Assessing the quality of chlorophyll-a concentration products under multiple spatial and temporal scales

Abstract

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.

Graphical abstract

Keywords

remote sensing / chlorophyll-a concentration / data coverage frequency / accuracy / validation / multiple spatial and temporal scales

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Acknowledgments

This research was funded by the Project for Fostering Outstanding Young talents of Henan Academy of Sciences (No. 210401001), Special Project for Team Building of Henan Academy of Sciences (No. 200501007), Science and Technology Research Project of Henan Province (Nos. 212102310424, 222102320467, and 212102310024); Major Scientific Research Focus Project of Henan Academy of Sciences (No. 210101007). The authors would like to thank the reviewers and the editor for their constructive comments.

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