In order to improve ecosystem service and protect nation ecology security, the government had designated lots of important ecosystem service protection areas, named national key ecological function zones (NKEFZ) in China. Water conservation service had been assessed with the help of multisource remote sensing data, and spatiotemporal features were analyzed from 2000 to 2014 in these ecological services zones. By assuming precipitation scenario as the constant, contribution for water conservation from human activities and climate change was analyzed, and result shows that, because of vegetation restoration by human activities, evapotranspiration increased obviously with the increase of the vegetation coverage. This could reduce the water conservation. However, actual annual increase of water conservation mainly comes from the increase of precipitation. Our analysis revealed that the choice of evaluation model played a decisive role in the reason analysis, which would affect the development of ecological policy.

Accurate high-resolution soil moisture mapping is critical for surface studies as well as climate change research. Currently, regional soil moisture retrieval primarily focuses on a spatial resolution of 1 km, which is not able to provide effective information for environmental science research and agricultural water resource management. In this study, we developed a quantitative retrieval framework for high-resolution (250 m) regional soil moisture inversion based on machine learning, multisource data fusion, and in situ measurement data. Specifically, we used various data sources, including the normalized vegetation index, surface temperature, surface albedo, soil properties data, precipitation data, topographic data, and soil moisture products from passive microwave data assimilation as input parameters. The soil moisture products simulated based on ground model simulation were used as supplementary data of the in situ measurements, together with the measured data from the Maqu Observation Network as the training target value. The study was conducted in the Zoige region of the Tibetan Plateau during the nonfreezing period (May–October) from 2009 to 2018, using random forests for training. The random forest model had good accuracy, with a correlation coefficient of 0.885, a root mean square error of 0.024 m³/m³, and a bias of −0.004. The ground-measured soil moisture exhibited significant fluctuations, while the random forest prediction was more accurate and closely aligned with the field soil moisture compared to the soil moisture products based on ground model simulation. Our method generated results that were smoother, more stable, and with less noise, providing a more detailed spatial pattern of soil moisture. Based on the permutation importance method, we found that topographic factors such as slope and aspect, and soil properties such as silt and sand have significant impacts on soil moisture in the southeastern Tibetan Plateau. This highlights the importance of fine-scale topographic and soil property information for generating high-precision soil moisture data. From the perspective of inter-annual variation, the soil moisture in this area is generally high, showing a slow upward trend, with small spatial differences, and the annual average value fluctuates between 0.3741 m³/m³ and 0.3943 m³/m³. The intra-annual evolution indicates that the monthly mean average soil moisture has a large geographical variation and a small multi-year linear change rate. These findings can provide valuable insights and references for regional soil moisture research.

In April 2023, China launched its first precipitation measurement satellite, FengYun-3G (FY-3G), to accurately measure the spatial and vertical structures of precipitation in the middle and lower latitudes of Earth. Equipped with advanced instruments, including a dual-frequency precipitation radar (DPR), a microwave radiometer, a visible and infrared (IR) imager, and a radio occultation sounder with reflectometry, FY-3G is designed for simultaneous measurements of precipitation, atmospheric temperature/moisture profiles, and cloud properties while capturing land and sea imaging at the same time. Additionally, it carries a polarized multiangle imager (PMAI) as an experimental payload to enhance the understanding of cloud microphysics. Six months after its launch, FY-3G has successfully completed the in-orbit commissioning phase. The detailed evaluation of the performance and the functionality of the platform as well as the instruments is presented in this article. Calibration and validation processes are also discussed. Since the end of 2023, sensor-dependent data records have been carefully validated and made available for public use. As the first of two planned precipitation missions in the FengYun satellite series, FY-3G offers independent and high-quality data on the occurrence, type, and intensity of precipitation, complementing the current observations made by the Global Precipitation Measurement satellite. This mission is expected to significantly advance the global understanding of precipitation dynamics, mechanisms, and the water cycle.

The passive microwave land surface emissivity (MLSE) plays a crucial role in retrieving various land surface and atmospheric parameters and in Numerical Weather Prediction models. The retrieval accuracy of MLSE depends on many factors, including the consistency of the input data acquisition time. The FengYun-3D (FY-3D) polar-orbiting meteorological satellite, equipped with passive microwave and infrared bands, offers time-consistent data crucial for MLSE retrieval. This study proposes a physics-based MLSE retrieval algorithm using all the input data from the FY-3D satellite. Based on the retrieved MLSE, the spatial distribution of the MLSE is closely correlated with the land cover types and topography. Lower emissivities prevailed over barren or sparsely vegetated regions, river basins, and coastal areas. Higher emissivities dominated densely vegetated regions and mountainous areas. Moderate emissivities dominated grasslands and croplands. Lower-frequency channels showed larger emissivity differences with different polarizations than those of higher-frequency channels in barren or sparsely vegetated regions. The MLSE across densely vegetated land areas, mountainous areas, and deserts showed small seasonal variations. However, woody savannas, grasslands, croplands, and seasonal snow-covered areas showed noticeable seasonal variations. For most land cover types, the differences between vertically and horizontally polarized emissivities remained relatively constant across seasons. However, certain grasslands in eastern Inner Mongolia and southern Mongolia showed clear seasonal variations. It is very difficult to verify the MLSE on a large scale. Consequently, the possible error sources in the retrieved MLSE were analyzed, including the brightness temperature errors (correlation coefficient ranging from 0.92 to 0.99) and the retrieved land surface temperature errors (Root Mean Square Error was 3.34 K and relation coefficient was 0.958).

A spaceborne microwave radiometer has a low spatial resolution limited by its antenna size. Enhancing the spatial resolution of data acquired by such sensors can improve the quality of subsequent applications. To improve the spatial resolution of the Microwave Radiation Imager (MWRI) onboard the Fengyun 3D satellite, this study used a Scatterometer Image Reconstruction (SIR) algorithm to generate resolution-enhanced swath brightness temperature data based on redundant information from overlaps between scanning points. These swath data have a higher pixel resolution that can reach 1/4 of the sampling frequency. The quality of reconstructed images, evaluated through visual comparison and quantitative analysis, revealed reasonable potential for providing more detailed depictions of surface information. Statistical analysis revealed a lower root mean square deviation of 0.8 K and a bias of 0.04 K following the SIR process. Analysis of the pixel spatial response function confirmed that the enhanced data have substantially finer spatial resolution than that of Level-1 data for 10–89 GHz vertical/horizontal channels, with an improvement of 9–39% in effective resolution. The findings of this study show that the SIR algorithm has potential for enhancing the quality of MWRI data and for widening the application domain to satellite product development, satellite data assimilation for numerical weather prediction, and other related fields.