Global coastal water clarity has increased due to human interventionem (code)

This software package contains four Jupyter Notebooks used to analyze long-term trends in suspended particulate matter (SPM) in global continental coastal waters from 2000 to 2023. The analyses are based on remote sensing data and statistical modeling, and include both global-scale evaluations and country-level summaries.Included notebooks:SPM_Trend_and_Distance.ipynbComputes the annual trend of coastal SPM concentrations and the landward recession of high-turbidity zones.SPM_Trend_Contribution.ipynbQuantifies the contribution of five classified SPM trend categories (e.g., Further Clearing, Further Turbidifying) using machine learning outputs.SPM_EEZ_Top20_SPMgt10.ipynbVisualizes the top 20 countries with the highest proportion of coastal grid cells (0.05°) where SPM exceeds 10 mg/L within their Exclusive Economic Zones (EEZs).SPM_ClearTrend_ByCountry_Top20.ipynbDisplays the top 20 countries ranked by the proportion of coastal waters showing clearing trends (Class 1 + 2), with China and Taiwan aggregated.All output figures are generated in high-resolution PDF format. Input CSV files (listed in the README) should be placed in a ./data/ folder, and plots will be saved to ./output/.The code supports reproducible analysis for researchers studying sediment dynamics, coastal water clarity, and long-term environmental change.