This paper intends to clarify that the spatial and temporal evolutionary patterns of regional vegetation and their relationship with climate form a premise of ecological conservation and environmental governance, and play an important role in maintaining regional ecosystem balance and promoting sustainable development. Based on measured data collected from NDVI remote sensing products and meteorological stations, NDVI variation in Anhui Province from 2001 to 2019 was determined through trend analysis and measurement methods involving coefficient of variation and Hurst index; in addition, the response to climatic factors was also explored. It was concluded that, firstly, in terms of spatiotemporal analysis, the interannual variation of NDVI in Anhui Province showed an increasing trend with a rate of 0.024/10 a, while the monthly variation showed a weak bimodal pattern, with the highest value in August and the lowest value in January. Furthermore, NDVI in Anhui Province showed significant spatial heterogeneity, with high values concentrated in mountainous regions in southern Anhui and Dabie Mountain region, and low values concentrated in the hilly areas of Jianghuai and areas along the Yangtze River. At the same time, the overall spatial variation of NDVI showed an increasing trend, and the areas with extremely significant and significant improvement in vegetation coverage accounted for 54.69% of the total area of Anhui Province. Secondly, in terms of the analysis on variation characteristics, the variation of NDVI in Anhui Province was generally stable, with an average CV coefficient of variation of 0.089, which, however, was quite different in different regions; meanwhile, the future trend of NDVI variation in the study areas was mostly in a random manner. Thirdly, the response of NDVI in Anhui Province to climatic factors showed significant spatial heterogeneity. NDVI was found to be positively correlated with precipitation and negatively correlated with temperature; in general, the impact of precipitation on NDVI was greater than that of temperature. In the 19 years studied, NDVI in Anhui Province showed an increasing trend; and climate, topography and human activities led to heterogeneous spatial distribution of vegetation. Therefore, in the future, the evolutionary trend of vegetation will be relatively random, and NDVI will be more greatly affected by temperature, than by precipitation.

Because of the anisotropy of snow surface reflectance, it is essential to select a proper snow bidirectional reflectance model for extracting snow cover and inverting snow properties from remote sensing image, especially during the period of snow rapidly changed. In this letter, the ability of reproducing snow bidirectional reflectance by three semiempirical bidirectional reflectance distribution function (BRDF) models (Ross–Li, Roujean, and Raman–Pinty–Verstraete) and the asymptotic radiative transfer theory (ART) model was evaluated using the polarization and directionality of the earth reflectance (POLDER) data. In addition, the ART model was compared with the bicontinuous geometric optics (bic-GORT) model based on field measurements. The results indicated that the root mean square errors (RMSEs) are small and similar for all models during the stable-snow period. The physical models perform better than the semiempirical models in capturing the bidirectional signatures of snow during the periods of snow rapidly changed. The bic-GORT model achieves higher accuracy, while the ART model holds the advantages of simple and efficient to be used.

Forest biomass estimation is an important parameter for calculating forest carbon storage, which is of great significance for formulating carbon-neutral strategies and forest resource management measures. We aimed at solving the problems of low estimation accuracy of forest biomass with complex canopy structure and high canopy density, and large differences in the estimation results of the same estimation model under complex forest conditions. The Huangfu Mountain Forest Farm in Chuzhou City was used as the research area. As predictors, we used Gaofen-1(GF-1) and Gaofen(GF-6) satellite high-resolution imaging satellite data, combined with digital elevation model (DEM) and forest resource data. Multiple stepwise regression, BP neural network and random forest estimation models were used to construct a natural subtropical secondary forest biomass estimation model with complex canopy structure and high canopy closure. We extracted image information as modeling factors, established multiple stepwise regression models of different tree types with a single data source and a comprehensive data source and determined the optimal modeling factors. On this basis, the BP neural network and random forest biomass estimation model were established for Pinus massoniana, Pinus elliottii, Quercus acutissima and mixed forests, with the coefficient of determination n (R²) and root mean square error (RMSE) as the judgment indices. The results show that the random forest model had the best biomass estimation effect among different forest types. The R² of Quercus acutissima was the highest, reaching 0.926, but the RMSE was 11.658 t/hm². The R² values of Pinus massoniana and mixed forest were 0.912 and 0.904, respectively. The RMSE reached 10.521 t/hm² and 6.765 t/hm², respectively; the worst result was the estimation result of Pinus elliottii, with an R² of 0.879 and an RMSE of 14.721 t/hm². The estimation result of the BP neural network was second only to that of the random forest model in the four forest types. From high precision to low precision, the order was Quercus acutissima, Pinus massoniana, mixed forest and Pinus elliottii, with R²s of 0.897, 0.877, 0.825 and 0.753 and RMSEs of 17.899 t/hm², 10.168 t/hm², 18.641 t/hm² and 20.419 t/hm², respectively. In this experiment, the worst biomass estimation performance was seen for multiple stepwise regression, which ranked the species in the order of Quercus acutissima, Pinus massoniana, mixed forest and Pinus elliottii, with R²s of 0.658, 0.622, 0.528 and 0.379 and RMSEs of 29.807 t/hm², 16.291 t/hm², 28.011 t/hm² and 23.101 t/hm², respectively. In conclusion, GF-1 and GF-6 combined with data and a random forest algorithm can obtain the most accurate results in estimating the forest biomass of complex tree species. The random forest estimation model had a good performance in biomass estimation of primary secondary forest. High-resolution satellite data have great application potential in the field of forest parameter inversion.

