1,956
Views
11
CrossRef citations to date
0
Altmetric
Research Article

Cropland data fusion and correction using spatial analysis techniques and the Google Earth Engine

&
Pages 1026-1045 | Received 27 Jun 2020, Accepted 13 Oct 2020, Published online: 28 Oct 2020

References

  • Allan, J. D. 2004. “Landscapes and Riverscapes: The Influence of Land Use on Stream Ecosystems.” Annual Review of Ecology, Evolution, and Systematics 35: 257–284.
  • Azzari, G., and D. B. Lobell. 2017. “Landsat-based Classification in the Cloud: An Opportunity for a Paradigm Shift in Land Cover Monitoring.” Remote Sensing of Environment 202: 64–74. doi:10.1016/j.rse.2017.05.025.
  • Bartholome, E., and A. S. Belward. 2005. “GLC2000: A New Approach to Global Land Cover Mapping from Earth Observation Data.” International Journal of Remote Sensing 26 (9): 1959–1977. doi:10.1080/01431160412331291297.
  • Benediktsson, J. A., P. H. Swain, and O. K. Ersoy. 1990. “Neural Network Approaches Versus Statistical Methods In Classification Of Multisource Remote Sensing Data.” IEEE Transactions on Geoscience and Remote Sensing 28 (4): 540–552. doi:10.1109/TGRS.1990.572944.
  • Bicheron, P., P. Defourny, C. Brockmann, L. Schouten, and O. Arino. 2008. “GLOBCOVER: Products Description and Validation Report.”
  • Breiman, L. 2001. “Random Forests. Machine Learning.” 45 (1), 5–32.
  • Brown, M. E., K. M. D. Beurs, and M. Marshall. 2012. “Global Phenological Response to Climate Change in Crop Areas Using Satellite Remote Sensing of Vegetation, Humidity and Temperature over 26 Years.” Remote Sensing of Environment 126: 174–183. doi:10.1016/j.rse.2012.08.009.
  • Chen, D., M. Lu, Q. Zhou, J. Xiao, Y. Ru, Y. Wei, and W. Wu. 2019. “Comparison of Two Synergy Approaches for Hybrid Cropland Mapping.” Remote Sensing 11 (3): 213. doi:10.3390/rs11030213.
  • Chen, G., K. G. Zhao, G. J. McDermid, and G. J. Hay. 2012. “The Influence of Sampling Density on Geographically Weighted Regression: A Case Study Using Forest Canopy Height and Optical Data.” International Journal of Remote Sensing 33 (9): 2909–2924. doi:10.1080/01431161.2011.624130.
  • Chen, J., J. Chen, A. Liao, X. Cao, L. Chen, X. Chen, C. He, G. Han, S. Peng, and M. Lu. 2015. “Global Land Cover Mapping at 30m Resolution: A POK-based Operational Approach.” ISPRS Journal of Photogrammetry & Remote Sensing 103: 7–27. doi:10.1016/j.isprsjprs.2014.09.002.
  • Congalton, R. G., and K. Green. 2019. Assessing the Accuracy of Remotely Sensed Data: Principles and Practices.. CRC press.
  • Congalton, R. G., J. Y. Gu, K. Yadav, P. Thenkabail, and M. Ozdogan. 2014. “Global Land Cover Mapping: A Review and Uncertainty Analysis.” Remote Sensing 6 (12): 12070–12093. doi:10.3390/rs61212070.
  • Dong, J., X. Xiao, M. A. Menarguez, G. Zhang, Y. Qin, D. Thau, C. Biradar, and B. Moore 3rd. 2016. “Mapping Paddy Rice Planting Area in Northeastern Asia with Landsat 8 Images, Phenology-based Algorithm and Google Earth Engine.” Remote Sensing of Environment 185: 142–154. doi:10.1016/j.rse.2016.02.016.
  • Dong, T.-Y., W.-J. Dong, Y. Guo, J.-M. Chou, S.-L. Yang, D. Tian, and -D.-D. Yan. 2018. “Future Temperature Changes over the Critical Belt and Road Region Based on CMIP5 Models.” Advances in Climate Change Research 9 (1): 57–65. doi:10.1016/j.accre.2018.01.003.
  • FAO. 2015. “FAOSTAT/Land Use.” Accessed December 13. http://www.fao.org/faostat/en/#data/RL/
  • Foley, J. A., R. DeFries, G. P. Asner, C. Barford, G. Bonan, S. R. Carpenter, F. S. Chapin, M. T. Coe, G. C. Daily, and H. K. Gibbs. 2005. “Global Consequences of Land Use.” Science 309 (5734): 570–574. doi:10.1126/science.1111772.
