Shaohua Lei

Inland lake ecosystems function as active nodes in the global carbon cycle, yet reliable basin-scale estimates of particulate organic carbon concentration (C_POC) remain scarce owing to the optical complexity of these environments. In this study, POC denotes the total particulate organic carbon pool, including organic matter from living and non-living particles. Here we present a semi-analytical retrieval scheme tailored to Sentinel-2 Multispectral Instrument (MSI) data and applied to 90 large eutrophic lakes (each >10 km²) within the Yangtze–Huaihe Plain (YHP) of eastern China. The scheme reconstructs the particulate absorption coefficient at 665 nm, a_p(665), from reflectance measurements at 665, 705, and 740 nm, reducing the interference of colored dissolved organic matter (a_CDOM) absorption and particulate backscattering (b_b). When evaluated against 60 field observations in the predefined concentration-stratified holdout, the algorithm achieved MAE = 0.59 mg/L, RMSE = 0.73 mg/L, and MAPE = 46.89%—outperforming three widely cited benchmark algorithms—and showed preliminary transfer potential in the two estuarine datasets. MSI-derived C_POC maps from 2018 to 2024 revealed systematic east-to-west and center-to-periphery concentration gradients across the 90-lake assemblage, shaped by catchment nutrient delivery, basin hydrodynamics, and phytoplankton standing stock. Correlation and attribution analyses identified chlorophyll-a concentration (C_Chla), atmospheric pressure, and total phosphorus (C_TP) as the three dominant environmental modulators of C_POC variability. The framework provides a field-calibrated approach for examining regional lake C_POC patterns. Its satellite application and ability to distinguish small concentration differences require further independent validation and uncertainty assessment.

Lake water color provides an integrated indicator of aquatic environmental change, yet its long-term phenology and resilience remain poorly understood in arid regions. In this study, Landsat-derived hue angle (α) records were reconstructed for 350 lakes larger than 1 km² across arid Northwest China from 1984 to 2025. Regional mean α declined significantly over the past 42 years (R² = 0.51), accompanied by pronounced spatial heterogeneity. Three lake water color phenological types were identified: Aseasonal (75.0%), Summer Greening (13.7%), and Spring Greening (11.3%). Aseasonal lakes were the most stable, whereas seasonal lakes showed greater temporal variability and a tendency to transition toward aseasonality. Lake water color resilience increased in 43% of lakes, declined in 21%, and remained unchanged in 36%, with resilience-loss lakes showing distinct spatial clustering. Lake morphology was most strongly associated with water color change, topography and thermal conditions with its phenology, and watershed population dynamics with its resilience. In situ observations further showed significant relationships between α and optically active constituents and their derived parameters, reflecting the combined optical effects of absorption and scattering and suggesting its potential as a proxy for broader water-quality assessment with more extensive field sampling and validation. These findings highlight contrasting controls on lake-color dynamics, phenology, and resilience, and demonstrate the value of hue angle for monitoring environmental change in vulnerable arid-zone lakes. Overall, these results advance understanding of the long-term environmental responses of arid-zone lakes and provide a scientific basis for targeted monitoring and adaptive management in arid regions.

With the advancement of remote sensing technology, the accuracy of satellite remote sensing data has continuously improved, and the precision of large‐scale soil and water conservation monitoring has steadily increased. However, owing to differences in sensor acquisition and processing methods, monitoring data obtained through various approaches frequently exhibit limited compatibility and data singularity, presenting challenges in meeting the requirements for advanced soil erosion monitoring. Recently, the emergence and application of multi‐source data fusion (MSDF) technology have effectively enhanced the spatiotemporal resolution of erosion monitoring. In this study, we employed the scientometric mapping tool CiteSpace using bibliometric methods to systematically review the research hotspots and development trends in multi‐source monitoring of soil erosion from 1999 to 2024. This review summarizes the classification and acquisition methods for multi‐source data, analyzes the characteristics and challenges of commonly used MSDF techniques, and elucidates the implementation pathways for applying MSDF technology to soil and water conservation monitoring in China. This review focuses on analyzing the application of multi‐source data to extract key factors of the soil loss equation, including rainfall erosivity, soil erodibility, vegetation coverage, and topographic parameters such as slope gradient and length. Additionally, it discusses the limitations of multi‐source data acquisition methods and soil loss equation, proposes innovative directions for the comprehensive application of multi‐source remote sensing fusion technology in erosion monitoring, and provides scientific recommendations to support the high‐quality development and scalable implementation of soil and water conservation informatization.

