Mapping of several soil properties using DAIS-7915 hyperspectral scanner data - a case study over clayey soils in Israel

Prediction of soil organic matter content in a litchi orchard of South China using spectral indices

“…In the 850-2350 nm range, the reflectance spectra changed gently. The reflectance spectra has three concaves at approximately 1370, 1900, and 2200 nm, which are thought to be relevant to the existence of hydroxide (OH) of free water (1400 and 1900) and the Al-OH lattice structure in clay minerals (2200 nm) (Ben-Dor et al, 2002;Viscarra Rossel et al, 2006;Summers et al, 2011). Chang et al (2001), Zornoza et al (2008), and Summers et al (2011) observed that increasing SOM would lower albedo across the whole VNIR spectrum.…”

Quantification of Soil Properties with Hyperspectral Data: Selecting Spectral Variables with Different Methods to Improve Accuracies and Analyze Prediction Mechanisms

“…The arable soils varied in the range of the parent materials (i.e., for example, debris from iron-rich sandstone, limestone, dolomite or clay sediments each overlain by layers with admixtures of loess) and were identified as Luvic and Haplic Stagnosols, Dystric, Eutric and Colluvic Cambisols, Dystric Regosols and Calcic Leptosols. Soil textures in topsoils (Ap horizons) ranged from (sample counts in parentheses) sand (2), to loamy sand (9), silty sand (8), sandy loam (2), silty loam and silt (11), clay loam (6) and silty clay (4). At each plot, soil was sampled from the top horizon (Ap, 0-10 cm depth).…”

Determination of soil physicochemical attributes in farming sites through visible, near-infrared diffuse reflectance spectroscopy and PLSR modeling

“…The premise of detecting and estimating SPA using VNIR-DRS analysis under laboratory conditions was extensively tested by scientists with some limits. Over the past few decades, many scientists have demonstrated that reflectance spectra of soil in the VNIR region can detect and predict the SPA along with its classification and mapping with hyperspectral datasets (Anne et al 2014;Ben-Dor et al 2002;Vibhute et al 2015). For instance, Rossel et al (2016) developed a soil spectral library and predicted some soil properties such as soil organic and inorganic carbon, pH, sand, silt, clay, cation exchange capacity (CEC), and iron contents of all land cover soils.…”