# Data-Free Area Detection and Evaluation for Marine Satellite Data Products
## 5 Conclusions
The experimental results (Table 5) indicate that the annual average missing rates of GOCI and MODIS are 25.81% and 27.04, respectively, which are much larger than the 10.05% of OLCI. In view of overcoming the effect of the perennial presence of clouds over the ocean, the anomalous missing rate is further used to measure the quality of product integrity. The experimental results show that the anomalous missing rates of GOCI and MODIS are 61.032% and 63.312%, respectively, which are much larger than the 1.115% of OLCI, and their anomalous missing rates are serious and similar, in general. The quality of the three products was evaluated from the perspective of integrity. The results indicate that OLCI is the superior product, followed by GOCI. Among the three products, MODIS has the worst integrity quality. 4. During the research process, we found that the data-free area has certain spatial and temporal distribution characteristics. Subsequently, we calculated the results of the spatiotemporal images of the data-free area to evaluate the product quality with respect to temporal and spatial patterns. Standard deviation and information entropy were applied to the spatiotemporal images for the quantitative evaluation of the information continuity of the ocean color products. The results (Table 7) indicate that OLCI is superior to GOCI and MODIS with respect to the spatiotemporal continuity of product information. However, as opposed to the results of the data information integrity evaluation, MODIS is superior to GOCI with respect to spatiotemporal continuity of information. In summary, OLCI is optimal with respect to both information integrity and the continuity of information. The ISAM algorithm can automatically determine the optimal threshold value for classification, thus improving the spatial continuity and accuracy of the results. The proposed method is a new reliable technical method that can support the recognition of target objects at sea. The data selected in this study are based on a unit of the four seasons,with three images per season (two images in winter) and a total of 33 scenes from three kinds of satellite data. Given the limitations in the statistical results, multi-scene images can be selected with a unit of months in future research. In principle, our method can be applied to other ocean color satellite data (e.g., GOCI-II, SwaWiFS, etc. ), but further work is needed. The obtained results for the Yellow and Bohai seas can be applied to other coastal regions in the World Ocean. The new method can objectively and subjectively analyze and evaluate the information in the product data-free area. Overall, this study can enrich the evaluation of ocean color products, and it provides a new direction for the optimization of atmospheric correction and cloud detection methods for ocean color data. Using different time series and different satellite combination data to fill data-free areas may be the future direction. It provides a certain reference value for producers and users in the aspect of product selection. Conceptualization, S.Z. and H.Z. ; methodology, S.Z. and H.Z. ; validation, S.Z., H.Z. and H.L. ; formal analysis, S.Z. and H.Z. ; investigation, S.Z. and H.Z. ; writing--original draft preparation, S.Z. ; writing--review and editing, S.Z., H.Z., J.L. and Y.Y. ; visualization, S.Z. ; supervision, H.Z. ; funding acquisition, H.Z. and H.L. All authors have read and agreed to the published version of the manuscript. This research was funded by the auspices of the National Natural Science Foundation of China (Grant No. 41971339), the National Key Research and Development Project of China (No. 2018 YFC1407605) and the SDUST Research Fund (No. 2019 TDJH103). Not applicable.