1. K. M. Chandrasekharan, C. Subasinghe, and A. Haileslassie, Mapping irrigated and rainfed agriculture in Ethiopia (2015-2016) using remote sensing methods. International Water Management Institute (IWMI), 2021. [DOI:10.5337/2021.206]

2. L. Zhang, K. Zhang, X. Zhu, H. Chen, and W. Wang, "Integrating remote sensing, irrigation suitability and statistical data for irrigated cropland mapping over mainland China," Journal of Hydrology, vol. 613, p. 128413, 2022. [DOI:10.1016/j.jhydrol.2022.128413]

3. K. S. Mpakairi, T. Dube, M. Sibanda, and O. Mutanga, "Fine-scale characterization of irrigated and rainfed croplands at national scale using multi-source data, random forest, and deep learning algorithms," ISPRS Journal of Photogrammetry and Remote Sensing, vol. 204, pp. 117-130, 2023. [DOI:10.1016/j.isprsjprs.2023.09.006]

4. Z. Gao, D. Guo, D. Ryu, and A. W. Western, "Enhancing the accuracy and temporal transferability of irrigated cropping field classification using optical remote sensing imagery," Remote Sensing, vol. 14, no. 4, p. 997, 2022. [DOI:10.3390/rs14040997]

5. E. Abdali, M. J. Valadan Zoej, A. Taheri Dehkordi, and E. Ghaderpour, "A parallel-cascaded ensemble of machine learning models for crop type classification in Google Earth Engine using multi-temporal sentinel-1/2 and landsat-8/9 remote sensing data," Remote Sensing, vol. 16, no. 1, p. 127, 2023. [DOI:10.3390/rs16010127]

6. L. Zhu, J. Suomalainen, J. Liu, J. Hyyppä, H. Kaartinen, and H. Haggren, "A review: Remote sensing sensors," Multi-purposeful application of geospatial data, vol. 19, pp. 19-42, 2018. [DOI:10.5772/intechopen.71049]

7. L. Ghayour et al., "Performance evaluation of Sentinel-2 and Landsat 8 OLI data for land cover/use classification using a comparison between machine learning algorithms," Remote Sensing, vol. 13, no. 7, p. 1349, 2021. [DOI:10.3390/rs13071349]

8. J. Li, B. Zhang, and X. Huang, "A hierarchical category structure based convolutional recurrent neural network (HCS-ConvRNN) for Land-Cover classification using dense MODIS Time-Series data," International Journal of Applied Earth Observation and Geoinformation, vol. 108, p. 102744, 2022. [DOI:10.1016/j.jag.2022.102744]

9. P. Dash, S. L. Sanders, P. Parajuli, and Y. Ouyang, "Improving the accuracy of land use and land cover classification of Landsat data in an agricultural watershed," Remote Sensing, vol. 15, no. 16, p. 4020, 2023. [DOI:10.3390/rs15164020]

10. H. Rakuasa, "Classification of Sentinel-2A Satellite Image for Ternate City land cover using Random Forest Classification in SAGA GIS Software," DNS-DIGITAL NEXUS SYSTEMATIC JOURNAL, vol. 1, no. 1, pp. 34-36, 2025. [DOI:10.69693/jesa.v2i1.14]

11. A. Qadir, S. Skakun, N. Kussul, A. Shelestov, and I. Becker-Reshef, "A generalized model for mapping sunflower areas using Sentinel-1 SAR data," Remote Sensing of Environment, vol. 306, p. 114132, 2024. [DOI:10.1016/j.rse.2024.114132]

12. S. a. Ibrahim and H. Balzter, "Evaluating Flood Damage to Paddy Rice Fields Using PlanetScope and Sentinel-1 Data in North-Western Nigeria: Towards Potential Climate Adaptation Strategies," Remote Sensing, vol. 16, no. 19, p. 3657, 2024. [DOI:10.3390/rs16193657]

