# Data-Free Area Detection and Evaluation for Marine Satellite Data Products
## 1 Introduction  
The domestic and international research on ocean remote sensing has gradually intensified along with the free access of remote sensing data and the commissioning of ocean color satellites, including the Sea-viewing Wide Field-of-view Sensor (SeaWiFS), Moderate Resolution Imaging Spectroradiometer (MODIS), Geostationary Ocean Color Imager (GOCI) and Ocean and Land Color Instrument (OLCI). Various ocean and atmospheric parameter products can be inferred from spectrometer signals measured by dedicated atmospherically corrected space-borne ocean color sensor data and related in-water biological algorithms [1]. However, the extensiveness of ocean color information relies on the inversion of an extremely small number of signals [2, 3]. Most signals received by ocean color satellites (e.g., atmospheric signals) are noisy, and only 10% are oceanic signals. Other problems include cloud occlusion, sensor failure or noise that hinders the acquisition of ocean color information [4, 5]. For example, 15 of the 20 detectors in band 6 of the Aqua MODIS are invalid. The sensor sometimes fails to generate meaningful results, even obtaining random error results due to missing pixels called dead pixels [6]. In more complex seaconditions, cloud masking or other denoising steps are necessary. Furthermore, depending on the choice of the cloud detection algorithm and denoising algorithm that vary with respect to capabilities or sizes, some normal image pixels are treated as clouds or masked pixels. Therefore, the ocean color data of a region of interest tend to have a high missing rate or even a complete missing result. The estuary of the Yangtze River has high sediment turbid waters, green tide algae disasters in summer and heavy eutrophication or areas with a highly suspended solid concentration [2]. In this area, cloud pixels are difficult to isolate from the high sediment and phytoplankton content, because the sea areas have an even higher reflectance compared with the clear water in the near-infrared band [7]. Such a situation may cause the loss of normal ocean color information. As a result, the ocean color products show a missing state at a certain spatial location, also called a data-free area. The existence of data-free areas seriously affects the spatial and temporal continuity of the information of the product data, reduces the value of data products and causes a great obstacle to the research of oceans. The quantification of uncertainties and quality aspects of satellite ocean color products is one of the central tasks of ocean color missions [8; 9]. From a satellite operation perspective, most of the validation and quality assessments of ocean color products are performed for individual or specific missions [10; 11; 12]. Image quality assessment methods usually include both subjective and objective evaluations. Experienced scientists usually perform subjective evaluations under the guidance of a complete scientific evaluation procedure for graphics [13]. By contrast, objective evaluation is based on the objective characteristics of images, and relevant rigorous mathematical models are adopted to evaluate the images and obtain evaluation results that are extremely close to human vision [14]. Objective evaluations can be divided into full-reference, weak-reference and no-reference image quality evaluations depending on the presence or absence of reference image information [15]. A full-reference image quality evaluation initially obtains a high-quality scene as a standard image and then compares the features with the image to be measured, and finally, certain similarities in the commonly used feature parameters (e.g., mean square error and peak signal-to-noise ratio) are calculated [16]. [PERSON] et al. [17] proposed the information fidelity criterion based on the mutual information approach.