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.
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
3. Polarimetric Imaging via Deep Learning: A Review X. Li et al.
4. Soil Moisture Linear Modeling by Using Decomposition and Selection of Fully Polarized SAR Features E. Khedri & M. Hasanlou
5. Soil Moisture Prediction Using Remote Sensing and Machine Learning Algorithms: A Review on Progress, Challenges, and Opportunities M. Lamichhane et al.
6. Soil moisture simulation of rice using optimized Support Vector Machine for sustainable agricultural applications P. Majumdar et al.