# Data-Free Area Detection and Evaluation for Marine Satellite Data Products
## 1 Introduction
[PERSON] [18; 19] implemented a reference-free image quality evaluation and used the grayscale mean gradient, image signal-to-noise ratio, and the information entropy metric to evaluate the quality of an image to be measured under the condition of a missing standard image. [PERSON] et al. [20] designed a watermarking algorithm based on motion blur degradation/restoration. There are also some classical algorithms, such as the semi-referential Special Sensor Microwave/Imager methods and VIF methods [21; 22; 23]. In the aforementioned methods, only a part of the feature information of the standard images is used as the basis for evaluation, and only low-quality standard images are considered. Furthermore, the aforementioned methods obtain data that are closer to human visual perception compared with those requiring image quality evaluation but that lack a reference image, which is also called a weak reference image quality evaluation. In this study, Level 1 data are used as the reference data, and Level 2 ocean color product data are used as the image for evaluation. Then, a robust weak reference method is used to mine the spectral information of the data-free area. Some of the non-reference indicators are also used to evaluate the information of the products. The acquisition of the ocean color information of highly turbid areas of the ocean has been a challenge for ocean color remote sensing researchers [24; 25; 26; 27]. The reasons for the absence of information to form a data-free area, the main types of marine objects contained in the data-free area and the spatial and temporal distribution characteristics exhibited by the absence are the central concerns of ocean color research. Traditionally, uncertainty and quality evaluations of ocean color products are based on field measurement data values that are used as the veracity-check or multi-sensor product values in deviation analysis [28; 29; 30; 31; 32]. Incidentally, high-quality standard images or actual measurement data are difficult to obtain. The content of ocean color product quality evaluation needs to be enriched. This study aims to investigate the evaluation of remotely sensed ocean color products from another perspective. We propose a method for the analysis and evaluation of Marine products based on regions with no data. The spectral information mining of Level 1 data is employed to achieve the following objectives: (1) to study the types of marine coverage objects contained in the data-free areas of remote sensing ocean color products in the Yellow Sea and the Bohai Sea; (2) to determine the presence or absence of temporal and spatial distribution characteristics of the missing information that correspond to the types of marine objects; (3) to conduct a correlation analysis of the missing temporal and spatial distribution characteristics of the different ocean color satellite data (GOCI, MODIS and OLCI); and (4) to analyze and evaluate the data based on two indicators, namely, data integrity and the continuity of ocean color information. In summary, we evaluated the missing pixels during the quality marking of the products. Moreover, the object of the study remains as the ocean color product itself, which we analyzed and evaluated in detail for both subjectivity and objectivity. This study is expected to enhance the scientific knowledge of data-free areas of ocean color data. It provides scientific and theoretical support to producers and users of the products and ensures the reliability of subsequent research and applications. Furthermore, this study explores the origin of the spatial and temporal patterns of the data-free areas and subsequently attempts to explain the related characteristic phenomena. We expect the detection and evaluation of ocean color products of data-free areas to help improve atmospheric correction algorithms, optimize cloud detection algorithms, reconstruct missing data, achieve evaluations of different satellite products, and enhance the application of the feature mining of data-free areas.