  • Friedl, M. A., D. Sulla-Menashe, B. Tan, A. Schneider, N. Ramankutty, A. Sibley, and X. Huang. 2010. “MODIS Collection 5 Global Land Cover: Algorithm Refinements and Characterization of New Datasets.” Remote Sensing of Environment 114 (1): 168–182. doi:10.1016/j.rse.2009.08.016.
  • Fritz, S., I. McCallum, C. Schill, C. Perger, R. Grillmayer, F. Achard, F. Kraxner, and M. Obersteiner. 2009. “Geo-Wiki.Org: The Use of Crowdsourcing to Improve Global Land Cover.” Remote Sensing 1 (3): 345–354. doi:10.3390/rs1030345.
  • Fritz, S., and L. See. 2008. “Identifying and Quantifying Uncertainty and Spatial Disagreement in the Comparison of Global Land Cover for Different Applications.” Global Change Biology 14 (5): 1057–1075. doi:10.1111/j.1365-2486.2007.01519.x.
  • Fritz, S., L. See, I. McCallum, C. Schill, M. Obersteiner, M. van der Velde, H. Boettcher, P. Havlík, and F. Achard. 2011a. “Highlighting Continued Uncertainty in Global Land Cover Maps for the User Community.” Environmental Research Letters 6 (4): 044005. doi:10.1088/1748-9326/6/4/044005.
  • Fritz, S., L. You, A. Bun, L. See, I. McCallum, C. Schill, C. Perger, J. Liu, M. Hansen, and M. Obersteiner. 2011b. “Cropland for sub-Saharan Africa: A Synergistic Approach Using Five Land Cover Data Sets.” Geophysical Research Letters 38 (4). doi:10.1029/2010GL046213.
  • Fritz, Steffen, Linda See, Ian McCallum, Liangzhi You, Andriy Bun, Elena Moltchanova, Martina Duerauer, Fransizka Albrecht, Christian Schill, and Christoph Perger. 2015. “Mapping global cropland and field size.” Global Change Biology 21 (5):1980–92.
  • GCOS. 2013. “GCOS Essential Climate Variables.” https://public.wmo.int/en/programmes/global-climate-observing-system?name=EssentialClimateVariables%3E%28accessed/
  • Gengler, S., and P. Bogaert. 2018. “Combining Land Cover Products Using a Minimum Divergence and a Bayesian Data Fusion Approach.” International Journal of Geographical Information Science 32 (4): 806–826. doi:10.1080/13658816.2017.1413577.
  • Gong, P., H. Liu, M. Zhang, C. Li, J. Wang, H. Huang, N. Clinton, et al. 2019. “Stable Classification with Limited Sample: Transferring a 30-m Resolution Sample Set Collected in 2015 to Mapping 10-m Resolution Global Land Cover in 2017.” Science Bulletin 64 (6): 370–373. doi:10.1016/j.scib.2019.03.002.
  • Gong, P., J. Wang, L. Yu, Y. Zhao, Y. Zhao, L. Liang, Z. Niu, et al. 2013. “Finer Resolution Observation and Monitoring of Global Land Cover: First Mapping Results with Landsat TM and ETM+ Data.” International Journal of Remote Sensing 34 (7): 2607–2654. doi:10.1080/01431161.2012.748992.
  • Gorelick, N., M. Hancher, M. Dixon, S. Ilyushchenko, D. Thau, and R. Moore. 2017. “Google Earth Engine: Planetary-scale Geospatial Analysis for Everyone.” Remote Sensing of Environment 202: 18–27. doi:10.1016/j.rse.2017.06.031.
  • Grekousis, G., G. Mountrakis, and M. Kavouras. 2015. “An Overview of 21 Global and 43 Regional Land-cover Mapping Products.” International Journal of Remote Sensing 36 (21): 5309–5335. doi:10.1080/01431161.2015.1093195.
  • Hansen, M. C., R. S. Defries, J. R. G. Townshend, and R. Sohlberg. 2000. “Global Land Cover Classification at 1km Spatial Resolution Using a Classification Tree Approach.” International Journal of Remote Sensing 21: 1331–1364. doi:10.1080/014311600210209.