Monitoring the dynamics of estuarine nutrients, specifically dissolved inorganic nitrogen (DIN) and silicate (DSi), is essential for assessing the ecological health of nearshore waters; however, traditional sampling methods struggle to provide the high-frequency temporal data and broad spatial coverage required for comprehensive assessment. Here, we developed a robust "two-step inversion" remote sensing framework (R2 > 0.75) that bridges the gap between remote sensing reflectance and salinity, subsequently linking salinity to DIN and DSi concentrations. By applying this model to the Jiulong River Estuary, we achieved a high-frequency, large-scale, and accurate reconstruction of surface DIN and DSi concentrations over the past decade. Both DIN and DSi exhibited a distinct land-to-sea gradient and significant seasonal fluctuations, characterized by higher concentrations in winter and lower concentrations in summer. The silicate-to-nitrogen ratio (Si/N) remained stable between 1.27 and 1.34 over the long term but displayed marked spatial heterogeneity during the summer. These nutrient dynamics are driven by coupled biogeochemical processes under the dual influence of natural cycles and human activity. While conservative mixing dictates spatial patterns, long-term trends are driven by temperature-regulated biological uptake and the balance between anthropogenic emissions and environmental governance. Our framework is an effective tool for monitoring non-optically active substances and has implications for eutrophication early-warning systems and integrated basin management.

The Danjiang River Basin is renowned as the water source for the South-to-North Water Diversion Middle Route Project. Catastrophic rainstorm-induced floods in this basin pose severe threats to regional socio-economic development, creating an urgent need for ultra-long-scale hydrological data to support flood control, disaster mitigation, and hydropower utilization. Palaeoflood hydrology provides a critical approach to reconstructing Holocene flood hydrological data in the Danjiang River Basin. At the Xiwan (XW) site in Shangnan County, China, a Holocene loess-paleosol profile containing two palaeoflood slackwater deposit (SWD) layers was identified. Proxy indicators such as magnetic susceptibility and grain-size distribution reveal that these palaeoflood SWDs were formed by suspended sediment deposition in a relatively stagnant environment due to high water levels during palaeofloods. Using AMS ¹⁴C dating and stratigraphic correlation, the two identified extreme palaeoflood events were dated to 5500–5000 a B.P. and 4200–4000 a B.P., periods corresponding to global abrupt climate shifts and transitional phases characterized by anomalous atmospheric circulation and frequent extreme climate events such as major floods and droughts. Based on the two palaeoflood SWD layers, the reconstructed palaeoflood stages were determined to be 352.3 m and 354.2 m, respectively. Using the Slope-area method, the corresponding peak discharges were estimated as 14,610 m³/s and 18,570 m³/s. By integrating instrumental flood records from the Jingziguan Hydrological Station, historical flood investigations, and the reconstructed extreme palaeofloods, a new long-term flood frequency and peak discharge relationship was established for the Danjiang River Basin. These findings significantly improve the accuracy of extreme flood risk assessment and provide robust evidence for predicting river system responses to climate variability.

Against the background of China’s ecological civilization construction and sustainable development strategies, how remote sensing courses adapt to the demands of the artificial intelligence era has become an urgent issue for undergraduate education in relevant disciplines at universities. This study proposed a trinity teaching reform path of “deep learning and remote sensing, and ecological sustainability”, aiming to cultivate interdisciplinary talents with capabilities in intelligent interpretation and practical application. The study established a three-stage curriculum objective system, integrating knowledge, ability, and literacy, designed a five-dimensional linkage teaching method combining case-driven teaching, modular training, and blended learning, and conducted teaching practices using mainstream deep learning frameworks and cloud platforms. Through hierarchical teaching practice cases and multi-dimensional evaluation data, it was shown that the reform effectively enhanced the experiment group students’ abilities in deep learning applications, complex remote sensing data processing, and ecological problem-solving. The achievement values for all five evaluation indicators exceeded 80%, with the highest improvement reaching 28% compared to the control group. The results indicate that this teaching reform not only enhances learning outcomes but also provides a valuable framework and practical pathway for remote sensing education empowered by artificial intelligence and the cultivation of professional talent in future sustainable development fields.

