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Articles

GEE-Based monitoring method of key management nodes in cotton production

ORCID Icon, , , , , & show all
Pages 1907-1922 | Received 07 Mar 2023, Accepted 19 May 2023, Published online: 28 May 2023

References

  • Abdi, A. M. 2020. “Land Cover and Land use Classification Performance of Machine Learning Algorithms in a Boreal Landscape Using Sentinel-2 Data.” GIScience & Remote Sensing 57 (1): 1–20. doi:10.1080/15481603.2019.1650447.
  • Adrian, J., V. Sagan, and M. Maimaitijiang. 2021. “Sentinel sar-Optical Fusion for Crop Type Mapping Using Deep Learning and Google Earth Engine.” ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 175: 215–235. doi:10.1016/j.isprsjprs.2021.02.018.
  • Amani, M., M. Kakooei, A. Moghimi, A. Ghorbanian, B. Ranjgar, S. Mahdavi, A. Davidson, et al. 2020. “Application of Google Earth Engine Cloud Computing Platform, Sentinel Imagery, and Neural Networks for Crop Mapping in Canada.” Remote Sensing 12 (21): 3561. doi:10.3390/rs12213561.
  • Arjasakusuma, S., S. Swahyu Kusuma, R. Rafif, S. Saringatin, and P. Wicaksono. 2020. “Combination of Landsat 8 oli and Sentinel-1 sar Time-Series Data for Mapping Paddy Fields in Parts of West and Central Java Provinces, Indonesia.” ISPRS International Journal of Geo-Information 9 (11): 663. doi:10.3390/ijgi9110663.
  • Bey, A., J. Jetimane, S. N. Lisboa, N. Ribeiro, A. Sitoe, and P. Meyfroidt. 2020. “Mapping Smallholder and Large-Scale Cropland Dynamics with a Flexible Classification System and Pixel-Based Composites in an Emerging Frontier of Mozambique.” REMOTE SENSING OF ENVIRONMENT 239 (111611), doi:10.1016/j.rse.2019.111611.
  • Breiman, L., J. H. Friedman, R. A. Olshen, and C. J. Stone. 1984. Classification and Regression Trees, Vol. 40. New York: Routledge.
  • Chen, P., F. Ouyang, G. Wang, H. Qi, W. Xu, W. Yang, Y. Zhang, et al. 2021. “Droplet Distributions in Cotton Harvest aid Applications Vary with the Interactions among the Unmanned Aerial Vehicle Spraying Parameters.” INDUSTRIAL CROPS AND PRODUCTS 163 (113324), doi:10.1016/j.indcrop.2021.113324.
  • Cohen, J. 1960. “A Coefficient of Agreement for Nominal Scales.” EDUCATIONAL AND PSYCHOLOGICAL MEASUREMENT 20 (1): 37–46. doi:10.1177/001316446002000104.
  • Elnashar, A., L. Wang, B. Wu, W. Zhu, and H. Zeng. 2021. “Synthesis of Global Actual Evapotranspiration from 1982 to 2019.” Earth System Science Data 13 (2): 447–480. doi:10.5194/essd-13-447-2021.
  • 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.
  • Kibret, K. S., C. Marohn, and G. Cadisch. 2020. “Use of Modis evi to map Crop Phenology, Identify Cropping Systems, Detect Land use Change and Drought Risk in Ethiopia – an Application of Google Earth Engine.” European Journal of Remote Sensing 53 (1): 176–191. doi:10.1080/22797254.2020.1786466.
  • Li, H., D. Fu, C. Huang, F. Su, Q. Liu, G. Liu, S. Wu, et al. 2020. “An Approach to High-Resolution Rice Paddy Mapping Using Time-Series Sentinel-1 sar Data in the mun River Basin, Thailand.” Remote Sensing 12 (23): 3959. doi:10.3390/rs12233959.
  • Liu, L., X. Xiao, Y. Qin, J. Wang, X. Xu, Y. Hu, Z. Qiao, et al. 2020. “Mapping Cropping Intensity in China Using Time Series Landsat and Sentinel-2 Images and Google Earth Engine.” REMOTE SENSING OF ENVIRONMENT 239 (111624), doi:10.1016/j.rse.2019.111624.
  • Luo, C., B. Qi, H. Liu, D. Guo, L. Lu, Q. Fu, Y Shao, et al. 2021. “Using Time Series Sentinel-1 Images for Object-Oriented Crop Classification in Google Earth Engine.” Remote Sensing 13 (4): 561. doi:10.3390/rs13040561.