  • Hansen, M. C., and B. Reed. 2000. “A Comparison of the IGBP DISCover and University of Maryland 1 Km Global Land Cover Products.” International Journal of Remote Sensing 9 (6–7): 1365–1373. doi:10.1080/014311600210218.
  • Herold, M., P. Mayaux, C. E. Woodcock, A. Baccini, and C. Schmullius. 2008. “Some Challenges in Global Land Cover Mapping: An Assessment of Agreement and Accuracy in Existing 1 Km Datasets.” Remote Sensing of Environment 112: 2538–2556. doi:10.1016/j.rse.2007.11.013.
  • Hu, Q., Y. X. Ma, B. D. Xu, Q. Song, H. J. Tang, and W. B. Wu. 2018. “Estimating Sub-Pixel Soybean Fraction from Time-Series MODIS Data Using an Optimized Geographically Weighted Regression Model.” Remote Sensing 10 (4): 491. doi:10.3390/rs10040491.
  • Huang, S., C. Ramirez, K. Kennedy, J. Mallory, J. Wang, and C. Chu. 2017. “Updating Land Cover Automatically Based on Change Detection Using Satellite Images: Case Study of National Forests in Southern California.” GIScience & Remote Sensing 54 (4): 495–514. doi:10.1080/15481603.2017.1286727.
  • Huang, W., B. DeVries, C. Huang, M. W. Lang, J. W. Jones, I. F. Creed, and M. L. Carroll. 2018. “Automated Extraction of Surface Water Extent from Sentinel-1 Data.” Remote Sensing 1 (5): 797. doi:10.3390/rs10050797.
  • Irons, J. R., J. L. Dwyer, and J. A. Barsi. 2012. “The Next Landsat Satellite: The Landsat Data Continuity Mission.” Remote Sensing of Environment 21: 11–21. doi:10.1016/j.rse.2011.08.026.
  • Jarnagin, S. T. 2004. “Regional and Global Patterns of Population, Land Use, and Land Cover Change: An Overview of Stressors and Impacts.” GIScience & Remote Sensing 41 (3): 207–227. doi:10.2747/1548-1603.41.3.207.
  • Jia, K., S. Liang, X. Wei, L. Zhang, Y. Yao, and S. Gao. 2014. “Automatic Land-cover Update Approach Integrating Iterative Training Sample Selection and a Markov Random Field Model.” Remote Sensing Letters 5 (2): 148–156. doi:10.1080/2150704X.2014.889862.
  • Jin, G., X. Deng, X. Chu, Z. Li, and Y. Wang. 2017. “Optimization of Land-use Management for Ecosystem Service Improvement: A Review.” Physics and Chemistry of the Earth, Parts A/B/C 101: 70–77. doi:10.1016/j.pce.2017.03.003.
  • Jung, M., K. Henkel, M. Herold, and G. Churkina. 2006. “Exploiting Synergies of Global Land Cover Products for Carbon Cycle Modeling.” Remote Sensing of Environment 101 (4): 534–553. doi:10.1016/j.rse.2006.01.020.
  • Latifovic, R., and I. Olthof. 2004. “Accuracy Assessment Using Sub-pixel Fractional Error Matrices of Global Land Cover Products Derived from Satellite Data.” Remote Sensing of Environment 38 (2): 153–165. doi:10.1016/j.rse.2003.11.016.
  • Liang, L., Q. Liu, G. Liu, H. Li, and C. Huang. 2019. “Accuracy Evaluation and Consistency Analysis of Four Global Land Cover Products in the Arctic Region.” Remote Sensing 11 (12): 1396. doi:10.3390/rs11121396.
  • Loveland, T. R., B. C. Reed, J. F. Brown, D. O. Ohlen, Z. Zhu, L. Yang, and J. W. Merchant. 2000. “Development of a Global Land Cover Characteristics Database and IGBP DISCover from 1 Km AVHRR Data.” International Journal of Remote Sensing 34 (6–7): 1303–1330. doi:10.1080/014311600210191.
  • Lu, B. B., M. Charlton, P. Harris, and A. S. Fotheringham. 2014. “Geographically Weighted Regression with a Non- Euclidean Distance Metric: A Case Study Using Hedonic House Price Data.” International Journal of Geographical Information Science 28: 660–681. doi:10.1080/13658816.2013.865739.
  • Lu, M., W. Wu, L. You, D. Chen, L. Zhang, P. Yang, and H. Tang. 2017. “A Synergy Cropland of China by Fusing Multiple Existing Maps and Statistics.” Sensors 17 (7): 1613. doi:10.3390/s17071613.
