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| Agricultural remote sensing big data: Management and applications |
| Yanbo Huang1, CHEN Zhong-xin2, YU Tao3, HUANG Xiang-zhi3, GU Xing-fa3 |
1 Crop Production Systems Research Unit, Agricultural Research Service, United States Department of Agriculture, MS 38776, USA
2 Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, P.R.China
3 Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, P.R.China |
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Abstract Big data with its vast volume and complexity is increasingly concerned, developed and used for all professions and trades. Remote sensing, as one of the sources for big data, is generating earth-observation data and analysis results daily from the platforms of satellites, manned/unmanned aircrafts, and ground-based structures. Agricultural remote sensing is one of the backbone technologies for precision agriculture, which considers within-field variability for site-specific management instead of uniform management as in traditional agriculture. The key of agricultural remote sensing is, with global positioning data and geographic information, to produce spatially-varied data for subsequent precision agricultural operations. Agricultural remote sensing data, as general remote sensing data, have all characteristics of big data. The acquisition, processing, storage, analysis and visualization of agricultural remote sensing big data are critical to the success of precision agriculture. This paper overviews available remote sensing data resources, recent development of technologies for remote sensing big data management, and remote sensing data processing and management for precision agriculture. A five-layer-fifteenlevel (FLFL) satellite remote sensing data management structure is described and adapted to create a more appropriate four-layer-twelve-level (FLTL) remote sensing data management structure for management and applications of agricultural remote sensing big data for precision agriculture where the sensors are typically on high-resolution satellites, manned aircrafts, unmanned aerial vehicles and ground-based structures. The FLTL structure is the management and application framework of agricultural remote sensing big data for precision agriculture and local farm studies, which outlooks the future coordination of remote sensing big data management and applications at local regional and farm scale.
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Received: 08 September 2017
Accepted:
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| Fund: This research was financially supported by the funding appropriated from USDA-ARS National Program 305 Crop Production and the 948 Program of Ministry of Agriculture of China (2016-X38). |
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Corresponding Authors:
Correspondence Yanbo Huang, Tel: +1-662-686-5354, Fax: +1-662-686-5422, E-mail: yanbo.huang@ars.usda.gov
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Cite this article:
Yanbo Huang, CHEN Zhong-xin, YU Tao, HUANG Xiang-zhi, GU Xing-fa.
2018.
Agricultural remote sensing big data: Management and applications. Journal of Integrative Agriculture, 17(09): 1915-1931.
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Anonymous. 2015. The 10 countries most active in space. [2017-10-25]. http://www.aerospace-technology.com/features/featurethe-10-countries-most-active-in-space-4744018/
Bastiaanssen W G M, Molden D J, Makin I W. 2000. Remote sensing for irrigated agriculture: Examples from research and possible applications. Agricultural Water Management, 46, 137–155.
Basu S, Ganguly S, Mukhopadhyay S, DiBiano R, Karti M, Nemani R. 2015. DeepSat - A learning framework for satellite imagery. In: Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems. Association for Computing Machinery, New York. pp. 1–37.
Bendig J, Bolten A, Bennertz S, Broscheit J, Eichfuss S, Bareth G. 2014. Estimating biomass of barley using crop surface models (CSMs) derived from UAV-based RGB imaging. Remote Sensing, 6, 10395–10412.
Bendre M R, Thool R C, Thool V R. 2015. Big data in precision agriculture: Weather forecasting for future farming. In: Proceedings of 1st International Conference on Next Generation Computing Technologies (NGCT). IEEE, Piscataway, NJ, USA. pp. 744–750.
Biswas S, Sen J. 2016. A proposed architecture for big data driven supply chain analytics. International Journal of Supply Chain Management (IUP), 6, 1–24.
Blaschke T. 2010. Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 65, 2–16.
Candiago S, Remondino F, De Giglio M, Dubbini M, Gattelli M. 2015. Evaluating multispectral images and vegetation indices for precision farming applications from UAV images. Remote Sensing, 7, 4026–4047.
Chen C L, Zhang C Y. 2014. Data-intensive applications, challenges, techniques and technologies: A survey on big data. Information Sciences, 275, 314–347.
Chen Y, Lin Z, Zhao X, Wang G, Gu Y. 2014. Deep learning-based classification of hyperspectral data. IEEE Journal of Selected Topics in Applied Erath Observations and Remote Sensing, 7, 2094–2107.
Chen Z, Zhou Q, Liu J, Wang L, Ren J, Huang Q, Deng H, Zhang L, Li D. 2011. CHARMS - China agricultural remote sensing monitoring system. In: Proceedings of the IEEE International Geoscience and Remote Sensing Symposium. IEEE Geoscience and Remote Sensing Society, USA.
