- Choudhary, K., Shi, W., Dong, Y., & Paringer, R. (2022). Random forest for rice yield mapping and prediction using Sentinel-2 data with Google Earth Engine. Advances in Space Research, 70(8), 2443-2457. https://doi.org/10.1016/j.asr.2022.06.073
- Copernicus Open Access Hub. (2021). Retrieved from https://scihub.copernicus.eu/
- Fei, H., Fan, Z., Wang, C., Zhang, N., Wang, T., Chen, R., & Bai, T. (2022). Cotton classification method at the county scale based on multi-features and random forest feature selection algorithm and classifier. Remote Sensing, 14(4), 829. https://doi.org/10.3390/rs14040829
- Gim, H.J., Ho, C.H., Jeong, S., Kim, J., Feng, S., & Hayes, M.J. (2020). Improved mapping and change detection of the start of the crop growing season in the US Corn Belt from long-term AVHRR NDVI. Agricultural and Forest Meteorology, 294, 108143.
- Ibrahim, E.S., Rufin, P., Nill, L., Kamali, B., Nendel, C., & Hostert, P. (2021). Mapping crop types and cropping systems in nigeria with sentinel-2 imagery. Remote Sensing, 13(17), 3523. https://doi.org/10.3390/rs13173523
- Kobayashi, N., Tani, H., Wang, X., & Sonobe, R. (2020). Crop classification using spectral indices derived from Sentinel-2A imagery. Journal of Information and Telecommunication, 4(1), 67-90. https://doi.org/10.3390/ rs13173523
- Kordi, F., Hamzeh, S., Atarchi, S., & Alavipanah, S.K. (2018). Agricultural product classification for optimal water resource management using the data time series of landsat8. Iranian Journal of Ecohydrology, 5(4), 1267-1283. (In Persian with English abstract). https://doi.org.10.22059/ije.2018.264578.943
- Kumar, S., Arya, S., & Jain, K. (2022). A SWIR-based vegetation index for change detection in land cover using multi-temporal Landsat satellite dataset. International Journal of Information Technology, 14, 2035–2048. https://doi.org/10.1007/s41870-021-00797-6
- Nouri, S., Sanaei Nejad, S.H., & Davari, K. (2018). Investigate of using of vegetation indices based on Satellite imagery in assessing agricultural drought (Case study: North Khorasan Province in Iran). Iranian Journal of Irrigation & Drainage, 11(6), 1076-1086. (In Persian with English abstract)
- Ok, A.O., Akar, O. and Gungor, O. (2012). Evaluation of random forest method for agricultural crop classification. European. Journal of Remote Sensing, 45(1), 421-432. https://doi.org/10.5721/EuJRS20124535
- Orynbaikyzy, A., Gessner, U., & Conrad, C. (2022) Spatial transferability of random forest models for crop type classification using Sentinel-1 and Sentinel-2. Remote Sensing. 14(6), 1493. https://doi.org/10.3390/rs14061493
- Pande, C.B. (2022) Land use/land cover and change detection mapping in Rahuri watershed area (MS), India using the google earth engine and machine learning approach, Geocarto International, 37(26), 13860-13880, https://doi.org/10.1080/10106049.2022.2086622
- Riahi, V., Zeaiean Firouzabadi, P., Azizpour, F., & Darouei, P. (2019). Identification and investigation of the area under cultivation in Lenjanat using Landsat 8 satellite images. Journal of Applied researches in Geographical Sciences, 19(52), 147-169. (In Persian with English abstract). https://doi.org.10.29252/jgs.19.52.147
- Saei Jamalabad, M., Mojardi, B., & Abkar, A.A. (2018). Winter wheat classification by multi-temporal optimized image analysis based on random forest algorithm. Journal of Geomatics Science and Technology, 8(2), 133-150. (In Persian)
- Sonobe, R., Yamaya, Y., Tani, H., Wang, X., Kobayashi, N., & Mochizuki, K.I. (2018). Crop classification from Sentinel-2-derived vegetation indices using ensemble learning. Journal of Applied Remote Sensing, 12(2), 026019-026019. https://doi.org/10.1117/1.JRS.12.026019.
- Tian, H., Wang, Y., Chen, T., Zhang, L., & Qin, Y. (2021). Early-season mapping of winter crops using sentinel-2 optical imagery. Remote Sensing, 13(19): 3822. https://doi.org/10.3390/rs13193822
- Zare khormizi, H., Ghafarian Malamiri, H.R., & Mortaz, M. (2020). Evaluation of supervised classification capability of Landsat-8 and Sentinel-2A Satellite images in determining type and area of pistachio cultivars. Journal of RS and GIS for Natural Resources, 11(1), 84-103. (In Persian with English abstract). https://doi.org.10.30495/girs.2020.672378
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