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  • Knadel, Maria  (2)
  • Marakkala Manage, Lashya P.  (2)
  • English  (2)
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  • English  (2)
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  • 1
    In: Soil Science Society of America Journal, Wiley, Vol. 82, No. 6 ( 2018-11), p. 1333-1346
    Abstract: Core Ideas The soil–water retention characteristics govern the water effect on soil vis‐NIR spectra. Soil clay, silt, sand content, and the water content at pF3 can be predicted with high degree of accuracy using spectroscopy at any field moisture condition. The OC and the derived properties of OC showed poorer prediction when the soil–water content exceeds field capacity (pF 〈 2). Soil physical characteristics are important drivers for soil functions and productivity. Field applications of near‐infrared spectroscopy (NIRS) are already deployed for in situ mapping of soil characteristics and therefore, fast and precise in situ measurements of the basic soil physical characteristics are needed at any given water content. Visible‐near‐infrared spectroscopy (vis–NIRS) is a fast, low‐cost technology for determination of basic soil properties. However, the predictive ability of vis–NIRS may be affected by soil‐water content. This study was conducted to quantify the effects of six different soil‐water contents (full saturation, pF 1, pF 1.5, pF 2.5, pF 3, and air‐dry) on the vis–NIRS predictions of six soil physical properties: clay, silt, sand, water content at pF 3, organic carbon (OC), and the clay/OC ratio. The effect of soil‐water content on the vis–NIR spectra was also assessed. Seventy soil samples were collected from five sites in Denmark and Germany with clay and OC contents ranging from 0.116 to 0.459 and 0.009 to 0.024 kg kg ‐1 , respectively. The soil rings were saturated and successively drained/dried to obtain different soil–water potentials at which they were measured with vis–NIRS. Partial least squares regression (PLSR) with leave‐one‐out cross‐validation was used for estimating the soil properties using vis–NIR spectra. Results showed that the effects of water on vis–NIR spectra were dependent on the soil–water retention characteristics. Contents of clay, silt, and sand, and the water content at pF 3 were well predicted at the different soil moisture levels. Predictions of OC and the clay/OC ratio were good at air‐dry soil condition, but markedly weaker in wet soils, especially at saturation, at pF 1 and pF 1.5. The results suggest that in situ measurements of spectroscopy are precise when soil‐water content is below field capacity.
    Type of Medium: Online Resource
    ISSN: 0361-5995 , 1435-0661
    RVK:
    Language: English
    Publisher: Wiley
    Publication Date: 2018
    detail.hit.zdb_id: 241415-6
    detail.hit.zdb_id: 2239747-4
    detail.hit.zdb_id: 196788-5
    detail.hit.zdb_id: 1481691-X
    SSG: 13
    SSG: 21
    Library Location Call Number Volume/Issue/Year Availability
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  • 2
    In: Soil Science Society of America Journal, Wiley, Vol. 83, No. 1 ( 2019-01), p. 37-47
    Abstract: Core Ideas The two‐compartment pedotransfer function successfully predicted soil particle density. Vis–NIR showed slightly poorer performance than the two‐compartment function for predicting soil ρ d . Spectroscopy or OM based pedotransfer models gave better estimates of ρ d when a wide range in soil OM data was used. The average particle density (ρ d ) is a fundamental soil property, used for calculating the total porosity. Traditional ρ d measurement by pycnometer method is tedious and time‐consuming. In this study, visible–near‐infrared (vis–NIR) spectroscopy and a simple two‐compartment linear and curvilinear pedotransfer function only requiring knowledge of soil organic matter content (OM) were tested and compared as alternative, indirect, rapid, and cost‐effective methods. Soil ρ d was measured by water pycnometer on 179 soils representing a wide range of OM (0.002–0.767 kg kg −1 ), whereas soil spectra were measured on air‐dry samples by vis–NIR spectroscopy. The ρ d models were developed using partial least squares regression with leave‐one‐out‐cross‐validation using vis–NIR spectral data, and a simple two‐compartment pedotransfer function, ρ d = A (OM) + B (1 − OM) using the OM content. Predictive abilities of these two methods were tested using three different datasets: (i) minerals soils (OM 〈 0.1 kg kg −1 ), (ii) organic soils (OM 〉 0.1 kg kg −1 ), and (iii) all soils. Calibrating the two‐compartment pedotransfer function for the entire dataset gave expected values for the individual particle densities of OM ( A = 1.244 g cm −3 ) and mineral particles ( B = 2.615 g cm −3 ). The vis–NIR spectroscopy model successfully predicted soil ρ d for the entire dataset ( R 2 = 0.87, RMSECV = 0.10 g cm −3 ), with a poorer performance than the two‐compartment linear model ( R 2 = 0.96, RMSE = 0.06 g cm −3 ). Using only the mineral soils data did not suffice to obtain realistic and accurate vis–NIR spectroscopy ( R 2 = 0.62, RMSECV = 0.02 g cm −3 ) or OM based ( R 2 = 0.80, RMSE = 0.02 g cm −3 ) models for ρ d , illustrating the importance of the wide range of OM content considered in the present study.
    Type of Medium: Online Resource
    ISSN: 0361-5995 , 1435-0661
    RVK:
    Language: English
    Publisher: Wiley
    Publication Date: 2019
    detail.hit.zdb_id: 241415-6
    detail.hit.zdb_id: 2239747-4
    detail.hit.zdb_id: 196788-5
    detail.hit.zdb_id: 1481691-X
    SSG: 13
    SSG: 21
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
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