Precise and spatially-explicit knowledge of leaf chlorophyll content (Chl) is crucial to adequately interpret the chlorophyll fluorescence (ChF) signal from space. Accompanying information about the reliability of the Chl estimation becomes more important than ever. Recently, a new statistical method was proposed within the family of nonparametric Bayesian statistics, namely Gaussian Processes regression (GPR). GPR is simpler and more robust than their machine learning family members while maintaining very good numerical performance and stability. Other features include: (i) GPR requires a relatively small training data set and can adopt very flexible kernels, (ii) GPR identifies the relevant bands and observations in establishing relationships with a variable, and finally (iii) along with pixelwise estimations GPR provides accompanying confidence intervals. We used GPR to retrieve Chl from hyperspectral reflectance data and evaluated the portability of the regression model to other images. Based on field Chl measurements from the SPARC dataset and corresponding spaceborne CHRIS spectra (acquired in 2003, Barrax, Spain), GPR developed a regression model that was excellently validated (r2: 0.96, RMSE: 3.82 μg/cm2). The SPARC-trained GPR model was subsequently applied to CHRIS images (Barrax, 2003, 2009) and airborne CASI flightlines (Barrax 2009) to generate Chl maps. The accompanying confidence maps provided insight in the robustness of the retrievals. Similar confidences were achieved by both sensors, which is encouraging for upscaling Chl estimates from field to landscape scale. Because of its robustness and ability to deliver confidence intervals, GPR is evaluated as a promising candidate for implementation into ChF processing chains.