With the rapid development of earth-observation technology, the amount of remote sensing data has increased exponentially, and traditional relational databases cannot satisfy the requirements of managing large-scale remote sensing data. To address this problem, this paper undertakes intensive research of the NoSQL (Not Only SQL) data management model, especially the MongoDB database, and proposes a new approach to managing large-scale remote sensing data. Firstly, based on the sharding technology of MongoDB, a distributed cluster architecture was designed and established for massive remote sensing data. Secondly, for the convenience in the unified management of remote sensing data, an archiving model was constructed, and remote sensing data, including structured metadata and unstructured image data, were stored in the above cluster separately, with the metadata stored in the form of a document, and image data stored with the GridFS mechanism. Finally, by designing different shard strategies and comparing MongoDB cluster with a typical relational database, several groups of experiments were conducted to verify the storage performance and access performance of the cluster. The experimental results show that the proposed method can overcome the deficiencies of traditional methods, as well as scale out the database, which is more suitable for managing massive remote sensing data and can provide technical support for the management of massive remote sensing data.

Unified representation of spatial earth data is an essential scientific issue. The analysis and mining of interdisciplinary spatial earth data resources can help discover hidden scientific knowledge, and even reveal the intrinsic relationship among different disciplines. However, the different description methods and inner structures among interdisciplinary spatial earth data bring significant challenges to unified data management and collaborative analysis in earth environment research. To address this issue, this paper proposes a unified representation method for interdisciplinary spatial earth data. First, this paper establishes a general metadata model and realizes the unified description of interdisciplinary data. Second, an entity data organization model is presented, which can realize the unified organization of entity data with different inner structures. Finally, we introduce the Spatial Earth Data Format (SEDF), a data format based on HDF5 for implementing the data organization model of interdisciplinary spatial earth data. Data representation experiments and validation are conducted to verify the availability and practicability of the proposed data representation method. The results suggest the powerful ability to represent spatial earth data efficiently and ensure data integrity, which is convenient for data management and application.

Remote sensing data acquisition is one of the most essential processes in the field of Earth observation. However, traditional methods to acquire data do not satisfy the requirements of current applications because large-scale data processing is required. To address this issue, this paper proposes a data acquisition framework that carries out remote sensing metadata planning and then realizes the online acquisition of large amounts of data. Firstly, this paper establishes a unified metadata cataloging model and realizes the catalog of metadata in a local database. Secondly, a coverage calculation model is presented, which can show users the data coverage information in a selected geographical region under the data requirements of a specific application. Finally, according to the data retrieval results and the coverage calculation, a machine-to-machine interface is provided to acquire target remote sensing data. Experiments were conducted to verify the availability and practicality of the proposed framework, and the results show the strengths and powerful capabilities of our framework by overcoming deficiencies in traditional methods. It also achieved the online automatic acquisition of large-scale heterogeneous remote sensing data, which can provide guidance for remote sensing data acquisition strategies.