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Research Article

Mapping paddy rice using Landsat time series data in the Ganfu Plain irrigation system, Southern China, from 1988−2017

, , , , , , & ORCID Icon show all
Pages 1556-1576 | Received 13 Sep 2019, Accepted 11 Sep 2020, Published online: 07 Dec 2020

References

  • Asilo, S., K. C. A. J. de Bie, A. Skidmore, A. Nelson, M. Barbieri, and A. Maunahan. 2014. “Complementarity of Two Rice Mapping Approaches: Characterizing Strata Mapped by Hypertemporal MODIS and Rice Paddy Identification Using Multitemporal SAR[J].” Remote Sensing 6 (12): 12789–12814. doi:10.3390/rs61212789.
  • Cai, Y., K. Guan, J. Peng, S. Wang, C. Seifert, B. Wardlow, and Z. Li. 2018. “A High-performance and In-season Classification System of Field-level Crop Types Using Time-series Landsat Data and A Machine Learning approach[J].” Remote Sensing of Environment 210: 35–47. doi:10.1016/j.rse.2018.02.045.
  • Cao, J., J. Tan, Y. Cui, and Y. Luo. 2019. “Irrigation Scheduling of Paddy Rice Using Short-term Weather Forecast data[J].” Agricultural Water Management 213: 714–723. doi:10.1016/j.agwat.2018.10.046.
  • Dong, J., and X. Xiao. 2016. “Evolution of Regional to Global Paddy Rice Mapping Methods: A review[J].” ISPRS Journal of Photogrammetry and Remote Sensing 119: 214–227. doi:10.1016/j.isprsjprs.2016.05.010.
  • Dong, J., X. Xiao, W. Kou, Y. Qin, G. Zhang, L. Li, C. Jin, et al. 2015. “Tracking the Dynamics of Paddy Rice Planting Area in 1986-2010 through Time Series Landsat Images and Phenology-based algorithms[J]”. Remote Sensing of Environment 160: 99–113. DOI:10.1016/j.rse.2015.01.004.
  • Dou, Y., R. Huang, L. R. Mansaray, and J. Huang. 2020. “Mapping High Temperature Damaged Area of Paddy Rice along the Yangtze River Using Moderate Resolution Imaging Spectroradiometer data[J].” International Journal of Remote Sensing 41 (2): 471–486. doi:10.1080/01431161.2019.1643936.
  • Hansen, M. C., and T. R. Loveland. 2012. “A Review of Large Area Monitoring of Land Cover Change Using Landsat data[J].” Remote Sensing of Environment 122 (SI): 66–74. doi:10.1016/j.rse.2011.08.024.
  • Huete, A. 1997. “A Comparison of Vegetation Indices over A Global Set of TM Images for EOS-MODIS[J].” Remote Sensing of Environment 59 (3): 440–451. doi:10.1016/S0034-4257(96)00112-5.
  • Huete, A., K. Didan, T. Miura, E. P. Rodriguez, X. Gao, and L. G. Ferreira. 2002. “Overview of the Radiometric and Biophysical Performance of the MODIS Vegetation indices[J].” Remote Sensing of Environment 83 (1–2): 195–213.
  • Jin, C., X. Xiao, J. Dong, Y. Qin, and Z. Wang. 2015. “Mapping Paddy Rice Distribution Using Multi-temporal Landsat Imagery in the Sanjiang Plain, Northeast China[J].” Frontiers in Earth Science 10: 1.
  • King, L., B. Adusei, S. V. Stehman, P. V. Potapov, X. Song, A. Krylov, C. Di Bella, T. R. Loveland, D. M. Johnson, and M. C. Hansen. 2017. “A Multi-resolution Approach to National-scale Cultivated Area Estimation of soybean[J].” Remote Sensing of Environment 195: 13–29. doi:10.1016/j.rse.2017.03.047.
  • Kontgis, C., A. Schneider, and M. Ozdogan. 2015. “Mapping Rice Paddy Extent and Intensification in the Vietnamese Mekong River Delta with Dense Time Stacks of Landsat data[J].” Remote Sensing of Environment 169: 255–269. doi:10.1016/j.rse.2015.08.004.
