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Hybrid spatiotemporal modeling of nutrient cycling in wetland ecosystems using advanced mapping techniques and machine learning approaches

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Abstract

Accurate spatiotemporal monitoring of nutrient cycling in wetlands is critical for conservation. However, traditional field-based methods are often inadequate for capturing the overall dynamics of wetlands. To address this challenge, we developed and validated a two-stage hybrid Random Forest regression framework that seamlessly integrated in-situ water quality data with wetland features and satellite imagery from Sentinel-1 and Sentinel-2 within the Google Earth Engine platform. The framework first models baseline nutrient concentrations using discrete wetland characteristics (stage 1) and then models the resulting spatial residual using continuous satellite-derived predictors (stage 2). We applied the model to predict quarterly nitrogen and phosphorus concentrations over four years (2021–2024) in the Beavercreek Wetlands Greenway (BWG), a mixed-use landscape. The two-stage model demonstrated exceptional predictive performance for both nitrogen (final (text {r}^{2}) = 0.90, RMSE = 0.129 mg/L) and phosphorus (final (text {r}^{2}) = 0.89, RMSE = 0.007 mg/L). The variable importance analysis revealed divergent predictive pathways: nitrogen concentration was driven by both landscape-level factors (e.g., land use classes, area and perimeter of wetlands, rainfall) and in-stream biophysical conditions captured by remote sensing (e.g., vegetation and SAR indices), whereas phosphorus was controlled by source-loading from developed land uses. We produced spatiotemporal maps to visualize these distinct patterns. The maps revealed that the BWG system is subject to seasonal nitrogen stress but has experienced significant recovery from prior phosphorus impairment. This study offers a diagnostic framework that could inform focused wetland management strategies and aid in monitoring the health and function of wetland ecosystems.

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Data availibility

The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.

References

  1. Zhang, X. et al. Global annual wetland dataset at 30m with a fine classification system from 2000 to 2022. Sci. Data 11, 310. https://doi.org/10.1038/s41597-024-03143-0 (2024).

    Google Scholar 

  2. Zhang, S. et al. Investigating the dynamic change and driving force of isolated marsh wetland in Sanjiang plain, northeast China. Land 13, 1969. https://doi.org/10.3390/land13111969 (2024).

    Google Scholar 

  3. Guelmami, A. Large-scale mapping of existing and lost wetlands: Earth observation data and tools to support restoration in the sebou and medjerda river basins. Euro-Mediter. J. Environ. Integr. 9, 169–182. https://doi.org/10.1007/s41207-023-00443-6 (2024).

    Google Scholar 

  4. Howard-Williams, C. Cycling and retention of nitrogen and phosphorus in wetlands: a theoretical and applied perspective. Freshw. Biol. 15, 391–431. https://doi.org/10.1111/j.1365-2427.1985.tb00212.x (1985).

    Google Scholar 

  5. Lacher, I. L. et al. Scale-dependent impacts of urban and agricultural land use on nutrients, sediment, and runoff. Sci. Total Environ. 652, 611–622. https://doi.org/10.1016/j.scitotenv.2018.09.370 (2019).

    Google Scholar 

  6. Fisher, J. & Acreman, M. C. Wetland nutrient removal: a review of the evidence. Hydrol. Earth Syst. Sci. 8, 673–685. https://doi.org/10.5194/hess-8-673-2004 (2004).

    Google Scholar 

  7. Jackson, C. R., Thompson, J. A. & Kolka, R. K. Wetland soils, hydrology, and geomorphology. In Ecology of Freshwater and Estuarine Wetlands (eds Batzer, D. P. & Sharitz, R. R.) 23–60 (University of California Press, Oakland, CA, USA, 2014).

    Google Scholar 

  8. Little, A. Sampling and analyzing wetland vegetation. In Wetland Techniques 273–324 (Springer, Dordrecht, 2013). https://doi.org/10.1007/978-94-007-6860-4_5.

    Google Scholar 

  9. Salari, A., Zakaria, M., Nielsen, C. C. & Boyce, M. S. Quantifying tropical wetlands using field surveys, spatial statistics and remote sensing. Wetlands 34, 565–574. https://doi.org/10.1007/s13157-014-0524-3 (2014).

    Google Scholar 

  10. Gallant, A. L. The challenges of remote monitoring of wetlands. Remote Sens. 74, 10938–10950. https://doi.org/10.3390/rs70810938 (2015).

    Google Scholar 

  11. Corcoran, J. M., Knight, J. F. & Gallant, A. L. Influence of multi-source and multi-temporal remotely sensed and ancillary data on the accuracy of random forest classification of wetlands in northern minnesotas. Remote Sens. 5, 3212–3238. https://doi.org/10.3390/rs5073212 (2013).

