Prediction of Soil Organic Carbon in a New Target Area by Near-Infrared Spectroscopy: Comparison of the Effects of Spiking in Different Scale Soil Spectral Libraries.

Near-infrared (NIR) spectroscopy is widely used to predict soil organic carbon (SOC) because it is rapid and accurate under proper calibration. However, the prediction accuracy of the calibration model may be greatly reduced if the soil characteristics of some new target areas are different from the existing soil spectral library (SSL), which greatly limits the application potential of the technology. We attempted to solve the problem by building a large-scale SSL or using the spiking method.

Quantitative Analysis of Soil Total Nitrogen Using Hyperspectral Imaging Technology with Extreme Learning Machine.

Soil nutrient detection is important for precise fertilization. A total of 150 soil samples were picked from Lishui City. In this work, the total nitrogen (TN) content in soil samples was detected in the spectral range of 900-1700 nm using a hyperspectral imaging (HSI) system.

Hyperspectral Imaging Analysis for the Classification of Soil Types and the Determination of Soil Total Nitrogen.

Soil is an important environment for crop growth. Quick and accurately access to soil nutrient content information is a prerequisite for scientific fertilization. In this work, hyperspectral imaging (HSI) technology was applied for the classification of soil types and the measurement of soil total nitrogen (TN) content.

[Quantitatively Determination of Available Phosphorus and Available Potassium in Soil by Near Infrared Spectroscopy Combining with Recursive Partial Least Squares].

Soil available phosphorus (P) and available potassium (K) don't possess direct spectral response in the near infrared (NIR) region. They are predictable because of their correlation with spectrally active constituents (organic matter, carbonates, clays, water, etc.).

[Research on rapid determination of organic matter concentration in aquaculture water based on ultraviolet/visible spectroscopy].

Ultraviolet/visible (UV/Vis) spectroscopy was investigated for the rapid determination of chemical oxygen demand (COD) which was an indicator to measure the concentration of organic matter in aquaculture water. A total number of 135 collected turtle breeding water samples were scanned for UV/Vis spectrum, uninformative variable elimination (UVE) and successive projections algorithm (SPA) were combined as a mixed variable selection method to perform characteristic wavelength selection from the full wavelength spectrum, 7 characteristic wavelengths were selected from full 201 UV/Vis spectral variables, which were just 3.48% number of the full range spectrum, and the calibration time and complexity of the modeling were greatly reduced.

[Visible and near infrared spectroscopy combined with recursive variable selection to quantitatively determine soil total nitrogen and organic matter].

In the present work, recursive variable selection methods (updating both the model coefficients and effective variables during the prediction process) were applied to maintain the predictive abilities of calibration models. This work compared the performances of partial least squares (PLS), recursive PLS (RPLS) and three recursive variable selection methods, namely vari- able importance in the projection combined with RPLS (VIP-RPLS), VIP-PLS, and uninformative variable elimination combined with PLS (UVE-PLS) for the measurement of soil total nitrogen (TN) and organic matter (OM) using Vis-NIR spectroscopy. The dataset consisted of 195 soil samples collected from eight towns in Wencheng County, Zhejiang Province, China.