## 1 Introduction  
Land surface processes are important in Earth system research, and accurate modeling of the state of the land surface is essential for predicting the weather, modeling climate, and simulating the water cycle [1, 2]. Land surface temperature (LST), which characterizes the state of surface heat balance, is an important physical parameter in modeling land surface processes [3, 4], whether in climate, hydrology, ecology, biogeochemistry, or agricultural research [5, 6, 7]. LST is usually measured by in situ observation, predicted by models, or retrieved from satellite-based remote sensing data. In situ measurements are accurate, but recording stations are sparsely distributed, and their data do not meet the needs of regional-scale research or applications [8]. Models can process long LST time series with high spatial and temporal resolutions, but several input parameters are required, and the accuracy of the product is compromised by land surface inhomogeneity, land cover diversity, and difficulties in parameterizing land surface processes [2]. Satellite-based remote sensing is the principal method of detecting LST, and it provides real-time spatial and temporal distributions of surface temperature at regional and global scales [9]. Of the many techniques used for remote sensing, geostationary satellite-based remote sensing technology uses measurements in the thermal infrared (TIR) spectral bands to obtain LST at high temporal and spatial resolutions. Remote sensing products require continuous comprehensive assessments to improve both detection technology and product performance [10]. Such activity promotes their use. Thus, new satellite detection technology and new products must be evaluated. The Fengyun-4 series (FY-4) represents the second generation of Chinese geostationary meteorological satellites. The prototype satellite FY-4A was launched on 11 December 2016. Its successful launch and the applications it carries make it a key component of the geosynchronous satellite constellation [11]. The advanced geostationary radiation imager (AGRI), the primary instrument of satellite payload, was designed principally to image land surface, atmosphere, and cloud targets with high spatial and temporal resolution [12]. FY-4A products, from the most advanced integrated atmospheric observation satellite of its time, have not been comprehensively evaluated. Validating remote sensing products uses many methods of testing and assessment, such as ground-based validation [13], radiance-based validation [14], and satellite product comparison [15]. Temperature and radiance measured in situ provide the most accurate data for validation [16]. Testing product accuracy by directly comparing product data with in situ data is conceptually straightforward, but land surface heterogeneity and scale mismatch can lead to errors in this type of validation [17]. Direct comparison of satellite products may not generate noise-free results and is therefore inadequate for evaluating a remote sensing product in terms of other products [18]. Triple collocation analysis (TCA) is a method of calculating differences in random error in a single geophysical variable between three collocated datasets [19] that does not require the use of a high-quality reference dataset. TCA has become an important method of assessing Earth observation data [20], but it is not widely used to assess LST products. However, the recent development of innovative remote sensing and reanalysis products has resulted in the creation of many independent LST data products, which allows the use of TCA to test the accuracy of remote sensing LST products. All metrics calculated by TCA theoretically lie between the evaluated product and the unknown ground truth value [21], which enables a comparison of the accuracy and error characteristics of different products [22; 23]. Various environmental factors distort the data recorded by satellite-based LST products [24; 25].