Urban Land Surface Temperature Retrieval From Landsat-9 Satellite Data Using Nonlinear Split-Window Algorithm

Land surface temperature (LST) is a key factor in monitoring and improving thermal environments. However, conventional LST retrieval algorithms have not sufficiently accounted for the cavity and adjacency effects caused by the three-dimensional (3D) structures in urban settings. In this study, we propose an urban multiple scattering radiative transfer model (UMS-RTM). Based on this model, we develop an urban nonlinear split-window (UNSW) algorithm to retrieve urban land surface temperature (ULST) from Landsat-9 satellite data. The UMS-RTM optimizes the thermal radiation transfer process by correcting the cavity and adjacency effects. Analysis shows that land surface emissivity (LSE) and sky view factor (SVF) are the primary factors influencing these effects. The cavity effect increases the effective LSE by 0.01 to 0.08, while the adjacency effect raises the ground-leaving brightness temperature (BT) by 0.82 K to 3.51 K. The UNSW algorithm’s coefficients were calibrated across various LST, water vapor content (WVC), and SVF groupings to eliminate atmospheric effects and correct for cavity and adjacency effects. Sensitivity analyses of instrument noise, WVC, effective LSE, and SVF uncertainties demonstrated the reliability of the UNSW algorithm. Validation using simulated data showed that the ULST retrieved by the UNSW algorithm had a root-mean-square error (RMSE) of 0.35 K. When applied to Landsat-9 satellite data, the UNSW algorithm revealed that conventional algorithms and LST products overestimate ULST by 0 K to 2 K, with overestimations exceeding 1 K in areas with low SVF. The UNSW algorithm provides more accurate ULST retrieval and finer spatial distribution details.