    Google Scholar 

  12. Drisiya, J. & Shibu, K. Assessment of nutrient dynamics in Sasthamkotta wetland using gis remote sensing and artificial neural network. Trans. Indian Natl. Acad. Eng. 10, 453–470. https://doi.org/10.1007/s41403-025-00528-4 (2025).

    Google Scholar 

  13. Ding, C. et al. Combining artificial neural networks with causal inference for total phosphorus concentration estimation and sensitive spectral bands exploration using modis. Water 12, 2372. https://doi.org/10.3390/w12092372 (2020).

    Google Scholar 

  14. Gaballah, M. S. & Lammers, R. W. Assessing the factors influencing constructed wetland performance for mitigating agricultural nutrient runoff in the U.S.. J. Water Process Eng. 71, 107293. https://doi.org/10.1016/j.jwpe.2025.107293 (2025).

    Google Scholar 

  15. Singh, S. et al. Machine learning application for nutrient removal rate coefficient analyses in horizontal flow constructed wetlands. ACS ES T Water 4, 2619–2631. https://doi.org/10.1021/acsestwater.4c00121 (2024).

    Google Scholar 

  16. Isles, P. D. F. A random forest approach to improve estimates of tributary nutrient loading. Water Res. 248, 120876. https://doi.org/10.1016/j.watres.2023.120876 (2024).

    Google Scholar 

  17. Ayele, G. T., Yu, B., Bruere, A. & Hamilton, D. P. Response of streamflow and nutrient loads in a small temperate catchment subject to land use change. Environ. Monit. Assess. 195, 1418. https://doi.org/10.1007/s10661-023-11828-z (2023).

    Google Scholar 

  18. Zhu, B. et al. Satellite remote sensing of water quality variation in a semi-enclosed bay (Yueqing bay) under strong anthropogenic impact. Remote Sens. 14, 550. https://doi.org/10.3390/rs14030550 (2022).

    Google Scholar 

  19. Euliss, N. H. J. et al. Placing prairie pothole wetlands along spatial and temporal continua to improve integration of wetland function in ecological investigations. J. Hydrol. 513, 490–503. https://doi.org/10.1016/j.jhydrol.2014.04.006 (2014).

    Google Scholar 

  20. Guo, B. et al. Using a two-stage scheme to map toxic metal distributions based on gf-5 satellite hyperspectral images at a northern chinese opencast coal mine. Remote Sens. 14, 5804. https://doi.org/10.3390/rs14225804 (2022).

    Google Scholar 

  21. Tarantino, C. et al. Intra-annual sentinel-2 time-series supporting grassland habitat discrimination. Remote Sens. 13, 277. https://doi.org/10.3390/rs13020277 (2021).

    Google Scholar 

  22. Xue, J., Yuan, C., Ji, X. & Zhang, M. Predictive modeling of nitrogen and phosphorus concentrations in rivers using a machine learning framework: a case study in an urban-rural transitional area in wenzhou china. Sci. Total Environ. 910, 168521. https://doi.org/10.1016/j.scitotenv.2023.168521 (2024).

    Google Scholar 

  23. Giri, S. & Qui, Z. Understanding the relationship of land uses and water quality in twenty first century: A review. J. Environ. Manag. 173, 41–48. https://doi.org/10.1016/j.jenvman.2016.02.029 (2016).

    Google Scholar 

  24. Wang, Y., Sun, J. & Wu, Y. Spatiotemporal variation of small and micro wetlands and their multiple responses to driving factors in the high-latitude region. Wetlands 44, 126. https://doi.org/10.1007/s13157-024-01882-9 (2024).

    Google Scholar 

  25. Nijiati, N., Rusuli, Y., Maimaitiaili, K. & Kuluwan, Y. Spatiotemporal variations and driving forces analysis of ecosystem health in the Bosten lake wetland in China. Land Degrad. Dev. https://doi.org/10.1002/ldr.5686 (2025).

    Google Scholar 

  26. Gaballah, M. S., Yousefyani, H., Karami, M. & Lammers, R. W. Free water surface constructed wetlands: review of pollutant removal performance and modeling approaches. Environ. Sci. Pollut. Res. 31, 44649–44668. https://doi.org/10.1007/s11356-024-34151-7 (2024).

    Google Scholar 

  27. Voli, M. T. et al. Fingerprinting the sources of suspended sediment delivery to a large municipal drinking water reservoir: Falls lake, neuse river, north carolina, usa. J. Soils Sediments 13, 1692–1707 (2013).

