This paper presents a novel semisupervised learning (SSL) technique defined in the context of ε-insensitive support vector regression (SVR) to estimate biophysical parameters from remotely sensed images. The proposed SSL method aims to mitigate the problems of small-sized biased training sets without collecting any additional samples with reference measures. This is achieved on the basis of two consecutive steps. The first step is devoted to inject additional priors information in the learning phase of the SVR in order to adapt the importance of each training sample according to distribution of the unlabeled samples. To this end, a weight is initially associated to each training sample based on a novel strategy that defines higher weights for the samples located in the high density regions of the feature space while giving reduced weights to those that fall into the low density regions of the feature space. Then, in order to exploit different weights for training samples in the learning phase of the SVR, we introduce a weighted SVR (WSVR) algorithm. The second step is devoted to jointly exploit labeled and informative unlabeled samples for further improving the definition of the WSVR learning function. To this end, the most informative unlabeled samples that have an expected accurate target values are initially selected according to a novel strategy that relies on the distribution of the unlabeled samples in the feature space and on the WSVR function estimated at the first step. Then, we introduce a restructured WSVR algorithm that jointly uses labeled and unlabeled samples in the learning phase of the WSVR algorithm and tunes their importance by different values of regularization parameters. Experimental results obtained for the estimation of single-tree stem volume show the effectiveness of the proposed SSL method.