How Hydrological Extremes Affect the Chlorophyll-a Concentration in Inland Water in Jiujiang City, China: Evidence from Satellite Remote Sensing

ISPRS International Journal of Geo-Information, Год журнала: 2025, Номер 14(2), С. 85 - 85

Опубликована: Фев. 15, 2025

From 2020 to 2022, hydrological extremes such as severe floods and droughts occurred successively in Jiujiang city, Poyang Lake Basin, posing a threat regional water quality safety. The chlorophyll-a (Chl-a) concentration is key indicator of river eutrophication. Until now, there has been lack empirical research exploring the Chl-a trend inland context extremes. In this study, Sentinel-2 satellite remote sensing data sourced from Google Earth Engine (GEE) cloud platform, along with hourly collected monitoring stations Jiangxi Province, China, are utilized develop quantitative inversion model for concentration. concentrations various types were estimated each quarter spatiotemporal distribution was analyzed. main findings follows: (1) validated via situ measurements, coefficient determination 0.563; (2) spatial estimates revealed slight increasing trend, by 0.1193 μg/L closely aligning monitoring-station data; (3) an extreme drought 2022 led less bodies, consequently, displayed significant upward especially Lake, where mean increased approximately 1 Q1 Q2 2022. These seasonal changes waters events, thus providing valuable information sustainable management city.

Язык: Английский

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The need for advancing algal bloom forecasting using remote sensing and modeling: Progress and future directions

Ecological Indicators, Год журнала: 2025, Номер 172, С. 113244 - 113244

Опубликована: Фев. 21, 2025

Язык: Английский

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Estimation of phytoplankton community composition from satellite data using a fuzzy and probabilistic combination model in mountainous reservoirs: A case of Huating Lake in spring and summer

Ecological Informatics, Год журнала: 2025, Номер unknown, С. 103153 - 103153

Опубликована: Апрель 1, 2025

Язык: Английский

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Diurnal distribution of phytoplankton in large shallow lakes based on time series clustering

Ecological Informatics, Год журнала: 2025, Номер unknown, С. 103250 - 103250

Опубликована: Июнь 1, 2025

Язык: Английский

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Estimation of chlorophyll-a in uncrewed aircraft systems imagery using autonomous surface vessel data with machine learning algorithms and feature selection techniques

Ecological Informatics, Год журнала: 2024, Номер unknown, С. 102954 - 102954

Опубликована: Дек. 1, 2024

Язык: Английский

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Enhancing model robustness to imbalanced species abundance distributions: Eliminating misclassified records via a model-agnostic approach, exemplified by tuna fisheries datasets

Ecological Informatics, Год журнала: 2024, Номер unknown, С. 102905 - 102905

Опубликована: Ноя. 1, 2024

Язык: Английский

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Marine ecological information prediction by using adjacent location spatiotemporal deep learning model with ensemble learning techniques

Ecological Informatics, Год журнала: 2024, Номер unknown, С. 102964 - 102964

Опубликована: Дек. 1, 2024

Язык: Английский

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