    Google Scholar 

  28. Arhonditsis, G. B., Javed, A., Saber, A., Neumann, A. & Arnillas, C. A. Characterization of biogeochemical cycles in agricultural watersheds: Integrating regional assessment with downstream water quality. SSRN 13, 52. https://doi.org/10.2139/ssrn.5249940 (2025).

    Google Scholar 

  29. Carvalho, W. S., Filho, F. J. C. M., Rodrigues, L. R. & Calheiros, C. S. C. Influence of land use and land cover on the quality of surface waters and natural wetlands in the Miranda river watershed, Brazilian Pantanal. Appl. Sci. 14, 5666. https://doi.org/10.3390/app14135666 (2024).

    Google Scholar 

  30. Kumwimba, M. N. et al. Nutrient and sediment retention by riparian vegetated buffer strips: impacts of buffer length, vegetation type, and season. Agric. Ecosyst. Environ. 369, 109050. https://doi.org/10.1016/j.agee.2024.109050 (2024).

    Google Scholar 

  31. Sendrowski, A., Castañeda-Moya, E., Twilley, R. & Passalacqua, P. Biogeochemical and hydrological variables synergistically influence nitrate variability in coastal deltaic wetlands. J. Geophys. Res.: Biogeosci. 126, e2020JG005737. https://doi.org/10.1029/2020JG005737 (2021).

    Google Scholar 

  32. Hogan, D. & Walbridge, M. Urbanization and nutrient retention in freshwater riparian wetlands. Ecol. Appl. 17, 1142–1155. https://doi.org/10.1890/06-0185 (2007).

    Google Scholar 

  33. Hobbie, S. et al. Contrasting nitrogen and phosphorus budgets in urban watersheds and implications for managing urban water pollution. Proc. Natl. Acad. Sci. 114, 4177–4182. https://doi.org/10.1073/pnas.1618536114 (2017).

    Google Scholar 

  34. Allan, J. D., Castillo, M. M. & Capps, K. A. Nutrient Dynamics. In Stream Ecology (Springer, Cham, 2021).

    Google Scholar 

  35. Jin, X. D., He, Y. L., Kirumba, G., Hassan, Y. & Li, J. B. Phosphorus fractions and phosphate sorption-release characteristics of the sediment in the Yangtze river estuary reservoir. Ecol. Eng. 55, 62–66. https://doi.org/10.1016/j.ecoleng.2013.02.001 (2013).

    Google Scholar 

  36. Hou, Y., Zhao, G., Chen, X. & Yu, X. Improving satellite retrieval of coastal aquaculture pond by adding water quality parameters. Remote Sens. 14, 3306. https://doi.org/10.3390/rs14143306 (2022).

    Google Scholar 

  37. Kadlec, R. H. The effects of wetland vegetation and morphology on nitrogen processing. Ecol. Eng. 33, 126–141. https://doi.org/10.1016/j.ecoleng.2008.02.012 (2008).

    Google Scholar 

  38. Zhang, J., Liu, X., Qin, Y., Fan, Y. & Cheng, S. Wetlands mapping and monitoring with long-term time series satellite data based on google earth engine, random forest, and feature optimization: A case study in gansu province, china. Land 13, 1527. https://doi.org/10.3390/land13091527 (2024).

    Google Scholar 

  39. Kull, A., Kull, A., Jaagus, J., Kuusemets, V. & Mander, U. The effects of fluctuating climatic conditions and weather events on nutrient dynamics in a narrow mosaic riparian peatland. Boreal Environ. Res. 13, 243–263 (2008).

    Google Scholar 

  40. Venkateswarlu, T. & Anmala, J. Importance of land use factors in the prediction of water quality of the upper green river watershed, Kentucky, USA, using random forest. Environ. Dev. Sustain. 26, 23961–23984. https://doi.org/10.1007/s10668-023-03630-1 (2024).

    Google Scholar 

  41. Weng, M. et al. Exploring the impact of land use scales on water quality based on the random forest model: A case study of the Shaying river basin, China. Water 16, 420. https://doi.org/10.3390/w16030420 (2024).

    Google Scholar 

  42. Kaplan, G. & Avdan, U. Sentinel-1 and sentinel-2 data fusion for wetlands mapping: Balikdami, turkey. In International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3, 729–734, https://doi.org/10.5194/isprs-archives-XLII-3-729-2018 (2018).

  43. Gonenc, A., Ozerdem, S. & Acar, E. Comparison of ndvi and rvi vegetation indices using satellite images. In International Conference on Agro-Geoinformatics, 1–4. https://doi.org/10.1109/Agro-Geoinformatics.2019.8820225 (Istanbul, Turkey, 2019).

