Empirical best linear unbiased predictors (EBLUPs) based on auxiliary information are efficient for estimating means or totals of environmental attributes in small domains. In small area estimation, the EBLUPs and their approximately unbiased mean square error (MSE) estimators are obtained under the premise of having a large number of population units in the target domain. In remote sensing, single pixels are regarded as population units, and EBLUPs and MSE estimators may also be required for subdomains containing a low number of pixels or even single pixels. In this study, EBLUPs, their MSE, and an unbiased estimator of the MSE are derived when predicting linear parameters for subdomains with a small number of population units that do not intersect with the training sample. In these situations, an additional MSE component should be considered to prevent MSE underestimation. A case study illustrates the applicability of the obtained results in the context of a Light Detection and Ranging‐based forest inventory, where estimates are provided for areas of interest sizing from single pixels or population units to thousands of hectares. Based on our results, we recommend to consider the additional component in the MSE estimator when estimating in pixels and domains of interest sizing few hectares.