Retrieval of global surface soil and vegetation temperatures based on multisource data fusion

Soil and vegetation temperatures are crucial for various fields, including ecology, agriculture, and climate change. However, there remains a lack of entirely observation-based global datasets for these two component temperatures. To fill this gap, this study developed a multisource data Fusion-based global surface Soil and Vegetation Temperature retrieval method (FuSVeT). This novel method not only utilizes temporal and spatial information from MODIS data by adopting a temperature cycle model to capture temporal variation and using adjacent pixels to consider spatial differences and increase the number of equations solved, but also leverages ERA5-Land data to reduce unknown parameters, effectively compensating for the limitations of satellite observations. Its performances were comprehensively evaluated with simulated data, high-resolution satellite products, and in situ measurements , demonstrating competitive accuracy with root mean square errors below 2 K and Biases of under 1 K in most cases. Compared to previous retrieval method that relies solely on satellite-based temporal and spatial information, FuSVeT present enhanced accuracy, more complete spatial coverage, and improved computational efficiency, making it more applicable for global soil and vegetation temperature mapping. Using this method, we generated global 0.05° monthly mean soil and vegetation temperatures for January and July 2020. These data can capture more pronounced temperature heterogeneities within biomes than existing soil temperature products, indicating its superiority for global analyses. Importantly, FuSVeT can also be applied to satellite observations with higher spatiotemporal resolution, holding significant potential for providing accurate, long-term, global maps of surface soil and vegetation temperatures. • A multisource data Fusion-based global surface Soil and Vegetation Temperature retrieval method (FuSVeT) was proposed. • Reanalysis data was employed to reduce the number of unknown parameters. • This novel method present enhanced accuracy, more complete spatial coverage, and improved computational efficiency compare to previous method. • The generated soil temperatures can capture more pronounced temperature heterogeneities within biomes than existing products.