Cross-calibration of Chinese Gaofen-5 thermal infrared images and its improvement on land surface temperature retrieval

Abstract Thermal infrared (TIR) remote sensing technology is capable of acquiring large-scale land surface temperature (LST), which is a key factor in the energy exchange between land surface and atmosphere. The visible and infrared multispectral sensor (VIMS) equipped in the Chinese Gaofen-5 (GF-5) satellite can obtain four channels of TIR images with a 40 m spatial resolution. However, due to the change of working environment, the TIR sensor suffers a low-accurate radiometric calibration that needs improvement. This paper puts forward a new cross-calibration for GF-5/VIMS TIR images by linking the top of the atmosphere (TOA) radiance of the vertical angle MODIS observation with the GF-5/VIMS image to estimate the radiometric calibration coefficients, Gain and Offset. To verify the recalibration performance, a new nonlinear two-channel split-window (SW) algorithm and a light gradient boosting machine (LightGBM) method which is used to refine LST from the SW algorithm by minimizing the residuals, were developed for the recalibrated GF-5/VIMS TIR 3 and TIR 4 channels. The radiometric cross-calibration algorithm and the optimized SW algorithm were applied to real GF-5/VIMS TIR images. The validation results showed that the brightness temperature and LST were improved significantly, and the LST retrieval error was reduced greatly to 1.79 K after recalibration, indicating a large improvement of the LST retrieval for GF-5/VIMS TIR image using the proposed cross-calibration and SW algorithms.