Remote sensing monitoring of land surface temperature – aiding research on global environmental change

In Earth science research, land surface temperature (LST) is a crucial variable. It serves as an input parameter for various surface process models, and remote sensing inversion of LST is a key method for estimating surface energy balance at regional and global scales. LST variations are influenced by a variety of factors, including topography, landforms, vegetation, and climate. Through remote sensing monitoring, we can obtain LST data for different regions and time periods, allowing us to explore the impact of these factors on temperature and subsequently study issues such as climate change, ecosystem responses, and urban development.

Advantages of surface temperature remote sensing monitoring

Land surface temperature monitoring and analysis based on remote sensing data offers advantages such as high spatiotemporal resolution, wide coverage, and low cost. Because remote sensing data can rapidly acquire extensive land surface temperature information, it enables real-time monitoring and analysis of temperature changes over large areas. Furthermore, the high spatiotemporal resolution of remote sensing data allows it to capture the spatial and temporal distribution characteristics of land surface temperature. Compared to traditional ground-based observations, remote sensing-based monitoring methods significantly improve monitoring efficiency and reduce costs.

CAS Xiguang Aerospace - Shanghai Surface Temperature Inversion

Shanghai is an important economic center city in my country. With its rapid socio-economic development, changes in its urban landscape layout and land use patterns have had a profound impact on the evolution of the urban ecological environment.Among the various ecological and environmental effects of urbanization, the urban heat island effect is closely related to changes in urban land cover and human socio-economic activities, and is a comprehensive summary and reflection of the urban ecological environment.In response, the CAS Xiguang team conducted remote sensing monitoring of surface temperature in Shanghai and generated an inversion result map.As shown in the figure, the surface temperature in Jinshan District, Songjiang District and Jiading District of Shanghai is higher than normal, while the surface temperature in Chongming District is lower than normal. 

△ Distribution of vegetation coverage in Wuxi City in August 2023

Widespread application of remote sensing monitoring of land surface temperature

Currently, land surface temperature products provided by remote sensing are widely used in environmental monitoring, drought monitoring, frost damage monitoring, agricultural water resource management, and forest fire prevention. The quality of land surface temperature is crucial to the simulation and prediction results of various land surface processes. In agricultural drought and thermal anomaly monitoring, long-term land surface temperature data is often used to obtain historical averages and serves as a reference for assessing drought or anomalies. In thermal environment monitoring, land surface temperature is the parameter that most directly reflects the spatial differences in the thermal environment, and therefore it is widely used in monitoring the urban heat island effect at different scales. In global change research, parameters characterizing the intensity and extent of land surface temperature data, mined from long-term land surface temperature data, represent a new area of ​​application for land surface temperature data products in recent years. As global land surface temperature data products continue to improve in terms of spatiotemporal resolution, accuracy, time span, and coverage integrity, land surface temperature will play an increasingly important role in global change research.

Currently, land surface temperature monitoring and analysis based on remote sensing data still faces several challenges. First, the quality and accuracy of remote sensing data significantly impact land surface temperature monitoring results. Therefore, data processing requires careful attention to correction and noise removal to improve the accuracy of monitoring results. Second, the interpretation and application of land surface temperature data necessitate comprehensive analysis in conjunction with other environmental factors to obtain more complete information. Furthermore, land surface temperature monitoring and analysis need to be combined with field observations and model simulations for mutual verification and supplementation, thereby enhancing the reliability and effectiveness of monitoring results.