{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:20:53Z","timestamp":1760239253869,"version":"build-2065373602"},"reference-count":40,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2020,10,25]],"date-time":"2020-10-25T00:00:00Z","timestamp":1603584000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Key Research and Development Program of China","award":["No. 2018YFE0122700"],"award-info":[{"award-number":["No. 2018YFE0122700"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Recently, increasing amounts of multi-source geospatial data (raster data of satellites and textual data of meteorological stations) have been generated, which can play a cooperative and important role in many research works. Efficiently storing, organizing and managing these data is essential for their subsequent application. HBase, as a distributed storage database, is increasingly popular for the storage of unstructured data. The design of the row key of HBase is crucial to improving its efficiency, but large numbers of researchers in the geospatial area do not conduct much research on this topic. According the HBase Official Reference Guide, row keys should be kept as short as is reasonable while remaining useful for the required data access. In this paper, we propose a new row key encoding method instead of conventional stereotypes. We adopted an existing hierarchical spatio-temporal grid framework as the row key of the HBase to manage these geospatial data, with the difference that we utilized the obscure but short American Standard Code for Information Interchange (ASCII) to achieve the structure of the grid rather than the original grid code, which can be easily understood by humans but is very long. In order to demonstrate the advantage of the proposed method, we stored the daily meteorological data of 831 meteorological stations in China from 1985 to 2019 in HBase; the experimental result showed that the proposed method can not only maintain an equivalent query speed but can shorten the row key and save storage resources by 20.69% compared with the original grid codes. Meanwhile, we also utilized GF-1 imagery to test whether these improved row keys could support the storage and querying of raster data. We downloaded and stored a part of the GF-1 imagery in Henan province, China from 2017 to 2018; the total data volume reached about 500 GB. Then, we succeeded in calculating the daily normalized difference vegetation index (NDVI) value in Henan province from 2017 to 2018 within 54 min. Therefore, the experiment demonstrated that the improved row keys can also be applied to store raster data when using HBase.<\/jats:p>","DOI":"10.3390\/ijgi9110625","type":"journal-article","created":{"date-parts":[[2020,10,26]],"date-time":"2020-10-26T02:34:54Z","timestamp":1603679694000},"page":"625","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["An Efficient Row Key Encoding Method with ASCII Code for Storing Geospatial Big Data in HBase"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5109-3812","authenticated-orcid":false,"given":"Quan","family":"Xiong","sequence":"first","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100083, China"},{"name":"Center for Spatial Information Science and Systems, George Mason University, 4400 University Dr, Fairfax, VA 22030, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6347-4973","authenticated-orcid":false,"given":"Xiaodong","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100083, China"},{"name":"Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture, Beijing 100083, China"},{"name":"Key Laboratory of Agricultural Land Quality and Monitoring, Ministry of Natural Resources, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8755-4115","authenticated-orcid":false,"given":"Wei","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8805-8914","authenticated-orcid":false,"given":"Sijing","family":"Ye","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenbo","family":"Du","sequence":"additional","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7025-2137","authenticated-orcid":false,"given":"Diyou","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dehai","family":"Zhu","sequence":"additional","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100083, China"},{"name":"Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture, Beijing 100083, China"},{"name":"Key Laboratory of Agricultural Land Quality and Monitoring, Ministry of Natural Resources, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8214-8345","authenticated-orcid":false,"given":"Zhe","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100083, China"},{"name":"Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture, Beijing 100083, China"},{"name":"Key Laboratory of Agricultural Land Quality and Monitoring, Ministry of Natural Resources, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8068-9415","authenticated-orcid":false,"given":"Xiaochuang","family":"Yao","sequence":"additional","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100083, China"},{"name":"Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture, Beijing 100083, China"},{"name":"Key Laboratory of Agricultural Land Quality and Monitoring, Ministry of Natural Resources, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,10,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.envsoft.2015.01.017","article-title":"Big data challenges in building the global earth observation system of systems","volume":"68","author":"Nativi","year":"2015","journal-title":"Environ. Model. Softw."