استفاده از ترکیب رویکردهای سنجش از دور و یادگیری ماشین در پیش‌بینی پارامترهای هیدرولوژیکی: یک مطالعه علم‌سنجی

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Amani, S., & Shafizadeh-Moghadam, H. (2023). A review of machine learning models and influential factors for estimating evapotranspiration using remote sensing and ground-based data. Agricultural Water Management, 284, 108324.‏

Amiri, A., Soltani, K., Ebtehaj, I., & Bonakdari, H. (2024). A novel machine learning tool for current and future flood susceptibility mapping by integrating remote sensing and geographic information systems. Journal of Hydrology, 632, 130936.

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Boutin, J., Yueh, S., Bindlish, R., Chan, S., Entekhabi, D., Kerr, Y., ... & Zribi, M. (2023). Soil moisture and sea surface salinity derived from satellite-borne sensors. Surveys in Geophysics, 44(5), 1449-1487.

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Hafeez, S., Wong, M. S., Abbas, S., & Asim, M. (2022). Evaluating landsat-8 and sentinel-2 data consistency for high spatiotemporal inland and coastal water quality monitoring. Remote Sensing, 14(13), 3155.‏،

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Kim, J., & Parolin, B. (2021). Deep learning-based soil moisture retrieval from remotely sensed data: A review. Remote Sensing, 13(19), 398

Kim, Y. I., Park, W. H., Shin, Y., Park, J.-W., Engel, B., Yun, Y. J., & Jang, W. S. (2024). Applications of Machine Learning and Remote Sensing in Soil and Water Conservation.

Krishnaraj, A., & Honnasiddaiah, R. (2022). Remote sensing and machine learning based framework for the assessment of spatio-temporal water quality in the Middle Ganga Basin. Environmental Science and Pollution Research, 29(43), 64939-64958.‏

Li, F., Yigitcanlar, T., Nepal, M., Nguyen, K., & Dur, F. (2023). Machine learning and remote sensing integration for leveraging urban sustainability: A review and framework. Sustainable Cities and Society, 96, 104653.

Li, Y., Wang, W., Wang, G., & Tan, Q. (2022). Actual evapotranspiration estimation over the Tuojiang River Basin based on a hybrid CNN-RF model. Journal of Hydrology, 610, 127788.

Liu, Q., Wu, Z., Cui, N., Jin, X., Zhu, S., Jiang, S., ... & Gong, D. (2023). Estimation of soil moisture using multi-source remote sensing and machine learning algorithms in farming land of Northern China. Remote Sensing, 15(17), 4214.

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Matyukira, C., & Mhangara, P. (2024). Advances in vegetation mapping through remote sensing and machine learning techniques: a scientometric review. European Journal of Remote Sensing, 2422330.

Mekonnen, Y. G., Alamirew, T., Chukalla, A. D., Malede, D. A., Yalew, S. G., & Mengistu, A. F. (2024). Remote sensing in hydrology: A systematic review of its applications in the Upper Blue Nile Basin, Ethiopia. HydroResearch.

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Pan, S., Pan, N., Tian, H., Friedlingstein, P., Sitch, S., Shi, H., Arora, V. K., Haverd, V., Jain, A. K., & Kato, E. (2020). Evaluation of global terrestrial evapotranspiration using state-of-the-art approaches in remote sensing, machine learning and land surface modeling. Hydrology and Earth System Sciences, 24(3), 1485-1509.

Pangali Sharma, T. P., Zhang, J., Khanal, N. R., Prodhan, F. A., Paudel, B., Shi, L., & Nepal, N. (2020). Assimilation of snowmelt runoff model (SRM) using satellite remote sensing data in Budhi Gandaki River Basin, Nepal. Remote Sensing, 12(12), 1951.

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Rostami, A., Raeini-Sarjaz, M., Chabokpour, J., & Chadee, A. A. (2023). Soil moisture monitoring by downscaling of remote sensing products using LST/VI space derived from MODIS products. Water Supply, 23(2), 688-705.‏

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Saha, A., & Pal, S. C. (2024). Application of machine learning and emerging remote sensing techniques in hydrology: A state-of-the-art review and current research trends. Journal of Hydrology, 130907.

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