# A Distributed Storage and Access Approach for Massive Remote Sensing Data in MongoDB
## 1 Introduction  
As a spatial information carrier, remote sensing data play significant roles in many fields, such as environmental monitoring, land resources survey, and disaster assessment, with its characteristics of strong timeliness and large area coverage [1; 2]. As earth-observation technologies (including remote sensing and satellite communication) and information technologies develop, a big data era has begun in the remote sensing field, where the amount of remote sensing data is massive and continues to increase exponentially [3; 4]. According to statistics, the daily data amount produced by different satellite platforms is increasing at the terabyte level. Landsat8, for example, launched by the National Aeronautics and Space Administration in 2013, generates four hundred global images every day;Sentinel, launched by the European Space Agency, produces thousands of data every day. Faced with massive remote sensing data, how to store and manage such with high efficiency is an issue that must be solved currently. Remote sensing data consist of unstructured image data and structured descriptive information attached to the image, which is also called metadata; these two files are commonly saved in the same directory. Previous studies have taken advantage of different methods to store remote sensing data, and the most commonly used method is combining the file system with a database, with the image data stored in the storage device and the metadata stored in the table of database [5]. However, this mode can neither achieve true storage nor reflect the nature of image data, and when the storage path is changed, there are many items that need to be modified in the database, which can reduce the safety and reliability of data to some extent. The second approach that has received much attention is storing image data in a database directly, using the data type \"BLOB\" [6; 7]. The above two methods are implemented based on a relational database management system. While as the amount of remote sensing data grows rapidly, the weaknesses of a relational database in the process of managing massive spatial data are becoming more and more obvious, including slow reading and writing speed and difficult horizontal expansion [8]. Especially the problem of low storage and access efficiency under the background of massive data, which can no longer meet the requirements for data management in a big data era. The last method that some applications adopt is the file system [9; 10]. In this condition, the image data are organized through the file mode in high-performance storage devices. However, the process of data retrieval and data acquisition is inconvenient using this method, and in many cases, users have to develop specialized programs to realize these functions. Thus, Not Only SQL (NoSQL) databases, which are typically represented by MongoDB, whose unique data management mode can solve the problems that existed in the relational database, are receiving more attention [11; 12]. In addition, the file storage mechanism GridFS within MongoDB can achieve the distributed storage of large binary files [13]. This paper proposes a method to manage remote sensing data based on MongoDB, with the metadata stored in the form of document and the image data stored with GridFS. Many studies use NoSQL databases to manage the spatial datasets. [PERSON] et al. proposed a management strategy based on MongoDB for the frequent modification and complex spatial analysis of large-scale GIS (Geographical Information System) data, and designed experiments to verify the feasibility and effectiveness of their strategy [14]. [PERSON] et al. flattened a hierarchical R-tree structure into a tabular MongoDB collection to manage planar spatial data, and the results showed that the planar spatial data could be effectively managed by this method [15].