High-resolution TIR data are essential for a wide range of studies, e.g., in environmental monitoring, urban effects, water stress, agricultural productivity, and land/water resources. Landsat series provides high-resolution TIR data, but data accuracy must be ensured to contribute to these fields. After the calibration problems of Landsat 8 (L8) TIRS due to a stray light effect, efforts were made in the design of Landsat 9 (L9) TIRS-2 to mitigate this effect. In any case, field calibration and validation activities are necessary to evaluate the accuracy of recently launched sensors. These activities require high-quality ground TIR radiance data that are representative at satellite spatial resolution and that quantify spatial-temporal fluctuations at the experimental sites concurrently to satellite overpasses. However, these requirements are not always accomplished in the literature.This paper provides vicarious calibrations of the L9 TIRS-2 data with accurate ground measurements acquired using multi-instrument spatial and temporal sampling in a rice paddy area from December 2021 to August 2023, in order to contribute to the calibration and validation of L9 TIRS-data.In March 1, 2023 L9 TIRS-2 scenes started to be reprocessed and calibration was updated. Both the original calibration and that after reprocessing were evaluated. Close results were obtained for band 10 (b10). When comparing satellite brightness temperatures and those determined from in situ LSTs, systematic differences of -0.3 K were shown, with root-mean-square differences (RMSDs) of 0.5 K. Results for band 11 showed a bias of -0.1 K for the original data and of 0.2 K after the reprocessing, with RMSDs of 0.6 K. The L2 LST product generated operationally from b10 data was also evaluated, together with alternative single-channel (SC) algorithms. A bias (RMSD) of 0.4 K (1.1 K) was obtained for the product. However, negative biases were shown both with the radiative transfer equation used on b10 radiances and the alternative SC algorithm. Finally, we validated six types of split-window (SW) algorithms applied to L9 TIRS-2 data before and after the reprocessing. A systematic difference of about-0.6 K between SW LST biases when using the reprocessed and original data was observed. In any case, for most of the algorithms with original and reprocessed data, LST biases and standard deviations were within the recommended threshold of 1 K, which proves the good performance of L9 TIRS-2 data to monitor LST at high spatial resolution.
