The spectral assessment of soil properties is handicapped by the fact that spectral predictive mechanisms often vary from one population to another. In a landscape approach, heterogeneous conditions with a wide variety of combinations of spectrally active factors have to be considered. Heterogeneity, however, is one main reason for poor predictions from spectroscopic data, as an optimal calibration needs limited but sufficient set heterogeneity. For our study, the investigated plots were located in an area that covered about 600 km²; geologic conditions and sampled soil types were highly variable. In total, 172 soil samples were taken from the top horizon of agricultural fields, afterwards analysed in the laboratory for total organic carbon (OC) and black carbon (BC) and additionally measured with a full range ASD FieldSpec-instrument. The heterogeneity of the sample set was reflected by both the analysed soil parameters and the measured soil spectra. As a consequence, one “global” calibration model (with PLSR) provided only moderate results for the studied soil variables. In the following we focused on two issues, which were i) to replace the global calibration by local calibration procedures, and ii) to study the effect of spectral variable selection for calibration success. For the CARS selection procedure (“competitive adaptive reweighted sampling”), the results demonstrated that more accurate estimates can be obtained using selected variables instead of the full spectrum.
The site for this study - located in Rhineland-Palatinate, Germany ("Bitburger Gutland") - covered different geological
substrates and agro-pedological zones. In total, 42 plots were sampled in the field; soil samples from the top horizon
were analysed in the laboratory for total organic carbon (OC), hot water-extractable C (HWE-C) and microbial C
(Cmic). In parallel to the ground campaign, a data set of the HyMapTM airborne imaging sensor was acquired on 27th of
August 2009. After pre-processing, HyMap spectra were used to assess the contents of OC, HWE-C and Cmic. As
calibration method we used partial least squares regression (PLSR), as it allows a handling of large input spaces and
noisy patterns. Since calibration quality was poor for HWE-C and Cmic (cross-validated r2 values were less than 0.5), we
additionally combined PLSR with a genetic algorithm (GA) to preselect an optimum set of spectral features instead of
using the full spectrum. With this GA-PLSR approach, results improved considerably for all constituents in the crossvalidation
(r2 ≥ 0.72). Very similar GA selection patterns for all carbon fractions suggest that spurious (indirect)
correlations may be relevant for assessing HWE-C and Cmic. For the GA approach, some overfitting due to a selection
based on chance correlations between C fractions and spectral variables cannot be excluded.
In the field of hydrological modelling, there is mostly a lack of spatially distributed data that may allow a detailed
analysis of simulation results. This study was to demonstrate that remote sensing can partly fill this gap, as combining
reflective and thermal data allows the retrieval of estimates for evapotranspiration (ET). Two Landsat-5 TM scenes were
analysed, and the results were afterwards compared to the daily output of the Precipitation Runoff Modeling System, a
conceptual model based on Hydrologic Response Units and designed for meso- to macroscale applications. For the study
site, the mesoscale Ruwer basin located in the low mountain range of Rhineland-Palatinate (Germany), an overall good
agreement of ET estimates retrieved by both approaches was found. At one date, some mismatches indicated clear
inconsistencies in the model structure and parameterisation scheme. Based on these findings, a modified soil module was
implemented to allow for a more realistic specification of land use dependant parameters. After this, PRMS provided ET
estimates now very similar to those from Landsat TM, and the RMSE was reduced from 1.30 to 0.86 mm. These results
indicate, that the representation of the hydrological cycle by hydrological modelling may be improved by the integration
of appropriate remote sensing data.
To assess the canopy water content of summer barley from spectroradiometer and HyMap image data, spectral indices and a canopy reflectance model (PROSPECT + SAIL) were applied. In addition to traditional indices (WI, NDWI), a Simple Ratio index (SR*) combining SWIR (1355 nm) and red edge (710 nm) reflectances was found to provide highly reliable estimates for the calibration data. After the adaption to HyMap resolution, the calibrated WI and SR* estimation models were applied to the image data. Here, limitations of the purely empirical index approach became apparent as the retrieved spatial patterns differed distinctly. The physically based approach of PROSPECT + SAIL provided estimates clearly too low but also highly correlated with the measured canopy water contents; the underestimation is due to the fact that some of the canopy water is partly unseeable for the remote sensor which can be traced back to erectophile canopy elements and thick or clumped tissues. Thus, PROSPECT + SAIL estimates were much better in line with the water content of the leaf fraction. In total, the PROSPECT + SAIL results kept stable when applied to synthetic and real HyMap data. This point gives confidence to the spatial pattern of water content as retrieved by PROSPECT + SAIL that differed clearly from the results obtained from the spectral indices.
Different empirical and physically based methods were employed to derive the vegetation water content of summer barley plots (n=22) from spectroradiometric measurements (ASD FieldSpec II). Data were acquired for two different phenological stages in May and June 2005. For the empirical approaches, a ratio index using the reflectances at 1355 and 710 nm and the partial least squares regression provided the best estimation results (r2 > 0.90). Canopy radiative transfer modeling was performed by coupling the PROSPECT and SAIL models. The retrieved values for Cw (equivalent leaf water thickness) × LAI were highly correlated (r2 = 0.86) with the measured canopy water contents, but showed distinct underestimates. For the vegetative phenological stage investigated in May, the PROSAIL results were very close to the measured water contents of the leaf fraction, but this was not valid for the data collected in June. Obviously, different phenological stages need specific model calibration, as the presence of undetectable water in non-leaf tissues is variable. All approaches were applied to synthetic HyMap data generated by resampling the spectroradiometer readings. Estimation results did not differ significantly; thus, by neglecting spatial scaling effects, the pure spectral information provided by both data sets is almost equivalent.
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