1. [1] Xiong, J., Thenkabail, P.S., Tilton, J.C., Gumma, M.K., Teluguntla, P., Oliphant, A., Congalton, R.G., Yadav, K. and Gorelick, N., "Nominal 30-m cropland extent map of continental Africa by integrating pixel-based and object-based algorithms using Sentinel-2 and Landsat-8 data on Google Earth Engine," Remote Sensing, vol. 9(10), p. 1065, 2017. [ DOI:10.3390/rs9101065] 2. [2] Forkuor, G., Conrad, C., Thiel, M., Landmann, T. and Barry, B., "Evaluating the sequential masking classification approach for improving crop discrimination in the Sudanian Savanna of West Africa," Computers and Electronics in Agriculture, vol. 118, pp. 380-389, 2015. [ DOI:10.1016/j.compag.2015.09.020] 3. [3] Vuolo, F., Neuwirth, M., Immitzer, M., Atzberger, C. and Ng, W.T., "How much does multi-temporal Sentinel-2 data improve crop type classification?," International journal of applied earth observation and geoinformation, pp. 122-130, 2018. [ DOI:10.1016/j.jag.2018.06.007] 4. [4] Aghighi, H., Azadbakht, M., Ashourloo, D., Shahrabi, H.S. and Radiom, S., "Machine learning regression techniques for the silage maize yield prediction using time-series images of Landsat 8 OLI," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 11(12), pp. 4563-4577, 2018. [ DOI:10.1109/JSTARS.2018.2823361] 5. [5] Niazmardi, S., omayouni, S., Safari, A., McNairn, H., Shang, J. and Beckett, K., "Histogram-based spatio-temporal feature classification of vegetation indices time-series for crop mapping," International journal of applied earth observation and geoinformation, vol. 72, pp. 34-41, 2018. [ DOI:10.1016/j.jag.2018.05.014] 6. [6] Ashourloo, D., Shahrabi, H.S., Azadbakht, M., Rad, A.M., Aghighi, H. and Radiom, S., "A novel method for automatic potato mapping using time series of Sentinel-2 images," Computers and Electronics in Agriculture, vol. 175, p. 105583, 2020. [ DOI:10.1016/j.compag.2020.105583] 7. [7] Zhong, L., Hu, L. and Zhou, H., "Deep learning based multi-temporal crop classification," Remote sensing of environment, vol. 221, pp. 430-443, 2019. [ DOI:10.1016/j.rse.2018.11.032] 8. [8] Zhou, Z., Li, S. and Shao, Y., "Crops classification from sentinel-2A multi-spectral remote sensing images based on convolutional neural networks," IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium, pp. 5300-5303, 2018. [ DOI:10.1109/IGARSS.2018.8518860] 9. [9] Ndikumana, E., Ho Tong Minh, D., Baghdadi, N., Courault, D. and Hossard, L., "Deep recurrent neural network for agricultural classification using multitemporal SAR Sentinel-1 for Camargue, France," Remote Sensing, vol. 10(8), p. 1217, 2018. [ DOI:10.3390/rs10081217] 10. [10] Sharma, A., Liu, X. and Yang, X., "Land cover classification from multi-temporal, multi-spectral remotely sensed imagery using patch-based recurrent neural networks," Neural Networks, vol. 105, pp. 346-355, 2018. [ DOI:10.1016/j.neunet.2018.05.019] 11. [11] Sedano, F., Molini, V. and Azad, M., "A mapping framework to characterize land use in the Sudan-Sahel Region from dense stacks of Landsat Data," Remote Sensing, vol. 11(6), p. 648, 2019. [ DOI:10.3390/rs11060648] 12. [12] Jiang, Y., Lu, Z., Li, S., Lei, Y., Chu, Q., Yin, X. and Chen, F., "Large-scale and high-resolution crop mapping in China using Sentinel-2 satellite imagery," Agriculture, vol. 10(10), p. 433, 2020. [ DOI:10.3390/agriculture10100433] 13. [13] Chang, L., Chen, Y.T., Wang, J.H. and Chang, Y.L., "Rice-Field Mapping with Sentinel-1A SAR Time-Series Data," Remote Sensing, vol. 13(1), p. 103, 2021. [ DOI:10.3390/rs13010103] 14. [14] Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D. and Moore, R., "Google Earth Engine: Planetary-scale geospatial analysis for everyone," Remote sensing of Environment, vol. 202, pp. 18-27, 2017. [ DOI:10.1016/j.rse.2017.06.031] 15. [15] Gumma, M.K., Thenkabail, P.S., Teluguntla, P.G., Oliphant, A., Xiong, J., Giri, C., Pyla, V., Dixit, S. and Whitbread, A.M., "Agricultural cropland extent and areas of South Asia derived using Landsat satellite 30-m time-series big-data using random forest machine learning algorithms on the Google Earth Engine cloud," GIScience & Remote Sensing, vol. 57(3), pp. 302-322, 2020. [ DOI:10.1080/15481603.2019.1690780] 16. [16] Liu, X., Zhai, H., Shen, Y., Lou, B., Jiang, C., Li, T., Hussain, S.B. and Shen, G., "Large-scale crop mapping from multisource remote sensing images in google earth engine," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 13, pp. 414-427, 2020. [ DOI:10.1109/JSTARS.2019.2963539] 17. [17] Huang, H., Chen, Y., Clinton, N., Wang, J., Wang, X., Liu, C., Gong, P., Yang, J., Bai, Y., Zheng, Y. and Zhu, Z., "Mapping major land cover dynamics in Beijing using all Landsat images in Google Earth Engine," Remote Sensing of Environment, vol. 202, pp. 166-176, 2017. [ DOI:10.1016/j.rse.2017.02.021] 18. [18] Ghorbanian, A., Kakooei, M., Amani, M., Mahdavi, S., Mohammadzadeh, A. and Hasanlou, M., "Improved land cover map of Iran using Sentinel imagery within Google Earth Engine and a novel automatic workflow for land cover classification using migrated training samples," ISPRS Journal of Photogrammetry and Remote Sensing, vol. 167, pp. 276-288, 2020. [ DOI:10.1016/j.isprsjprs.2020.07.013] 19. [19] Nyaga, J.W., Markert, K.N., Thomas, A.B., Mugo, R.M., Wahome, A.M. and Irwin, D., "Water Quality Monitoring of In-Land Lakes in East Africa: How Open Source Tethys and Google Earth Engine Platforms are Improving Water Quality Data Analysis, Visualization and Decision Making," In AGU Fall Meeting Abstracts, pp. IN11B-20, 2019. 20. [20] Chen, B., Xiao, X., Li, X., Pan, L., Doughty, R., Ma, J., Dong, J., Qin, Y., Zhao, B., Wu, Z. and Sun, R., "A mangrove forest map of China in 2015: Analysis of time series Landsat 7/8 and Sentinel-1A imagery in Google Earth Engine cloud computing platform," ISPRS Journal of Photogrammetry and Remote Sensing, vol. 131, pp. 104-120, 2017. [ DOI:10.1016/j.isprsjprs.2017.07.011] 21. [21] Liu, X., Hu, G., Chen, Y., Li, X., Xu, X., Li, S., Pei, F. and Wang, S., "High-resolution multi-temporal mapping of global urban land using Landsat images based on the Google Earth Engine Platform," Remote sensing of environment, vol. 209, pp. 227-239, 2018. [ DOI:10.1016/j.rse.2018.02.055] 22. [22] Drusch, M., Del Bello, U., Carlier, S., Colin, O., Fernandez, V., Gascon, F., Hoersch, B., Isola, C., Laberinti, P., Martimort, P. and Meygret, A., "Sentinel-2: ESA's optical high-resolution mission for GMES operational services," Remote sensing of Environment, vol. 120, pp. 25-36, 2012. [ DOI:10.1016/j.rse.2011.11.026] 23. [23] Carrasco, L., O'Neil, A.W., Morton, R.D. and Rowland, C.S., "Evaluating combinations of temporally aggregated Sentinel-1, Sentinel-2 and Landsat 8 for land cover mapping with Google Earth Engine," Remote Sensing, vol. 11(3), p. 288, 2019. [ DOI:10.3390/rs11030288] 24. [24] Tucker, C.J., "ed and photographic infrared linear combinations for monitoring vegetation," Remote sensing of Environment, vol. 8(2), pp. 127-150, 1979. [ DOI:10.1016/0034-4257(79)90013-0]
|