Penetrometer-mounted VisNIR spectroscopy: Application of EPO-PLS to in situ VisNIR spectra

Ackerson, Jason P.; Morgan, Cristine L.S.; Ge, Yufeng

doi:10.1016/j.geoderma.2016.10.018

Cited by 58 publications

(30 citation statements)

References 20 publications

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“…Our results demonstrated that moist spectra and dried ground spectra differed greatly from each other (Figure 3a). This corresponded well with other studies: with increasing soil water content, reflectance spectra decreased over the entire wavelength range [4,9,[47][48][49][50][51]. This is mainly because soil water replaces the air within the micro and macro pores, leading to increasing the forward scattering of light and increasing the absorption at all wavelengths [52].…”

Section: Discussionsupporting

confidence: 91%

Combining Fractional Order Derivative and Spectral Variable Selection for Organic Matter Estimation of Homogeneous Soil Samples by VIS–NIR Spectroscopy

Hong

Chen

et al. 2018

Remote Sensing

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Visible and near-infrared (VIS-NIR) spectroscopy has been extensively applied to estimate soil organic matter (SOM) in the laboratory. However, if field/moist VIS-NIR spectra can be directly applied to estimate SOM, then much of the time and labor would be avoided. Spectral derivative plays an important role in eliminating unwanted interference and optimizing the estimation model. Nonetheless, the conventional integer order derivatives (i.e., the first and second derivatives) may neglect some detailed information related to SOM. Besides, the full-spectrum generally contains redundant spectral variables, which would affect the model accuracy. This study aimed to investigate different combinations of fractional order derivative (FOD) and spectral variable selection techniques (i.e., competitive adaptive reweighted sampling (CARS), elastic net (ENET) and genetic algorithm (GA)) to optimize the VIS-NIR spectral model of moist soil. Ninety-one soil samples were collected from Central China, with their SOM contents and reflectance spectra measured. Support vector machine (SVM) was applied to estimate SOM. Results indicated that moist spectra differed greatly from dried ground spectra. With increasing order of derivative, the spectral resolution improved gradually, but the spectral strength decreased simultaneously. FOD could provide a better tool to counterbalance the contradiction between spectral resolution and spectral strength. In full-spectrum SVM models, the most accurate estimation was achieved by SVM model based on 1.5-order derivative spectra, with validation R 2 = 0.79 and ratio of the performance to deviation (RPD) = 2.20. Of all models studied (different combinations of FOD and variable selection techniques), the highest validation model accuracy for SOM was achieved when applying 1.5 derivative spectra and GA method (validation R 2 = 0.88 and RPD = 2.89). Among the three variable selection techniques, overall, the GA method yielded the optimal predictability. However, due to its long computation time, one alternative was to use CARS method. The results of this study confirm that a suitable combination of FOD and variable selection can effectively improve the model performance of SOM in moist soil.

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Section: Discussionsupporting

confidence: 91%

Combining Fractional Order Derivative and Spectral Variable Selection for Organic Matter Estimation of Homogeneous Soil Samples by VIS–NIR Spectroscopy

Hong

Chen

et al. 2018

Remote Sensing

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“…Soil moisture has a distinct impact on soil reflectance by decreasing the albedo [113]. To that end, methods to eliminate this effect such as EPO and DS have been proposed with relatively good results [51,142]. Attempts to create samples with variable soil moisture content have also been made, though they are somewhat time consuming [73,80,143].…”

Section: Discussionmentioning

confidence: 99%

From Laboratory to Proximal Sensing Spectroscopy for Soil Organic Carbon Estimation—A Review

et al. 2020

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Rapid and cost-effective soil properties estimations are considered imperative for the monitoring and recording of agricultural soil condition for the implementation of site-specific management practices. Conventional laboratory measurements are costly and time-consuming, and, therefore, cannot be considered appropriate for large datasets. This article reviews laboratory and proximal sensing spectroscopy in the visible and near infrared (VNIR)–short wave infrared (SWIR) wavelength region for soil organic carbon and soil organic matter estimation as an alternative to analytical chemistry measurements. The aim of this work is to report the progress made in the last decade on data preprocessing, calibration approaches, and system configurations used for VNIR-SWIR spectroscopy of soil organic carbon and soil organic matter estimation. We present and compare the results of over fifty selective studies and discuss the factors that affect the accuracy of spectroscopic measurements for both laboratory and in situ applications.

