Evaluation of Fengyun-4A Detection Accuracy: A Case Study of the Land Surface Temperature Product for Hunan Province, Central China

Abstract

Land surface temperature (LST) is an important parameter in determining surface energy balance and a fundamental variable detected by the advanced geostationary radiation imager (AGRI), the main payload of FY-4A. FY-4A is the first of a new generation of Chinese geostationary satellites, and the detection product of the satellite has not been extensively validated. Therefore, it is important to conduct a comprehensive assessment of this product. In this study, the performance of the FY-4A LST product in the Hunan Province was authenticity tested with in situ measurements, triple collocation analyzed with reanalysis products, and impact analyzed with environmental factors. The results confirm that FY-4A captures LST well (R = 0.893, Rho = 0.915), but there is a general underestimation (Bias = −0.6295 °C) and relatively high random error (RMSE = 8.588 °C, ubRMSE = 5.842 °C). In terms of accuracy, FY-4A LST is more accurate for central-eastern, northern, and south-central Hunan Province and less accurate for western and southern mountainous areas and Dongting Lake. FY-4A LST is not as accurate as Himawari-8 LST; its accuracy also varies seasonally and between day and night. The accuracy of FY-4A LST decreases as elevation, in situ measured LST, surface heterogeneity, topographic relief, slope, or NDVI increase and as soil moisture decreases. FY-4A LST is also more accurate when the land cover is cultivated land or artificial surfaces or when the landform is a platform for other land covers and landforms. The conclusions drawn from the comprehensive analysis of the large quantity of data are generalizable and provide a quantitative baseline for assessing the detection capability of the FY-4A satellite, a reference for determining improvement in the retrieval algorithm, and a foundation for the development and application of future domestic satellite products.

Validation of FY-4A/AGRI LST and High Temporal–Spatial Resolution Analysis of Surface Heat Resources in Hunan Province, Central China

“…Comparative analysis shows (Figure 3) that the FY-4A product captured changes in surface temperature for Hunan Province well (R = 0.893), but that it generally underestimated LST (Bias = −6.295 • C) and had some deviation from in situ measurement (RMSE = 8.58 • C; ubRMSE = 5.842 • C), for which ubRMSE was significantly lower than RMSE, but still with a relatively high error value, which could also indicates that the FY-4A LST product was greatly affected by systematic error and random error at the same time. Compared with relevant research, the error level of the FY-4A LST product was higher than that of similar advanced Himawari imager (AHI) from Himawari-8 [18].…”

“…The validation of the LST product from the FY-4 series geostationary satellite and its application in meteorological business have been our team's primary research focus. In the initial stage, we published an article on the comprehensive assessment of accuracy for the FY-4A/AGRI LST product and analyzed the influencing mechanisms of environmental factors [18]. Building upon this foundation, the present study further expands the spatiotemporal analysis of FY-4A product accuracy based on observational data, thereby exploring its potential application in surface heat resources analysis.…”

“…With the application of FY-4A products in China's meteorological business in recent years, technicians generally reflect that the accuracy of the product is still short of that of the international mainstream products. The authority does not provide a quality report for FY-4A/AGRI LST; however, relevant studies indicate that this product exhibits certain detection errors and a general underestimation phenomenon [18,19], with larger detection errors compared to mainstream Landsat and MODIS products [7,20]. The comparison of FY-4A and MODIS LST products in relevant studies also confirms their high correlation, with a significantly increased difference in higher LST during summer and autumn [21].…”

“…Previous studies have confirmed that the Ulivieri (1985) algorithm can effectively retrieve FY-4A LST with an RMSE of 3 K, demonstrating comparable accuracy to MODIS data [21]. Additionally, the National Oceanic and Atmospheric Administration Joint Polar Satellite System enterprise algorithm has shown promising results in retrieving FY-4A LST, achieving an RMSE within 3 K when compared to in situ-measured land surface emissivity-derived LST val- In the aforementioned validation analysis, FY-4A LST exhibited a high overall error level and an unexpectedly significant underestimation compared to in situ measurements at high LSTs, surpassing MODIS [20], Landsat [7], and Himawari-8 [18] counterparts in terms of error level. The phenomenon under investigation may be attributed to various factors.…”

“…Atmosphere 2023, 14, 1777 8 of 17 In the aforementioned validation analysis, FY-4A LST exhibited a high overall error level and an unexpectedly significant underestimation compared to in situ measurements at high LSTs, surpassing MODIS [20], Landsat [7], and Himawari-8 [18] counterparts in terms of error level. The phenomenon under investigation may be attributed to various factors.…”