  • Lu, M., W. Wu, L. Zhang, A. Liao, S. Peng, and H. Tang. 2016. “A Comparative Analysis of Five Global Cropland Datasets in China.” Science China Earth Sciences 59 (12): 2307–2317. doi:10.1007/s11430-016-5327-3.
  • Maeda, E. E., A. R. Formaggio, and Y. E. Shimabukuro. 2008. “Impacts of Land Use and Land Cover Changes on Sediment Yield in a Brazilian Amazon Drainage Basin.” GIScience & Remote Sensing 45 (4): 443–453. doi:10.2747/1548-1603.45.4.443.
  • Markham, B. L., J. C. Storey, D. L. Williams, and J. R. Irons. 2004. “Landsat Sensor Performance: History and Current Status.” IEEE Transactions on Geoscience and Remote Sensing 42 (12): 2691–2694. doi:10.1109/TGRS.2004.840720.
  • Mayaux, P., H. Eva, J. Gallego, A. H. Strahler, M. Herold, S. Agrawal, S. Naumov, E. E. De Miranda, C. M. Di Bella, and C. Ordoyne. 2006. “Validation of the Global Land Cover 2000 Map.” IEEE Transactions on Geoscience and Remote Sensing 44 (7): 1728–1739. doi:10.1109/TGRS.2006.864370.
  • National Bureau of Statistics of China. 2015. “China Statistical Yearbook 2015/Agriculture.” Accessed 13 December 2019. http://www.stats.gov.cn/tjsj/ndsj/2015/indexch.htm/
  • Oliphant, A. J., P. S. Thenkabail, P. Teluguntla, J. Xiong, M. K. Gumma, R. G. Congalton, and K. Yadav. 2019. “Mapping Cropland Extent of Southeast and Northeast Asia Using Multi-year Time-series Landsat 30-m Data Using a Random Forest Classifier on the Google Earth Engine Cloud.” International Journal of Applied Earth Observation and Geoinformation 81: 110–124. doi:10.1016/j.jag.2018.11.014.
  • Olofsson, P., G. M. Foody, M. Herold, S. V. Stehman, C. E. Woodcock, and M. A. Wulder. 2014. “Good Practices for Estimating Area and Assessing Accuracy of Land Change.” Remote Sensing of Environment 148: 42–57. doi:10.1016/j.rse.2014.02.015.
  • Peel, M. C., B. L. Finlayson, and T. A. McMahon. 2007. Updated World Map of the Koppen-Geiger Climate Classification Hydrology and Earth System Sciences 11, 1633–1644. doi:10.5194/hess-11-1633-2007
  • Pekel, J. F., A. Cottam, N. Gorelick, and A. S. Belward. 2016. “High-resolution Mapping of Global Surface Water and Its Long-term Changes.” Nature 540: 418–422. doi:10.1038/nature20584.
  • Pelletier, C., S. Valero, J. Inglada, N. Champion, and G. Dedieu. 2016. “Assessing the Robustness of Random Forests to Map Land Cover with High Resolution Satellite Image Time Series over Large Areas.” Remote Sensing of Environment 187: 156–168. doi:10.1016/j.rse.2016.10.010.
  • Perez-Hoyos, A., F. J. Garcia-Haro, and J. San-Miguel-Ayanz. 2012. “A Methodology to Generate A Synergetic Land-cover Map by Fusion of Different Land-cover Products.” International Journal of Applied Earth Observation and Geoinformation 19: 72–87. doi:10.1016/j.jag.2012.04.011.
  • Pérez-Hoyos, A., F. Rembold, H. Kerdiles, and J. Gallego. 2017. “Comparison of Global Land Cover Datasets for Cropland Monitoring.” Remote Sensing 9 (11): 1118. doi:10.3390/rs9111118.
  • Portmann, F. T., S. Siebert, and P. Döll. 2010. “MIRCA2000—Global monthly irrigated and rainfed crop areas around the year 2000: A new high‐resolution data set for agricultural and hydrological modeling.” Global Biogeochemical Cycles 24 (1).
  • Poulter, B., N. MacBean, A. Hartley, I. Khlystova, O. Arino, R. Betts, S. Bontemps, M. Boettcher, C. Brockmann, and P. Defourny. 2015. “Plant Functional Type Classification for Earth System Models: Results from the European Space Agency’s Land Cover Climate Change Initiative.” Geoscientific Model Development 8: 2315–2328. doi:10.5194/gmd-8-2315-2015.
