VNIR/SWIR Laboratory Imaging Spectroscopy for Wall-to-Wall Mapping of Elemental Concentrations in Soil Cores

Schreiner, Simon; Buddenbaum, Henning; Emmerling, Christoph; Steffens, Markus

doi:10.1127/pfg/2015/0279

Cited by 10 publications

(10 citation statements)

References 9 publications

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“…The patches in the 2Bw horizon of the sand and silt maps were limestone fragments, which was consistent with the white patches in the digital images shown in Figure 1. Similar patches of organic residue and Ca from lime application were found by Schreiner et al (2015) using visible-near infrared and shortwave infrared imaging spectroscopy.…”

Section: Maps Of Profile Variationsupporting

confidence: 67%

“…Similar patches of organic residue and Ca from lime application were found by Schreiner et al . () using visible‐near infrared and shortwave infrared imaging spectroscopy.…”

Section: Discussionmentioning

confidence: 99%

“…Digital soil morphometrics uses instruments and techniques to measure and quantify soil profile attributes (Hartemink & Minasny, 2014). Imaging spectroscopy has been used to predict and map elements and diagnostic horizons of a Luvisol (Steffens & Buddenbaum, 2013) and a Regosol and Cambisol (Schreiner et al, 2015). Portable X-ray fluorescence (pXRF) spectrometry has been used in situ to study vertical variation in elements and pedogenesis (Stockmann et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

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Digital mapping of a soil profile

Zhang

Hartemink

2018

European J Soil Science

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Summary Detailed information on soil profiles is required for site characterization, soil mapping, studies on pedogenesis, soil–landscape modelling and soil classification. In this study, we used digital soil mapping techniques to map the profile wall of an Alfisol (90‐cm depth × 100‐cm width). Geochemical data (sampled at 10 cm × 10 cm) and digital images (resolution 1 cm × 1 cm) were used as input data in models that predicted a range of properties across the soil profile. Fuzzy c‐means clustering was applied to the profile and colour maps, and a confusion index was calculated. The colour coordinates were strongly correlated with SOC (soil organic carbon), sand and silt contents, and weathering indices. Random forest using the RGB model (R2 = 0.57, RMSE (root mean square error) = 2.41) showed less accurate prediction of SOC content than random forest using the CIE L*a*b* (R2 = 0.84, RMSE = 1.57) and HSV, hue, saturation, chroma (R2 = 0.85, RMSE = 1.46) models. Detailed profile maps showed considerable within‐horizon variation and identified animal holes, roots and limestone fragments. Colour and profile maps of soil properties and weathering indices matched field‐delineated soil horizons. Clusters obtained by soil properties and weathering indices represented the field‐delineated horizons and had the largest overall accuracy (P = 0.85 and 0.86, respectively). The confusion index showed that the horizons had wavy and somewhat irregular boundaries. We conclude that colour coordinates are useful for predicting and mapping soil properties in soil profiles. Random forest can be used to produce high‐resolution soil profile maps, and fuzzy c‐means clustering is useful for delineating soil horizons. Highlights We investigated the use of digital images in soil horizon delineation and within soil horizon variation We used various colour models to map soil profile properties and define horizons Colour and profile maps of soil properties and weathering indices matched field delineated soil horizons. Random forest can produce high‐resolution soil profile maps, and fuzzy c‐means clustering can delineate soil horizons

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Section: Maps Of Profile Variationsupporting

confidence: 67%

“…Similar patches of organic residue and Ca from lime application were found by Schreiner et al . () using visible‐near infrared and shortwave infrared imaging spectroscopy.…”

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Digital mapping of a soil profile

Zhang

Hartemink

2018

European J Soil Science

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“…Given the strong association of infrared (IR) spectra with SOC content 22 , this spectral information can potentially be used to derive SOC spatial distribution in an undisturbed soil sample. Although laboratory hyperspectral imaging is promising for assessing the information content of soil at very high scale resolution, its application to date has been largely limited to near surface horizons 23 , 24 and its use in deep subsoils is yet to be thoroughly tested.…”