13. F. Bioresita, T. S. F. Larastika, M. Taufik, and N. Hayati, "Integration of Texture and PCA Information from Sentinel-1 SAR Data for Land Cover-Analysis using Random Forest Classifier Method in Sidoarjo Regency, Indonesia," in Forum Geografi, 2025, vol. 39, no. 1, pp. 38-52. [DOI:10.23917/forgeo.v39i1.6045]

14. M. Vizzari, G. Lesti, and S. Acharki, "Crop classification in Google Earth Engine: leveraging Sentinel-1, Sentinel-2, European CAP data, and object-based machine-learning approaches," Geo-spatial Information Science, pp. 1-16, 2024. [DOI:10.1080/10095020.2024.2341748]

15. C. Eisfelder et al., "Cropland and Crop Type Classification with Sentinel-1 and Sentinel-2 Time Series Using Google Earth Engine for Agricultural Monitoring in Ethiopia," Remote Sensing, vol. 16, no. 5, p. 866, 2024. [DOI:10.3390/rs16050866]

16. H. Xing et al., "Mapping irrigated, rainfed and paddy croplands from time-series Sentinel-2 images by integrating pixel-based classification and image segmentation on Google Earth Engine," Geocarto International, vol. 37, no. 26, pp. 13291-13310, 2022. [DOI:10.1080/10106049.2022.2076923]

17. A. K. Sharma et al., "Identifying seasonal groundwater-irrigated cropland using multi-source NDVI time-series images," Remote sensing, vol. 13, no. 10, p. 1960, 2021. [DOI:10.3390/rs13101960]

18. E. Erdanaev, M. Kappas, and D. Wyss, "The Identification of Irrigated Crop Types Using Support Vector Machine, Random Forest and Maximum Likelihood Classification Methods with Sentinel-2 Data in 2018: Tashkent Province, Uzbekistan," International Journal of Geoinformatics, vol. 18, no. 2, 2022.

19. M. Alami Machichi et al., "Crop mapping using supervised machine learning and deep learning: A systematic literature review," International Journal of Remote Sensing, vol. 44, no. 8, pp. 2717-2753, 2023. [DOI:10.1080/01431161.2023.2205984]

20. H. Tamiminia, B. Salehi, M. Mahdianpari, L. Quackenbush, S. Adeli, and B. Brisco, "Google Earth Engine for geo-big data applications: A meta-analysis and systematic review," ISPRS Journal of Photogrammetry and Remote Sensing, vol. 164, pp. 152-170, 2020. [DOI:10.1016/j.isprsjprs.2020.04.001]

21. Z. Zhao et al., "Comparison of three machine learning algorithms using Google Earth Engine for land use land cover classification," Rangeland ecology & management, vol. 92, pp. 129-137, 2024. [DOI:10.1016/j.rama.2023.10.007]

22. M. Kazemi Garajeh, F. Haji, M. Tohidfar, A. Sadeqi, R. Ahmadi, and N. Kariminejad, "Spatiotemporal monitoring of climate change impacts on water resources using an integrated approach of remote sensing and Google Earth Engine," Scientific reports, vol. 14, no. 1, p. 5469, 2024. [DOI:10.1038/s41598-024-56160-9]

23. C. B. Pande, "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, vol. 37, no. 26, pp. 13860-13880, 2022. [DOI:10.1080/10106049.2022.2086622]

24. A. Amindin, N. Siamian, N. Kariminejad, J. J. Clague, and H. R. Pourghasemi, "An integrated GEE and machine learning framework for detecting ecological stability under land use/land cover changes," Global Ecology and Conservation, vol. 53, p. e03010, 2024. [DOI:10.1016/j.gecco.2024.e03010]

25. J. Zhi et al., "Rapid Large-Scale Monitoring of Pine Wilt Disease Using Sentinel-1/2 Images in GEE," Forests, vol. 16, no. 6, p. 981, 2025. [DOI:10.3390/f16060981]

26. A. Liepa et al., "Harmonized NDVI time-series from Landsat and Sentinel-2 reveal phenological patterns of diverse, small-scale cropping systems in East Africa," Remote Sensing Applications: Society and Environment, vol. 35, p. 101230, 2024. [DOI:10.1016/j.rsase.2024.101230]