  44. Jafarzadeh, H., Mahdianpari, M., Gill, E. W. & Mohammadimanesh, F. Enhancing wetland mapping: Integrating sentinel-1/2, gedi data, and google earth engine. Sensors 24, 1651. https://doi.org/10.3390/s24051651 (2024).

    Google Scholar 

  45. Behrouz, M. S., Yazdi, M. N. & Sample, D. J. Using random forest, a machine learning approach to predict nitrogen, phosphorus, and sediment event mean concentrations in urban runoff. J. Environ. Manag. 317, 115412. https://doi.org/10.1016/j.jenvman.2022.115412 (2020).

    Google Scholar 

  46. Harrison, J. W., Lucius, M. A., Farrell, J. L., Eichler, L. W. & Relyea, R. A. Prediction of stream nitrogen and phosphorus concentrations from high-frequency sensors using random forests regression. Sci. Total Environ. 763, 143005. https://doi.org/10.1016/j.scitotenv.2020.143005 (2021).

    Google Scholar 

  47. Bhattarai, A., Dhakal, S., Gautam, Y. & Bhattarai, R. Prediction of nitrate and phosphorus concentrations using machine learning algorithms in watersheds with different landuse. Water 13, 3096. https://doi.org/10.3390/w13213096 (2021).

    Google Scholar 

  48. Wang, X., Jiang, W., Peng, K., Li, Z. & Rao, P. A framework for fine classification of urban wetlands based on random forest and knowledge rules: taking the wetland cities of haikou and yinchuan as examples. GIScience Remote Sens. 59, 2144–2163. https://doi.org/10.1080/15481603.2022.2152926 (2022).

    Google Scholar 

  49. Breiman, L. Random forests. Mach. Learn. 45, 5–32. https://doi.org/10.1023/A:1010933404324 (2001).

    Google Scholar 

  50. Salas, E. A. L., Subburayalu, S. K., Partee, E. B., Willis, L. P. & Mitchell, K. Potential of mapping dissolved oxygen in the little Miami river using sentinel-2 images and machine learning algorithms. Remote Sens. Appl. Soc. Environ. 26, 100759. https://doi.org/10.1016/j.rsase.2022.100759 (2022).

    Google Scholar 

  51. Ohijeagbon, O., Waheed, A. & Ajayi, O. Machine learning-based solar radiation forecasting models for enhancing renewable energy integration in Nigeria. Available at SSRN: https://ssrn.com/abstract=5200160, https://doi.org/10.2139/ssrn.5200160 (2025).

  52. Santos, V. O., Rocha, P. A. C., Thé, J. V. G. & Gharabaghi, B. Evaluation of machine learning methods for forecasting turbidity in river networks using sentinel-2 remote sensing data. Ecol. Inform. 90, 103313. https://doi.org/10.1016/j.ecoinf.2025.103313 (2025).

    Google Scholar 

  53. Tamiminia, H. et al. Google earth engine for geo-big data applications: A meta-analysis and systematic review. ISPRS J. Photogramm. Remote Sens. 164, 152–170. https://doi.org/10.1016/j.isprsjprs.2020.04.001 (2020).

    Google Scholar 

  54. Merchant, M. et al. Leveraging google earth engine cloud computing for large-scale arctic wetland mapping. Int. J. Appl. Earth Obs. Geoinf. 125, 103589. https://doi.org/10.1016/j.jag.2023.103589 (2023).

    Google Scholar 

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Funding

This research was supported by the Evans-Allen funds of the U.S. Department of Agriculture, National Institute of Food and Agriculture [NI241445XXXXG004].

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Authors and Affiliations

  1. Agricultural Research Development Program (ARDP), Central State University, Wilberforce, OH, 45384, USA

    Eric Ariel L. Salas, Kellsie Schrack, Sakthi S. Kumaran & Robert Bennett

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  1. Eric Ariel L. Salas
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  2. Kellsie Schrack
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Contributions

E.A.L.S. conceptualized the study. E.A.L.S. and K.S. conducted the experiments. E.A.L.S. and S.S.K. provided inputs for methodology, software, and resources. S.S.K. project administration. E.A.L.S., K.S., and R.B. conducted the field survey and experiments. E.A.L.S. and K.S. analyzed the results. R.B. and K.S. conducted the laboratory experiments. All authors read and approved the final manuscript.

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Eric Ariel L. Salas.

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Salas, E.A.L., Schrack, K., Kumaran, S.S. et al. Hybrid spatiotemporal modeling of nutrient cycling in wetland ecosystems using advanced mapping techniques and machine learning approaches.
Sci Rep (2026). https://doi.org/10.1038/s41598-026-40585-5

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