Development of a national VNIR soil-spectral library for soil classification and prediction of organic matter concentrations

Shi, Zhou; Wang, Qian-long; Peng, Jie; Ji, Wenjun; Liu, HuanJun; Li, Xi; Rossel, Raphael A. Viscarra

doi:10.1007/s11430-013-4808-x

Cited by 171 publications

(118 citation statements)

References 21 publications

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“…This finding may be explained by the high variability of the high number of samples included in this study. Additionally, the diversity of soil genesis and mineralogical backgrounds may cause differences in soil reflectance, and thus lead to a decrease in the accuracy of predictions [21]. Therefore, there is still room for improvement of the prediction accuracy of LWR using CSSL, such as by optimizing pre-processing methods and optimal model parameters.…”

Section: Prediction Of Plsr and Lwrmentioning

confidence: 99%

“…These libraries consist of soil spectra and corresponding laboratory analytical data, which can be used to develop calibration models for the prediction of soil properties in a local field by using spectra only. Several of such databases exist today, such as those described by Shepherd and Walsh [15], Brown et al [16], Sankey et al [17], Knadel et al [18], Viscarra Rossel and Webster [19], Stevens et al [20], Shi et al [21], and Gogé et al [22]. Among physical and chemical soil properties, soil N, organic carbon (C), clay content, pH, and cation-exchange capacity (CEC) have been reported to be the most successfully predicted with moderate or better accuracies using different multivariate calibration methods.…”

Section: Introductionmentioning

confidence: 99%

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Potential of VIS-NIR-SWIR Spectroscopy from the Chinese Soil Spectral Library for Assessment of Nitrogen Fertilization Rates in the Paddy-Rice Region, China

Chen

et al. 2015

Remote Sensing

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Abstract:To meet growing food demand with limited land and reduced environmental impact, soil testing and formulated fertilization methods have been widely adopted around the world. However, conventional technology for investigating nitrogen fertilization rates (NFR) is time consuming and expensive. Here, we evaluated the use of visible near-infrared shortwave-infrared (VIS-NIR-SWIR: 400-2500 nm) spectroscopy for the assessment of NFR to provide necessary information for fast, cost-effective and precise fertilization rating. Over 2000 samples were collected from paddy-rice fields in 10 Chinese provinces; samples were added to the Chinese Soil Spectral Library (CSSL). Two kinds of modeling strategies for NFR, quantitative estimation of soil N prior to classification and qualitative by classification, were employed using partial least squares regression (PLSR), locally weighted regression (LWR), and support vector machine discriminant analogy (SVMDA). Overall, both LWR and SVMDA had moderate accuracies with Cohen's kappa coefficients of 0.47 and 0.48, respectively, while PLSR had fair accuracy (0.37). We conclude that VIS-NIR-SWIR spectroscopy coupled with the CSSL appears to be a viable, rapid means for the assessment of NFR in paddy-rice soil. Based on qualitative classification of soil spectral data only, it is recommended that the SVMDA be adopted for rapid implementation.

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Section: Prediction Of Plsr and Lwrmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Potential of VIS-NIR-SWIR Spectroscopy from the Chinese Soil Spectral Library for Assessment of Nitrogen Fertilization Rates in the Paddy-Rice Region, China

Chen

et al. 2015

Remote Sensing

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“…To the best of our knowledge, this is the first VisNIR study that involves close to 20,000 soil samples collected from the conterminous United States at one fixed point in time. There have been a number of studies that reported the VisNIR modeling of soil databases at the national scales (Brown et al, 2006;Shi et al, 2014;Terra et al, 2015;Viscarra Rossel and Webster, 2012), but all of them used legacy soil samples. The number of samples being analyzed and modeled in this study is one of the largest in the soil VisNIR literature.…”

Section: A Brief Introduction Of the Us Rapid Carbon Assessment Promentioning

confidence: 99%

Prediction of Soil Carbon in the Conterminous United States: Visible and Near Infrared Reflectance Spectroscopy Analysis of the Rapid Carbon Assessment Project

