This study evaluated the land surface model-simulated soil moisture (SM) product from the China Land Data Assimilation System (CLDAS). This was achieved using three densely instrumented in situ observation networks in China that have different environmental conditions, i.e., the Hebi, Naqu and Heihe sites. The European Space Agency Climate Change Initiative multi-satellite-retrieved SM product (CCI-SM) was also included for inter-comparison purposes. Standard validation scores indicated that the CLDAS-SM product has high correlation and low uncertainty with both surface and root-zone soil moisture observations. The target accuracy (0.04 m³ m⁻³) was achieved over all three sites. Compared with the CCI-SM product, the CLDAS-SM product showed higher accuracy for the Hebi and Heihe sites but slightly lower accuracy for the Naqu site located at the centre of the Tibetan Plateau. Regionally, the unbiased root mean square difference between the CLDAS-SM and CCI-SM products was noticeably smaller within China than in neighbouring countries. Given that the performance of the CCI-SM product should be unaffected by country boundaries, the better performance of the CLDAS-SM product in China can be attributed primarily to the high-quality meteorological forcing data.

Triple collocation (TC) is a popular technique for determining the data quality of three products that estimate the same geophysical variable using mutually independent methods. When TC is applied to a triplet of one point-scale in situ and two coarse-scale datasets that have the similar spatial resolution, the TC-derived performance metric for the point-scale dataset can be used to assess its spatial representativeness. In this study, the spatial representativeness of in situ snow depth measurements from the meteorological stations in northeast China was assessed using an unbiased correlation metric estimated with TC. Stations are considered representative if ; that is, in situ measurements explain no less than 50% of the variations in the “ground truth” of the snow depth averaged at the coarse scale (0.25°). The results confirmed that TC can be used to reliably exploit existing sparse snow depth networks. The main findings are as follows. 1) Among all the 98 stations in the study region, 86 stations have valid values, of which 57 stations are representative for the entire snow season (October–December, January–April). 2) Seasonal variations in are large: 63 stations are representative during the snow accumulation period (December–February), whereas only 25 stations are representative during the snow ablation period (October–November, March–April). 3) The is positively correlated with mean snow depth, which largely determines the global decreasing trend in from north to south. After removing this trend, residuals in can be explained by heterogeneity features concerning elevation and conditional probability of snow presence near the stations.

This study explores a new scheme to retrieve the global aerosol optical depth over land for the advanced Medium Resolution Spectral Imager (MERSI-II) aboard on Fengyun-3D (FY-3D) satellite, on the basis of dark target (DT) algorithm. The main improvement is that the global surface reflectance model non-linearly varying with solar zenith angle and NDVIswir is made, which is more complex relative to that of MODIS operational algorithm. Our AOD retrievals are compared with AERONET AOD and cross-evaluated with Aqua/MODIS respectively. Overall, the MERSI-II retrieved results over global scale have good consistency with the AERONET observations, on the same condition, the percentage of matchups within the expected error (EE: ±0.05±0.15AOD) is 67.06%, which is slightly lower than the percentage of MODIS (79.84%). On a spatial scale, the coverage of MERSI-II retrievals at one granule is significantly higher than that of MODIS, which is related to successful inversion of haze pixels and has retrieval ability in urban, grassland and other surface types. The monthly mean AOD values retrieved by MERSI-II are close to those of MODIS, indicating that MERS-II has similar quantitative capability and application potential as international counterparts.

Fractional Vegetation Cover (FVC) is an important vegetation structure factor for applications in agriculture, forestry, ecology, etc. Due to its simplicity, the normalized difference vegetation index (NDVI)-based mixture model is widely used to estimate FVC from remotely sensed data. However, the accuracy and efficiency of FVC estimation require the precise calculation of two key parameters: the NDVI of fully covered vegetation and bare soil. Despite their importance, these two endmember NDVI values have not yet been produced as large-scale maps. Traditional statistical methods for obtaining endmember NDVI from satellite datasets highly rely on the assumption that a certain amount of pure pixels of vegetation and soil must be present, which is often invalid for many areas. This study generated 30 m resolution maps of endmember NDVI across China using the MultiVI algorithm, incorporating multi-angle remote sensing data. The quality and accuracy of the endmember NDVI maps were evaluated using various validation data, including statistically obtained pure NDVI, soil spectra from a soil library, and field-measured FVC. The NDVI values for bare soil derived from the MultiVI algorithm were consistent with those obtained from the soil spectral library. Additionally, the FVC estimated using the MultiVI-derived endmember NDVI and the VI-based mixture model exhibited reasonable accuracy compared to the field measurements. The root mean square deviation (RMSD) values for MultiVI FVC were below 0.13 in the Heihe, Hebei, and Three Gorges Reservoir regions of China. These regions include typical arid and humid zones, facilitating the evaluation of the algorithm's performance under diverse climatic conditions. Furthermore, the MultiVI FVC outperformed those calculated using the traditional statistical methods. The endmember NDVI maps provide a convenient and reliable source of key parameters for the accurate and rapid estimation of FVC at large scales. The 30 m pure NDVI maps of 2014 are publicly available at https://doi.org/10.5281/zenodo.15720622 (Zhao et al., 2024a).

Medium Resolution Spectral Imager-II (MERSI-II) on board FY-3D satellite is a visible and infrared spectral imaging instrument featured with six 250-m spatial resolution channels (blue, green, red, NIR, 10.8 µm, 12.0 µm). Such unique spectral and spatial attributes provide significant opportunities and challenges for land cover (LC) mapping research. This paper reports the production of a 250-m spatial resolution global LC classification (under IGBP scheme) from MERSI-II NDVI operational products from May 2019 to April 2020. To extract robust features for discriminating LC types, we adopted three classic compositing criteria (MaxNDVI, Mean8NDVI, and Mean4BT) and two new compositing criteria (3minRedBlue and 3minRedNIR) to obtain high-quality annual composites. Random Forest (RF) algorithm was used for supervised learning, and 10-fold cross validation (CV) based on ‘site-splits’ was implemented to evaluate classification accuracy. The derived MERIS-II LC map was compared at pixel level with two widely used LC products (MODIS and VIIRS), and consistency among the three maps was investigated. Results showed that the higher resolution afforded by 250-m MERSI-II data in combination with a refined input features produced a global LC map with an overall accuracy of 79–86%. Most classes exhibited low commission and omission errors except for some classes of mixed nature (i.e. wetland, cropland/natural vegetation mosaic, savanna, woody savanna). The MERSI-II LC map generally agreed better with MODIS than with VIIRS, and the proportion of land pixels (excluding Antarctic) for which MERSI-II shows the same LC label with either MODIS or VIIRS is very large (0.70–0.82). Composite data contributed differently to total accuracy, with Mean8NDVI being the most spatial coherent and most contributive and MaxNDVI being the opposite. The other three composites (Mean4BT, 3minRedBlue, and 3minRedNIR) showed moderate spatial coherence and contributed to classification variedly by extracting information from different phenology phases. The unique 250-m blue and green bands of MERSI-II were found to be helpful to discriminate between wetland and needleleaf forest in high latitudes and between cities and grassland. This study confirms that with the RF algorithm (one of the state-of-art supervised machine learning algorithms) and image compositing techniques, accurate LC map can be derived from MERSI-II. Future work will attempt to include more bands and compositing criteria to further improve classification accuracy.