  • Magidi, J., L. Nhamo, S. Mpandeli, and T. Mabhaudhi. 2021. “Application of the Random Forest Classifier to map Irrigated Areas Using Google Earth Engine.” Remote Sensing 13 (5): 876. doi:10.3390/rs13050876.
  • Markos, A., N. Sims, and G. Giuliani. 2023. “Beyond the sdg 15.3.1 Good Practice Guidance 1.0 Using the Google Earth Engine Platform: Developing a Self-Adjusting Algorithm to Detect Significant Changes in Water use Efficiency and net Primary Production.” Big Earth Data 7 (1): 59–80. doi:10.1080/20964471.2022.2076375.
  • Naboureh, A., J. Bian, G. Lei, and A. Li. 2021. “A Review of Land use/Land Cover Change Mapping in the China-Central Asia-West Asia Economic Corridor Countries.” Big Earth Data 5 (2): 237–257. doi:10.1080/20964471.2020.1842305.
  • Nagasubramanian, K., S. Jones, S. Sarkar, A. K. Singh, A. Singh, and B. Ganapathysubramanian. 2018. “Hyperspectral Band Selection Using Genetic Algorithm and Support Vector Machines for Early Identification of Charcoal rot Disease in Soybean Stems.” Plant Methods 14(1). doi:10.1186/s13007-018-0349-9.
  • Otsu, N. 1979. “A Threshold Selection Method from Gray-Level Histograms.” IEEE Transactions on Systems, Man, and Cybernetics 9 (1): 62–66. doi:10.1109/TSMC.1979.4310076.
  • Paludo, A., W. R. Becker, J. Richetti, L. C. D. A. Silva, and J. A. Johann. 2020. “Mapping Summer Soybean and Corn with Remote Sensing on Google Earth Engine Cloud Computing in Parana State - Brazil.” International Journal of Digital Earth 13 (12): 1624–1636. doi:10.1080/17538947.2020.1772893.
  • Perilla, G. A., and J. Mas. 2019. “High-resolution Mapping of Protected Agriculture in Mexico, Through Remote Sensing Data Cloud Geoprocessing.” European Journal of Remote Sensing 52 (1): 532–541. doi:10.1080/22797254.2019.1686430.
  • Phalke, A. R., M. özdoğan, P. S. Thenkabail, T. Erickson, N. Gorelick, K. Yadav, et al. 2020. “Mapping Croplands of Europe, Middle East, Russia, and Central Asia Using Landsat, Random Forest, and Google Earth Engine.” ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 167: 104–122. doi:10.1016/j.isprsjprs.2020.06.022.
  • Saah, D., G. Johnson, B. Ashmall, G. Tondapu, K. Tenneson, M. Patterson, A. Poortinga, et al. 2019. “Collect Earth: An Online Tool for Systematic Reference Data Collection in Land Cover and use Applications.” ENVIRONMENTAL MODELLING & SOFTWARE 118: 166–171. doi:10.1016/j.envsoft.2019.05.004.
  • Sheykhmousa, M., M. Mahdianpari, H. Ghanbari, F. Mohammadimanesh, P. Ghamisi, and S. Homayouni. 2020. “Support Vector Machine Versus Random Forest for Remote Sensing Image Classification: A Meta-Analysis and Systematic Review.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13: 6308–6325. doi:10.1109/JSTARS.2020.3026724.
  • Tang, W., J. Hu, H. Zhang, P. Wu, and H. He. 2015. “Kappa Coefficient: A Popular Measure of Rater Agreement.” Shanghai Archives of Psychiatry 27 (1): 62–67. doi:10.11919/j.issn.1002-0829.215010.
  • Thieme, A., S. Yadav, P. C. Oddo, J. M. Fitz, S. Mccartney, L. King, J. Keppler, et al. 2020. “Using Nasa Earth Observations and Google Earth Engine to map Winter Cover Crop Conservation Performance in the Chesapeake bay Watershed.” REMOTE SENSING OF ENVIRONMENT 248 (111943), doi:10.1016/j.rse.2020.111943.
  • Tiwari, V., M. A. Matin, F. M. Qamer, W. L. Ellenburg, B. Bajracharya, K. Vadrevu, B. R. Rushi, et al. 2020. “Wheat Area Mapping in Afghanistan Based on Optical and sar Time-Series Images in Google Earth Engine Cloud Environment.” Frontiers in Environmental Science 8 (77), doi:10.3389/fenvs.2020.00077.
  • Tucker, C. J., J. H. Elgin, J. E. Mcmurtrey, and C. J. Fan. 1979. “Monitoring Corn and Soybean Crop Development with Hand-Held Radiometer Spectral Data.” REMOTE SENSING OF ENVIRONMENT 8 (3): 237–248. doi:10.1016/0034-4257(79)90004-X.