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"527","DOI":"10.3390\/rs10040527","article-title":"Spatiotemporal fusion of multisource remote sensing data: Literature survey, taxonomy, principles, applications, and future directions","volume":"10","author":"Zhu","year":"2018","journal-title":"Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1007\/s11707-018-0699-7","article-title":"Onshore-offshore wind energy resource evaluation based on synergetic use of multiple satellite data and meteorological stations in Jiangsu Province, China","volume":"13","author":"Wei","year":"2019","journal-title":"Front. Earth Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1080\/20964471.2018.1432115","article-title":"Big spatial vector data management: A review","volume":"2","author":"Yao","year":"2018","journal-title":"Big Earth Data"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"He, W., and Yokoya, N. (2018). Multi-Temporal Sentinel-1 and-2 Data Fusion for Optical Image Simulation. ISPRS Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7100389"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Tan, Z., Yue, P., Di, L., and Tang, J. (2018). Deriving high spatiotemporal remote sensing images using deep convolutional network. Remote Sens., 10.","DOI":"10.3390\/rs10071066"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1109\/MGRS.2018.2890023","article-title":"Multisource and multitemporal data fusion in remote sensing: A comprehensive review of the state of the art","volume":"7","author":"Ghamisi","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zhuo, W., Huang, J., Li, L., Zhang, X., Ma, H., Gao, X., Huang, H., Xu, B., and Xiao, X. (2019). Assimilating soil moisture retrieved from Sentinel-1 and Sentinel-2 data into WOFOST model to improve winter wheat yield estimation. Remote Sens., 11.","DOI":"10.3390\/rs11131618"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1016\/j.agrformet.2015.10.013","article-title":"Assimilating a synthetic Kalman filter leaf area index series into the WOFOST model to improve regional winter wheat yield estimation","volume":"216","author":"Huang","year":"2016","journal-title":"Agric. For. Meteorol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.agrformet.2015.02.001","article-title":"Improving winter wheat yield estimation by assimilation of the leaf area index from Landsat TM and MODIS data into the WOFOST model","volume":"204","author":"Huang","year":"2015","journal-title":"Agric. For. Meteorol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1016\/j.rse.2017.03.015","article-title":"The Australian geoscience data cube\u2014foundations and lessons learned","volume":"202","author":"Lewis","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Yao, X., Li, G., Xia, J., Ben, J., Cao, Q., Zhao, L., Ma, Y., Zhang, L., and Zhu, D. (2020). Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges. Remote Sens., 12.","DOI":"10.3390\/rs12010062"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1376","DOI":"10.3390\/rs10091376","article-title":"RDCRMG: A Raster Dataset Clean & Reconstitution Multi-Grid Architecture for Remote Sensing Monitoring of Vegetation Dryness","volume":"10","author":"Ye","year":"2018","journal-title":"Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Han, D., and Stroulia, E. (July, January 28). Hgrid: A data model for large geospatial data sets in hbase. Proceedings of the 2013 IEEE Sixth International Conference on Cloud Computing, Santa Clara, CA, USA.","DOI":"10.1109\/CLOUD.2013.78"},{"key":"ref_15","unstructured":"Ye, S. (2016). Research on Application of Remote Sensing Tupu-Take Monitoring of Meteorological Disaster for Example. [Ph.D. Thesis, China Agricultural University]."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.cageo.2013.08.012","article-title":"A pole-oriented discrete global grid system: Quaternary quadrangle mesh","volume":"61","author":"Zhou","year":"2013","journal-title":"Comput. Geosci."},{"key":"ref_17","unstructured":"Dutton, G. (2000, January 26\u201328). Universal geospatial data exchange via global hierarchical coordinates. Proceedings of the International Conference on Discrete Global Grids, Santa Barbara, CA, USA."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"11088","DOI":"10.1073\/pnas.1202383109","article-title":"Next-generation digital earth","volume":"109","author":"Goodchild","year":"2012","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1080\/13658816.2012.698017","article-title":"Generic cumulative annular bucket histogram for spatial selectivity estimation of spatial database management system","volume":"27","author":"Cheng","year":"2013","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_20","first-page":"164","article-title":"Hipparchus. Data Structure: Points, Lines and Regions in Spherical Voronoi Grid","volume":"9","author":"Lukatela","year":"1989","journal-title":"Proceedings Auto-Carto."},{"key":"ref_21","first-page":"1111","article-title":"Multi-level QTM Based Algorithm for Generating Spherical Voronoi Diagram","volume":"40","author":"Wang","year":"2015","journal-title":"Geomat. Inf. Sci. Wuhan Univ."},{"key":"ref_22","first-page":"1","article-title":"Spatial information multi-grid and its functions","volume":"3","author":"Li","year":"2005","journal-title":"Geospat. Inf."