Investigating the impact of urban 3D variables on satellite land surface temperature estimates

Land surface temperature (LST) is of fundamental importance to many aspects of geosciences, such as, net radiation budget, evaluation and monitoring of forest and crops. The LST is also a key-parameter to derive the Surface Urban Heat Island (SUHI) or health related indices, for example, the Discomfort Index (DI). It is therefore an essential prerequisite to understand the urban climate and to support the definition of mitigation strategies, health risk management plans, public policies among other initiatives to effectively address the adverse effects of heat. While the LST is commonly retrieved from data acquired in the TIR (Thermal InfraRed) spectral domain by remote multispectral sensors with about 1K accuracy for natural surfaces, its retrieval over urban areas is not trivial. Urban landscapes possess tremendous challenges in LST estimates due to its high heterogeneity of surfaces and materials, and the three-dimensional (3D) configuration of the elements that are present on urban areas. The Thermal InfraRed Imaging Satellite for High-resolution Natural resource Assessment (TRISHNA) is planned for launch in 2025 and features a TIR instrument that will image the Earth every three days, at 57 m resolution, providing the research community with critical information to understand the radiative interactions and impacts al local level. This new satellite mission will offer an unprecedented opportunity to support urban microclimate studies. Based on extensive radiative transfer simulations using the Discrete Anisotropic Radiative Transfer Model (DART) and sensitivity analysis, this work investigates the impacts of 3D urban structures (e.g., road width, building height, building density) and materials with different optical properties on LST estimation at the TRISHNA spatial resolution. In fine, the idea is to develop a method to minimize these impacts on LST estimated from the TRISHNA data. First, a processing chain has been set up to simulate TRISHNA LST with DART, by using as inputs i) the configuration of the sensor and ii) 3D urban forms with different geometric and optical properties. Second, the radiative transfer modeling for simulation of the TIR remote sensing signal is performed. Finally, by correlating the simulated TRISHNA LST and the surface characteristics for each scene, the main parameters impacting the LST in urban environments have been identified. From these results, a correction method at satellite scale to minimize the impacts of urban 3D variables on LST will be formulated.