Enhancing river discharge inversion for the midstream Jialing River (China) using the Google Earth Engine platform

Journal of Hydroinformatics, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 3, 2025

ABSTRACT This study focused on the Jinxi section of Jialing River, utilizing data from Landsat 8, Sentinel-2, and MODIS accessed via Google Earth Engine (GEE) platform. The objective was to estimate runoff by applying three models: improved Manning's formula (Model 1), relationship fitting method 2), C/M signal 3). models were evaluated based their accuracy in inversion, influence hydraulic parameters, suitability for medium-sized rivers. results indicated that all performed well simulating runoff, with Nash–Sutcliffe Efficiency coefficients exceeding 0.90. root mean square error (RMSE) formula, method, 50.2, 117.1, 69.5 m³/s, respectively, corresponding relative RMSE (RRMSE) 4.71, 16.15, 5.88%. It observed both generally underestimated flow, while tended overestimate it. Overall, outperformed terms applicability.

Language: Английский

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Spatiotemporal dynamics of Ramsar wetlands and freshwater resources: Technological innovations for ecosystem conservation

Water Environment Research, Journal Year: 2025, Volume and Issue: 97(5)

Published: May 1, 2025

Abstract Aquatic ecosystems, particularly wetlands, are vulnerable to natural and anthropogenic influences. This study examines the Saman Bird Sanctuary Keetham Lake, both Ramsar sites, using advanced remote sensing for water occurrence, land use cover (LULC), quality assessments. Sentinel data, processed in cloud computing, enabled land‐use classification, boundary delineation, seasonal occurrence mapping. A combination of Modified Normalized Difference Water Index (MNDWI), OTSU threshold segmentation, Canny edge detection provided precise boundaries. Study utilized a MNDWI, methods. These approaches allowed delineation Sixteen parameters including pH, turbidity, dissolved oxygen (DO), chemical demand (COD), total hardness (TH), alkalinity (TA), solid (TDS), electrical conductivity (EC), phosphates (PO 4 ), nitrate (NO 3 chloride (Cl − fluoride (F carbon dioxide (CO 2 silica (Si), iodine (I chromium (Cr ) were analyzed compared sites. Results showed significant LULC changes, at Saman, with scrub forest, built‐up areas, agriculture increasing, while flooded vegetation open declined. Significant changes observed near Marsh wetland, where positive up 42.17% seen surrounding regions, an increase 5.43 ha 2021 from 3.14 2017. Positive change was forests 21.02%, rise 2.18 ha. Vegetation marsh region, grasses hydrophytes, has shown extent 0.39 7.12%. Spatiotemporal across pre‐monsoon, monsoon, post‐monsoon seasons Sentinel‐1 data. The highlights role field‐based monitoring understanding ecological shifts pressures on wetlands. By integrating analysis, this research provides critical information planning conservation efforts. It vital insights planning, advocating continued adaptive management sustain these ecosystems. Practitioner Points surface two geographically different wetlands—lake wetland; its analysis evaluate impact wetlands environment—positive negative changes; Boundary examine identify low‐lying areas during pre‐ post‐monsoon; Comparative wetlands; Insectivorous plant— Utricularia stellaris , recorded Northern India first time.

Language: Английский

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Retrieving Inland Water Quality Parameters via Satellite Remote Sensing: Sensor Evaluation, Atmospheric Correction, and Machine Learning Approaches

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(10), P. 1734 - 1734

Published: May 15, 2025

Satellite remote sensing provides a cost-effective and large-scale alternative to traditional methods for retrieving water quality parameters inland waters. Effective parameter retrieval via optical satellite requires three key components: (1) sensor whose measurements are sensitive variations in quality; (2) accurate atmospheric correction eliminate the effect of absorption scattering atmosphere retrieve water-leaving radiance/reflectance; (3) bio-optical model used estimate from signal. This study literature review an evaluation these components. First, decommissioned, active, upcoming sensors is presented, highlighting their advantages limitations, ranking method introduced assess suitability chlorophyll-a, colored dissolved organic matter, non-algal particles can aid selecting appropriate future studies. Second, strengths weaknesses algorithms over waters examined. The results show that no algorithm performed consistently across all conditions. However, understanding allows users select most suitable specific use case. Third, challenges, recent advances machine learning models discussed. Machine have including low generalizability, dimensionality, spatial/temporal autocorrelation, information leakage. These issues highlight importance locally trained models, rigorous cross-validation methods, integrating auxiliary data enhance dimensionality. Finally, recommendations promising research directions provided.

Language: Английский

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Investigation of hydromorphological changes of river corridor using satellite images

International Journal of Environmental Science and Technology, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 28, 2024

Language: Английский

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Reservoir Water Surface Area Estimation Using Sentinel-1 and Sentinel-2 Imagery

Korean Journal of Remote Sensing, Journal Year: 2024, Volume and Issue: 40(5-1), P. 643 - 656

Published: Oct. 31, 2024

Language: Английский

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