Monitoring Dissolved Organic Carbon Concentration and Flux in the Qiantang Riverine System Using Sentinel-2 Satellite Images DOI Creative Commons

Yujia Yan,

Xianqiang He, Yan Bai

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(22), P. 4254 - 4254

Published: Nov. 15, 2024

Real-time monitoring of riverine-dissolved organic carbon (DOC) and its controlling factors is critical for formulating strategies regarding the river basin marginal seas pollution prevention control. In this study, we established a linear regression formulation that relates permanganate index (CODMn) to DOC concentration based on in situ measurements collected five field surveys 2023–2024. This was used large number data from automatic stations Qiantang River area construct daily quasi-in database concentration. By combining Sentinel-2 measurements, an enhanced algorithm empirical estimation developed (R2 = 0.66) using extreme gradient boosting (XGBoost) method spatial temporal variations were analyzed 2016 2023. Spatially, main stream exhibited overall decreasing increasing trend influenced by population density, economic development, pollutant discharge area, distribution controlled meteorological conditions. The contents had highest summer, primarily due high rainfall leaching. inter-annual variation total annual runoff volumes, with minimum level 2.24 mg L−1 2023 maximum 2.45 2019. monthly fluxes ranged 6.3 13.8 × 104 t, values coinciding volumes June July. levels remained relatively recent years (2016–2023). study enables concerned stakeholders researchers better understand transportation dynamics coastal areas.

Language: Английский

Integration of remote sensing data and GIS technologies in river management system DOI Creative Commons

Chatrabhuj,

Kundan Meshram,

Umank Mishra

et al.

Discover Geoscience, Journal Year: 2024, Volume and Issue: 2(1)

Published: Oct. 2, 2024

Abstract Effective River system management is essential for conserving water resources, improving agricultural productivity, and sustaining ecological health. Remote sensing crucial evaluating tracking several elements of river systems. The study explores the incorporation remote into Geographic Information Systems (GIS) Artificial Intelligence (AI) to acquire a thorough comprehension dynamics accurately record minor fluctuations in conditions. demonstrates utilization satellite series such as Landsat, Sentinel enhance monitoring methods through analysis high-resolution imagery data. AI helps by automating data processing, finding patterns, making predictions about conditions trends. Machine learning techniques analytical capabilities GIS classifying land cover, predicting flood events, quality. research highlights novel approaches utilizing tackle issues related accessibility, analysis, verification. also acknowledges specific constraints difficulties, concerns over accessibility data, intricacies processes involved validation. statement underscores importance ongoing research, technical progress, collaboration among stakeholders overcome these limitations fully exploit sensing, artificial intelligence, geographic information An integrated approach development successful policies strategies that improve resilience sustainable This eventually promotes resource practices preservation.

Language: Английский

Citations

5
A review of studies on assessing water quality parameters based on the Google Earth Engine imagery DOI
Yusef Kheyruri, Ahmad Sharafati, Reza Farzad

et al.

Remote Sensing Applications Society and Environment, Journal Year: 2025, Volume and Issue: unknown, P. 101581 - 101581

Published: May 1, 2025

Language: Английский

Citations

0
Estimating of antibiotic resistance genes in the sediments of Erhai Lake, China: Based on multi-source remote sensing data DOI

Z P Chen,

Qihao Chen, Xiong Pan

et al.

Journal of Hydrology, Journal Year: 2025, Volume and Issue: unknown, P. 133350 - 133350

Published: April 1, 2025

Language: Английский

Citations

0
Satellite retrievals of total phosphorus in Taihu Lake using Sentinel-2 images and an optimized XGBoost model DOI
Jiangnan Cui,

Shiqiang Wu,

Jiangyu Dai

et al.

Ecological Indicators, Journal Year: 2025, Volume and Issue: 175, P. 113563 - 113563

Published: May 6, 2025

Language: Английский

Citations

0
Adjacency effect on Rayleigh scattering radiance for satellite remote sensing of river waters DOI
Yaqi Zhao, Xianqiang He, Yan Bai

et al.

IEEE Transactions on Geoscience and Remote Sensing, Journal Year: 2024, Volume and Issue: 62, P. 1 - 20

Published: Jan. 1, 2024

Language: Английский

Citations

2
Nitrifying bacteria for the remediation of organic nitrogen‐contaminated waters: a review DOI
Kelvin Adrian Sanoja‐López,

Nikolt Stephanie Loor-Molina,

Rafael Luque

et al.

Biofuels Bioproducts and Biorefining, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 12, 2024

Abstract The rapid growth of the world's population and its environmental impact has increased demand for effective water treatment methods. Surface systems, including rivers, streams, lakes, ponds, have suffered contamination, leading to urgent need contaminant removal. Organic nitrogen compounds nitrates are particular concern because they pose risks health can cause damage. However, traditional water‐treatment methods often prove ineffective addressing these issues. For this reason, research focuses on harnessing capabilities nitrifying bacteria denitrify organic in water, exploring various bacterial strains, their functions, ability obtain sources carbon. study also investigates innovative approaches such as biofilm mixed cultures, combined processes, which shown stronger potential Overall, it provides valuable insights into use bacteria‐based technologies remediation face growing challenges.

Language: Английский

Citations

0
Monitoring Dissolved Organic Carbon Concentration and Flux in the Qiantang Riverine System Using Sentinel-2 Satellite Images DOI Creative Commons

Yujia Yan,

Xianqiang He, Yan Bai

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(22), P. 4254 - 4254

Published: Nov. 15, 2024

Real-time monitoring of riverine-dissolved organic carbon (DOC) and its controlling factors is critical for formulating strategies regarding the river basin marginal seas pollution prevention control. In this study, we established a linear regression formulation that relates permanganate index (CODMn) to DOC concentration based on in situ measurements collected five field surveys 2023–2024. This was used large number data from automatic stations Qiantang River area construct daily quasi-in database concentration. By combining Sentinel-2 measurements, an enhanced algorithm empirical estimation developed (R2 = 0.66) using extreme gradient boosting (XGBoost) method spatial temporal variations were analyzed 2016 2023. Spatially, main stream exhibited overall decreasing increasing trend influenced by population density, economic development, pollutant discharge area, distribution controlled meteorological conditions. The contents had highest summer, primarily due high rainfall leaching. inter-annual variation total annual runoff volumes, with minimum level 2.24 mg L−1 2023 maximum 2.45 2019. monthly fluxes ranged 6.3 13.8 × 104 t, values coinciding volumes June July. levels remained relatively recent years (2016–2023). study enables concerned stakeholders researchers better understand transportation dynamics coastal areas.

Language: Английский

Citations

0