Microwave remote sensing can provide long-term near-surface soil moisture data on regional and global scales. Conducting standardized authenticity tests is critical to the effective use of observed data products in models, data assimilation, and various terminal scenarios. Global Land Data Assimilation System (GLDAS) soil moisture data were used as a reference for comparative analysis, and triple collocation analysis was used to validate data from four mainstream passive microwave remote sensing soil moisture products: Soil Moisture and Ocean Salinity (SMOS), Soil Moisture Active and Passive (SMAP), Global Change Observation Mission–Water using the Advanced Microwave Scanning Radiometer 2 (AMSR2) instrument, and Fengyun-3C (FY-3C). The effects of topography, land cover, and meteorological factors on the accuracy of soil moisture observation data were determined. The results show that SMAP had the best overall performance and AMSR2 the worst. Passive microwave detection technology can accurately capture soil moisture data in areas at high altitude with uniform terrain, particularly if the underlying surface is soil, and in areas with low average temperatures and little precipitation, such as the Qinghai–Tibet Plateau. FY-3C performed in the middle of the group and was relatively optimal in northeast China but showed poor data integrity. Variation in accuracy between products, together with other factors identified in the study, provides a baseline reference for the improvement of the retrieval algorithm, and the research results provide a quantitative basis for developing better use of passive microwave soil moisture products.

Long-term near-surface soil moisture (SM) data can be obtained on a regional scale through microwave remote sensing. Therefore, to quantitatively analyze the accuracy of multisource remote sensing–based observation products, improve the retrieval algorithm, and effectively use in terminal environments, a standardized comprehensive evaluation is imperative. The SM data obtained by the China Meteorological Administration and Ministry of Water Resources were used as reference data to verify the performance of six passive microwave remote sensing–based SM products from the SMOS, SMAP, GCOM-W, FY-3B, and FY-3C satellites in Hunan province, China. These data were also used to analyze the effects of topographical, land cover, and meteorological factors on SM retrieval accuracy. Results show that SMAP shows the best overall performance in Hunan province; furthermore, it exhibits stable accuracy and is not easily affected by environmental factors. The FY series of satellite products shows the worst performance, and some grid remote sensing data are negatively correlated with the ground measurement data. AMSR2 possesses the largest amount of data and the largest deviation, and only this product exhibits significant differences with the fluctuation trend of the measured SM and precipitation. Passive microwave detection technology presents the best performance in the central part of Hunan province and the largest bias in the Dongting Lake area. SMOS-L3 and SMOS-IC, two products of the same satellite, show the lowest bias but present differences in the SM fluctuation range, orbital accuracy, as well as dry or wet bias. Furthermore, FY-3B and FY-3C, two satellites belonging to the same series, exhibit excellent consistency in performance. The evaluation results and accuracy variation between products as well as other factors identified in the study provide a baseline reference for improving the retrieval algorithm. This study provides a quantitative basis for developing improved applications of passive microwave SM products.

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.

Land surface temperature (LST) is a crucial parameter in climate and ecology, exerting significant influence on meteorological conditions, ecosystems, and human life. LST data sources are diverse, with remote sensing being the prevailing means of acquisition. FY-4A/AGRI offers high-quality LST products for East Asia. We conducted a comprehensive evaluation and refined analysis of surface heat resources in Hunan Province, central China, over a two-year period using the 4 km/1 h resolution product in this study. The results demonstrate that the FY-4A LST product effectively captures surface temperature (R = 0.893), albeit with a relatively high error level (Bias = −6.295 °C; RMSE = 8.58 °C), particularly in capturing high LST values. The performance of this product is superior in the eastern flat terrain area of Hunan Province compared to its performance in the western mountainous region due to environmental conditions causing systematic errors that contribute to instability in detection deviation for this product. Surface heat resources are more abundant in eastern Hunan Province than in mountainous areas located west and southwardly, and the detailed distribution of them at finer scales is mainly influenced by terrain and climate conditions. There is no obvious seasonal difference in the distribution of heat resources except in winter, and rapid urbanization within Chang–Zhu–Tan urban agglomeration over two years has significantly altered the spatial distribution pattern of surface heat resources across Hunan Province. These findings provide a quantitative baseline for assessing FY-4A satellite’s detection capability while serving as a reference for further application of its LST products and establishing foundations for divisional classification and utilization strategies pertaining to surface heat resources within Hunan Province.

Land surface temperature (LST) is a crucial parameter in climate and ecology, exerting significant influence on meteorological conditions, ecosystems, and human life. LST data sources are diverse, with remote sensing being the prevailing means of acquisition. FY-4A/AGRI offers high-quality LST products for East Asia. In this study, we evaluated the performance of the product in Hunan Province and conducted refined analysis of surface heat resources based on the 4km/1h resolution product over a two-year period. The results demonstrate that the FY-4A LST product effectively captures surface temperature (R=0.893), albeit with a relatively high error level (Bias=-6.295℃; RMSE=8.58℃), particularly in capturing high LST values. The performance of this product is superior in the eastern flat terrain area of Hunan Province compared to its performance in the western mountainous region due to environmental conditions causing systematic errors that contribute to instability in detection deviation for this product. Surface heat resources are more abundant in eastern Hunan Province than in mountainous areas located west and southwardly, and the detailed distribution of them at finer scales is mainly influenced by the terrain and climate conditions. There is no obvious seasonal difference in the distribution of heat resources except in winter, and rapid urbanization within Chang-Zhu-Tan urban agglomeration during two years has significantly altered the spatial distribution pattern of surface heat resources across Hunan Province. These findings provide a quantitative baseline for assessing FY-4A satellite's detection capability while serving as a reference for further application of its LST products and establishing foundations for divisional classification and utilization strategies pertaining to surface heat resources within Hunan Province.