Assessing the performance of UAS-compatible multispectral and hyperspectral sensors for soil organic carbon prediction

Soil laboratory spectroscopy has proved its reliability for the estimation of soil organic carbon (SOC) by exploiting the relationship between electromagnetic radiation and key spectral features of organic matter located in the VIS-NIR-SWIR (350-2500 nm) region. It currently allows estimating soil variables at sampled points, however geo-statistical techniques have to be used to infer continuous spatial information on soil properties. In this regard, the use of proximal or remote sensing data could be very useful to provide detailed spectral sampling on soil spatial variability at the field or even regional scale. However, the factors affecting the quality of spectral acquisition in outdoor conditions need to be taken into account. In this perspective, we designed a study to investigate the capabilities of two portable hyperspectral sensors (STS-VIS and STS-NIR), and two multispectral cameras with narrow bands in the VIS-NIR region (Parrot Sequoia and Mini-MCA6), against a more sensitive reference hyper-spectral sensor (ASD Fieldspec-Pro 3) to provide data for SOC modelling from ground-based measurements. The aim of the comparison was to assess the performance of Partial Least Squares Regression (PLSR) models, when moving from laboratory to outdoor conditions, namely changing illumination, air conditions and sensor distance. Moreover, to verify the transferability of the prediction models between different measurement setups, we tested a methodology to align spectra acquired under different conditions (laboratory and outdoor) or by different instruments, by means of a calibration factor based on an internal soil standard. The results, in terms of Ratio of Performance to Deviation (RPD), showed that: i) the best performance for SOC modelling under outdoor conditions were obtained using the VIS-NIR range (RPD: 4.2), while the addition of the SWIR region resulted in a worsening of the prediction accuracy (RPD: 2.9); ii) modelling on the narrow bands of the two multispectral cameras (Parrot Sequoia and Tetracam Mini-MCA6) gave better performances (RPD: 4.2 and 3.4 respectively) than with the STS hyperspectral sensors (RPD: 2.6); iii) the STS employment in the outdoor benefitted from a laboratory model calibration adopting a spectral transfer using an internal soil standard, with the RPD increasing from 1.4 to 2.9 after the alignment. We therefore suggest that the employment of VIS-NIR-based portable instrument could be a strategy to obtain accurate and spatially distributed SOC data. Moreover, the perspective of their employment on UAS could represent a cost-effective solution for precision farming applications

Uncertainties in assessing tillage erosion – How appropriate are our measuring techniques?

Tillage erosion on arable land is a very important process leading to a net downslope movement of soil and soil constitutes. Tillage erosion rates are commonly in the same order of magnitude as water erosion rates and can be even higher, especially under highly mechanized agricultural soil management. Despite its prevalence and magnitude, tillage erosion is still understudied compared to water erosion. The goal of this study was to bring together experts using different techniques to determine tillage erosion and use the different results to discuss and quantify uncertainties associated with tillage erosion measurements. The study was performed in northeastern Germany on a 10 m by 50 m plot with a mean slope of 8%. Tillage erosion was determined after two sequences of seven tillage operations. Two different micro-tracers (magnetic iron oxide mixed with soil and fluorescent sand) and one macro-tracer (passive radio-frequency identification transponders (RFIDs), size: 4 × 22 mm) were used to directly determine soil fluxes. Moreover, tillage induced changes in topography were measured for the entire plot with two different terrestrial laser scanners and an unmanned aerial system for structure from motion topography analysis. Based on these elevation differences, corresponding soil fluxes were calculated. The mean translocation distance of all techniques was 0.57 m per tillage pass, with a relatively wide range of mean soil translocation distances ranging from 0.39 to 0.72 m per pass. A benchmark technique could not be identified as all used techniques have individual error sources, which could not be quantified. However, the translocation distances of the macro-tracers used were consistently smaller than the translocation distances of the micro-tracers (mean difference = − 26 ± 12%), which questions the widely used assumption of non-selective soil transport via tillage operations. This study points out that tillage erosion measurements, carried out under almost optimal conditions, are subject to major uncertainties that are far from negligible