The Innovation Team of Agricultural Remote Sensing of the Institute of Agricultural Resources and Regional Planning (IARRP) of the Chinese Academy of Agricultural Sciences (CAAS) have made significant progress in the correction of angular effects in remotely sensed land surface temperature. Their related research, titled "Angular effect correction of remotely sensed land surface temperature by integrating geostationary and polar-orbiting satellite data," has been published in the top-tier Remote Sensing of Environment journal (IF=11.1).

Land surface temperature is a crucial remote sensing parameter that reflects soil moisture, crop transpiration, and crop health. However, land surface temperature exhibits distinct directional characteristics, meaning that radiation temperatures vary under different viewing angles. To address this issue, the Agricultural Remote Sensing Team proposed a new method for angular effect correction of land surface temperature products based on a kernel-driven model and multi-source remote sensing data.

This method leverages the advantages of the kernel-driven model in balancing physical accuracy and practicality. It utilizes a satellite virtual network composed of one geostationary satellite and four polar-orbiting satellites to construct a satellite dataset of multi-angle land surface temperature. By calibrating the kernel-driven model in different seasons and land cover types, the method eliminates the angular effect of land surface temperature. In comparison to previous studies, this method uses satellites with different operational modes, incorporates a richer angular sampling in the constructed multi-angle dataset, and combines a model calibration approach based on clustering scale to address the challenge of obtaining kernel coefficients. As a result, a more robust kernel-driven model is established, effectively mitigating the impact of directionality on land surface temperature and obtaining highly accurate land surface temperature data.

This research outcome can provide data support for crop growth monitoring, drought early warning, and precision irrigation, thereby enhancing agricultural production efficiency and resource utilization, and promoting the sustainable development of modern agriculture.

Wei Ran, a master's student from the IARRP Innovation Team of Agricultural Remote Sensing is the first author of the study, with Professor Duan Sibo as the corresponding author. The research was supported by the National Key Laboratory for Efficient Utilization of Northern Arid and Semi-Arid Farmland and the National Natural Science Foundation of China, among other projects.

Original article link: https://www.sciencedirect.com/science/article/pii/S0034425725001920