The particle absorption coefficient of pigments (a_ph(λ)), a critical indicator of phytoplankton spectral absorption properties, is essential for bio-optical models and water quality monitoring. To enhance the accuracy of a_ph(λ) retrieval in complex aquatic environments, this study proposes a novel two-stage framework integrating optical classification and machine learning regression. Focusing on inland waters—key areas for eutrophication monitoring—we first developed an intelligent clustering method combining Kernel Principal Angle-based Component (KPAC) dimensionality reduction and Chameleon Swarm Algorithm (CSA)-optimized k-medoids to classify water bodies into four optical types based on hyperspectral reflectance features. Subsequently, an XGBoost regression model with L1-norm feature selection was applied to inversely derive a_ph(440), a_ph(555), a_ph(675), and a_ph(709) for each class. Experimental results demonstrated that optical classification significantly improved inversion accuracy: the determination coefficients R² all exceeded 0.9 in classified datasets, with RMSE reduced by up to 93.1% compared to unclassified scenarios. This indicates that the strategy based on optical classification and regression inversion can effectively enhance the accuracy of pigment particle absorption coefficient inversions. In summary, this study, with the central objective of accurately measuring the pigment particle absorption coefficient, successfully developed a comprehensive set of optical classification and regression inversion methods applicable to various aquatic environments. This new scientific approach and powerful tool provide a means for monitoring and interpreting the pigment particle absorption characteristics in water bodies using remote sensing technology.

Accurate and efficient extraction of rice planting structures, coupled with comprehensive analysis of their spatiotemporal dynamics and driving factors, is crucial for rice yield estimation and optimized water resource management in the Poyang Lake region. However, traditional approaches face significant limitations: single machine learning models often yield insufficient classification accuracy, while existing fusion models typically involve complex processing workflows and exhibit low computational efficiency. To address these challenges, this study developed an efficient and simplified fusion model based on a scoring strategy to determine rice planting structures from 2018 to 2023, followed by an in-depth analysis of their spatiotemporal patterns and underlying drivers. The evaluation results demonstrated that four individual classification models of K-Nearest Neighbors (KNN), Random Forest (RF), Support Vector Machine (SVM), and Gradient Boosting Decision Tree (GBDT) achieved Overall Accuracy of 85.29%–90.07%, Kappa coefficients of 0.786–0.855, User Accuracy of 80.51%–93.02%, and Mapping Accuracy of 80.87%–92.63%. The proposed scoring-based fusion model significantly enhanced these metrics, improving Overall Accuracy by 3.36%–9.16%, Kappa coefficient by 5.15%–14.38%, User Accuracy by 0.37%–11.13%, and Mapping Accuracy by 0.48%–10.71%. Spatiotemporal analysis revealed distinct trends in rice cultivation patterns: single-cropping rice and regenerated rice showed consistent expansion, both in planting area and proportion, with a spatial tendency towards flat regions. Conversely, double-cropping rice exhibited a gradual decline, with its cultivation areas contracting towards the central lake region. These shifts were primarily driven by socioeconomic factors, particularly rural labor migration and rising fertilizer prices, which have incentivized farmers to adopt less labor-intensive and lower-input cultivation systems, such as single-cropping and regenerated rice. The findings offer a novel methodological framework for precise extraction of crop planting structures, and a scientific foundation for local governments to develop targeted water resource management strategies.

Clarifying carbon (C) cycling in small ponds is vital for understanding C transport in lowland agricultural landscape. Quantifying C flux is crucial for learning C cycling, but is challenging due to its complex cycling and significant impacts from intensive human activities. Here, we developed a process‐based model (CDP) to achieve a daily estimation of C dynamics in agricultural ponds within lowland artificial watersheds (polders), and proposed a dual evaluation approach (concentration and flux) to assess the model's performance using two data sets obtained from eight typical polders in the Lake Taihu Basin. The developed model captured pond C dynamics, achieving a Nash‐Sutcliffe efficiency of 0.44 ± 0.27. Our C flux estimations based on the newly‐developed model showed large C emissions, primarily through carbon dioxide (CO₂) (497.5 g C m⁻² yr⁻¹), along with significant C burial (27.8 g C m⁻² yr⁻¹) with a hot moment in summer. Scenario simulations revealed the distinct impacts of pond C emissions and burial associated with the growth and death of phytoplankton and macrophytes. A 10% increase in macrophyte growth rates associated with a 1.8 g C m⁻² yr⁻¹ increase in CO₂ emission, while a similar increase in phytoplankton growth rates related to a 12.2–16.2% increase in C burial. This study revealed a quick response of C flux to phytoplankton‐macrophyte dominance, and highlighted the high potential of the process‐based model for high‐resolution (daily) quantification of C fluxes, thereby enhancing our understanding of C cycling in lowland agricultural ponds.