Conference Agenda

Land Surface Temperature (LST) is a crucial parameter in the Earth's energy cycle, essential for understanding the interactions between the surface and the atmosphere. This abstract provides a summary of the Dragon-5 project (All-Weather Land Surface Temperature At High Spatial Resolution: Validation and Applications, 59318), including (1) generating all-weather LST datasets; (2) developing methods for improving LST validation; and (3) Application of the all-weather LST.

In all-weather LST datasets: the project team carried out research both on method exploration and dataset generation. The traditional all-weather LST is usually retrieved from passive microwave (PMW) brightness temperature (BT). However, limited by the satellite orbit, it is difficult to obtain the all-weather LST in spatial. Therefore, the research team carried out a series of research on it, such as retrieval from PMW BTs [1], gap filling for PMW data [2], and integrating PMW/reanalysis data with thermal infrared LST [3], [4], etc. Then, the all-weather LST dataset from 2000 to 2022 for China’s landmass and surrounding areas has been generated, namely TRIMS LST, with a spatial resolution of 1 km and a temporal resolution of four observations per day[5]. Furthermore, the Chinese team extends the integration method to Chinese geostationary meteorological satellites [6], [7], which makes it possible to obtain a high temporal-resolution all-weather LST dataset based on historical archived records and near real-time datasets. The European team integrated the clear-sky LST retrieved from the European geostationary meteorological satellite (MSG/SEVIRI) with the LST estimated by a land surface energy balance model to fill the gaps caused by clouds and generated hourly all-weather LST, namely MLST-AS [8].

In LST validation: apart from the validation in all-weather LST retrieval and generation, the team also conducted a special validation for the developed two all-weather LST datasets [9]. The results show that for terrestrial sites, MLST-AS showed better accuracy, with an RMSE of 1.6–2.1 K, while TRIMS LST had an RMSE of 1.9–3.1 K; both datasets exhibited higher LST accuracy under clear sky conditions at all sites compared to non-clear sky conditions. At the same time, based on the analysis of long-term in-situ LST at Gobabeb’s meteorological station, the performance of the two all-weather LST in various atmospheric and surface conditions in Gobabeb was comprehensively analyzed [10]. In addition, the spatial representativeness of the LST validation stations on pixel scales [11] and the near-surface atmospheric influence on longwave radiation-based in-situ LST [12] were also studied, which provides theoretical support for LST validation.

There are two main applications for the all-weather LST. Firstly, the team carried an attempt to obtain a higher resolution all-weather LST, such as downscaling TRIMS LST using LightGBM from 1 km to 250 m over Southeast Tibet [13], and using the geographically weighted regression and random forest from 1 km to 100 m [14]. Secondly, the real SUHI of five major cities in China under all-weather conditions was investigated and found that clouds have a significant impact on UHIs, highlighting the necessity of developing high-resolution all-weather LST data [15].

In summary, these advancements not only enhance our understanding of the spatiotemporal variations in LST but also provide valuable, continuous LST data resources for global change research. We look forward to more breakthroughs in the construction and application of all-weather LST datasets in the future, contributing to the improvement of weather and climate system models and the development of Earth system science.

References

[1] S. Wang et al., “Estimating Land Surface Temperature from Satellite Passive Microwave Observations with the Traditional Neural Network, Deep Belief Network, and Convolutional Neural Network,” Remote Sensing, 2020 12(17): 2691.

[2] X. Zhang et al., “Estimation of 1-km all-weather remotely sensed land surface temperature based on reconstructed spatial-seamless satellite passive microwave brightness temperature and thermal infrared data,” ISPRS Journal of Photogrammetry and Remote Sensing, 2020 167: 321–344.

[3] W. Tang et al., “Near-Real-Time Estimation of 1-km All-Weather Land Surface Temperature by Integrating Satellite Passive Microwave and Thermal Infrared Observations,” IEEE Geosci. Remote Sensing Lett., 2022 19: 1–5.

[4] X. Zhang et al., “A practical reanalysis data and thermal infrared remote sensing data merging (RTM) method for reconstruction of a 1-km all-weather land surface temperature,” Remote Sensing of Environment, 2021 260: 112437.

[5] W. Tang et al., “TRIMS LST: a daily 1 km all-weather land surface temperature dataset for China’s landmass and surrounding areas (2000–2022),” Earth Syst. Sci. Data, 2024 16(1): 387–419.

[6] L. Ding et al., “Near-Real-Time Estimation of Hourly All-Weather Land Surface Temperature by Fusing Reanalysis Data and Geostationary Satellite Thermal Infrared Data,” IEEE Trans. Geosci. Remote Sensing, 2023 61: 1–18.

[7] L. Ding et al., “Reconstruction of Hourly All-Weather Land Surface Temperature by Integrating Reanalysis Data and Thermal Infrared Data from Geostationary Satellites (RTG),” IEEE Trans. Geosci. Remote Sensing, 2022 60: 1–17.

[8] J. P. A. Martins et al., “An All-Weather Land Surface Temperature Product Based on MSG/SEVIRI Observations,” Remote Sensing, 2019 11(24): 3044.

[9] Y. Meng et al., “Investigation and validation of two all-weather land surface temperature products with in-situ measurements,” Geo-spatial Information Science, 2023.

[10] F.-M. Göttsche et al., “Validation of Satellite-Retrieved Land SurfaceTemperature (LST) Products at Gobabeb, Namibia,” Journal Namibia Scientific Society, 2022 69.

[11] J. Ma et al., “Continuous evaluation of the spatial representativeness of land surface temperature validation sites,” Remote Sensing of Environment, 2021 265: 112669.

[12] J. Ma et al., “An atmospheric influence correction method for longwave radiation-based in-situ land surface temperature,” Remote Sensing of Environment, 2023 293: 113611.

[13] Z. Huang et al., “Toward the method for generating 250-m all-weather land surface temperature for glacier regions in Southeast Tibet,” National Remote Sensing Bulletin, 2021 25(8): 1873–1888.

[14] L. Ding et al., “A Spatial Downscaling Approach for Land Surface Temperature by Considering Descriptor Weight,” IEEE Geosci. Remote Sensing Lett., 2023 20: 1–5.

[15] Y. Liao et al., “Surface urban heat island detected by all-weather satellite land surface temperature,” Science of The Total Environment, 2022 811: 151405.