Validation requirements for diffuse reflectance soil characterization models with a case study of VNIR soil C prediction in Montana

Brown, David J.; Bricklemyer, Ross S.; Miller, Perry R.

doi:10.1016/j.geoderma.2005.01.001

Cited by 205 publications

(201 citation statements)

References 33 publications

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“…To build a robust calibration model, validation of model developed has to be carried out using validation samples, which were not used for model development. This means that validation samples of the independent validation set (25 samples collected from the field 1 in the Duck End farm) should not be used for the development of calibration models in cross-validation, avoiding this way any influence in the prediction capacity of the model selected and the consequent overestimation (Brown et al, 2005). The same samples of calibration and validation were selected for each property.…”

Section: Establishment Of Calibration Modelsmentioning

confidence: 99%

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On-line measurement of soil properties without direct spectral response in near infrared spectral range

Marín-González

Kuang

Quraishi

et al. 2013

Soil and Tillage Research

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This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and sharing with colleagues.Other uses, including reproduction and distribution, or selling or licensing copies, or posting to personal, institutional or third party websites are prohibited.

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Section: Establishment Of Calibration Modelsmentioning

confidence: 99%

“…The latent variable of the first minimum value of residual variance was selected (Brown et al, 2005). The residual sample variance and predicted vs. measured plots were assessed for outliers determination after running the PLSR.…”

Section: Establishment Of Calibration Modelsmentioning

confidence: 99%

On-line measurement of soil properties without direct spectral response in near infrared spectral range

Marín-González

Kuang

Quraishi

et al. 2013

Soil and Tillage Research

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“…Under in-situ measurement conditions, additional challenges associated with the variation of soil-to-sensor distance affect the accuracy of the measurement [8,9]. In addition, a wide range of soil spectral measurements are being gathered around the globe, which return different results as they have been collected with different protocols, sampling techniques, sample preparation, instrument specifications, spectral acquisition and analytical algorithms, and can severely affect the prediction performance of spectroscopic models and outputs [4,10,11]. For instance, differences in water content of air-dried samples (depending on the laboratory protocol), due to fluctuations in relative humidity of the ambient air in the laboratory, affect the spectral shape and peaks, especially around 1415 and 1915 nm [12,13].…”

Section: Introductionmentioning

confidence: 99%

Agricultural Soil Spectral Response and Properties Assessment: Effects of Measurement Protocol and Data Mining Technique

et al. 2017

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Abstract:Soil spectroscopy has shown to be a fast, cost-effective, environmentally friendly, non-destructive, reproducible and repeatable analytical technique. Soil components, as well as types of instruments, protocols, sampling methods, sample preparation, spectral acquisition techniques and analytical algorithms have a combined influence on the final performance. Therefore, it is important to characterize these differences and to introduce an effective approach in order to minimize the technical factors that alter reflectance spectra and consequent prediction. To quantify this alteration, a joint project between Czech University of Life Sciences Prague (CULS) and Tel-Aviv University (TAU) was conducted to estimate Cox, pH-H 2 O, pH-KCl and selected forms of Fe and Mn. Two different soil spectral measurement protocols and two data mining techniques were used to examine seventy-eight soil samples from five agricultural areas in different parts of the Czech Republic. Spectral measurements at both laboratories were made using different ASD spectroradiometers. The CULS protocol was based on employing a contact probe (CP) spectral measurement scheme, while the TAU protocol was carried out using a CP measurement method, accompanied with the internal soil standard (ISS) procedure. Two spectral datasets, acquired from different protocols, were both analyzed using partial least square regression (PLSR) technique as well as the PARACUDA II ® , a new data mining engine for optimizing PLSR models. The results showed that spectra based on the CULS setup (non-ISS) demonstrated significantly higher albedo intensity and reflectance values relative to the TAU setup with ISS. However, the majority of statistics using the TAU protocol was not noticeably better than the CULS spectra. The paper also highlighted that under both measurement protocols, the PARACUDA II ® engine proved to be a powerful tool for providing better results than PLSR. Such initiative is not only a way to unlock current limitations of soil spectroscopy, but also offers considerable efficiency and cost-and time-saving possibilities, which lead to further improvements in prediction performance of spectral models.

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“…In order to improve the accuracy of SOC calibration models with VIS/NIR spectral data, the raw spectral data are often pre-processed before modeling [9,32,33]. Pre-processing is usually regarded as an integral part of chemometrics modeling with spectral data.…”

Section: Introductionmentioning

confidence: 99%

Estimating Soil Organic Carbon Using VIS/NIR Spectroscopy with SVMR and SPA Methods

et al. 2014

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With 298 heterogeneous soil samples from Yixing (Jiangsu Province), Zhongxiang and Honghu (Hubei Province), this study aimed to combine a successive projections algorithm (SPA) with a support vector machine regression (SVMR) model (SPA-SVMR model) to improve the estimation accuracy of soil organic carbon (SOC) contents using the laboratory-based visible and near-infrared (VIS/NIR, 350−2500 nm) spectroscopy of soils. The effects of eight spectra pre-processing methods, i.e., Log (1/R), Log (1/R) coupled with Savitzky-Golay (SG) smoothing (Log (1/R) + SG), first derivative with SG smoothing (FD), second derivative with SG smoothing (SD), SG, standard normal variate (SNV), mean center (MC) and multiplicative scatter correction (MSC), on SPA-based informative wavelength selection were explored. The SVMR model (i.e., SVMR without SPA) and SPA-PLSR model (i.e., SPA combined with partial least squares regression (PLSR)) were developed and compared with the SPA-SVMR model in order to evaluate the performance of SPA-SVMR. The results indicated that the variables selected by SPA and their distributions were strongly affected by different pre-processing methods, and SG was the optimal pre-processing method for SPA-SVMR model development; the SPA-SVMR model using SG pre-processing and 28 SPA-selected wavelengths obtained a better result (R 2 V = 0.73, RMSE V = 2.78 g·kg and RPD V = 1.63). Most of the spectral bands used by the SPA-SVMR model over the near-infrared region were important wavelengths for SOC content estimation. This study demonstrated that the combination of SPA and SVMR is feasible and reliable for estimating SOC content from the VIS/NIR spectra of soils in regions with multiple soil and land-use types.

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Validation requirements for diffuse reflectance soil characterization models with a case study of VNIR soil C prediction in Montana

Cited by 205 publications

References 33 publications

On-line measurement of soil properties without direct spectral response in near infrared spectral range

On-line measurement of soil properties without direct spectral response in near infrared spectral range

Agricultural Soil Spectral Response and Properties Assessment: Effects of Measurement Protocol and Data Mining Technique

Estimating Soil Organic Carbon Using VIS/NIR Spectroscopy with SVMR and SPA Methods

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