Land-surface temperature retrieval at high spatial and temporal resolutions based on multi-sensor fusion

Land-surface temperature(LST)is of great significance for the estimation of radiation and energy budgets associated with land-surface processes.However,the available satellite LST products have either low spatial resolution or low temporal resolution,which constrains their potential applications.This paper proposes a spatiotemporal fusion method for retrieving LST at high spatial and temporal resolutions.One important characteristic of the proposed method is the consideration of the sensor observation differences between different land-cover types.The other main contribution is that the spatial correlations between different pixels are effectively considered by the use of a variation-based model.The method was tested and assessed quantitatively using the different sensors of Landsat TM/ETM,moderate resolution imaging spectroradiometer and the geostationary operational environmental satellite imager.The validation results indicate that the proposed multisensor fusion method is accurate to about 2.5 K.

Validation of Landsat land surface temperature product in the conterminous United States using in situ measurements from SURFRAD, ARM, and NDBC sites

Since 1982,Landsat series of satellite sensors continuously acquired thermal infrared images of the Earth’s land surface.In this study,Landsat 5,7,and 8 land surface temperature(LST)products in the conterminous United States from 2009 to 2019 were validated using in situ measurements collected at 6 SURFRAD(Surface Radiation Budget Network)sites,6 ARM(Atmospheric Radiation Measurement)sites,and 9 NDBC(National Data Buoy Center)sites.The results indicate that a relatively consistent performance among Landsat 5,7,and 8 LST products is obtained for most sites due to the consistent LST retrieval algorithm in conjunction with the same atmospheric compensation and land surface emissivity(LSE)correction methods for Landsat 5,7,and 8 sensors.Large bias and root mean square error(RMSE)of Landsat LST product are obtained at some vegetated sites due to incorrect LSE estimation where LSE is invariant with the increasing of normalized difference vegetation index(NDVI).Except for the sites with incorrect LSE estimation,a mean bias(RMSE)of the differences between Landsat LST and in situ LST is 1.0 K(2.1 K)over snow-free land surfaces,−1.1 K(1.6 K)over snow surfaces,and−0.3 K(1.1 K)over water surfaces.