Internal soil standard method for the Brazilian soil spectral library: Performance and proximate analysis

Romero, D.J.; Ben‐Dor, Eyal; Demattê, José Alexandre Melo; Souza, Arnaldo Barros e; Vicente, L. E.; Tavares, Tiago Rodrigues; Martello, Maurício; Strabeli, Taila Fernanda; Barros, Pedro Paulo; Fiorio, Peterson Ricardo; Gallo, Bruna Cristina; Sato, Marcus Vinicius; Eitelwein, Mateus Tonini

doi:10.1016/j.geoderma.2017.09.014

Cited by 55 publications

(34 citation statements)

References 20 publications

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“…Another challenge is the lack in comparability between different studies since the model evaluation and accuracy is not measured with the same methods and studies are deprived of certain information that is necessary for the comparison between them. In addition, different protocols for soil sampling and measurements together with different instrumentation are factors that also hinder the reliability of the results, while predictions are affected by the reference method the soil properties were measured [28,108].…”

Section: Future Of Soil Spectroscopy In Soc Estimationmentioning

confidence: 99%

Remote Sensing Techniques for Soil Organic Carbon Estimation: A Review

et al. 2019

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Towards the need for sustainable development, remote sensing (RS) techniques in the Visible-Near Infrared–Shortwave Infrared (VNIR–SWIR, 400–2500 nm) region could assist in a more direct, cost-effective and rapid manner to estimate important indicators for soil monitoring purposes. Soil reflectance spectroscopy has been applied in various domains apart from laboratory conditions, e.g., sensors mounted on satellites, aircrafts and Unmanned Aerial Systems. The aim of this review is to illustrate the research made for soil organic carbon estimation, with the use of RS techniques, reporting the methodology and results of each study. It also aims to provide a comprehensive introduction in soil spectroscopy for those who are less conversant with the subject. In total, 28 journal articles were selected and further analysed. It was observed that prediction accuracy reduces from Unmanned Aerial Systems (UASs) to satellite platforms, though advances in machine learning techniques could further assist in the generation of better calibration models. There are some challenges concerning atmospheric, radiometric and geometric corrections, vegetation cover, soil moisture and roughness that still need to be addressed. The advantages and disadvantages of each approach are highlighted and future considerations are also discussed at the end.

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Section: Future Of Soil Spectroscopy In Soc Estimationmentioning

confidence: 99%

Remote Sensing Techniques for Soil Organic Carbon Estimation: A Review

et al. 2019

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“…The spectral reflectance of soil samples was obtained using a FieldSpec 3 spectroradiometer (Analytical Spectral Devices Inc., Boulder, CO, USA) in the VIS-NIR-SWIR range (350-2500 nm), following standard laboratory procedure of the Brazilian Soil Spectral Library (Romero et al, 2018). The samples were placed in a petri dish and shaken to ensure a smooth surface for spectrum acquisition.…”

Section: Spectral Measurementsmentioning

confidence: 99%

A Regional Legacy Soil Dataset for Prediction of Sand and Clay Content with Vis-Nir-Swir, in Southern Brazil

Silva

Giasson

Dotto

et al. 2019

Rev. Bras. Ciênc. Solo

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The success of soil prediction by VIS-NIR-SWIR spectroscopy has led to considerable investment in large soil spectral libraries. The aims of this study were 1) to develop a soil VIS-NIR-SWIR spectroscopy approach using legacy soil samples to improve spectral soil information in a regional scale; (2) to compare six spectral preprocessing techniques; and (3) to compare the performance of linear and non-linear multivariate models for prediction of sand and clay content. A total of 1,534 legacy soil samples, stored by Epagri, were collected from agricultural areas in 2009 on a regional scale, covering 260 municipalities of Santa Catarina. Six spectral preprocessing techniques were applied and compared with reflectance spectra (control treatment) in the development of sand and clay prediction models. Five multivariate regression models, Support Vector Machines, Gaussian Process Regression, Cubist, Random Forest, and Partial Least Square Regression were compared. The scatter-corrective preprocessing groups produced similar or better performance than spectral-derivatives. In addition, preprocessing spectra prior to regression analysis does not improve sand prediction, since reflectance spectra achieved the best performance using Cubist, SVM, and PLS models. In general, clay content presented better prediction accuracy than sand content. The best multivariate model to predict sand and clay content from soil VIS-NIR-SWIR spectra was Cubist. The best Cubist performance was achieved combined with reflectance spectra (R 2 = 0.73; root mean square error = 10.60 %; ratio of the performance to the interquartile range = 2.36) and MSC (R 2 = 0.83; root mean square error = 7.29 %; ratio of the performance to the interquartile range = 3.70) for sand and clay content, respectively. Considering the mean RMSE values of the validation set, the predictive ability of the multivariate models decreased in the following order: Cubist>PLS>RF>GPR>SVM for both properties. The predictive ability of VIS-NIR-SWIR reflectance spectroscopy achieved in this study for sand and clay content using legacy soil data and heterogeneous samples confirmed the potential of the spectroscopy approach.

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“…The geometry parameters of the spectrometer were illustrated as follows: one external 50 W halogen light matched with the spectrometer was placed 30 cm away from the sample, with a 45 • incidence angle; the sensor was positioned vertically and 12 cm above the sample surface. At the beginning of the spectral measurements and after every ten soil samples, the spectrometer was calibrated and optimized using a white Spectralon ® panel [35]. Ten scans were obtained for each soil sample, and then averaged to one spectrum as the final reflectance spectra.…”

Section: Spectral Measurement and Pre-processingmentioning

confidence: 99%

“…Three statistical parameters were applied to evaluate the predictive abilities of the estimated models in the courses of cross-validation and validation: the coefficient of determination (R 2 ), root mean squared error (RMSE) and ratio of the performance to deviation (RPD) [35]. Interpretations of RPD values were classified into five classes: RPD ≥ 2.5 (excellent models/predictions); 2.0 ≤ RPD < 2.5 (very good models/predictions); 1.8 ≤ RPD <2.0 (good models/predictions); 1.4 ≤ RPD < 1.8 (fair models/predictions); RPD < 1.4 (unsuccessful models/predictions).…”

Section: Model Calibration and Validationmentioning

confidence: 99%

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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Internal soil standard method for the Brazilian soil spectral library: Performance and proximate analysis

Cited by 55 publications

References 20 publications

Remote Sensing Techniques for Soil Organic Carbon Estimation: A Review

Remote Sensing Techniques for Soil Organic Carbon Estimation: A Review

A Regional Legacy Soil Dataset for Prediction of Sand and Clay Content with Vis-Nir-Swir, in Southern Brazil

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

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