Global canopy temperature estimation by integrating satellite observations and ground measurements

Canopy temperature is a vital indicator of plant-environment interactions, playing a key role in assessing drought impacts and water use efficiency and understanding the global carbon cycle. Despite existing efforts on ground measurements at FLUXNET and ICOS sites, satellite Land Surface Temperature (LST) has been widely used as a proxy for canopy temperature in studies at different scales. However, satellite-based LST represents a mixture of vegetation, snow and soil temperatures, leading to biases in estimating plant-related processes, particularly for temperature-limited regions. Up to now, there is still no available global canopy temperature product.

To bridge the research gap, this study first combines in-situ canopy temperature measurements (from outgoing longwave radiation and thermal camera), satellite LST into a machine learning model for global canopy temperature mapping. In the algorithm, vegetation cover, ERA5 soil and air temperatures as well as other auxiliary data were adopted for estimating canopy temperature, by removing the contributions of soil and air information to satellite LST. The primary validation results over more than global 130 sites, by separating training group (2/3) and evaluation group (1/3), indicated the retrieval of canopy temperature is encouraging, by achieving average RMSE (Root Mean Squared Error) of 2.80 K, Bias of 0.12 K and correlation coefficient (R) of 0.96, against ground measurements. In the next steps, more auxiliary data including net radiation, vegetation water, vapor pressure deficit, wind speed, soil moisture and vegetation parameters, will be included for the retrieving and final global product development. In the process, the interactions of these variables with the targeted canopy temperature will be also investigated. The final global daily canopy temperature for more than 20 years, with a spatial resolution of 0.25°, will be released and further used to evaluate the land surface version of the dynamic vegetation model, LPJ-GUESS, to assess the impacts of canopy instead of air temperature on influencing global terrestrial water-carbon cycles.