A Comprehensive Validation Scheme for Satellite-Derived Land Surface Temperature Dataset

Land surface temperature (LST) is a widely focused parameter between the land surface and the atmosphere. Currently, satellite remote sensing is the main approach to obtaining regional and global LST. Validation of satellite-derived LST can promote its application and provide feedback for the retrieval algorithms and parameterization schemes. The current widely used temperature-based method faces many influences, e.g., obtaining the ground truth on the pixel scale and its uncertainty. Here, a comprehensive validation scheme is proposed for validating the satellite-derived LST by combining the near-surface atmospheric correction for longwave-radiation-based in situ LST and considering the validation station's spatial representativeness, and applying it in the validation of AVHRR-derived LST. The LST "ground truth" of three validation stations in Heihe River Basin, China was obtained, with a mean uncertainty of 0.87 K (range: $0.47\sim 2.96$ K), 1.07 K ( $0.49\sim 1.81$ K), and 0.61 K ( $0.47\sim 1.13$ K) for A'rou superstation (ARS), daman superstation (DMS), and sidaoqiao superstation (SDQ), respectively. Validation of AVHRR-derived LST against the obtained "ground truth" shows that the random error is lower than 3 K, and the system error is station-dependent, with a range of $- 1.02\sim 3.93$ K. Further comparison indicated significant systematic error differences (range: $- 1.5\sim 3.45$ K) and inapparent random errors difference ( $- 0.84\sim 0.43$ K) between the proposed comprehensive scheme and the classic scheme at the selected stations. Since the main influences are considered in the proposed comprehensive validation scheme, the validation results are more objective and credible. The comprehensive validation scheme provides a reference for LST validation and could be extended to the validation of related hydrothermal parameters.