Chi M, Plaza A, Benediktsson J A, Sun Z, Shen J, Zhu Y. 2016. Big data for remote sensing: Challenges and opportunities. Proceedings of the IEEE, 104, 2207–2219.
Clasen M, Hege H C. 2006. Terrain rendering using spherical climaps. In: Proceedings of EuroVis06 Joint Eurographics - IEEE-VGTC Symposium on Visualization. Eurographics, Geneve, Switzerland. pp. 91–98.
Di L, Kobler B. 2000. NASA standards for earth remote sensing data. International Archives of Photogrammetry and Remote Sensing, XXXIII(B2), 147–155.
Fu P, Sun J. 2010. Web GIS: Principles and Applications. ESRI Press, USA.
Gong P. 2009. Some essential questions in remote sensing science and technology. Journal of Remote Sensing, 13, 1–23.
Gu X, Yu T, Xie D, Guo H, Hu X, Li J, Cheng T. 2013. A Hierarchically Organized Method Based on the Latitude and Longitude Grid Data. China Patent. No. CN102346923 B. 2013-10-23. (in Chinese)
Hall D L, Llinas J. 2009. Multisensor data fusion. In: Liggins M E, Hall D L, Llinas J, eds., Handbook of Multisensor Data Fusion: Theory and Practice. 2nd ed. CRC Press, Boca Raton, FL. pp. 1–13.
Huang J, Ma H, Wei S, Zhang X, Huang Y, Liu J, Fan J. 2015a. Jointly assimilating MODIS LAI and ET products into SWAP model for winter wheat yield estimation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8, 4060–4071.
Huang J, Sedano F, Huang Y, Ma H, Li X, Liang S, Tian L, Wu W. 2015b. Assimilating a synthetic Kalman filter leaf area index series into the WOFOST model to improve regional winter wheat yield estimation. Agricultural and Forest Meteorology, 216, 188–202.
Huang J, Tian L, Liang S, Becker-Reshef I, Huang Y, Su W, Fan J, Wu W. 2015c. Improving winter wheat yield estimation by assimilation of the leaf area index from Landsat TM and MODIS data into the WOFOST model. Agricultural and Forest Meteorology, 204, 106–221.
Huang Q, Zhou Q, Wu W, Wang L, Zhang L. 2012. Extraction of plaingting areas of major crops and crop growth monitoring in Northeast China. Intelligent Automation & Soft Computing, 18, 1023–1033.
Huang Y. 2009. Advances in artificial neural networks - methodological development and applications. Algorithms, 2, 973–1007.
Huang Y, Brand H, Sui R, Thomson S J, Furukawa T, Ebelhar M W. 2017. Cotton yield estimation using very high-resolution digital images acquired on a low-cost small unmanned aerial vehicle. Transactions of the ASABE, 59, 1563–1574.
Huang Y, Lan Y, Thomson S J, Fang A, Hoffmann W C, Lacey R E. 2010a. Development of soft computing and applications in agricultural and biological engineering. Computers and Electronics in Agriculture, 71, 107–127.
Huang Y, Reddy K N. 2015. Unmanned aerial vehicles: A unique platform for low-altitude remote sensing for crop management. In: Proceedings of the Plenary and Lead Papers of the 25th Asian-Pacific Weed Science Society Conference. Hyderabad, India. pp. 185–192.
Huang Y, Reddy K N, Thomson S J, Yao H. 2015. Assessment of soybean injury from glyphosate using airborne multispectral remote sensing. Pesticide Management Science, 71, 545–552.
Huang Y, Sui R, Thomson S J, Fisher D K. 2013a. Estimation of cotton yield with varied irrigation and nitrogen treatments using aerial multispectral imagery. International Journal of Agricultural and Biological Engineering, 6, 37–41.
Huang Y, Thomson S J, Hoffman W C, Lan Y, Fritz B K. 2013b. Development and prospect of unmanned aerial vehicles for agricultural production management. International Journal of Agricultural and Biological Engineering, 6, 1–10.
Huang Y, Thomson S J, Ortiz B V, Reddy K N, Ding W, Zablotowicz R M, Bright Jr J R. 2010b. Airborne remote sensing assessment of the damage to cotton caused by spray drift from aerially applied glyphosate through spray deposition measurements. Biosystems Engineering, 107, 212–220.
Jacquemoud S, Baret F. 1990. PROSPECT: A model of leaf optical properties spectra. Remote Sensing of Environment, 34, 75–91.