  • Li, L., M. A. Friedl, Q. Xin, J. Gray, Y. Pan, and S. Frolking. 2014. “Mapping Crop Cycles in China Using MODIS-EVI Time series[J].” Remote Sensing 6 (3): 2473–2493. doi:10.3390/rs6032473.
  • Liheng, Z., G. Peng, and G. S. Biging. 2014. “Efficient Corn and Soybean Mapping with Temporal Extendability: A Multi-year Experiment Using Landsat imagery[J].” Remote Sensing of Environment 140: 1–13. doi:10.1016/j.rse.2013.08.023.
  • Liu, J., W. Kuang, Z. Zhang, X. Xu, Y. Qin, J. Ning, W. Zhou, et al. 2014. “Spatiotemporal Characteristics, Patterns, and Causes of Land-use Changes in China since the Late 1980s[J].” Journal of Geographical Sciences 24 (2): 195–210. DOI:10.1007/s11442-014-1082-6.
  • Onojeghuo, A. O., G. A. Blackburn, Q. Wang, P. M. Atkinson, D. Kindred, and Y. Miao. 2018. “Mapping Paddy Rice Fields by Applying Machine Learning Algorithms to Multi-temporal Sentinel-1A and Landsat data[J].” International Journal of Remote Sensing 39 (4): 1042–1067. doi:10.1080/01431161.2017.1395969.
  • Qin, Y., X. Xiao, J. Dong, Y. Zhou, Z. Zhu, G. Zhang, G. Du, et al. 2015. “Mapping Paddy Rice Planting Area in Cold Temperate Climate Region through Analysis of Time Series Landsat 8 (OLI), Landsat 7 (ETM+) and MODIS imagery[J]”. ISPRS Journal of Photogrammetry and Remote Sensing 105: 220–233. DOI:10.1016/j.isprsjprs.2015.04.008.
  • Qiu, B., D. Lu, Z. Tang, C. Chen, and F. Zou. 2017. “Automatic and Adaptive Paddy Rice Mapping Using Landsat Images: Case Study in Songnen Plain in Northeast China[J].” Science of the Total Environment 598: 581–592. doi:10.1016/j.scitotenv.2017.03.221.
  • Shihua, L., X. Jingtao, N. Ping, Z. Jing, W. Hongshu, and W. Jingxian. 2014. “Monitoring Paddy Rice Phenology Using Time Series MODIS Data over Jiangxi Province, China[J].” International Journal of Agricultural and Biological Engineering 7 (6): 28–36.
  • Sun, B., L. Zhang, L. Yang, F. Zhang, D. Norse, and Z. Zhu. 2012. “Agricultural Non-Point Source Pollution in China: Causes and Mitigation Measures[J].” Ambio 41 (4): 370–379. doi:10.1007/s13280-012-0249-6.
  • Teluguntla, P., P. S. Thenkabail, J. Xiong, M. K. Gumma, R. G. Congalton, A. Oliphant, J. Poehnelt, K. Yadav, M. Rao, and R. Massey. 2017. “Spectral Matching Techniques (Smts) and Automated Cropland Classification Algorithms (Accas) for Mapping Croplands of Australia Using MODIS 250-m Time-series (2000-2015) data[J].” International Journal of Digital Earth 10 (9): 944–977. doi:10.1080/17538947.2016.1267269.
  • Tian, H., M. Wu, L. Wang, and Z. Niu. 2018. “Mapping Early, Middle and Late Rice Extent Using Sentinel-1A and Landsat-8 Data in the Poyang Lake Plain, China[J].” Sensors 18 (UNSP): 1851. doi:10.3390/s18010185.
  • Waldner, F., G. S. Canto, and P. Defourny. 2015. “Automated Annual Cropland Mapping Using Knowledge-based Temporal features[J].” ISPRS Journal of Photogrammetry and Remote Sensing 110: 1–13. doi:10.1016/j.isprsjprs.2015.09.013.
  • Wang, J., J. Huang, P. Gao, C. Wei, and L. R. Mansaray. 2016. “Dynamic Mapping of Rice Growth Parameters Using HJ-1 CCD Time Series data[J].” Remote Sensing 8 (11): 931. doi:10.3390/rs8110931.
