Improved surface temperature estimates with MASTER/AVIRIS sensor fusion

Abstract Land surface temperature (LST) is an important parameter in many ecological studies. The current Root Mean Square Error (RMSE) in standard MODIS and ASTER LST products is greater than 1 K, and for ASTER can be as large as 4 K for graybody pixels such as vegetation. Errors of 3 to 8 K have been observed for ASTER in humid conditions, making knowledge of atmospheric water vapor content critical in retrieving accurate LST. For this reason improved accuracy in LST measurements through the synthesis of visible-to-shortwave-infrared (VSWIR) derived water vapor maps and Thermal-Infrared (TIR) data is one goal of the Hyperspectral Infrared Imager, or HyspIRI, mission. The 2011 ER-2 Delano/Lost Hills flights acquired data with both the MODIS/ASTER Simulator (MASTER) and Airborne Visible InfraRed Imaging Spectrometer (AVIRIS) instruments flown concurrently. This study compares LST retrieval accuracies from the standard JPL MASTER temperature products produced using the temperature–emissivity separation (TES) algorithm, and the water vapor scaling (WVS) atmospheric correction method proposed for HyspIRI. The two retrieval methods are run both with and without high spatial resolution AVIRIS-derived water vapor maps to assess the improvement from VSWIR synthesis. We find improvement using VSWIR derived water vapor maps, with the WVS method being most accurate overall. For closed canopy agricultural vegetation we observed temperature retrieval RMSEs of 0.49 K and 0.70 K using the WVS method on MASTER data with and without AVIRIS derived water vapor, respectively.

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