## 4 Discussion  
Many factors affect the accuracy of LST data retrieved from satellite TIR data, such as atmospheric profiles, sensor parameters, surface parameters, and systematic errors due to the parameterization step of the retrieval algorithm [41]. It is therefore important to test the authenticity and comprehensively evaluate the LST product, especially for new satellite products. There have been many validation studies of the LST products of Landsat, MODIS, Himawari and other satellites [16; 42], and the effects of the retrieval algorithm, day/night observation, season, and land cover type on the accuracy of remote sensing LST products have been documented, quantified and modeled. In this study, we conducted a comprehensive assessment of the FY-4A LST product, which has not previously been extensively validated. We used high-quality in situ measured data from CMA together with reanalysis products, which gave us a large quantity of data to ensure our research was superior to other similar validation studies [13; 16; 41]. The direct accuracy comparison analyzed more than 500,000 data points, and TCA used >65 million data points for a 2 y period; the study results were therefore more generalizable and provide a baseline reference for improving and promoting the FY-4A product. The authenticity test in the study adopted direct comparison between FY-4A LST with in situ measurement and TCA between FY-4A LST with two reanalysis products. The different methods of analysis allowed us to make more comprehensive and more generalizable conclusions. The scale difference between different data products in the direct comparison may have affected the results. The resolution of FY-4A/AGRI (4 km resolution for both TIR bands and LST product) differs from the resolution of Himawari-8/AHI (2 km resolution for TIR bands and 0.045\({}^{\circ}\) resolution for LST product), and the remote sensing grid product scale is different from the in situ measured single point data scale. However, the research results of the TCA method partially compensate for the impact of scale differences on the results. In contrast to the Bias values that were within \(\pm\)2 K according to the _Quality Assessment Report_ of Copernicus Global Land Operations, the Bias values we obtained were generally greater than the values for in situ measurement. This result may be due to the sparse distribution of observation stations (thus increasing the scale mismatch between remote sensing data and in situ measurements) and the fact that most meteorological observatories were situated on hills at elevations that are not conducive to accurate remote sensing detection (Figure 6). However, the different but still similar observational accuracy of FY-4A LST when compared with Himawari-8 data, and the greater accuracy of the LST product than other remote sensing parameters [38], illustrate the capability of the TIR-based split-window algorithm of FY-4A/AGRI to retrieve accurate LST. The seasonal and diurnal trends of FY-4A LST (Bias, RMSE; Table 3) were consistent with the findings of [PERSON] et al. [43] for Himawari-8 and MODIS LST in western China; the LST products in that study also showed the largest variation when the land cover was forest. The error coefficients (Bias, Bias\({}_{r}\), and RMSE) calculated in the comprehensive assessment by [PERSON] et al. [24] using FY-3C/MWRI LST and in situ measurement in Hunan Province were similar to those we obtained in this study. [PERSON] et al. [24] also found a trend of decreasing accuracy with increasing elevation in remotely sensed LST together with a trend of gradually increasing underestimation when daytime LST was >20 \({}^{\circ}\)C (descending orbit). [PERSON] et al. [16] validated remote sensing LST products using in situ radiation measurement and found that the accuracy of such products varied with season, day/night, and land cover, similar to our findings, and that observation variations in subtropical and forest sites were similar to those of FY-4A LST.