Content area
Remote sensing monitoring of small-lake eutrophication faces challenges such as sparse data, insufficient synergy of multi-source data, and limited model generalization performance. Hence, this study developed a scenario-aware modeling framework for the trophic level index (TLI) by integrating multi-source imagery data from Sentinel-2, GF-1, HJ-2, and PlanetScope, using Dongqian Lake in Zhejiang Province, China as the case study. The cross-sensor prediction accuracy was evaluated using algorithms such as CatBoost Regression (CBR), XGBoost Regression (XGBR), TabPFN Regression (TPFNR), and Linear Regression (LR). Meanwhile, the influence of input features was quantified by SHapley Additive exPlanations (SHAP). The main results found that : (1) Overall annual mean values of total nitrogen/total phosphorus ratio (TN/TP) and TLI were 22.13 and 37.36 ± 4.99, respectively, indicating a mesotrophic and phosphorus-limited state in Dongqian Lake. (2) TLI exhibited the strongest correlation with water color and algal spectral indices, including Normalized Difference Water Index (NDWI), Normalized Green–Red Difference Index (NGRDI), and Blue–Green Ratio (BGR). (3) CBR demonstrated the strongest cross-sensor generalization capability across different imagery, with only minor variations in prediction accuracy (ΔR ≈ 0.07–0.15). Feature attribution analysis identified NDWI, NGRDI, and BGR as primary contributing features for the CBR model. (4) Integrating high-frequency multi-source remote sensing imagery with 27 field surveys achieved seamless monitoring of the TLI. The spatial distribution of TLI showed distinct seasonal variations, with higher values observed in nearshore areas and lower values in the lake center. TLI values were relatively low in spring, but surged sharply and remained elevated in summer. This study provided a reference basis for detailed remote sensing monitoring and management of eutrophication in small lakes.
Details
Accuracy;
Deep learning;
Phosphorus;
Water shortages;
Chemical oxygen demand;
Normal distribution;
Eutrophication;
Water color;
Remote sensing;
Remote monitoring;
Spatial distribution;
Seasonal variations;
Machine learning;
Monitoring systems;
Performance evaluation;
Probability distribution;
Algae;
Nitrogen;
Water quality;
Regression;
Trophic levels;
Correlation analysis;
Imagery
1 College of Geography and Environmental Sciences, Zhejiang Normal University, Jinghua, China (ROR: https://ror.org/01vevwk45) (GRID: grid.453534.0) (ISNI: 0000 0001 2219 2654)
2 Department of Social Sciences and Policy Studies, Education University of Hong Kong, Tai Po, Hong Kong SAR, China (ROR: https://ror.org/000t0f062) (GRID: grid.419993.f) (ISNI: 0000 0004 1799 6254)
3 GeoInformatic Unit, Geography Section, School of Humanities, Universiti Sains Malaysia, George Town, Penang, Malaysia (ROR: https://ror.org/02rgb2k63) (GRID: grid.11875.3a) (ISNI: 0000 0001 2294 3534); Environmental and Atmospheric Sciences Research Group, Scientific Research Center, Al-Ayen University, Thi-Qar, Nasiriyah, Iraq (ROR: https://ror.org/02t6wt791)
4 Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, China (ROR: https://ror.org/034t30j35) (GRID: grid.9227.e) (ISNI: 0000000119573309)
5 Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China (ROR: https://ror.org/034t30j35) (GRID: grid.9227.e) (ISNI: 0000000119573309)
6 Faculty of Resources and Environmental Science, Hubei University, Wuhan, China (ROR: https://ror.org/03a60m280) (GRID: grid.34418.3a) (ISNI: 0000 0001 0727 9022)