Leaf chlorophyll content plays a vital role in plant photosynthesis. The PROSPECT model has been widely used for retrieving leaf chlorophyll content from remote sensing data over various plant species. However, despite wide variations in leaf surface reflectance across different plant species and environmental conditions, leaf surface reflectance is assumed to be the same for different leaves in the PROSPECT model. This work extends the PROSPECT model by taking into account the variation of leaf surface reflection. In the modified model named PROSPECT-Rsurf, an additional surface layer with a variable refractive index is bounded on the N elementary layers. Leaf surface reflectance (Rs) is characterized by the difference between the refractive indices of leaf surface and interior layers. The specific absorption coefficients of the leaf total chlorophyll and carotenoids were recalibrated using a cross-calibration method and the refractive indices of leaf surface and interior layers were obtained during model inversion. Chlorophyll content (C_ab) retrieval and spectral reconstruction in the visible spectral region (VIS, 400–750 nm) were greatly improved using PROSPECT-Rsurf, especially for leaves covered by heavy wax or hard cuticles that lead to high surface reflectance. The root mean square error (RMSE) of chlorophyll estimates decreased from 11.1 µg/cm² to 8.9 µg/cm² and the Pearson’s correlation coefficient (r) increased from 0.81 to 0.88 (p < 0.01) for broadleaf samples in validation, compared to PROSPECT-5. For needle leaves, r increased from 0.71 to 0.89 (p < 0.01), but systematic overestimation of C_ab was found due to the edge effects of needles. After incorporating the edge effects in PROSPECT-Rsurf, the overestimation of C_ab was alleviated and its estimation was improved for needle leaves. This study explores the influence of leaf surface reflectance on C_ab estimation at the leaf level. By coupling PROSPECT-Rsurf with canopy models, the influence of leaf surface reflectance on canopy reflectance and therefore canopy chlorophyll content retrieval can be investigated across different spatial and temporal scales.

Precisely delineating the distribution of moso bamboo forests is critical for forestry management and regional carbon cycle research. The unique phonological characteristics (i.e., on- and off-year phenomenon) of bamboo impose difficulties in bamboo identification. This study aims to develop a new algorithm for mapping bamboo distribution using remote sensing data with the consideration of bamboo phenological characteristics. Three optical indices were proposed based on canopy reflectance retrieved from Sentinel-2 and field inventory data, including modified bamboo index (MBI), bamboo phenological characteristic index (BPCI), and BPCI 2 (BPCI-2). The collaboration of these three indices with the recursive feature elimination (RFE) and extreme gradient boosting (XGBoost) methods can precisely map bamboo distribution and its phenological status. The model based on MBI, BPCI, and BPCI-2 outperformed the model driven by the existing bamboo extracting indices, i.e., bamboo index (BI), yearly change bamboo index (YCBI), and monthly change bamboo index (MCBI), increasing in overall accuracy (OA) by about 1.5%. Additionally, the proposed indices were calculated using the data synthesized from Sentinel-1 synthetic aperture radar (SAR) imageries by the cycle-consistent adversarial network (CycleGAN) method under the condition without cloudy-free Sentinel-2 data available to fill the time series data gaps. The performance of the model based on augmented data improved notably in comparison with the model driven only by indices from original optical images, with the identification accuracy for on- and off-year bamboo samples over 96%. The generated moso bamboo distribution map aligns well with forestry inventory data in terms of both area and spatial distribution. The proposed indices are less sensitive to terrain than the existing bamboo extracting indices. This merit is valuable for better mapping bamboo forests, which are mostly distributed in mountainous areas.

Rapid urbanization exerts profound pressure on urban biodiversity, yet long-term assessments integrating multi-source remote sensing data remain scarce. Objective: Focusing on the Hangzhou Bay Urban Agglomeration, a rapidly developing region in China’s Yangtze River Delta, this study aims to construct a remote sensing-based Biodiversity Index (BI) and analyze its spatiotemporal evolution and underlying drivers. Six Essential Biodiversity Variables derived from satellite observations (2000–2024) were integrated using Principal Component Analysis. Spatial autocorrelation and Geodetector models were then applied to examine BI dynamics and driving factors. The regional BI declined gradually from 0.80 in 2000 to 0.72 in 2024, with the rate of decline slowing after 2020 and a partial recovery observed in Zhoushan. Marked inter-city heterogeneity exists: Huzhou retains the highest and most stable BI due to extensive forest cover, whereas Jiaxing exhibits the lowest BI and the most pronounced decline, driven by rapid expansion of construction land. Land use/cover (LULC) and fractional vegetation cover (FVC) emerge as the dominant drivers (average q-values of 0.196 and 0.208, respectively), and their interaction explains over 46% of the spatial variance in BI. Road density shows a consistently increasing influence over time. This study demonstrates the utility of remote sensing-based frameworks for monitoring urban biodiversity dynamics and provides actionable insights for evidence-based land use planning and ecological restoration.

Natural wetland loss constitutes a primary threat to waterbirds worldwide, increasingly forcing them to rely on expanding artificial wetlands. Extensive underground coal mining across the North China Plain has created numerous subsidence wetlands, which could serve as important alternative habitats for migratory and wintering waterbirds. However, the effects of different management regimes on waterbirds in these novel artificial wetlands remain poorly understood, hindering effective strategies for reconciling human development with waterbird conservation. Here, we conducted a long-term field survey (2017–2025) of wintering waterbirds across 15 subsidence wetlands under four distinct human management regimes in the Huaibei coal mining area. We recorded 22,712 waterbirds of 45 species. We found that high-intensity aquaculture and floating photovoltaic systems were associated with reduced waterbird diversity, increased community dissimilarity, altered species composition, and the loss of multiple threatened species from survey records. We also found that ecological aquaculture and unutilized wetlands may serve as favorable habitats as alternatives to natural wetlands. Our findings demonstrate that subsidence wetlands can provide vital wintering habitats when managed sustainably, but intensive development severely compromises their conservation value. Future research should integrate habitat variables and year-round surveys to optimize management strategies for these expanding artificial ecosystems.

During crop growth, leaf photosynthetic capacity changes continuously, and the vertical distribution of leaf nitrogen (Na) and chlorophyll (Chla) affects photosynthesis in different canopy layers. Understanding stratified photosynthesis is vital for accurate prediction of crop photosynthetic capacity. We conducted a two-year field study on winter wheat and paddy rice in Eastern China, measuring leaf maximum carboxylation rate (Vc_max25), maximum electron transport rate (J_max25), Na, and Chla every 7–10 days from greening to maturity. We analyzed vertical variations of these parameters in upper (T-1), middle (T-2), and lower (T-3) canopy layers and explored relationships between Na/Chla and Vc_max25. Results showed significant vertical variations: Vc_max25 and J_max25 in T-1 were higher than T-2, and T-2 higher than T-3. The vertical distribution of Na and Vc_max25 was more pronounced than Chla. Correlation between Na and Vc_max25 increased from T-1 to lower layers, while Vc_max25-Chla correlation decreased. A single Vc_max25 estimation model based on Na performed well across layers (R²=0.619, RMSE=15.751 µmol m⁻² s⁻¹). Differentiating T-1 from T-2/T-3 improved Chla-based models. Na was better than Chla for characterizing Vc_max25 vertical variation, with Chla-based models requiring separation of T-1 from T-2/T-3. This study provides key insights for remote sensing of photosynthetic parameters and improves understanding of crop canopy photosynthesis.