Land Surface Emissivity Retrieval From Landsat 9 Data in Combination With Land Cover Data and Spectral Library

Abstract

Land surface emissivity (LSE) is crucial for retrieving land surface temperature (LST) from Landsat 9 TIRS-2 thermal infrared (TIR) data. However, the single-band LSE product (band 10) provided officially is insufficient for the split-window (SW) algorithm requiring dual-band emissivity inputs. This letter proposes a land cover and channel transformed-LSE (LCCT-LSE) method to estimate band 11 LSE and enables LST retrieval using the SW algorithm on Google Earth Engine. Cross-validation with MOD21 LSE products showed that the LCCT-LSE method achieved a mean absolute error (MAE) of 0.004 and a root mean square error (RMSE) of 0.005, outperforming the classification-based method, NDVI threshold method, and vegetation cover vegetation cover-based method (VCM) methods. In situ validation showed SW-retrieved LST attains MAE/RMSE of 1.27/2.13 K, with consistent accuracy across diverse land covers (water: 0.86 K, soil: 1.58 K, desert: 1.71 K, sand: 1.80 K, and vegetation: 0.87 K). A comparison with the official Landsat 9 LST product indicated that the bias of retrieved LST is within 1 K for all land cover classes (cropland, forest, grassland, shrubland, water, barren, and impervious) in Beijing. These results demonstrated that the LCCT-LSE method is capable of estimating the LSE in Landsat 9 band 11 with a reliable and accurate result. This study provides a new insight for LST retrieval from Landsat 9 data.