Lange Priscila K, Fachon Evangeline, Nielsen Jens M, Brosnahan Michael, Zhang Jiaxu, Mordy Calvin W, Gann Jeanette C, Lomas Michael W, Pate Emma, Sheffield Gay, Stabeno Phyllis, Robinson Dale, Pathare Mrunmayee, Lefebvre Kathi A, Anderson Donald M, Eisner Lisa B
Bigelow Laboratory for Ocean Sciences, East Boothbay, ME, United States; Departamento de Meteorologia, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ, Brazil; Blue Marble Space Institute of Science, Seattle, WA, United States.
Woods Hole Oceanographic Institution, Woods Hole, MA, United States; Department of Earth Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, MA, United States.
J Environ Manage. 2025 Apr;380:125042. doi: 10.1016/j.jenvman.2025.125042. Epub 2025 Mar 24.
Harmful algal blooms (HABs) of the toxic dinoflagellate Alexandrium catenella are increasing in the Pacific Arctic due to ocean warming. threatening ecosystems and to coastal communities that rely on marine resources for their subsistence. This study explores the potential of the Sentinel-3 remote sensing reflectance (R(λ)) to detect and quantify dinoflagellate blooms in the Bering and Chukchi seas using an A. catenella cell abundance dataset to regionally parameterize and evaluate new algorithm combinations (color indexes and principal component regression - PCR). The color indexes utilize the fluorescence (FLH), green (GLH), and blue line heights (BLH) and the spectral difference in FLH peak to identify dinoflagellate blooms. The algorithms were parameterized and validated using 45 satellite match-ups with in situ A. catenella abundances measured over summer 2022 in the North Bering and Chukchi seas. Assuming the dinoflagellate bloom is dominated by A. catenella, the dinoflagellate index DINI (GLH-based) and enhanced bloom index EBI (BLH-based) provide reliable cell abundance estimates at concentrations higher than 10,000 (R = 0.53) and 3000 cells/L (R = 0.67), respectively. The PCR model resolves estimates at lower cell abundances (>1000 cells/L, R = 0.68). Despite their higher uncertainty, color index models provide early detection and tracking of potential A. catenella blooms, as demonstrated during Summers 2023 and 2024. By providing timely and accurate information on bloom dynamics, these satellite products can significantly augment HAB monitoring systems in the Pacific Arctic.
由于海洋变暖，太平洋北极地区有毒甲藻链状亚历山大藻引发的有害藻华正在增加，这对生态系统以及依赖海洋资源维持生计的沿海社区构成了威胁。本研究利用链状亚历山大藻细胞丰度数据集对新的算法组合（颜色指数和主成分回归 - PCR）进行区域参数化和评估，探讨哨兵 - 3遥感反射率（R(λ)）检测和量化白令海和楚科奇海甲藻藻华的潜力。颜色指数利用荧光（FLH）、绿光（GLH）和蓝光线高（BLH）以及FLH峰值的光谱差异来识别甲藻藻华。这些算法通过45次卫星与2022年夏季在北白令海和楚科奇海测量的链状亚历山大藻现场丰度的匹配进行参数化和验证。假设甲藻藻华以链状亚历山大藻为主，基于GLH的甲藻指数DIN1和基于BLH的增强藻华指数EBI分别在浓度高于10,000（R = 0.53）和3000个细胞/升（R = 0.67）时提供可靠的细胞丰度估计。PCR模型可解析较低细胞丰度（>1000个细胞/升，R = 0.68）时的估计值。尽管颜色指数模型的不确定性较高，但如2023年和2024年夏季所示，它们能够早期检测和跟踪潜在的链状亚历山大藻藻华。通过提供有关藻华动态的及时准确信息，这些卫星产品可显著增强太平洋北极地区的有害藻华监测系统。
