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Use of A Portable Camera for Proximal Soil Sensing with Hyperspectral Image Data
摘要： In soil proximal sensing with visible and near-infrared spectroscopy, the currently available hyperspectral snapshot camera technique allows a rapid image data acquisition in a portable mode. This study describes how readings of a hyperspectral camera in the 450–950 nm region could be utilised for estimating soil parameters, which were soil organic carbon (OC), hot-water extractable-C, total nitrogen and clay content; readings were performed in the lab for raw samples without any crushing. As multivariate methods, we used PLSR with full spectra (FS) and also combined with two conceptually different methods of spectral variable selection (CARS, “competitive adaptive reweighted sampling” and IRIV, “iteratively retaining informative variables”). For the accuracy of obtained estimates, it was beneficial to use segmented images instead of image mean spectra, for which we applied a regular decomposing in sub-images all of the same size and k-means clustering. Based on FS-PLSR with image mean spectra, obtained estimates were not useful with RPD values less than 1.50 and R2 values being 0.51 in the best case. With segmented images, improvements were marked for all soil properties; RPD reached values ≥ 1.68 and R2 ≥ 0.66. For all image data and variables, IRIV-PLSR slightly outperformed CARS-PLSR.
关键词： spectral variable selection,hyperspectral snapshot camera,partial least squares regression,multivariate calibration,hyperspectral imaging,proximal soil sensing
更新于2025-09-23 15:22:29
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Comparison of Calibration Approaches in Laser-Induced Breakdown Spectroscopy for Proximal Soil Sensing in Precision Agriculture
摘要： The lack of soil data, which are relevant, reliable, a?ordable, immediately available, and su?ciently detailed, is still a signi?cant challenge in precision agriculture. A promising technology for the spatial assessment of the distribution of chemical elements within ?elds, without sample preparation is laser-induced breakdown spectroscopy (LIBS). Its advantages are contrasted by a strong matrix dependence of the LIBS signal which necessitates careful data evaluation. In this work, di?erent calibration approaches for soil LIBS data are presented. The data were obtained from 139 soil samples collected on two neighboring agricultural ?elds in a quaternary landscape of northeast Germany with very variable soils. Reference analysis was carried out by inductively coupled plasma optical emission spectroscopy after wet digestion. The major nutrients Ca and Mg and the minor nutrient Fe were investigated. Three calibration strategies were compared. The ?rst method was based on univariate calibration by standard addition using just one soil sample and applying the derived calibration model to the LIBS data of both ?elds. The second univariate model derived the calibration from the reference analytics of all samples from one ?eld. The prediction is validated by LIBS data of the second ?eld. The third method is a multivariate calibration approach based on partial least squares regression (PLSR). The LIBS spectra of the ?rst ?eld are used for training. Validation was carried out by 20-fold cross-validation using the LIBS data of the ?rst ?eld and independently on the second ?eld data. The second univariate method yielded better calibration and prediction results compared to the ?rst method, since matrix e?ects were better accounted for. PLSR did not strongly improve the prediction in comparison to the second univariate method.
关键词： laser-induced breakdown spectroscopy,soil nutrients,elemental composition,proximal soil sensing,LIBS
更新于2025-09-12 10:27:22
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Applications of Laser-Induced Breakdown Spectroscopy for Soil Analysis, Part I: Review of Fundamentals and Chemical and Physical Properties
摘要： Laser-induced breakdown spectroscopy (LIBS) has become a prominent analytical technique in recent years for real-time characterization of soil properties. However, only a few studies of soil chemical and physical properties have been reported using LIBS until recently. The aims of this article are to: (1) provide the basic principles of LIBS for soil analysis and (2) present the use of LIBS for soil pH, soil texture, and humification degree of soil organic matter (SOM). The second article will cover soil classification and soil elemental analysis, including plant nutrients, carbon (C), and toxic elements. LIBS is a multi-element analytical technique based on atomic spectroscopy that employs a high-energy laser pulse focused onto a sample surface to create a transient plasma. It is a spectroscopic analytical technique that requires very little or no sample preparation, examines each sample in seconds, and offers a flexible platform for the examination of a broad array of elements in the sample. LIBS also can be used to infer soil chemical and physical properties if a relationship exists between the chemical composition and the soil properties. With proper calibration, LIBS has a great potential for real-time in-field soil analysis and precision farming that could lead to improved soil management and agricultural production, and reduced agricultural environmental impacts.
关键词： humification degree of soil organic matter,soil texture,precision agriculture,soil sensing,soil analysis,soil pH
更新于2025-09-11 14:15:04