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“…Proximal soil sensing has made soil analysis convenient, easier, cheaper and faster [1,2] over the conventional laboratory soil analysis methods, which are laborious, costly, time consuming and destructive [3,4]. One of the best proximal soil sensors is visible and near infrared (vis-NIR) diffuse reflectance spectroscopy, which is a simple, non-destructive and rapid technique, needs no sample preparation for field applications, and can be used for off-line (the sensor is in a fixed position in the laboratory or the field) [5][6][7][8] and on-line (the sensor is being on the move during measurement) [9][10][11][12] measurement modes. The unique feature of the on-line mode is that it offers high sampling resolution (≈ 2000 samples per ha) data, compared to the laboratory and in situ modes, provided with sufficient prediction accuracy for several precision agricultural applications [13].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Secondary Soil Properties by Fusion of Laboratory and On-Line Measured Vis–NIR Spectra

2019

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Visible and near infrared (vis–NIR) diffuse reflectance spectroscopy has made invaluable contributions to the accurate estimation of soil properties having direct and indirect spectral responses in NIR spectroscopy with measurements made in laboratory, in situ or using on-line (while the sensor is moving) platforms. Measurement accuracies vary with measurement type, for example, accuracy is higher for laboratory than on-line modes. On-line measurement accuracy deteriorates further for secondary (having indirect spectral response) soil properties. Therefore, the aim of this study is to improve on-line measurement accuracy of secondary properties by fusion of laboratory and on-line scanned spectra. Six arable fields were scanned using an on-line sensing platform coupled with a vis–NIR spectrophotometer (CompactSpec by Tec5 Technology for spectroscopy, Germany), with a spectral range of 305–1700 nm. A total of 138 soil samples were collected and used to develop five calibration models: (i) standard, using 100 laboratory scanned samples; (ii) hybrid-1, using 75 laboratory and 25 on-line samples; (iii) hybrid-2, using 50 laboratory and 50 on-line samples; (iv) hybrid-3, using 25 laboratory and 75 on-line samples, and (v) real-time using 100 on-line samples. Partial least squares regression (PLSR) models were developed for soil pH, available potassium (K), magnesium (Mg), calcium (Ca), and sodium (Na) and quality of models were validated using an independent prediction dataset (38 samples). Validation results showed that the standard models with laboratory scanned spectra provided poor to moderate accuracy for on-line prediction, and the hybrid-3 and real-time models provided the best prediction results, although hybrid-2 model with 50% on-line spectra provided equally good results for all properties except for pH and Na. These results suggest that either the real-time model with exclusively on-line spectra or the hybrid model with fusion up to 50% (except for pH and Na) and 75% on-line scanned spectra allows significant improvement of on-line prediction accuracy for secondary soil properties using vis–NIR spectroscopy.

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Penetrometer-mounted VisNIR spectroscopy: Application of EPO-PLS to in situ VisNIR spectra

Cited by 58 publications

References 20 publications

Combining Fractional Order Derivative and Spectral Variable Selection for Organic Matter Estimation of Homogeneous Soil Samples by VIS–NIR Spectroscopy

Combining Fractional Order Derivative and Spectral Variable Selection for Organic Matter Estimation of Homogeneous Soil Samples by VIS–NIR Spectroscopy

From Laboratory to Proximal Sensing Spectroscopy for Soil Organic Carbon Estimation—A Review

Estimation of Secondary Soil Properties by Fusion of Laboratory and On-Line Measured Vis–NIR Spectra

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