},{"key":"ref_23","first-page":"52","article-title":"Research on grid division and encoding of spatial information multi-grids","volume":"1","author":"Li","year":"2006","journal-title":"Acta Geod. Cartogr. Sin."},{"key":"ref_24","unstructured":"Li, D., Shao, Z., Zhu, X., and Zhu, Y. (2004, January 20\u201324). From digital map to spatial information multi-grid. Proceedings of the 2004 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2004), Anchorage, AK, USA."},{"key":"ref_25","first-page":"238","article-title":"A Global Grid Model Based on \"Constant Area\" Quadrilaterals","volume":"3","author":"Grytten","year":"2003","journal-title":"ScanGIS Citeseer"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"653","DOI":"10.1080\/13658810410001705334","article-title":"Examination of a constant-area quadrilateral grid in representation of global digital elevation models","volume":"18","author":"Nilsen","year":"2004","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Ghemawat, S., Gobioff, H., and Leung, S.T. (2003, January 19\u201322). The Google file system. Proceedings of the Nineteenth ACM Symposium on Operating Systems Principles, Bolton Landing, NY, USA.","DOI":"10.1145\/945445.945450"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Palankar, M.R., Iamnitchi, A., Ripeanu, M., and Garfinkel, S. (2008, January 25). Amazon S3 for science grids: A viable solution?. Proceedings of the 2008 International Workshop on Data-Aware Distributed Computing, Boston, MA, USA.","DOI":"10.1145\/1383519.1383526"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Eldawy, A., and Mokbel, M.F. (2015, January 13\u201317). Spatialhadoop: A mapreduce framework for spatial data. Proceedings of the 2015 IEEE 31st International Conference on Data Engineering, Seoul, Korea.","DOI":"10.1109\/ICDE.2015.7113382"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"785","DOI":"10.1007\/s10707-018-0325-6","article-title":"St-hadoop: A mapreduce framework for spatio-temporal data","volume":"22","author":"Alarabi","year":"2018","journal-title":"GeoInformatica"},{"key":"ref_31","first-page":"21","article-title":"The hadoop distributed file system: Architecture and design","volume":"11","author":"Borthakur","year":"2007","journal-title":"Hadoop Proj. Website"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Liu, X., Han, J., Zhong, Y., Han, C., and He, X. (September, January 31). Implementing WebGIS on Hadoop: A case study of improving small file I\/O performance on HDFS. Proceedings of the 2009 IEEE International Conference on Cluster Computing and Workshops, New Orleans, LA, USA.","DOI":"10.1109\/CLUSTR.2009.5289196"},{"key":"ref_33","unstructured":"Khetrapal, A., and Ganesh, V. (2006). HBase and Hypertable for Large Scale Distributed Storage Systems, Department of Computer Science, Purdue University."},{"key":"ref_34","unstructured":"Apache HBase (2020, August 08). The Apache Software Foundation. Available online: http:\/\/hadoop.apache.org."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Kaplanis, A., Kendea, M., Sioutas, S., Makris, C., and Tzimas, G. (2015, January 13\u201317). HB+ tree: Use hadoop and HBase even your data isn\u2019t that big. Proceedings of the 30th Annual ACM Symposium on Applied Computing, Salamanca, Spain.","DOI":"10.1145\/2695664.2695723"},{"key":"ref_36","unstructured":"Team, A.H. (2020, August 08). Apache Hbase Reference Guide, Available online: https:\/\/hbase.apache.org\/book.html."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Liu, Y., Chen, B., He, W., and Fang, Y. (2013, January 20\u201322). Massive image data management using HBase and MapReduce. Proceedings of the 2013 21st International Conference on Geoinformatics, Kaifeng, China.","DOI":"10.1109\/Geoinformatics.2013.6626187"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Wang, L., Cheng, C., Wu, S., Wu, F., and Teng, W. (2015, January 26\u201331). Massive remote sensing image data management based on HBase and GeoSOT. Proceedings of the 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy.","DOI":"10.1109\/IGARSS.2015.7326842"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Nishimura, S., Das, S., Agrawal, D., and El Abbadi, A. (2011, January 6\u20139). Md-hbase: A scalable multi-dimensional data infrastructure for location aware services. Proceedings of the 2011 IEEE 12th International Conference on Mobile Data Management, Lulea, Sweden.","DOI":"10.1109\/MDM.2011.41"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Wang, L., Chen, B., and Liu, Y. (2013, January 20\u201322). Distributed storage and index of vector spatial data based on HBase. Proceedings of the 2013 21st international conference on geoinformatics, Kaifeng, China.","DOI":"10.1109\/Geoinformatics.2013.6626052"}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/9\/11\/625\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:27:46Z","timestamp":1760178466000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/9\/11\/625"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,25]]},"references-count":40,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2020,11]]}},"alternative-id":["ijgi9110625"],"URL":"https:\/\/doi.org\/10.3390\/ijgi9110625","relation":{},"ISSN":["2220-9964"],"issn-type":[{"type":"electronic","value":"2220-9964"}],"subject":[],"published":{"date-parts":[[2020,10,25]]}}}