# Data-Free Area Detection and Evaluation for Marine Satellite Data Products
## 5 Conclusions  
The ocean color products of the ocean cannot obtain effective results in special environments. For example, the concentrations of algal blooms are high in June and July each year in the shallow seas of northern Jiangsu to the Jiaodong Peninsula, the concentrations of suspended particles are high in the Yangtze River Delta, the anthropogenic activities are frequent, eutrophication differs among the three ways of the Bohai Sea, etc. Therefore, this study uses the proposed method to analyze and evaluate the data product information of the sea areas. We found that the missing data information is mostly due to the intensity difference of the cloud mask algorithm, which mistakenly identifies some marine coverage as clouds. On the one hand, certain temporal and spatial patterns are apparent in the data-free areas of the aforementioned products, obtaining a regular representation of common ocean-covered objects in time and space. On the other hand, a concomitant phenomenon between GOCI and MODIS has been determined for ocean coverage objects in data-free areas. Presumably, a concomitant phenomenon exists between the cloud mask algorithm results for the identification of marine targets of GOCI and MODIS. Therefore, the different product data should be optimized with respect to atmospheric correction and cloud detection to ensure the integrity and continuity of information. In particular, the missing information is the most serious GOCI and MODIS data. In this study, the proposed ISAM algorithm was employed for the 2019 products of three ocean color satellites (i.e., GOCI, MODIS and OLCI). We obtained some characteristicsof the data-free areas through detection. Then, we utilized the proposed ocean color information integrity and continuity indicators and applied them to three product evaluations while analyzing the spatial and temporal distribution characteristics of marine coverage objects in the data-free areas. The conclusions can be summarized as follows. 1. The integrity of the ocean colored product information is fundamentally based on the capability of the cloud detection algorithm. Most of the commonly used algorithms for cloud detection are the spectrum-oriented single-band or multi-band threshold methods. In this study, we employed an improved version of the spectrum-related SAM recognition algorithm and named it ISAM. The ISAM algorithm can reduce the fragmentation performance of the results and can increase the spatial continuity of the information. The ISAM algorithm can also reduce the fragmentation of the results, and it performs sufficiently in the recognition of marine objects. The obtained classification accuracy and Kappa coefficients are high. The ISAM algorithm is also applicable for multispectral data to a certain extent. 2. The spatial distributions of green tide algae and sea ice in the data-free areas of GOCI and MODIS are mainly manifested by the accompanying occurrence of their endmembers and the surrounding clean water. Sea ice is mostly accompanied by turbid water because of its geographical location. The shape performance of different ocean coverage objects varies, but a certain pattern can be observed over time. The number of green tide algae is higher in June and July every year, sea ice is most apparent from December to February and the missing amount in turbid waters is greater in spring and autumn. The absence of clean water shows a positive variation over time with cloud amount, mostly with the irregular spatial distribution around the accompanying clouds (above, below, left and right areas). By contrast, the missing amount of turbid water has an inverse variation over time with the cloud amount. The anomalous missing information in the data-free area of OLCI usually appears spatially as individual pixels or sporadic distribution of multiple pixels. The occurrence of accompanying phenomena is also rare, hence the minimal total amount of missing product information. 3.