Wijewardane

Wills

et al. 2016

Soil Science Soc of Amer J

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Visible and near infrared reflectance spectroscopy (VisNIr) has been used across a number of spatial scales to predict soil organic carbon (OC) content. The rapid Carbon Assessment Project (raCA) is a nationwide project that collected 144,000+ soil samples from across the conterminous United States for C stock mapping using VisNIr. The objective of this study was to calibrate and validate the VisNIr soil OC and total C (TC) models with ~20,000 samples from raCA. Models were developed with either partial least squares regression (PlSr) or the Artificial Neural Network (ANN) model. Four auxiliary variables-raCA region, land Use land Cover, Master Horizon, and Textural Class-were tested to stratify the dataset and to develop local models. The results showed that OC and TC models calibrated with ANN (R 2 > 0.94; rPD > 4.0) outperformed those of PlSr (R 2 = 0.83; rPD = 2.4). For PlSr, local models developed with all four auxiliary variables exhibited improvement in prediction accuracy, and the improvement was only marginal for ANN models. Master Horizon and Textural Class appeared to be more effective in stratifying samples into homogeneous groups because they gave an overall lower root mean squared error of prediction (rMSE P ) for the validation samples. For the majority of the local Textural Class models, the rMSE P of OC prediction ranged from 0.5 to 1.5%. To maximize the applicability of the raCA spectral library on external soil samples, PlSr local models developed from Master Horizon or Textural Class appeared to be more favorable.Abbreviations: ANN, Artificial Neural Network; IC, inorganic carbon; LULC, land use land cover classes; OC, organic carbon; PLSR, partial least squares regression; RaCA, Rapid Carbon Assessment Project; RPD, ratio of performance to deviation; RPIQ, ratio of performance to interquartile range; TC, total carbon; VisNIR, visible and near infrared reflectance spectroscopy. S oil organic carbon (OC) is a key soil property that plays many critical roles, from agriculture production to biogeochemical cycling to ecosystems functioning (Adhikari and Hartemink, 2016;Chen et al., 2000;Lal, 2004; van Wesemael et al., 2010). The capability of soils to sequester C and therefore regulate atmospheric CO 2 concentration and mitigate climate change is widely recognized and has been an active area of research (Grunwald et al., 2011;Lal, 2004;West and Post, 2002). Up-to-date, baseline soil C stock maps across different scales are a very useful tool for researchers, stakeholders, and policymakers for a wide variety of applications, ranging from best land management practices to natural resource conservation to C auditing (de Gruijter et al., 2016;Minasny et al., 2011).Producing large-scale (national or continental) soil C maps (e.g., for the purpose of C inventory and understanding of soil C sourcing and sinking) is highly challenging. Many previous studies have used legacy soil data (Aitkenhead and Coull, 2016;Minasny et al., 2013;Mulder et al., 2016 Core Ideas• We modeled ~20,000 samples in raCA from...

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“…Soil spectroscopy, as a fast, cost-effective, and environmental-friendly analytical technique, has successfully been utilised to retrieve soil properties and has experienced a tremendous increase in the past years. It has been shown that soil spectra across the Visible Near-Infrared Shortwave Infrared (VIS-NIR-SWIR; 400-2500 nm) spectral region are characterised by significant spectral signals [3][4][5][6], which makes it possible for quantitative analysis of soil properties. Furthermore, the wide spread use of visible and infrared spectroscopy can resolve the trade-off between the growing need of large scale soil information and its high cost [7].…”

Section: Introductionmentioning

confidence: 99%

Combining Partial Least Squares and the Gradient-Boosting Method for Soil Property Retrieval Using Visible Near-Infrared Shortwave Infrared Spectra

Liu

Buchroithner

2017

Remote Sensing

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Abstract:Soil spectroscopy has experienced a tremendous increase in soil property characterisation, and can be used not only in the laboratory but also from the space (imaging spectroscopy). Partial least squares (PLS) regression is one of the most common approaches for the calibration of soil properties using soil spectra. Besides functioning as a calibration method, PLS can also be used as a dimension reduction tool, which has scarcely been studied in soil spectroscopy. PLS components retained from high-dimensional spectral data can further be explored with the gradient-boosted decision tree (GBDT) method. Three soil sample categories were extracted from the Land Use/Land Cover Area Frame Survey (LUCAS) soil library according to the type of land cover (woodland, grassland, and cropland). First, PLS regression and GBDT were separately applied to build the spectroscopic models for soil organic carbon (OC), total nitrogen content (N), and clay for each soil category. Then, PLS-derived components were used as input variables for the GBDT model. The results demonstrate that the combined PLS-GBDT approach has better performance than PLS or GBDT alone. The relative important variables for soil property estimation revealed by the proposed method demonstrated that the PLS method is a useful dimension reduction tool for soil spectra to retain target-related information.

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Development of a national VNIR soil-spectral library for soil classification and prediction of organic matter concentrations

Cited by 171 publications

References 21 publications

Potential of VIS-NIR-SWIR Spectroscopy from the Chinese Soil Spectral Library for Assessment of Nitrogen Fertilization Rates in the Paddy-Rice Region, China

Potential of VIS-NIR-SWIR Spectroscopy from the Chinese Soil Spectral Library for Assessment of Nitrogen Fertilization Rates in the Paddy-Rice Region, China

Prediction of Soil Carbon in the Conterminous United States: Visible and Near Infrared Reflectance Spectroscopy Analysis of the Rapid Carbon Assessment Project

Combining Partial Least Squares and the Gradient-Boosting Method for Soil Property Retrieval Using Visible Near-Infrared Shortwave Infrared Spectra

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