从太空中对复杂的沿海水域的甲估计 - - 通过空间解析的缩放因子进行新的优化方法
Andreas Holbach1, Sanjina Upadhyay Stæhr1, Peter Anton Upadhyay Stæhr1
1Aarhus University, Department of Ecoscience, Frederiksborgvej 399, 4000, Roskilde, Denmark.
The Science of the total environment
|November 13, 2025
概括
一个新的空间优化框架改善了复杂沿海水域的卫星甲 (Chla) 监测. 这种方法提高了海洋管理和优化评估的准确性.
科学领域:
- 海洋学 海洋学 海洋学
- 遥感 遥感 遥感 遥感
- 环境科学 环境科学
背景情况:
- 在光学复杂的沿海水域中,精确的叶绿素a (Chla) 监测对于海洋色彩遥感至关重要,但具有挑战性.
- 现有的Sentinel-3 OLCI 2级产品使用Case 2 Regional CoastColour (C2RCC) 算法显示这些环境的Chla检索性能受到限制.
研究的目的:
- 开发和验证一个新的空间优化框架,以加强从丹麦海域的Sentinel-3 OLCI 2级产品中提取Chla.
- 为了提高基于卫星的Chla数据的准确性,用于海洋监测和海洋肥胖和浮游植物动态的评估.
主要方法:
- 开发了一个地理加权回归 (GWR) 方法,结合成本距离指标来考虑复杂的海岸线.
- 通过将来自卫星的植物浮游生物吸收与来自丹麦国家监测计划 (NOVANA) (2018-2023) 的现场Chla度联系起来,获得了空间分辨率的缩放因子.
主要成果:
- 与默认C2RCC产品相比,GWR衍生的缩放因子显著改善了与现场Chla数据的一致性 (R从0.59增加到0.65),并减少了RMSE (从0.29降低到0.27).
- 开发的方法在精确匹配和长期平均值方面表现优于默认C2RCC和OC4ME算法.
- 该框架产生了空间连续的,区域调整的Chla地图.
结论:
- 新型空间优化框架提供了一种透明,可重复和操作可行的方法,用于在光学复杂的沿海水域增强Chla检索.
- 这种方法推进了沿海环境中的定量遥感,并支持与欧盟指令保持一致的运营性海洋监测.
- 区域调整的Chla地图为评估肥胖化和浮游植物动态提供了有价值的数据.
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