By admin
Armstrong M P and Densham P J. 1992. Domain decomposition for parallel processing of spatial problems. Computers, Environment and Urban Systems, 16(6): 497-513 [DOI: 10.1016/0198-9715(92)90041-Ohttp://dx.doi.org/10.1016/0198-9715(92)90041-O]
Bansal S, Kumar P and Singh K. 2006. An improved two-step algorithm for task and data parallel scheduling in distributed memory machines. Parallel Computing, 32(10): 759-774 [DOI: 10.1016/j.parco.2006.08.004http://dx.doi.org/10.1016/j.parco.2006.08.004]
Baumann P. 2017. The datacube manifesto[EB/OL]. https://earthserver.eu/tech/datacube-manifesto/The-Datacube-Manifesto.pdfhttps://earthserver.eu/tech/datacube-manifesto/The-Datacube-Manifesto.pdf.
Baumann P, Dehmel A, Furtado P, Ritsch R, Widmann N. 1999. Spatio-temporal retrieval with RasDaMan//Proceedings of the 25th International Conference on Very Large Data Bases. Edinburgh, Scotland, UK: Morgan Kaufmann Publishers Inc.: 746-749
Baumann P, Rossi A P, Bell B, Clements O, Evans B, Hoenig H, Hogan P, Kakaletris G, Koltsida P, Mantovani S, Marco Figuera R, Merticariu V, Misev D, Pham H B, Siemen S and Wagemann J. 2018. Fostering cross-disciplinary earth science through datacube analytics//Earth Observation Open Science and Innovation. Switzerland: Spring: 91-119 [DOI: 10.1007/978-3-319-65633-5_5http://dx.doi.org/10.1007/978-3-319-65633-5_5]
CEOS. 2020a. Committee on earth observation satellites[EB/OL]. http://ceos.org/http://ceos.org/
CEOS. 2020b. CEOS Open Data Cube[EB/OL]. http://datacube-core.readthedocs.io/en/latest/http://datacube-core.readthedocs.io/en/latest/
Chaudhuri S, Dayal U. 1997. An overview of data warehousing and OLAP technology. ACM SIGMOD Record, 26(1): 65-74 [DOI: 10.1145/248603.248616http://dx.doi.org/10.1145/248603.248616]
Crippen R E. 1990. Calculating the vegetation index faster. Remote Sensing of Environment, 34(1): 71-73 [DOI: 10.1016/0034-4257(90)90085-Zhttp://dx.doi.org/10.1016/0034-4257(90)90085-Z]
DASK. 2020. DASK: Scalable analytics in Python[EB/OL]. https://dask.org/https://dask.org/
Gao B C. 1996. NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58(3): 257-266 [DOI: 10.1016/S0034-4257(96)00067-3http://dx.doi.org/10.1016/S0034-4257(96)00067-3]
Huete A, Didan K, Miura T, Rodriguez E P, Gao X and Ferreira L G. 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83(1/2): 195-213 [DOI: 10.1016/S0034-4257(02)00096-2http://dx.doi.org/10.1016/S0034-4257(02)00096-2]
Huete A R. 1988. A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25(3): 295-309 [DOI: 10.1016/0034-4257(88)90106-Xhttp://dx.doi.org/10.1016/0034-4257(88)90106-X]
Kopp S, Becker P, Doshi A, Wright D J, Zhang K X and Xu H. 2019. Achieving the full vision of earth observation data cubes. Data, 4(3): 94 [DOI: 10.3390/data4030094http://dx.doi.org/10.3390/data4030094]
Lewis A, Lacey J, Mecklenburg S, Ross J, Siqueira A, Killough B, Szantoi Z, Tadono T, Rosenqvist A, Goryl P, Miranda N and Hosford S. 2018. CEOS analysis ready data for land (CARD4L) overview//2018 IEEE International Geoscience and Remote Sensing Symposium. Valencia, Spain: IEEE: 7407-7410 [DOI: 10.1109/IGARSS.2018.8519255http://dx.doi.org/10.1109/IGARSS.2018.8519255]
Li D R, Zhang L P and Xia G S. 2014. Automatic analysis and mining of remote sensing big data. Acta Geodaetica et Cartographica Sinica, 43(12): 1211-1216
李德仁, 张良培, 夏桂松. 2014. 遥感大数据自动分析与数据挖掘. 测绘学报, 43(12): 1211-1216 [DOI: 10.13485/j.cnki.11-2089.2014.0187http://dx.doi.org/10.13485/j.cnki.11-2089.2014.0187]
Liao X J. 2021. Scientific and technological progress and development prospect of the earth observation in China in the past 20 years. Journal of Remote Sensing, 25(1): 267-275
廖小罕. 2021. 中国对地观测20年科技进步和发展. 遥感学报, 25(1): 267-275 [DOI: 10.11834/jrs.20211017http://dx.doi.org/10.11834/jrs.20211017]
Mahecha M D, Gans F, Brandt G, Christiansen R, Cornell S E, Fomferra N, Kraemer G, Peters J, Bodesheim P, Camps-Valls G, Donges J F, Dorigo W, Estupinan-Suarez L M, Gutierrez-Velez V H, Gutwin M, Jung M, Londoño M C, Miralles D G, Papastefanou P and Reichstein M. 2020. Earth system data cubes unravel global multivariate dynamics. Earth System Dynamics, 11(1): 201-234 [DOI: 10.5194/esd-11-201-2020http://dx.doi.org/10.5194/esd-11-201-2020]
