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RESEARCH PRODUCT

A Comparative Study of Land Surface Emissivity Retrieval from NOAA Data

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Abstract A comparative study has been carried out on the most recent algorithms for the estimation of land surface emissivity (ϵ) using Advanced Very High Resolution Radiometer (AVHRR) data. Three of the algorithms are based on the Temperature-Independent Spectral Indices (TISI) concept using atmospherically corrected channels 3, 4, and/or 5, namely: (1) TISI BL , (2) TS-RAM, and (3) Δ day. The fourth is a simplified method based on the estimation of ϵ using atmospherically corrected data in the visible and near-infrared channels, called Normalized Difference Vegetation Index (NDVI) Thresholds Method (NDVI THM ). This method integrates a wide spectral data set of bare soil reflectivity measurements in the 0.4–14-μm band and uses different approaches in function of the NDVI value. All methods have been applied to the Iberian Peninsula using AVHRR/National Oceanic and Atmospheric Administration (NOAA-14) data during March 17th, 1997. In terms of emissivity, the results show that the difference between the NDVI THM method and the other methods is always positive, with a bias of less than 0.010 and a root mean square (rms) error of less than 0.010 when compared to the TISI BL method, a bias of 0.018 and an rms error of 0.020 when compared to the Δday method, and a bias of 0.025 and an rms error of 0.012 when compared to the TS-RAM method. In terms of land surface temperature, the NDVI THM method shows a bias of less than −0.4 K and an rms error of less than 1.0 K when compared to TISI BL , a bias of −1.0 K and a rms error of 1.3 K when compared to Δday, and a bias of −1.1 K and an rms error of 0.5 K when compared to TS-RAM. In conclusion, although the TISI BL is a precise method, it needs the AVHRR channel 3 for its application, which is not always available, for example, as in the AVHRR archives provided in the frame of the Pathfinder AVHRR Land project (more than 18 years of data). In this case we have shown that the NDVI THM method shows promising results and can be applied to obtain land surface temperature and emissivity from NOAA data without losing accuracy.