ESTIMATING SOIL MOISTURE USING POLSAR DATA: A MACHINE LEARNING APPROACH

6 citations as recorded by crossref.

1. Estimate soil moisture of maize by combining support vector machine and chaotic whale optimization algorithm B. He et al. https://doi.org/10.1016/j.agwat.2022.107618
2. EnSSNet: An advanced ensemble self-supervised learning framework with mini batch-graph convolutional network and deep curriculum learning for robust PolSAR image classification M. Darvishnezhad & M. Sebt https://doi.org/10.1016/j.knosys.2025.114744
3. Polarimetric Imaging via Deep Learning: A Review X. Li et al. https://doi.org/10.3390/rs15061540
4. Soil Moisture Linear Modeling by Using Decomposition and Selection of Fully Polarized SAR Features E. Khedri & M. Hasanlou https://doi.org/10.29252/jgit.6.3.59
5. Soil Moisture Prediction Using Remote Sensing and Machine Learning Algorithms: A Review on Progress, Challenges, and Opportunities M. Lamichhane et al. https://doi.org/10.3390/rs17142397
6. Soil moisture simulation of rice using optimized Support Vector Machine for sustainable agricultural applications P. Majumdar et al. https://doi.org/10.1016/j.suscom.2023.100924