27. E. Heller et al., "Mapping crop types, irrigated areas, and cropping intensities in heterogeneous landscapes of Southern India using multi-temporal medium-resolution imagery," Photogrammetric Engineering & Remote Sensing, vol. 78, no. 8, pp. 815-827, 2012. [DOI:10.14358/PERS.78.8.815]

28. E. S. Ibrahim, P. Rufin, L. Nill, B. Kamali, C. Nendel, and P. Hostert, "Mapping crop types and cropping systems in Nigeria with Sentinel-2 imagery," Remote sensing, vol. 13, no. 17, p. 3523, 2021. [DOI:10.3390/rs13173523]

29. N. Kussul, M. Lavreniuk, S. Skakun, and A. Shelestov, "Deep learning classification of land cover and crop types using remote sensing data," IEEE Geoscience and Remote Sensing Letters, vol. 14, no. 5, pp. 778-782, 2017. [DOI:10.1109/LGRS.2017.2681128]

30. L. Breiman, "Random forests," Machine learning, vol. 45, pp. 5-32, 2001. [DOI:10.1023/A:1010933404324]

31. H. Deng, W. Zhang, X. Zheng, and H. Zhang, "Crop classification combining object-oriented method and random forest model using unmanned aerial vehicle (UAV) multispectral image," Agriculture, vol. 14, no. 4, p. 548, 2024. [DOI:10.3390/agriculture14040548]

32. A. Orynbaikyzy, U. Gessner, and C. Conrad, "Spatial transferability of random forest models for crop type classification using Sentinel-1 and Sentinel-2," Remote Sensing, vol. 14, no. 6, p. 1493, 2022. [DOI:10.3390/rs14061493]

33. I. Baronian, R. Borna, K. Jafarpour Ghalehteimouri, M. Zohoorian, J. Morshedi, and M. A. Khaliji, "Unveiling the thermal impact of land cover transformations in Khuzestan province through MODIS satellite remote sensing products," Paddy and Water Environment, vol. 22, no. 4, pp. 503-520, 2024. [DOI:10.1007/s10333-024-00981-x]

34. H. Zhao, S. Duan, J. Liu, L. Sun, and L. Reymondin, "Evaluation of five deep learning models for crop type mapping using sentinel-2 time series images with missing information," Remote Sensing, vol. 13, no. 14, p. 2790, 2021. [DOI:10.3390/rs13142790]

35. L. Liu et al., "Mapping cropping intensity in China using time series Landsat and Sentinel-2 images and Google Earth Engine," Remote Sensing of Environment, vol. 239, p. 111624, 2020. [DOI:10.1016/j.rse.2019.111624]

36. F. Rahimi-Ajdadi, "Land suitability assessment for second cropping in terms of low temperature stresses using Landsat TIRS sensor," Computers and Electronics in Agriculture, vol. 200, p. 107205, 2022. [DOI:10.1016/j.compag.2022.107205]

37. P. Potin et al., "Sentinel-1 mission status," in Proceedings of EUSAR 2016: 11th European conference on synthetic aperture radar, 2016: VDE, pp. 1-6.

38. L. Yang, X. Meng, and X. Zhang, "SRTM DEM and its application advances," International Journal of Remote Sensing, vol. 32, no. 14, pp. 3875-3896, 2011. [DOI:10.1080/01431161003786016]

39. Q. Li, J. Tian, and Q. Tian, "Deep learning application for crop classification via multi-temporal remote sensing images," Agriculture, vol. 13, no. 4, p. 906, 2023. [DOI:10.3390/agriculture13040906]

40. S. L. Ermida, P. Soares, V. Mantas, F.-M. Göttsche, and I. F. Trigo, "Google earth engine open-source code for land surface temperature estimation from the landsat series," Remote Sensing, vol. 12, no. 9, p. 1471, 2020. [DOI:10.3390/rs12091471]