The Simplified Level 2 Product Prototype Processor (SL2P) for estimating Leaf Area index (LAI), fraction of vegetation cover (fCover) and Canopy Water Content (CWC) from Sentinel-2/MSI and Landsat-8/OLI data was validated over an agricultural region. In-situ data collected during the SMAP Validation Experiment 2016 field campaign were used as a reference. SL2P processor performance varied substantially between crop type and biophysical variable. Over all crops, SL2P underestimated in-situ LAI and CWC measurements when using either MSI (slope (bias) of 0.70 (- 0.37) for LAI and 0.42 (- 0.37 kg/m(2)) for CWC) or OLI (slope (bias) of 0.59 (-1.21) for LAI and 0.24 (- 0.23 kg/m (2)) for CWC) data. The accuracy of SL2P fCover estimates, over all crops, was higher (slope (bias) of 0.99 (1.84%) using MSI and 0.93 (- 3.75%) using OLI). The RMSE between biophysical variables estimated using SL2P from MSI (OLI) in comparison to in-situ data was 0.98 (1.63) for LAI, 11.39% (10.95%) for fCover and 0.66 kg/m(2) (0.96 kg/m (2)) for CWC. Slightly better results are generally obtained using locally calibrated vegetation indices models, when compared to SL2P estimates using the corresponding sensor data. Uncertainty metrics of vegetation biophysical variables derived from both MSI and OLI, when compared to interpolated in-situ data time series, are found comparable to results obtained for cross-validation suggesting the possibility of using interpolated in-situ data time series for validating decametric resolution remote sensing products sparsely sampled in time.