Externally funded project

Synergetic use of mobile and lab-based spectroscopic techniques (vis-NIR, lab and hand-held MIR, portable hyperspectral frame camera) to optimize the determination of soil properties with high variability in time and space (Variable Bodeneigenschaften)


Abstract

Total contents of soil organic carbon (SOC), nitrogen (N) and phosphorus
(P) are only of limited use for studies of management (e.g.
fertilizations or tillage) on soil fertility; SOC and N fractions as
well as soil microbial properties are much more sensitive indicators.
However, a high spatial and temporal density of samples can only be
achieved with non-destructive sampling techniques. In this context, the
project studies the potentials of spectroscopic techniques to determine
key soil properties (SOC, N, pH, fractions of SOC and N, P, sulphur,
potassium, iron, cation exchange capacity, soil texture, microbial and
hot water-soluble C and N) with high accuracy by combining non-imaging
spectroscopy in the near and middle (vis-NIR and MIR) domain with
hyperspectral imaging. In addition to the lab scale, we focus on the
field scale with on-site spectroscopic measurements, which is favoured
by new instrumental developments, a portable MIR spectrometer and a
portable hyperspectral frame camera. The MIR range is essential for soil
spectroscopy, as fundamental bands of chemical groups can be measured
(different from the NIR range with only combination bands and
overtones). For a total of eight arable sites with soils of differing
textures, top soils and soil profiles will be sampled to investigate the
potentials of lab spectroscopy compared to on-site spectroscopy by
combining the different spectroscopic techniques for the estimation of
the soil properties mentioned above. To improve obtained accuracies,
methods of multivariate calibration will be optimized by using e.g.
Support Vector Machines or Random Forest instead of PLSR, by applying
spectral variable selection techniques, by substituting global by local
calibrations (i.e., a sample-wise selection of appropriate calibration
samples is performed) and by using the approach of spiking to locally
adapt calibration models. The optimized techniques will then be
validated on existing data sets. Additionally, it will be analysed,
whether and to what extent disturbances originating from different soil
surface roughness or from different soil water contents can be
compensated. Already existing soil spectral libraries (LUCAS,
ICRAF-ISRIC) are evaluated to select appropriate samples which may
support the definition and optimization of calibration models. In
addition, the underlying spectral predictive mechanisms will be analysed
(e.g., by 2D-correlation spectroscopy) to elucidate whether a direct or
only indirect spectral prediction is feasible for each of the studied
soil properties. This is fundamental to clarify whether a prediction
model, once calibrated, may be in principle transferred in space and
time.


Last updated on 2018-22-11 at 13:36