Can mid infrared diffuse reflectance analysis replace soil extractions?

Janik, L. J.; Merry, R. H.; Skjemstad, JO

doi:10.1071/ea97144

Cited by 408 publications

(309 citation statements)

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“…Terra et al [32] could not predict well Ca, Mg, P, K, pH, SB and V value in the VIS-NIR region for four States in the Brazilian soils. For instance, the P model in our study had an R 2 of 0.11, comparable to that presented by Janik et al [36] (R 2 of 0.07) and Araújo et al [37] (R 2 of 0.05) using MIR spectra, whereas high R 2 (0.81) was obtained by Daniel et al [38] using VIS-NIR spectra.…”

Section: Predictions Of Soil Chemical Propertiessupporting

confidence: 70%

Prediction of Soil Physical and Chemical Properties by Visible and Near-Infrared Diffuse Reflectance Spectroscopy in the Central Amazon

et al. 2017

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Visible and near-infrared diffuse reflectance spectroscopy (VIS-NIR) has shown levels of accuracy comparable to conventional laboratory methods for estimating soil properties. Soil chemical and physical properties have been predicted by reflectance spectroscopy successfully on subtropical and temperate soils, whereas soils from tropical agro-forest regions have received less attention, especially those from tropical rainforests. A spectral characterization provides a proficient pathway for soil characterization. The first step in this process is to develop a comprehensive VIS-NIR soil library of multiple key soil properties to be used in future soil surveys. This paper presents the first VIS-NIR soil library for a remote region in the Central Amazon. We evaluated the performance of VIS-NIR for the prediction of soil properties in the Central Amazon, Brazil. Soil properties measured and predicted were: pH, Ca, Mg, Al, H, H+Al, P, organic C (SOC), sum of bases, cation exchange capacity (CEC), percentage of base saturation (V), Al saturation (m), clay, sand, silt, silt/clay (S/C), and degree of flocculation. Soil samples were scanned in the laboratory in the VIS-NIR range (350-2500 nm), and forty-one pre-processing methods were tested to improve predictions. Clay content was predicted with the highest accuracy, followed by SOC. Sand, S/C, H, Al, H+Al, CEC, m and V predictions were reasonably good. The other soil properties were poorly predicted. Among the soil properties predicted well, SOC is one of the critical soil indicators in the global carbon cycle. Besides the soil property of interest, the landscape position, soil order and depth influenced in the model performance. For silt content, pH and S/C, the model performed better in well-drained soils, whereas for SOC best predictions were obtained in poorly drained soils. The association of VIS-NIR spectral data to landforms, vegetation classes, and soil types demonstrate potential for soil characterization.

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Section: Predictions Of Soil Chemical Propertiessupporting

confidence: 70%

Prediction of Soil Physical and Chemical Properties by Visible and Near-Infrared Diffuse Reflectance Spectroscopy in the Central Amazon

et al. 2017

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“…In many recent studies, the application of remote sensing techniques has gained much attention, mainly because these techniques have been shown to generate faster and cheaper characterizations, earning them credibility in the scientific community. The application of remote sensing techniques in soil studies began in the 1960s (Bowers & Hanks, 1965) and expanded to various applications, including quick and nondestructive quantification of soil attributes (Janik et al, 1998;Shepherd & Walsh, 2002, Dunn et al, 2002ViscarraRossel, et al, 2006a;Demattê & Nanni, 2006;Brown, et al,. 2006), soil survey and classification (Demattê et al, 2004, Ben-Dor et al, 2008, mineralogical measurements (Madeira-Netto, 1996;Viscarra-Rossel et al, 2006b;Sellito et al, 2009), digital soil mapping (Viscarra-Rossel & McBractney, 2008), precision agriculture (Thomasson et al, 2001Mouazen et al, 2007) and quantification of heavy metals (Wu Zhao et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

Soil spectral library and its use in soil classification

Bellinaso

Demattê

Romeiro

2010

Rev. Bras. Ciênc. Solo

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SUMMARYSoil science has sought to develop better techniques for the classification of soils, one of which is the use of remote sensing applications. The use of ground sensors to obtain soil spectral data has enabled the characterization of these data and the advancement of techniques for the quantification of soil attributes. In order to do this, the creation of a soil spectral library is necessary. A spectral library should be representative of the variability of the soils in a region. The objective of this study was to create a spectral library of distinct soils from several agricultural regions of Brazil. Spectral data were collected (using a

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“…From Figure 4, we find that the N uptake amount can be simulated by ( ) = 556.70 . (6) where N(t) is the soil nutrient absorption rate, and t is the DOG. The N absorption rate can be calculated by the derivative of Equation (6), which is similar to Equation (5),…”

Section: Calibration Of Soil Nutrient Absorption Equationmentioning

confidence: 99%

Preliminary Study of Soil Available Nutrient Simulation Using a Modified WOFOST Model and Time-Series Remote Sensing Observations

et al. 2018

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Abstract:The approach of using multispectral remote sensing (RS) to estimate soil available nutrients (SANs) has been recently developed and shows promising results. This method overcomes the limitations of commonly used methods by building a statistical model that connects RS-based crop growth and nutrient content. However, the stability and accuracy of this model require improvement. In this article, we replaced the statistical model by integrating the World Food Studies (WOFOST) model and time series of remote sensing (T-RS) observations to ensure stability and accuracy. Time series of HJ-1 A/B data was assimilated into the WOFOST model to extrapolate crop growth simulations from a single point to a large area using a specific assimilation method. Because nutrient-limited growth within the growing season is required and the SAN parameters can only be used at the end of the growing season in the original model, the WOFOST model was modified. Notably, the calculation order was changed, and new soil nutrient uptake algorithms were implemented in the model for nutrient-limited growth estimation. Finally, experiments were conducted in the spring maize plots of Hongxing Farm to analyze the effects of nutrient stress on crop growth and the SAN simulation accuracy. The results confirm the differences in crop growth status caused by a lack of soil nutrients. The new approach can take advantage of these differences to provide better SAN estimates. In general, the new approach can overcome the limitations of existing methods and simulate the SAN status with reliable accuracy.

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Can mid infrared diffuse reflectance analysis replace soil extractions?

Cited by 408 publications

References 0 publications

Prediction of Soil Physical and Chemical Properties by Visible and Near-Infrared Diffuse Reflectance Spectroscopy in the Central Amazon

Prediction of Soil Physical and Chemical Properties by Visible and Near-Infrared Diffuse Reflectance Spectroscopy in the Central Amazon

Soil spectral library and its use in soil classification

Preliminary Study of Soil Available Nutrient Simulation Using a Modified WOFOST Model and Time-Series Remote Sensing Observations

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