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A Simplified and Robust Surface Reflectance Estimation Method (SREM) for Use over Diverse Land Surfaces Using Multi-Sensor Data

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Journal Article - Open Access

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Naval Research Laboratory Washington United States

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Surface reflectance SR estimation is the most critical preprocessing step for deriving geophysical parameters in multi-sensor remote sensing. Most state-of-the-art SR estimation methods, such as the vector version of the Second Simulation of the Satellite Signal in the Solar Spectrum 6SV radiative transfer RT model, depend on accurate information on aerosol and atmospheric gases. In this study, a Simplified and Robust Surface Reflectance Estimation Method SREM based on the equations from 6SV RT model, without integrating information of aerosol particles and atmospheric gasses, is proposed and tested using Landsat 5 Thematic Mapper TM, Landsat 7 Enhanced Thematic Mapper plus ETM , and Landsat 8 Operational Land Imager OLI data from 2000 to 2018. For evaluation purposes, i the SREM SR retrievals are validated against in situ SR measurements collected by Analytical Spectral Devices ASD from the South Dakota State University SDSU site, USA ii cross-comparison between the SREM and Landsat spectral SR products, i.e., Landsat Ecosystem Disturbance Adaptive Processing System LEDAPS and Landsat 8 Surface Reflectance Code LaSRC, are conducted over 11 urban 2013-2018, 13 vegetated 2013-2018, and 11 desertarid 2000 to 2018 sites located over different climatic zones at a global scale iii the performance of the SREM spectral SR retrievals for low to high aerosol loadings is evaluated iv spatio-temporal cross-comparison is conducted for six Landsat pathsrows located in Asia, Africa, Europe, and the United States of America from 2013 to 2018 to consider a large variety of land surfaces and atmospheric conditions v cross-comparison is also performed for the Normalized Difference Vegetation Index NDVI, the Enhanced Vegetation Index EVI, and the Soil

Subject Categories:

  • Geology, Geochemistry and Mineralogy
  • Miscellaneous Detection and Detectors

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