal infrared land surface temperature products, retrieves land surface temperature from the hyperspectral thermal infrared data, land surface emissivity, and the integrated inversion method for temperature and humidity profiles. It studies and improves crop remote sensing recognition algorithms and develops new process models based on remote sensing invertible parameters.

Research objectives:

The team works to innovate the multi-mode collaborative inversion theory and methods of agricultural quantitative remote sensing, improve the accuracy and efficiency of crop remote sensing recognition, develop the agricultural monitoring and forecast models based on remote sensing invertible parameters, innovate remote sensing data and process mechanism models and methods, and establish an quantitative-remote-sensing-based agricultural monitoring and forecast comprehensive system through coupling the remote sensing model and the process mechanism model.

Research direction: 

Agricultural quantitative remote sensing is based on remote sensing retrieval of key parameters of land surface water and heat balance; agricultural remote sensing research is focused on crop remote sensing recognition algorithms and models, new crop yield estimation models, and an agricultural monitoring and forecasting integrated retrieval system.

Team members:

Established in 2013, the team has 20 members. They are: