A Coupled Statistical Learning and Machine Learning Framework for Global Daily Seamless Secchi Disk Depth Reconstruction (1998-2023)
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摘要
Secchi Disk Depth (ZSD) is a crucial indicator characterizing marine ecological environment, playing a key role in monitoring water clarity and understanding climate-driven processes. Despite recent advances in satellite remote sensing retrieval of ZSD, existing products remain constrained by deficiencies in reliability, temporal continuity, and spatial completeness in coastal waters. To address these limitations, we propose a coupled reconstruction framework (DCT-XGB) to generate a global, daily, high-accuracy, and seamless ZSD dataset (65°N-65°S, 4 km resolution) from multi-satellite merged OC-CCI remote sensing reflectance data (1998–2023). Building upon reliable retrieval results (APDm = 28.9%), we developed the DCT-XGB model (APDm = 23.5%) to reconstruct missing regions by integrating traditional statistical learning (Discrete Cosine Transform-Penalized Least Squares, DCT-PLS) and machine learning (Extreme Gradient Boosting, XGBoost). Specifically, it fuses the DCT-PLS-derived ZSD background field with environmental and temporal drivers (Mixed Layer Depth, Sea Surface Temperature, Month, Day of Year) to produce the final gap-free product. Independent evaluation with in situ observation reveals that this product has substantially higher overall accuracy (APDm = 27.5%) than the existing GlobColour product (APDm = 56.1%). Notably, in low-ZSD waters (ZSD≤5m), the new product improves accuracy by 37%. Furthermore, the new product reliably reproduces the time-series characteristics of in situ ZSD at validation stations, with higher correlation (R2 = 0.59±0.15) and lower error (APDm = 16.3%±5.3%) compared to GlobColour (R2 = 0.22±0.19, APDm = 36.0%±16.6%). Trend analysis based on the new product reveals a significant contracting trend (–1.9×10⁴ km² yr⁻¹, P < 0.01) in low-ZSD coastal waters across the global ocean over the past 26 years, a feature that cannot be captured by the GlobColour product due to its limited accuracy.  In summary, the proposed framework and the resulting global daily ZSD product represent a significant breakthrough in spatial completeness, temporal continuity, and coastal accuracy. By effectively overcoming the spatiotemporal discontinuities of existing satellite records, this work provides high-quality, long-term data essential for marine ecological monitoring and global climate change research.
 
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报告人
Xiang Jinzhao
Sun Yat-sen University

稿件作者
Xiang Jinzhao Sun Yat-sen University
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重要日期
  • 会议日期

    01月12日

    2027

    01月15日

    2027

  • 07月21日 2026

    初稿截稿日期

  • 01月15日 2027

    注册截止日期

主办单位
State Key Laboratory of Marine Environmental Science, Xiamen University (MEL)
Department of Earth Sciences, National Natural Science Foundation of China (NSFC)
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