Jacquemoud S, Verhoef W, Baret F, Bacour C, Zarco-Tejada P J, Asner G P, Francois C, Ustin S L. 2009. PROSPECT+SAIL models: A review of use for vegetation characterization. Remote Sensing of Environment, 113, 556–566.
Jacquemoud S, Verhoef W, Baret F, Zarco-Tejada P J, Asner G P, Francois C, Ustin S L. 2006. PROSPECT+SAIL: 15 years of use for land surface characterization. In: Proceedings of IEEE International Conference on Geoscience and Remote Sensing Symposium, 2006. IEEE Geoscience and Remote Sensing Society, USA. pp. 1992–1995.
Jagannathan S. 2016. Real-time big data analytics architecture for remote sensing application. In: Proceedings of 2016 International Conference on Signal Processing, Communication, Power and Embedded System. IEEE, USA. pp. 1912–1916.
Lewis A, Lymburner L, Purss M B J, Brooke B, Evans B, Ip A, Dekker A G, Irons J R, Minchin S, Mueller N, Oliver S, Roberts D, Ryan B, Thankappan M, Woodcock R, Wyborn L. 2016. Rapid, high-resolution detection of environmental change overcontinental scales from satellite data - the Earth Observation Data Cube. International Journal of Digital Earth, 9, 106–111.
Liu P. 2015. A survey of remote-sensing big data. Frontiers in Environmental Science, 3, 1–6.
Ma Y, Wu H, Wang L, Huang B, Ranjan R, Zamaya A, Wei J. 2015. Remote sensing big data computing: Challenges and opportunities. Future Generation Computer Systems, 51, 47–60.
Mathews A J, Jensen J L R. 2013. Visualizing and quantifying vineyard canopy LAI using an unmanned aerial vehicle (UAV) collected high density structure from motion point cloud. Remote Sensing, 5, 3164–2183.
Mohanty S P, Hughes D, Salathe M. 2016. Using deep learning for image-based plant disease detection. Frontiers in Plant Science, 7, 1–10.
Moran M S, Inoue Y, Barnes E M. 1997. Opportunities and limitations for image-based remote sensing in precision crop management. Remote Sensing of Environment, 61, 319–346.
Mueller N, Lewis A, Roberts D, Ring S, Melrose R, Sixsmith J, Lymburner L, McIntyre A, Tan P, Curnow S, Ipa A. 2016. Water observations from space: Mapping surface water from 25 years of Landsat imagery across Australia. Remote Sensing of Environment, 174, 341–352.
Mulla D J. 2013. Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps. Biosystems Engineering, 114, 358–371.
Pinter P J, Hatfield Jr J L, Schepers J S, Barnes E M, Moran M S, Daughtry C S T, Upchurch D R. 2003. Remote sensing for crop management. Photogrammetric Engineering and Remote Sensing, 69, 647–664.
Piwowar J M. 2001. Getting your imagery at the right level.Cartouche. No. 41 (Winter).
Rathore M M U, Paul A, Ahmad A, Chen B, Huang B, Ji W. 2015. Real-time big data analytical architecture for remote sensing application. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8, 4610–4621.
Reddy K N, Huang Y, Lee M A, Nandula V K, Fletcher R S, Thomson S J, Zhao F. 2014. Glyphosate-resistant and glyphosate-susceptible Palmer amaranth (Amaranthus palmeri S. Wats.): hyperspectral reflectance properties of plants and potential for classification. Pest Management Science, 70, 1910–1917.
Ren J, Chen Z, Tang H, Zhou Q, Qin J. 2011. Regional crop yield simulation based on crop growth model and remote sensing data. Transactions of the Chinese Society of Agricultural Engineering, 27, 257–264.
Ren J, Chen Z, Zhou Q, Liu J, Tang H. 2015. MODIS vegetation index data used for estimating corn yield in USA. Journal of Remote Sensing, 19, 568–577.
Ren J, Chen Z, Zhou Q, Tang H. 2008. Regional yield estimation for winter wheat with MODIS-NDVI data in Shandong, China. International Journal of Applied Earth Observation and Geoinformation, 10, 403–413.
Rosenqvist A, Milne A., Lucas R, Imhoff M, Dobson C. 2003. A review of remote sensing technology in support of the Kyoto Protocol. Environmental Science & Policy, 6, 441–455.
Rosnell T, Honkavaara E. 2012. Point cloud generation from aerial image data acquired by a quadrocopter type micro unmanned aerial vehicle and a digital still camera. Sensors, 12, 453–480.
Rubin Y. 2016. Challenges with precision agriculture: Finding the balance between big data and local conditions. [2016-08-12]. http://myobservatoryblog.blogspot.com/2016/08/challenges-with-precision-agriculture.html?m=1
Sabarina K, Priya N. 2015. Lowering data dimensionality in big data for the benefit of precision agriculture. Procedia Computer Science, 48, 548–554.
