Spatiotemporal Analysis of River Health Using Remote Sensing and Machine Learning Approach

Disclosing the spatiotemporal (ST) fluctuations of nutrients in coastal waters is essential for comprehending and assessing the coastal environment, consequently offering practicalrecommendations for aquatic restoration.This study introduced a MachineLearning (ML) thatintegrates STdata, facilitating the establishment of quantitative connections between determined external variables and extensive satellite imagery, while reducingestimation errors exceeding 40% compared to traditional ML models lacking STintegration.The STpatterns of Dissolved InorganicNitrogen (DIN) and Dissolved InorganicPhosphorus (DIP) throughout 45000 km² of the Sea were acquired on an 8-day interval.The STvariations illustrated the water quality trends, revealing fluctuations of two critical nutrients in harbors affected by complex anthropogenic impacts, typical waterways with multiple river components, and open oceans with significant fisheries.Despite a 25% and 20% reduction in DIN and DIP concentrations over nine years, the inshore ocean's water condition has not improved, particularly during fall and winter.The research conducted a quantitative analysis of the primary causes ofwater degradation and offered scientific recommendations for focused surveillance and regional cooperation in governance.

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