【摘要】 海面漂浮绿潮受风场和流场等的影响，具有较强的漂移特征，开展不同光学数据间的对比分析具有一定的挑战。本研究遍历中国黄海2015～2021年Sentinel-2 MSI和MODIS影像数据对，筛选出2组准同步数据对，其观测时间间隔小于10 min，绿潮漂移偏差不超过1个MODIS像元。以10 m空间分辨率MSI数据的绿潮监测结果为真值，模拟25×25像元范围内（对应1个MODIS像元）的“含藻像元占比”（Alga-containing Pixel Ratio, APR）作为绿潮聚集参数，从而对准同步250 m空间分辨率MODIS数据的绿潮探测效能进行了评估。结果表明：当对应APR>13%时，大部分绿潮能被MODIS探测到；而当对应APR<13%时，大部分绿潮难以被MODIS探测到，这些不能被MODIS监测到的绿潮斑块分布分散，不易聚集，特别是分布于江苏近海海域。低空间分辨率MODIS数据对绿潮估算的不确定性，主要产生于对绿潮小斑块的监测能力差异；此外，基于高空间分辨率影像开展低聚集度绿潮的精细化监测，为准确了解绿潮生消和辐聚过程提供了参考。更多还原

【Abstract】 In The movement characteristics of floating green tides are strongly influenced by wind and flow fields, making it challenging to conduct a synchronous comparison analysis. Following an analysis of data from 2015 to 2021 in the Yellow Sea of China, two quasi-synchronous high-precision data pairs from Sentinel-2 MSI and MODIS, with imaging intervals of less than 10 minutes, were identified. These exhibited algae drift deviations of less than one MODIS pixel. In order to examine the authenticity of the 10 m MSI identification results and the detection efficiency of MODIS data, this study employs a simulation in which the Algae-containing Pixel Ratio(APR) is calculated within a coverage area of 25×25 MSI pixels(equivalent to one MODIS pixel) as an aggregation parameter of green tides. The results demonstrated that the majority of green tide patches can be detected by MODIS when the APR in the simulated images is greater than 13%. In contrast, algae with an APR of less than 13%, which are primarily composed of dispersed low-aggregation green tide patches, are difficult to detect and are particularly concentrated around the Jiangsu offshore region. The uncertainty in green tide detection by MODIS data with its coarse spatial resolution is primarily due to differences in its ability to monitor low-aggregation patches. Additionally, fine monitoring of small algae patches using high-resolution images is valuable for the timely detection of the generation, extinction, and convergence of green tide evolution with better accuracy.更多还原