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This letter presents an experimental analysis of the application of the ε-insensitive support vector regression (SVR) technique to soil moisture content estimation from remotely sensed data at field/basin scale. SVR has attractive properties, such as ease of use, good intrinsic generalization capability, and robustness to noise in the training data, which make it a valid candidate as an alternative to more traditional neural-network-based techniques usually adopted in soil moisture content estimation. Its effectiveness in this application is assessed by using field measurements and considering various combinations of the input features (i.e., different active and/or passive microwave measurements acquired using various sensor frequencies, polarizations, and acquisition geometries). The performance of the SVR method (in terms of estimation accuracy, generalization capability, computational complexity, and ease of use) is compared with that achieved using a multilayer perceptron neural network, which is considered as a benchmark in the addressed application. This analysis provides useful indications for building soil moisture estimation processors for upcoming satellites or near-real-time applications.