Section: Introductionmentioning

confidence: 99%

Hotspots of soil organic carbon storage revealed by laboratory hyperspectral imaging

Hobley

Steffens

Bauke

et al. 2018

Sci Rep

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Subsoil organic carbon (OC) is generally lower in content and more heterogeneous than topsoil OC, rendering it difficult to detect significant differences in subsoil OC storage. We tested the application of laboratory hyperspectral imaging with a variety of machine learning approaches to predict OC distribution in undisturbed soil cores. Using a bias-corrected random forest we were able to reproduce the OC distribution in the soil cores with very good to excellent model goodness-of-fit, enabling us to map the spatial distribution of OC in the soil cores at very high resolution (~53 × 53 µm). Despite a large increase in variance and reduction in OC content with increasing depth, the high resolution of the images enabled statistically powerful analysis in spatial distribution of OC in the soil cores. In contrast to the relatively homogeneous distribution of OC in the plough horizon, the subsoil was characterized by distinct regions of OC enrichment and depletion, including biopores which contained ~2–10 times higher SOC contents than the soil matrix in close proximity. Laboratory hyperspectral imaging enables powerful, fine-scale investigations of the vertical distribution of soil OC as well as hotspots of OC storage in undisturbed samples, overcoming limitations of traditional soil sampling campaigns.

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“…However, geoscientific applications are not the only ones, which profit from extensive laboratory studies. Ecological [ 2 ] and soil [ 3 ] related applications have benefited a lot from the integration of the laboratory scans using imaging spectroscopy to study and link, e.g., plant [ 2 ] and soil properties [ 3 ] to spectroscopic signatures. This is critical for the understanding of large areas from other imaging spectroscopy scales (UAV-based, airborne and spaceborne).…”

Section: Introductionmentioning

confidence: 99%

Translational Imaging Spectroscopy for Proximal Sensing

Rogaß

Koerting

Mielke

et al. 2017

Sensors

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Proximal sensing as the near field counterpart of remote sensing offers a broad variety of applications. Imaging spectroscopy in general and translational laboratory imaging spectroscopy in particular can be utilized for a variety of different research topics. Geoscientific applications require a precise pre-processing of hyperspectral data cubes to retrieve at-surface reflectance in order to conduct spectral feature-based comparison of unknown sample spectra to known library spectra. A new pre-processing chain called GeoMAP-Trans for at-surface reflectance retrieval is proposed here as an analogue to other algorithms published by the team of authors. It consists of a radiometric, a geometric and a spectral module. Each module consists of several processing steps that are described in detail. The processing chain was adapted to the broadly used HySPEX VNIR/SWIR imaging spectrometer system and tested using geological mineral samples. The performance was subjectively and objectively evaluated using standard artificial image quality metrics and comparative measurements of mineral and Lambertian diffuser standards with standard field and laboratory spectrometers. The proposed algorithm provides highly qualitative results, offers broad applicability through its generic design and might be the first one of its kind to be published. A high radiometric accuracy is achieved by the incorporation of the Reduction of Miscalibration Effects (ROME) framework. The geometric accuracy is higher than 1 μpixel. The critical spectral accuracy was relatively estimated by comparing spectra of standard field spectrometers to those from HySPEX for a Lambertian diffuser. The achieved spectral accuracy is better than 0.02% for the full spectrum and better than 98% for the absorption features. It was empirically shown that point and imaging spectrometers provide different results for non-Lambertian samples due to their different sensing principles, adjacency scattering impacts on the signal and anisotropic surface reflection properties.

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VNIR/SWIR Laboratory Imaging Spectroscopy for Wall-to-Wall Mapping of Elemental Concentrations in Soil Cores

Cited by 10 publications

References 9 publications

Digital mapping of a soil profile

Digital mapping of a soil profile

Hotspots of soil organic carbon storage revealed by laboratory hyperspectral imaging

Translational Imaging Spectroscopy for Proximal Sensing

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