  • Wang, W., Y. Ding, Q. Shao, J. Xu, X. Jiao, Y. Luo, and Z. Yu. 2017. “Bayesian Multi-model Projection of Irrigation Requirement and Water Use Efficiency in Three Typical Rice Plantation Region of China Based on CMIP5[J].” Agricultural and Forest Meteorology 232: 89–105. doi:10.1016/j.agrformet.2016.08.008.
  • Xiao, X. M., S. Boles, J. Y. Liu, D. F. Zhuang, S. Frolking, C. S. Li, W. Salas, and B. Moore. 2005. “Mapping Paddy Rice Agriculture in Southern China Using Multi-temporal MODIS images[J].” Remote Sensing of Environment 95 (4): 480–492. doi:10.1016/j.rse.2004.12.009.
  • Xu, Y., L. Yu, D. Peng, X. Cai, Y. Cheng, J. Zhao, Y. Zhao, et al. 2018. “Exploring the Temporal Density of Landsat Observations for Cropland Mapping: Experiments from Egypt, Ethiopia, and South Africa[J].” International Journal of Remote Sensing 39 (21): 7328–7349. DOI:10.1080/01431161.2018.1468115.
  • Xu, Y., L. Yu, Y. Zhao, D. Feng, Y. Cheng, X. Cai, and P. Gong. 2017. “Monitoring Cropland Changes along the Nile River in Egypt over past Three Decades (1984-2015) Using Remote sensing[J].” International Journal of Remote Sensing 38 (15): 4459–4480. doi:10.1080/01431161.2017.1323285.
  • Yang, S., S. Shen, B. Li, T. L. Toan, and W. He. 2008. “Rice Mapping and Monitoring Using ENVISAT ASAR data[J].” IEEE Geoscience and Remote Sensing Letters 5 (1): 108–112. doi:10.1109/LGRS.2007.912089.
  • Zha, Y., J. Gao, and S. Ni. 2003. “Use of Normalized Difference Built-up Index in Automatically Mapping Urban Areas from TM imagery[J].” International Journal of Remote Sensing 24 (3): 583–594. doi:10.1080/01431160304987.
  • Zhang, G., X. Xiao, C. M. Biradar, J. Dong, Y. Qin, M. A. Menarguez, Y. Zhou, et al. 2017. “Spatiotemporal Patterns of Paddy Rice Croplands in China and India from 2000 to 2015[J]”. Science of the Total Environment 579: 82–92. DOI:10.1016/j.scitotenv.2016.10.223.
  • Zhang, G., X. Xiao, J. Dong, W. Kou, C. Jin, Y. Qin, Y. Zhou, J. Wang, M. A. Menarguez, and C. Biradar. 2015. “Mapping Paddy Rice Planting Areas through Time Series Analysis of MODIS Land Surface Temperature and Vegetation Index data[J].” ISPRS Journal of Photogrammetry and Remote Sensing 106: 157–171. doi:10.1016/j.isprsjprs.2015.05.011.
  • Zhang, X., Q. Xiong, L. Di, J. Tang, J. Yang, H. Wu, Y. Qin, R. Su, and W. Zhou. 2018. “Phenological Metrics-based Crop Classification Using HJ-1 CCD Images and Landsat 8 imagery[J].” International Journal of Digital Earth 11 (12): 1219–1240. doi:10.1080/17538947.2017.1387296.
  • Zhang, X., M. Zhang, Y. Zheng, and B. Wu. 2016. “Crop Mapping Using PROBA-V Time Series Data at the Yucheng and Hongxing Farm in China[J].” Remote Sensing 8: 91511. doi:10.3390/rs8110915.
  • Zhong, L., P. Gong, and G. S. Biging. 2014. “Efficient Corn and Soybean Mapping with Temporal Extendability: A Multi-year Experiment Using Landsat imagery[J].” Remote Sensing of Environment 140: 1–13.
  • Zhong, L., T. Hawkins, G. Biging, and P. Gong. 2011. “A Phenology-based Approach to Map Crop Types in the San Joaquin Valley, California[J].” International Journal of Remote Sensing 32 (22): 7777–7804. doi:10.1080/01431161.2010.527397.
  • Zhu, Z., and C. E. Woodcock. 2014. “Continuous Change Detection and Classification of Land Cover Using All Available Landsat data[J].” Remote Sensing of Environment 144: 152–171. doi:10.1016/j.rse.2014.01.011.

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