The Wetland Cover Datasets on the Large Wetlands of International Importance in 2001 and 2013 by Remote Sensing Data Integration

The arrival of the era of big data for Earth observation (EO) indicates that traditional data management models have been unable to meet the needs of remote sensing data in big data environments. With the launch of the first remote sensing satellite, the volume of remote sensing data has also been increasing, and traditional data storage methods have been unable to ensure the efficient management of large amounts of remote sensing data. Therefore, a professional remote sensing big data integration method is sorely needed. In recent years, the emergence of some new technical methods has provided effective solutions for multi-source remote sensing data integration. This paper proposes a multi-source remote sensing data integration framework based on a distributed management model. In this framework, the multi-source remote sensing data are partitioned by the proposed spatial segmentation indexing (SSI) model through spatial grid segmentation. The designed complete information description system, based on International Organization for Standardization (ISO) 19115, can explain multi-source remote sensing data in detail. Then, the distributed storage method of data based on MongoDB is used to store multi-source remote sensing data. The distributed storage method is physically based on the sharding mechanism of the MongoDB database, and it can provide advantages for the security and performance of the preservation of remote sensing data. Finally, several experiments have been designed to test the performance of this framework in integrating multi-source remote sensing data. The results show that the storage and retrieval performance of the distributed remote sensing data integration framework proposed in this paper is superior. At the same time, the grid level of the SSI model proposed in this paper also has an important impact on the storage efficiency of remote sensing data. Therefore, the remote storage data integration framework, based on distributed storage, can provide new technical support and development prospects for big EO data.

Download Full-text

The multi-source remote sensing data integration and fusion to recognize uninhabited islands

10.1117/12.832410 ◽

2009 ◽

Author(s):

Zhijun Liu ◽

Bo Du ◽

Chenxing Wang

Keyword(s):

Remote Sensing ◽

Data Integration ◽

Remote Sensing Data ◽

Sensing Data

Download Full-text

Ocean Remote Sensing Data Integration and Products

Proceedings of OceanObs'09: Sustained Ocean Observations and Information for Society ◽

10.5270/oceanobs09.pp.12 ◽

2010 ◽

Cited By ~ 3

Author(s):

Bertrand Chapron ◽

A. Bingham ◽

Fabrice Collard ◽

Craig Donlon ◽

Johnny A. Johannessen ◽

...

Keyword(s):

Remote Sensing ◽

Data Integration ◽

Remote Sensing Data ◽

Sensing Data ◽

Ocean Remote Sensing

Download Full-text

Improving Site-Specific Maize Yield Estimation by Integrating Satellite Multispectral Data into a Crop Model

Agronomy ◽

10.3390/agronomy9110719 ◽

2019 ◽

Vol 9 (11) ◽

pp. 719 ◽

Cited By ~ 2

Author(s):

Vijaya R. Joshi ◽

Kelly R. Thorp ◽

Jeffrey A. Coulter ◽

Gregg A. Johnson ◽

Paul M. Porter ◽

...

Keyword(s):

Remote Sensing ◽

Data Integration ◽

Remote Sensing Data ◽

Maize Yield ◽

Crop Model ◽

Estimation Accuracy ◽

Yield Estimation ◽

Site Specific ◽

Sensing Data ◽

Integration Approach

Integrating remote sensing data into crop models offers opportunities for improved crop yield estimation. To compare site-specific yield estimation accuracy of a stand-alone crop model with a data-integration approach, a study was conducted in 2016–2017 with nitrogen (N)-fertilized and unfertilized treatments across a heterogeneous 7-ha maize field. For each treatment, yield data were grouped into five classes resulting in 109 spatial zones. In each zone, the Crop Environment Resource Synthesis (CERES)-Maize model was run using the GeoSim plugin within Quantum GIS. In the data integration approach, maize biomass values estimated using satellite imagery at the five (V5) and ten (V10) leaf collar stages were used to optimize the total soil nitrogen concentration (SLNI) and soil fertility factor (SLPF) in CERES-Maize. Without integration, maize yield was simulated with root mean square error (RMSE) of 1264 kg ha−1. Optimization of SLNI improved yield simulations at both V5 and V10. However, better simulations were obtained from optimization at V10 (RMSE 1026 kg ha−1) as compared to V5 (RMSE 1158 kg ha−1). Optimization of SLPF together with SLNI did not further improve the yield simulations. This study shows that integrating remote sensing data into a crop model can improve site-specific maize yield estimations as compared to the stand-alone crop modeling approach.

Download Full-text