Soil samples from grassland plots in the Hase floodplain near Osnabrück (NW Germany) were analyzed using visible and near‐infrared laboratory spectroscopy (VNIRS) by means of an ASD FieldSpec II Pro FR‐ spectroradiometer (spectral range 0.4–2.5 µm), paralleled by atomic‐absorption spectrometry (AAS), automated combustion for Corg quantification, and texture analysis. By AAS, contents of Cu, Zn, and Pb were found to be clearly elevated which is due to industrial effluents into the river in the 19th and 20th century. As these heavy metals (HMs) cannot be assessed directly by VNIRS, it was one major task to clarify whether they can be quantified indirectly using intercorrelations with spectrally active soil components. A second goal was to identify the specific spectroscopic predictive mechanism which may also be applicable to assess trace‐HM contents of other soil samples similar to those investigated here. For the latter, Corg was found to be most indicative, whereas the binding of the metals to other constituents (Fe oxides, clay) was not utilizable in the spectroscopic approach. The measured spectra were subjected to a multiplicative scatter correction and afterwards used to establish partial least‐squares regression (PLSR) models to estimate the contents of the different soil constituents. For Corg, very reliable estimates were obtained for both calibration and validation samples (in the validation, r² amounted to 0.90 and the percentage root mean square error [RMSE] was equal to 30.6%). Estimation accuracies obtained by PLSR for the trace HMs were considerably lower (r² between 0.56 and 0.71, percentage RMSE > 50%), which can be traced back to moderate correlations with Corg as main spectral determinant. According to these results, VNIRS can be applied as rapid and precise screening method for Corg to complement traditional analytical methods and to be used efficiently for a large number of samples. The method of VNIRS may also be applied for an indirect estimation of Corg‐associated metals to address their spatial variability, for example. This issue appears to be of high importance for digital soil mapping by imaging spectroscopy on a local to medium spatial scale.

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With the acceleration in climate warming and land surface drying, the role of temperature on water cycle becomes increasingly critical due to its effect on the evaporation component. Theoretically, evaporation (E) from vegetated canopies depends on the temperature at the canopy-air space, which is also called the aerodynamic temperature (T 0 ). Because T 0 is not measurable at the global scale, operational E mapping from thermal infrared (TIR) Earth

Biogas production from energy crops by anaerobic digestion is becoming increasingly important. The amount of biogas that can be produced per unit of biomass is referred to as the biomethane potential (BMP). For energy crops, the BMP varies among varieties and with crop state during the vegetation period. Traditional ways of analytical BMP determination are based on fermentation trials and require a minimum of 30 days. Here, we present a faster method for BMP retrievals using near infrared spectroscopy and partial least square regression (PLSR). PLSR prediction models were developed based on two different sets of spectral reflectance data: (i) laboratory spectra of silage samples and (ii) airborne imaging spectra (HyMap) of maize canopies under field (in situ) conditions. Biomass was sampled from 35 plots covering different maize varieties and the BMP was determined as BMP per mass (BMP FM , Nm 3 biogas/t fresh matter (Nm 3 /t FM)) and BMP per area (BMP area , Nm 3 biogas/ha (Nm 3 /ha)). We found that BMP FM significantly differs among maize varieties; it could be well retrieved from silage samples in the laboratory approach (R cv 2 = 0.82, n = 35), especially at levels >190 Nm 3 /t. In the in situ approach PLSR prediction quality declined (R cv 2 = 0.50, n = 20). BMP area , on the other hand, was found to be strongly correlated with total biomass, but could not be satisfactorily predicted using airborne HyMap imaging data and PLSR.

<p style="text-align: justify;"><strong>Aims</strong>: The present investigation in a Luxembourgish vineyard aimed at evaluating the potential of multispectral, multi-angular UAS (unmanned aerial system) imagery to separate four soil management strategies, to predict physiological variables (chlorophyll, nitrogen, yield etc.) and to follow seasonal changes in grapevine physiology in relation to soil management.</p><p style="text-align: justify;"><strong>Methods and results</strong>: Multi-angular (nadir and 45° off-nadir) multispectral imageries (530-900 nm) were taken in the years 2011 and 2012. Image grey values and reflectance-derived vegetation indices were computed and canopy and vigour properties were monitored in the field. All four soil management strategies could be significantly discriminated (box-plots, linear discriminant analysis) and vegetation properties estimated (linear regression) in 2011. For 2012, global models predicted chlorophyll contents and nitrogen balance index values with a R²_cv of 0.65 and 0.76, respectively.</p><p style="text-align: justify;"><strong>Conclusions</strong>: Soil management strategies strongly affect plant vigour and reflectance. Differences were best detectable by oblique visible/near-infrared (Vis/nIR) UAS data of illuminated canopies.</p><p style="text-align: justify;"><strong>Significance and impact of the study</strong>: UAS imaging is a flexible tool for applications in precision viticulture.</p>