McFeeters S K. 1996. The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. International Journal of Remote Sensing, 17(7): 1425-1432 [DOI: 10.1080/01431169608948714http://dx.doi.org/10.1080/01431169608948714]
Mueller N, Lewis A, Roberts D, Ring S, Melrose R, Sixsmith J, Lymburner L, McIntyre A, Tan P, Curnow S and lp A. 2016. Water observations from space: mapping surface water from 25 years of Landsat imagery across Australia. Remote Sensing of Environment, 174: 341-352 [DOI: 10.1016/j.rse.2015.11.003http://dx.doi.org/10.1016/j.rse.2015.11.003]
Nativi S, Mazzetti P and Craglia M. 2017. A view-based model of data-cube to support big earth data systems interoperability. Big Earth Data, 1(1/2): 75-99 [DOI: 10.1080/20964471.2017.1404232http://dx.doi.org/10.1080/20964471.2017.1404232]
Ren Y B, Chen Z J, Chen G, Han Y and Wang Y J. 2017. A hybrid process/thread parallel algorithm for generating DEM from LiDAR points. ISPRS International Journal of Geo-Information, 6(10): 300 [DOI: 10.3390/ijgi6100300http://dx.doi.org/10.3390/ijgi6100300]
Sudmanns M, Tiede D, Lang S, Bergstedt H, Trost G, Augustin H, Baraldi A and Blaschke T. 2020. Big Earth data: disruptive changes in Earth observation data management and analysis?. International Journal of Digital Earth, 13(7): 832-850 [DOI: 10.1080/17538947.2019.1585976http://dx.doi.org/10.1080/17538947.2019.1585976]
Voidrot M F and Percivall G. 2020. OGC geospatial coverages data cube community practice. IOP Conference Series: Earth and Environmental Science, 509: 012058 [DOI: 10.1088/1755-1315/509/1/012058http://dx.doi.org/10.1088/1755-1315/509/1/012058]
Wagemann J, Clements O, Figuera R M, Rossi A P and Mantovani S. 2018. Geospatial web services pave new ways for server-based on-demand access and processing of Big Earth Data. International Journal of Digital Earth, 11(1): 7-25 [DOI: 10.1080/17538947.2017.1351583http://dx.doi.org/10.1080/17538947.2017.1351583]
Wang S W and Armstrong M P. 2009. A theoretical approach to the use of cyberinfrastructure in geographical analysis. International Journal of Geographical Information Systems, 23(2): 169-193 [DOI: 10.1080/13658810801918509http://dx.doi.org/10.1080/13658810801918509]
Wulder M A and Coops N C. 2014. Satellites: make Earth observations open access. Nature, 513(7516): 30-31 [DOI: 10.1038/513030ahttp://dx.doi.org/10.1038/513030a]
Xu H Q. 2006. Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. International Journal of Remote Sensing, 27(14): 3025-3033 [DOI: 10.1080/01431160600589179http://dx.doi.org/10.1080/01431160600589179]
You S M, Zhang J T and Gruenwald L. 2015. Large-scale spatial join query processing in cloud//The 31st IEEE International Conference on Data Engineering Workshops. Seoul, Korea (South): IEEE: 34-41 [DOI: 10.1109/ICDEW.2015.7129541http://dx.doi.org/10.1109/ICDEW.2015.7129541]
Yue P, Gao F, Shangguan B Y and Yan Z R. 2020. A machine learning approach for predicting computational intensity and domain decomposition in parallel geoprocessing. International Journal of Geographical Information Science, 34(11): 2243-2274 [DOI: 10.1080/13658816.2020.1730850http://dx.doi.org/10.1080/13658816.2020.1730850]
Yue P, Ramachandran R, Baumann P, Khalsa S J S, Deng M X and Jiang L C. 2016. Recent activities in earth data science [technical committees]. IEEE Geoscience and Remote Sensing Magazine, 4(4): 84-89 [DOI: 10.1109/MGRS.2016.2600528http://dx.doi.org/10.1109/MGRS.2016.2600528]
Yue P, Zhang M D and Tan Z Y. 2015. A geoprocessing workflow system for environmental monitoring and integrated modelling. Environmental Modelling and Software, 69: 128-140 [DOI: 10.1016/j.envsoft.2015.03.017http://dx.doi.org/10.1016/j.envsoft.2015.03.017]
Zaharia M, Chowdhury M, Franklin M J, Shenke S and Stoica I. 2010. Spark: cluster computing with working sets//Proceedings of the 2nd USENIX Conference on Hot Topics in Cloud Computing. Boston, MA: USENIX Association
Zhang L F, Chen H, Sun X J, Fu D J, Tong Q X. 2017. Designing spatial-temporal-spectral integrated storage structure of multi-dimensional remote sensing images. Journal of Remote Sensing, 21(1): 62-73
张立福, 陈浩, 孙雪剑, 付东杰, 童庆禧. 2017. 多维遥感数据时空谱一体化存储结构设计. 遥感学报, 21(1): 62-73 [DOI: 10.11834/jrs.20176091http://dx.doi.org/10.11834/jrs.20176091]