  • Wang, S., G. Azzari, and D. B. Lobell. 2019a. “Crop Type Mapping Without Field-Level Labels: Random Forest Transfer and Unsupervised Clustering Techniques.” REMOTE SENSING OF ENVIRONMENT 222: 303–317. doi:10.1016/j.rse.2018.12.026.
  • Wang, G., Y. Lan, H. Qi, P. Chen, A. Hewitt, and Y. Han. 2019b. “Field Evaluation of an Unmanned Aerial Vehicle (uav) Sprayer: Effect of Spray Volume on Deposition and the Control of Pests and Disease in Wheat.” PEST MANAGEMENT SCIENCE 75 (6): 1546–1555. doi:10.1002/ps.5321.
  • Xu, W., W. Yang, S. Chen, C. Wu, P. Chen, and Y. Lan. 2020. “Establishing a Model to Predict the Single Boll Weight of Cotton in Northern Xinjiang by Using High Resolution uav Remote Sensing Data.” COMPUTERS AND ELECTRONICS IN AGRICULTURE 179: 105762. doi:10.1016/j.compag.2020.105762.
  • Yan, Y., and Y. Ryu. 2021. “Exploring Google Street View with Deep Learning for Crop Type Mapping.” ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 171: 278–296. doi:10.1016/j.isprsjprs.2020.11.022.
  • Yang, Z., S. Duan, X. Dai, Y. Sun, and M. Liu. 2022. “Mapping of Lakes in the Qinghai-Tibet Plateau from 2016 to 2021: Trend and Potential Regularity.” International Journal of Digital Earth 15 (1): 1692–1714. doi:10.1080/17538947.2022.2131008.
  • Yang, Z., W. Li, Q. Chen, S. Wu, S. Liu, and J. Gong. 2019. “A Scalable Cyberinfrastructure and Cloud Computing Platform for Forest Aboveground Biomass Estimation Based on the Google Earth Engine.” International Journal of Digital Earth 12 (9): 995–1012. doi:10.1080/17538947.2018.1494761.
  • Yang, W., W. Xu, C. Wu, B. Zhu, P. Chen, L. Zhang, Y. Lan, et al. 2021. “Cotton Hail Disaster Classification Based on Drone Multispectral Images at the Flowering and Boll Stage.” COMPUTERS AND ELECTRONICS IN AGRICULTURE 180: 105866. doi:10.1016/j.compag.2020.105866.
  • Yi, L., Y. Lan, H. Kong, F. Kong, and X. Han. 2019. “Exploring the Potential of uav Imagery for Variable Rate Spraying in Cotton Defoliation Application.” International Journal of Precision Agricultural Aviation 2 (1): 42–45. doi:10.33440/j.ijpaa.20190201.0018.
  • Yin, H., A. Brandão, J. Buchner, D. Helmers, B. G. Iuliano, N. E. Kimambo, K. E. Lewinska, et al. 2020. “Monitoring Cropland Abandonment with Landsat Time Series.” REMOTE SENSING OF ENVIRONMENT 246 (111873), doi:10.1016/j.rse.2020.111873.
  • Yu, F., W. Du, Z. Guo, C. Zhou, D. Wang, and T. Xu. 2019. “Uav Hyperspectral Inversion Modeling of Rice Nitrogen Content Based on Woa-elm.” International Journal of Precision Agricultural Aviation 2 (2): 43–48. doi:10.33440/J.IJPAA.20190202.39.
  • Yuan, H., G. Yang, C. Li, Y. Wang, J. Liu, H. Yu, H. Feng, et al. 2017. “Retrieving Soybean Leaf Area Index from Unmanned Aerial Vehicle Hyperspectral Remote Sensing: Analysis of rf, ann, and svm Regression Models.” Remote Sensing 9 (4): 309. doi:10.3390/rs9040309.
  • Yue, L., B. Li, S. Zhu, Q. Yuan, and H. Shen. 2023. “A Fully Automatic and High-Accuracy Surface Water Mapping Framework on Google Earth Engine Using Landsat Time-Series.” International Journal of Digital Earth 16 (1): 210–233. doi:10.1080/17538947.2023.2166606.
  • Zhang, H., and K. Yemoto. 2019. “Uas-based Remote Sensing Applications on the Northern Colorado Limited Irrigation Research Farm.” International Journal of Precision Agricultural Aviation 1 (2): 1–10. doi:10.33440/j.ijpaa.20190202.50.