A genetic algorithm-optimized backpropagation neural network model for predicting soil moisture content using spectral data
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11368-024-03792-z.pdf
Reference44 articles.
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3. Araya SN, Fryjoff-Hung A, Anderson A, Viers JH, Ghezzehei TA (2020) Machine learning based soil moisture retrieval from unmanned aircraft system multispectral remote sensing. In: IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium. pp 4598–4601. https://doi.org/10.1109/IGARSS39084.2020.9324117
4. de Lima RS, Li K, Vain A, Lang M, Bergamo TF, Kokamägi K, Burnside NG, Ward RD, Sepp K (2022) The potential of optical UAS data for predicting surface soil moisture in a peatland across time and sites. Remote Sens Basel 14(10):2334–2334. https://doi.org/10.3390/rs14102334
5. Dong L, Wang W, Jin R, Xu FA, Zhang Y (2023) Surface soil moisture retrieval on Qinghai-Tibetan Plateau using sentinel-1 synthetic aperture radar data and machine learning algorithms. Remote Sens Basel 15(1):153. https://doi.org/10.3390/rs15010153
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