This study aims to enhance the aquatic eco-functional zoning by incorporating the spatial variability of hydrological processes during the zoning process. We propose a method for watershed eco-functional zoning based on distributed hydrological modeling. Using the Jinjiang Basin in Southeast China as a case study, we applied the Soil Water and Assessment Tool (SWAT) model to delineate the basic zoning units and simulate their hydrological processes. We integrated natural environmental indicators—specifically topography, vegetation, meteorology, and hydrology—with land use as a measure of human activity, while accounting for their spatial variability. This approach enabled us to conduct both first-level and second-level eco-functional zoning of the watershed. The results indicated that (1) the Jinjiang Basin can be categorized into three main groups consisting of six first-level aquatic ecological zones, which reflect the spatial variability of terrestrial natural environmental factors and their influence on aquatic ecosystems; (2) building on this categorization, the first-level aquatic ecological regions were further divided into five categories comprising 18 second-level aquatic ecological functional zones, emphasizing the impact of human activities on aquatic ecosystems and their associated first-level ecological service functions; and (3) the application of hydrological simulation techniques allows for a comprehensive assessment of the spatial variability of hydrological processes, thereby enhancing the validity of the ecological function zoning results and providing robust technical support for watershed ecological function zoning.

Under China’s “Dual Carbon” goals, accurate assessment of regional carbon balance is of critical importance. Land use acts as a core factor governing carbon balance. However, the differentiated effects of land use intensity (LUI)—a comprehensive indicator quantifying human intervention on carbon storage, sequestration, and emissions, as well as their spa-tial driving factors, remain poorly understood. Here we incorporate multi-source remote sensing datasets and the GeoDetector model to systematically explore the spatio-temporal linkages between LUI and carbon balance components in Anhui Province, Chi-na, during the period 2000–2020. Our results reveal that LUI exhibits dual spatial associations on car-bon balance components: LUI shows significant negative spatial correlations with carbon storage (CSt), sequestration (CSeq), and net carbon (NetC), but a significant positive corre-lation with carbon emissions (CE). Notably, in 2020, negative association clusters, covering approximately 22% of the study area, were located in the southern mountains. In contrast, the “High LUI - High CE” agglomerations are centered in the northern urban clusters. GeoDetector further demonstrates that the spatial patterns of CSt and CSeq are primarily associated with interactions between LUI and natural factors (NDVI) (q > 0.4), while the spatial pattern of CE is dominated by interactions between LUI and socio-economic factors (GDP) (q > 0.6). These findings reveal dual interaction patterns through which LUI is associated with carbon balance—a strong joint explanatory effect of LUI and NDVI in the south and a strong joint explanatory effect of LUI and GDP in the north—providing scientific support for regional carbon governance strategies that target these two distinct patterns: protecting existing sinks in the south and curbing emissions in the north.

As a critical area for the ecological security barrier in the middle and lower reaches of the Yangtze River, the Jianghuai Watershed Area holds significant importance in coordinating ecological conservation and economic development through the assessment of its water ecosystem service value (ESV). This study focuses on Chuzhou, a typical city within the Jianghuai Watershed Area, to explore the spatiotemporal evolution of water ESV and analyze the spatiotemporal change of the Consistency between water Ecosystem services and Economy (CEE) from 2000 to 2022 based on remote sensing. Firstly, the water was extracted through normalized snow water index (NSWI) and Otsu threshold method (OTSU). Subsequently, the water ESV dynamic evaluation model was constructed. Using this model, the water ESV in Chuzhou was quantified, and its spatiotemporal distribution characteristics were analyzed. Finally, by calculating the coefficient of variation (CV) between ESV and GDP, the deviation between water ESV and GDP in Chuzhou was obtained. At the same time, the coordination between water ESV and economic development of Chuzhou was obtained by calculating CEE, so as to evaluate the Spatiotemporal coordination and regional differences between them. This study has found that: (1) The water ESV in Chuzhou showed a phased change of “fluctuating decline and subsequent recovery” from 2000 to 2022. (2) In terms of spatial distribution, the water ESV exhibits a gradient pattern of “high ESV in the northwest and low ESV in the southeast of the Chuzhou”. (3) In terms of ecological and economic coordination, the northwest of the Chuzhou maintain a healthy CEE. However, the southeast of Chuzhou has exacerbated the imbalance in CEE due to the water space occupied by construction land. This study could provide a decision-making basis for formulating ecological priority development strategies in the Jianghuai Watershed Area.