Nondestructive proximal sensors can be an efficient source of information of N status in crops for localized and rapid adjustment of fertilization applications. The aim of this study was to compare two transmittance/reflectance‐based sensors (SPAD, ASD) and a florescence‐based sensor (Multiplex) in their ability to measure N content in corn (Zea mays L.), spring and winter barley (Hordeum vulgare L.), and rye (Secale cereale L.), both at the leaf and canopy level. Measurements of leaves and canopies from six fertilization field trials in 2019 and 2020 were analyzed to establish relationships between sensor information and laboratory‐determined N content in crops. Analyses included linear regression for single sensor variables and machine learning for multivariate approaches, to assess the relative accuracy of the proximal sensors to measure N. The ASD is time‐intensive and requires post hoc analyses of the spectra. However, the spectral outputs of this device were clearly correlated with the N status of leaves and canopies. At the leaf level, SPAD showed higher accuracy than any of the single Multiplex variables to predict plant N. Multiplex performance could be improved by combining three of its variables. At the canopy level, interpolated SPAD values and the best‐performing Multiplex variables showed similar accuracy. It could be concluded that the relationship sensor‐N status is species specific. Despite the high standard deviation recorded in some raw Multiplex variable, the derived indices showed a comparable low standard deviation. At both, leaf and canopy levels an integrated sensor solution would combine the multidimensionality of Multiplex and ASD, and the accuracy and practicality of SPAD.

Environmental services of mires and peatlands and negative impacts of their alteration are comprehensively documented. A spatial detection of these organic soils is therefore essential. This paper examines the detectability of organic soils in forests using open ge-ospatial and remote sensing data combined with mapped soil and water level infor-mation in two Random Forest (RF) approaches. Either surrounded in or covered by for-est, organic soils of the study region exhibit elevated soil water content reaching satura-tion during the hydrological winter. Consequently, terrain indices from Digital Elevation Models (DEM) and soil moisture from L-band ALOS PALSAR signals are used as pre-dictors in RF algorithms. CORINE Land Cover data help assess how different forest cov-er types (FCT) influence RF models. Substantial agreement is reached in the target classi-fication when FCT is included and when using the higher spatial resolution DEM. The Boolean approach is less affected by different compositions of predictor variables, but is more sensitive to the level of imbalance in the reference data. This becomes evident when comparing the “event error” and “no event error”. In all RF models, the variable importance of soil moisture pixel values retrieved from L-band ALOS PALSAR is the highest when FCT is not in-cluded.