  • Radoux, J., C. Lamarche, E. Van Bogaert, S. Bontemps, C. Brockmann, and P. Defourny. 2014. “Automated Training Sample Extraction for Global Land Cover Mapping.” Remote Sensing 6 (5): 3965–3987. doi:10.3390/rs6053965.
  • Ran, Y., X. Li, and L. Lu. 2009. “China Land Cover Classification at 1 Km Spatial Resolution Based on a Multi-source Data Fusion Approach.” Advances in Earth Science 24: 192–203.
  • Ran, Y. H., X. Li, L. Lu, and Z. Y. Li. 2012. “Large-scale Land Cover Mapping with the Integration of Multi-source Information Based on the Dempster–Shafer Theory.” International Journal of Geographical Information Science 26 (1): 169–191. doi:10.1080/13658816.2011.577745.
  • Rodriguez-Galiano, V. F., B. Ghimire, J. Rogan, M. Chica-Olmo, and J. P. Rigol-Sanchez. 2012. “An Assessment of the Effectiveness of a Random Forest Classifier for Land-cover Classification.” ISPRS Journal of Photogrammetry and Remote Sensing 67: 93–104. doi:10.1016/j.isprsjprs.2011.11.002.
  • Sakti, A. D., W. Takeuchi, and K. Wikantika. 2017. “Development of Global Cropland Agreement Level Analysis by Integrating Pixel Similarity of Recent Global Land Cover Datasets.” Journal of Environmental Protection 8 (12): 1509–1529. doi:10.4236/jep.2017.812093.
  • Santoro, M., G. Kirches, J. Wevers, M. Boettcher, C. Brockmann, C. Lamarche, and P. Defourny. 2017. “Land Cover CCI: Product User Guide Version 2.0.” Accessed December 13. http://maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC/
  • Schepaschenko, D., L. See, M. Lesiv, I. McCallum, S. Fritz, C. Salk, E. Moltchanova, et al. 2015. “Development of a Global Hybrid Forest Mask through the Synergy of Remote Sensing, Crowdsourcing and FAO Statistics.” Remote Sensing of Environment 162: 208–220. doi:10.1016/j.rse.2015.02.011.
  • See, L., D. Schepaschenko, M. Lesiv, I. McCallum, S. Fritz, A. Comber, C. Perger, et al. 2015. “Building a Hybrid Land Cover Map with Crowdsourcing and Geographically Weighted Regression.” ISPRS Journal of Photogrammetry and Remote Sensing 103: 48–56. doi:10.1016/j.isprsjprs.2014.06.016.
  • See, L. M., and S. Fritz. 2006. “A Method to Compare and Improve Land Cover Datasets: Application to the GLC-2000 and MODIS Land Cover Products.” IEEE Transactions on Geoscience and Remote Sensing 44 (7): 1740–1746. doi:10.1109/TGRS.2006.874750.
  • Sexton, J. O., X.-P. Song, M. Feng, P. Noojipady, A. Anand, C. Huang, D.-H. Kim, K. M. Collins, S. Channan, and C. DiMiceli. 2013. “Global, 30-m Resolution Continuous Fields of Tree Cover: Landsat-based Rescaling of MODIS Vegetation Continuous Fields with Lidar-based Estimates of Error.” International Journal of Digital Earth 6 (5): 427–448. doi:10.1080/17538947.2013.786146.
  • Story, M., and R. G. Congalton. 1986. “Accuracy Assessment: A User’s Perspective.” Photogrammetric Engineering and Remote Sensing 52: 397–399.
  • Teluguntla, P., P. S. Thenkabail, A. Oliphant, J. Xiong, M. K. Gumma, R. G. Congalton, K. Yadav, and A. Huete. 2018. “A 30-m Landsat-derived Cropland Extent Product of Australia and China Using Random Forest Machine Learning Algorithm on Google Earth Engine Cloud Computing Platform.” ISPRS Journal of Photogrammetry and Remote Sensing 144: 325–340. doi:10.1016/j.isprsjprs.2018.07.017.
  • Tian, F., B. Wu, H. Zeng, X. Zhang, and J. Xu. 2019. “Efficient Identification of Corn Cultivation Area with Multitemporal Synthetic Aperture Radar and Optical Images in the Google Earth Engine Cloud Platform.” Remote Sensing 4 (6): 629. doi:10.3390/rs11060629.
