The chlorophyll-a concentration data obtained through remote sensing are important for a wide range of scientific concerns. However, cloud cover and limitations of inversion algorithms of chlorophyll-a concentration lead to data loss, which critically limits studying the mechanism of spatial-temporal patterns of chlorophyll-a concentration in response to marine environment changes. If the commonly used operational chlorophyll-a concentration products can offer the best data coverage frequency, highest accuracy, best applicability, and greatest robustness at different scales remains debatable to date. Therefore, in the present study, four commonly used operational multi-sensor multi-algorithm fusion products were compared and subjected to validation based on statistical analysis using the available data measured at multiple spatial and temporal scales. The experimental results revealed that in terms of spatial distribution, the chlorophyll-a concentration products generated by averaging method (Chl1-AV/AVW) and GSM model (Chl1-GSM) presented a relatively high data coverage frequency in Case I water regions and extremely low or no data coverage frequency in the estuarine coastal zone regions and inland water regions, the chlorophyll-a concentration products generated by the Neural Network algorithm (Chl2) presented high data coverage frequency in the estuarine coastal zone Case 2 water regions. The chlorophyll-a concentration products generated by the OC5 algorithm (ChlOC5) presented high data coverage frequency in Case I water regions and the turbid Case II water regions. In terms of absolute precision, the Chl1-AV/AVW and Chl1-GSM chlorophyll-a concentration products performed better in Class I water regions, and the Chl2 product performed well only in Case II estuarine coastal zones, while presenting large errors in absolute precision in the Case I water regions. The ChlOC5 product presented a higher precision in Case I and Case II water regions, with a better and more stable performance in both regions compared to the other products.

通过遥感获得的叶绿素-a 浓度数据对于广泛的科学问题非常重要。然而，云量和叶绿素a浓度反演算法的局限性导致数据丢失，这严重限制了叶绿素a浓度时空格局响应海洋环境变化机制的研究。常用的可操作叶绿素-a浓度产品是否能够在不同尺度上提供最佳的数据覆盖频率、最高的准确性、最佳的适用性和最大的稳健性仍然存在争议。因此，在本研究中，使用在多个空间和时间尺度上测量的可用数据，对四种常用的操作多传感器多算法融合产品进行了比较和基于统计分析的验证。实验结果表明，从空间分布来看，平均法（Chl1-AV/AVW）和GSM模型（Chl1-GSM）生成的叶绿素a浓度产物在案例I水域呈现出较高的数据覆盖频率，且极河口沿岸地区和内陆水域的数据覆盖频率较低或无数据覆盖，而神经网络算法（Chl2）生成的叶绿素a浓度产品在河口沿岸地区案例2水域中呈现出较高的数据覆盖频率。OC5算法(ChlOC5)生成的叶绿素-a浓度产品在案例I水区和浑浊水区中呈现出较高的数据覆盖频率。从绝对精度来看，Chl1-AV/AVW和Chl1-GSM叶绿素-a浓度产品在I类水域表现较好，Chl2产品仅在案例II河口沿岸区域表现较好，但绝对精度存在较大误差在案例一水域。ChlOC5产品在Case I和Case II水区表现出更高的精度，与其他产品相比，在这两个区域都有更好、更稳定的性能。Chl1-AV/AVW和Chl1-GSM叶绿素-a浓度产品在I类水域表现较好，Chl2产品仅在案例II河口沿岸区域表现良好，而在案例I水域绝对精度存在较大误差。ChlOC5产品在Case I和Case II水区表现出更高的精度，与其他产品相比，在这两个区域都有更好、更稳定的性能。Chl1-AV/AVW和Chl1-GSM叶绿素-a浓度产品在I类水域表现较好，Chl2产品仅在案例II河口沿岸区域表现良好，而在案例I水域绝对精度存在较大误差。ChlOC5产品在Case I和Case II水区表现出更高的精度，与其他产品相比，在这两个区域都有更好、更稳定的性能。