Sladojevic S, Arsenovic M, Anderla A, Culibrk D, Stefanovic D. 2016. Deep neural networks based recognition of plant diseases by leaf image classification. Computational Intelligence and Neuroscience, 2016, 3289801.
Snipes C E, Nichols S P, Poston D H, Walker T W, Evans L P, Robinson H R. 2005. Current Agricultural Practices of the Mississippi Delta. Mississippi Agricultural & Forestry Experiment Station, Bulletin 1143. Mississippi State, Mississippi, USA. p. 18.
Snyder J P. 1993. Flattening the Earth: Two Thousand Years of Map Projections. The University of Chicago Press, Chicago, IL. pp. 5–8.
Song G. 2008. Review and outlook of development of GIS platform software techniques. [2018-02-01]. http://www.supermap.com.cn/magazine/200812/main/JD/index2.htm
Suomalainen J, Anders N, Iqbal S, Roerink G, Franke J, Wenting P, Hünniger D, Bartholomeus H, Becker R, Kooistra L. 2014. A lightweight hyperspectral mapping system and photogrammetric processing chain for unmanned aerial vehicles. Remote Sensing, 6, 11013–11030.
Thomson S J, Ouellet-Plamondon C M, DeFauw S L, Huang Y, Fisher D K, English P J. 2012. Potential and challenges in use of thermal imaging for humid region irrigation system management. Journal of Agricultural Science, 4, 103–116.
Turner D, Lucieer A, Watson C. 2012. An automated technique for generating georectified mosaics from ultra-high resolution unmanned aerial vehicle (UAV) imagery, based on structure from motion (SfM) point clouds. Remote Sensing, 4, 1392–1410.
Verhoef W. 1984. Light scattering by leaf layers with application to canopy reflectance model: The SAIL model. Remote Sensing of Environment, 16, 125–141.
Walter V. 2004. Object-based classification of remote sensing data for change detection. Journal of Photogrammetry and Remote Sensing, 58, 225–238.
Wang D, Zheng F, Lai J, Yu T, Li J, Guo S. 2012. A new parallel algorithm based on five-layer fifteen-level remote sensing data organization. Microcomputer Information, 1, 1–5.
Wilkinson G G. 1996. A review of current issues in the integration of GIS and remote sensing data. International Journal of Geographical Information Systems, 10, 85–101.
Wolfert S, Lan G, Verdouw C, Bogaardt M. 2017. Big data in small farming - a review. Agricultural Systems, 153, 69–80.
Xu B, Yang X, Tao W, Qin Z, Liu H, Miao J, Bi Y. 2008. MODIS-based remote sensing monitoring of grass production in China. International Journal of Remote Sensing, 29, 5313–5327.
Xu B, Yang X, Tao W, Miao J, Yang Z, Liu Z, Jin Y, Zhu X, Qin Z, Lv H, Li J. 2013. MODIS-based remote-sensing monitoring of the spatiotemporal patterns of China’s grassland vegetation growth. International Journal of Remote Sensing, 34, 3867–3878.
Yao H, Huang Y. 2013. Remote sensing applications to precision farming. In: Wang G, Weng Q, eds., Remote Sensing of Natural Resources. Chap 18. CRC Press, Boca Raton, FL. pp. 333–352.
Yu T, Li J, Cheng T, Hu X, Xie D, Guo H, Gu X. 2012. Data Grading Organization Method Based on Longitude and Latitude Grid. China Patent. No. CN102346923 A. 2012-02-06. (in Chinese)
Zhang C, Kovacs J M. 2012. The application of small unmanned aerial systems for precision agriculture: a review. Precision Agriculture, 13, 693–712.
Zhang L, Zhang L, Du B. 2016. Deep learning for remote sensing data: A technical tutorial on the state of the art. IEEE Geoscience and Remote Sensing Magazine, 4, 22–40.
Zhang N, Wang M, Wang N. 2002. Precision agriculture - a worldwide overview. Computers and Electronics in Agriculture, 36, 13–132.
Zhao F, Gu X, Verhoef W, Wang Q, Yu T, Liu Q. 2010. A spectral directional reflectance model of row crops. Remote Sensing of Environment, 114, 265–285.
Zhao F, Li Y, Dai X, Verhoef W, Guo Y, Shang H, Gu X, Huang H, Yu T. 2014. Simulated impact of sensor field of view and distance on field measurements of bidirectional reflectance factors for row crops. Remote Sensing of Environment, 156, 129–142. |
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