In Situ Characterization of Soil Clay Content with Visible Near‐Infrared Diffuse Reflectance Spectroscopy

Waiser, Travis H.; Morgan, Cristine L.S.; Brown, David J.; Hallmark, C. T.

doi:10.2136/sssaj2006.0211

Cited by 199 publications

(143 citation statements)

References 25 publications

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“…In general, when compared to the validation indexes from other studies that used proximal sensors, the models in our study performed well. In the studies of [33,[70][71][72], the RMSE values range from 2.66 to 7.9, which is similar to the RMSE values of our study. Table 5.…”

Section: Soil Particle Size Distribution Predictive Modelssupporting

confidence: 88%

Proximal Sensing and Digital Terrain Models Applied to Digital Soil Mapping and Modeling of Brazilian Latosols (Oxisols)

et al. 2016

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Digital terrain models (DTM) have been used in soil mapping worldwide. When using such models, improved predictions are often attained with the input of extra variables provided by the use of proximal sensors, such as magnetometers and portable X-ray fluorescence scanners (pXRF). This work aimed to evaluate the efficiency of such tools for mapping soil classes and properties in tropical conditions. Soils were classified and sampled at 39 locations in a regular-grid design with a 200-m distance between samples. A pXRF and a magnetometer were used in all samples, and DTM values were obtained for every sampling site. Through visual analysis, boxplots were used to identify the best variables for distinguishing soil classes, which were further mapped using fuzzy logic. The map was then validated in the field. An ordinary least square regression model was used to predict sand and clay contents using DTM, pXRF and the magnetometer as predicting variables. Variables obtained with pXRF showed a greater ability for predicting soil classes (overall accuracy of 78% and 0.67 kappa index), as well as for estimating sand and clay contents than those acquired with DTM and the magnetometer. This study showed that pXRF offers additional variables that are key for mapping soils and predicting soil properties at a detailed scale. This would not be possible using only DTM or magnetic susceptibility.

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Section: Soil Particle Size Distribution Predictive Modelssupporting

confidence: 88%

Proximal Sensing and Digital Terrain Models Applied to Digital Soil Mapping and Modeling of Brazilian Latosols (Oxisols)

et al. 2016

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“…There is no evidence of RMSE values much lower than 2% in studies limited to even smaller regional, farm or field scale calibrations (Malley et al, 2000;Viscarra Rossel et al, 2006c;Waiser et al, 2007;Wetterlind et al, 2008a,b), although in these cases RMSE values were among the lowest published (Table 3) and RPD values usually higher than 2 and in some cases close to three 3. …”

Section: Soil Texturementioning

confidence: 95%

Visible and Near Infrared Spectroscopy in Soil Science

Stenberg

Rossel

Mouazen

et al. 2010

Advances in Agronomy

1,105

845

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This chapter provides a review on the state of soil visible-near infrared (vis-NIR) spectroscopy. Our intention is for the review to serve as a source of up-to date information on the past and current role of vis-NIR spectroscopy in soil science. It should also provide critical discussion on issues surrounding the use of vis-NIR for soil analysis and on future directions. To this end, we describe the fundamentals of visible and infrared diffuse reflectance spectroscopy and spectroscopic multivariate calibrations. A review of the past and current role of vis-NIR spectroscopy in soil analysis is provided, focusing on important soil attributes such as soil organic matter (SOM), minerals, texture, nutrients, water, pH, and heavy metals. We then discuss the performance and generalization capacity of vis-NIR calibrations, with particular attention on sample pre-tratments, co-variations in data sets, and mathematical data preprocessing. Field analyses and strategies for the practical use of vis-NIR are considered. We conclude that the technique is useful to measure soil water and mineral composition and to derive robust calibrations for SOM and clay content. Many studies show that we also can predict properties such as pH and nutrients, although their robustness may be questioned. For future work we recommend that research should focus on: (i) moving forward with more theoretical calibrations, (ii) better understanding of the complexity of soil and the physical basis for soil reflection, and (iii) applications and the use of spectra for soil mapping and monitoring, and for making inferences about soils quality, fertility and function. To do this, research in soil spectroscopy needs to be more collaborative and strategic.

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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].…”

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 Situ Characterization of Soil Clay Content with Visible Near‐Infrared Diffuse Reflectance Spectroscopy

Cited by 199 publications

References 25 publications

Proximal Sensing and Digital Terrain Models Applied to Digital Soil Mapping and Modeling of Brazilian Latosols (Oxisols)

Proximal Sensing and Digital Terrain Models Applied to Digital Soil Mapping and Modeling of Brazilian Latosols (Oxisols)

Visible and Near Infrared Spectroscopy in Soil Science

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

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