  • United Nations Population Division. 2019. “World Population Prospects: The 2019 Revision. Accessed 13 December 2019. https://data.un.org/Data.aspx?q=population&d=PopDiv&f=variableID%3a12/
  • Vancutsem, C., E. Marinho, F. Kayitakire, L. See, and S. Fritz. 2012. “Harmonizing and Combining Existing Land Cover/land Use Datasets for Cropland Area Monitoring at the African Continental Scale.” Remote Sensing 5 (1): 19–41. doi:10.3390/rs5010019.
  • Verburg, P. H., K. Neumann, and L. Nol. 2011. “Challenges in Using Land Use and Land Cover Data for Global Change Studies.” Global Change Biology 17 (2): 974–989. doi:10.1111/j.1365-2486.2010.02307.x.
  • Waldner, F., S. Fritz, A. Di Gregorio, and P. Defourny. 2015. “Mapping Priorities to Focus Cropland Mapping Activities: Fitness Assessment of Existing Global, Regional and National Cropland Maps.” Remote Sensing 7: 7959–7986. doi:10.3390/rs70607959.
  • Wessels, K. J., F. Van den Bergh, D. P. Roy, B. P. Salmon, K. C. Steenkamp, B. MacAlister, D. Swanepoel, and D. Jewitt. 2016. “Rapid Land Cover Map Updates Using Change Detection and Robust Random Forest Classifiers.” Remote Sensing 8: 888.
  • World Bank, FAO, and the United Nations Statistical Commission. 2011. “Global Strategy to Improve Agricultural and Rural Statistics.” Accessed 13 December 2019. http://www.fao.org/docrep/015/am082e/am082e00.pdf/
  • Wulder, M. A., J. C. White, T. R. Loveland, C. E. Woodcock, A. S. Belward, W. B. Cohen, E. A. Fosnight, J. Shaw, J. G. Masek, and D. P. Roy. 2016. “The Global Landsat Archive: Status, Consolidation, and Direction.” Remote Sensing of Environment 185: 271–283. doi:10.1016/j.rse.2015.11.032.
  • Xu, G., H. R. Zhang, B. Z. Chen, H. F. Zhang, J. W. Yan, J. Chen, M. L. Che, X. F. Lin, and X. M. Dou. 2014. “A Bayesian Based Method to Generate a Synergetic Land-Cover Map from Existing Land-Cover Products.” Remote Sensing 6 (6): 5589–5613. doi:10.3390/rs6065589.
  • Yang, Y. K., P. F. Xiao, X. Z. Feng, and H. X. Li. 2017. “Accuracy Assessment of Seven Global Land Cover Datasets over China.” ISPRS Journal of Photogrammetry and Remote Sensing 125: 156–173. doi:10.1016/j.isprsjprs.2017.01.016.
  • Yu, L., and P. Gong. 2012. “Google Earth as a Virtual Globe Tool for Earth Science Applications at the Global Scale: Progress and Perspectives.” International Journal of Remote Sensing 33 (12): 3966–3986. doi:10.1080/01431161.2011.636081.
  • Zhang, H. K., and D. P. Roy. 2017. “Using the 500 M MODIS Land Cover Product to Derive a Consistent Continental Scale 30 M Landsat Land Cover Classification.” Remote Sensing of Environment 197: 15–34. doi:10.1016/j.rse.2017.05.024.
  • Zhang, J. H., L. L. Feng, and F. M. Yao. 2014. “Improved Maize Cultivated Area Estimation over a Large Scale Combining MODIS–EVI Time Series Data and Crop Phenological Information.” ISPRS Journal of Photogrammetry and Remote Sensing 94: 102–113. doi:10.1016/j.isprsjprs.2014.04.023.
  • Zhang, X., B. Wu, G. Ponce-Campos, M. Zhang, S. Chang, and F. Tian. 2018. “Mapping up-to-Date Paddy Rice Extent at 10 M Resolution in China through the Integration of Optical and Synthetic Aperture Radar Images.” Remote Sensing 10 (8): 1200. doi:10.3390/rs10081200.
  • Zhong, Y., C. Luo, X. Hu, L. Wei, X. Wang, and S. Jin. 2019. “Cropland Product Fusion Method Based on the Overall Consistency Difference: A Case Study of China.” Remote Sensing 11 (9): 1065. doi:10.3390/rs11091065.
  • Zhu, Z., and C. E. Woodcock. 2012. “Object-based Cloud and Cloud Shadow Detection in Landsat Imagery.” Remote Sensing of Environment 118: 83–94. doi:10.1016/j.rse.2011.10.028.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.