Near infrared reflectance spectroscopy for assessment of spatial soil variation in an agricultural field

Odlare, Monica; Svensson, Kalle; Pell, Mikael

doi:10.1016/j.geoderma.2004.09.013

Cited by 77 publications

(42 citation statements)

References 24 publications

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“…Basic methods for selecting samples out of principal component (PC) scores to recover the original variation with few samples have been tested for country and field scale (Stenberg and Nordkvist, 1996;Stenberg et al, 1995). The strategy improved the variation within sub-sets compared with random sampling for at least clay, total carbon and CEC at the country scale, and at the field scale also total N, pH, and available Mg. For a single field, Odlare et al (2005) compared variograms and kriged maps from PC scores of vis-NIR spectra with those of conventionally analyzed total C, clay, and pH. Compared with the other parameters they found the variogram of PC 1 to be the most robust, but they did not claim a specific representativity to any of the soil parameters analyzed.…”

Section: Screening and Mapping Of Overall Soil Variabilitymentioning

confidence: 99%

Visible and Near Infrared Spectroscopy in Soil Science

Stenberg

Rossel

Mouazen

et al. 2010

Advances in Agronomy

1,109

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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Section: Screening and Mapping Of Overall Soil Variabilitymentioning

confidence: 99%

Visible and Near Infrared Spectroscopy in Soil Science

Stenberg

Rossel

Mouazen

et al. 2010

Advances in Agronomy

1,109

845

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“…Predicted SOC showed nugget-sill ratios of 30.3% and 28.6%, respectively, and ranges of 1618 m and 1477 m, respectively. Nugget-sill ratios reported in the literature vary greatly depending on the local soil conditions and the sampling and analysis methods [32,61].…”

Section: Spatial Structure Analysis and Influence Of Sensor Resolutionmentioning

confidence: 99%

Prediction of Common Surface Soil Properties Based on Vis-NIR Airborne and Simulated EnMAP Imaging Spectroscopy Data: Prediction Accuracy and Influence of Spatial Resolution

et al. 2016

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Abstract:With the upcoming availability of the next generation of high quality orbiting hyperspectral sensors, a major step toward improved regional soil mapping and monitoring and delivery of quantitative soil maps is expected. This study focuses on the determination of the prediction accuracy of spectral models for the mapping of common soil properties based on upcoming EnMAP (Environmental Mapping and Analysis Program) satellite data using semi-operational soil models. Iron oxide (Fe d ), clay, and soil organic carbon (SOC) content are predicted in test areas in Spain and Luxembourg based on a semi-automatic Partial-Least-Square (PLS) regression approach using airborne hyperspectral, simulated EnMAP, and soil chemical datasets. A variance contribution analysis, accounting for errors in the dependent variables, is used alongside classical error measurements. Results show that EnMAP allows predicting iron oxide, clay, and SOC with an R 2 between 0.53 and 0.67 compared to Hyperspectral Mapper (HyMap)/Airborne Hyperspectral System (AHS) imagery with an R 2 between 0.64 and 0.74. Although a slight decrease in soil prediction accuracy is observed at the spaceborne scale compared to the airborne scale, the decrease in accuracy is still reasonable. Furthermore, spatial distribution is coherent between the HyMap/AHS mapping and simulated EnMAP mapping as shown with a spatial structure analysis with a systematically lower semivariance at the EnMAP scale.

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“…The study of soil attributes through remote sensing can assist in soil survey and mapping techniques, as well as prediction of the erosion and a more accurate fertilizing management practices (Shepard and Walsh, 2002;Odlare et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

Spectral behavior of some modal soil profiles from São Paulo State, Brazil

2012

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Remote sensing has a high potential for environmental evaluation. However, a necessity exists for a better understanding of the relations between the soil attributes and spectral data. The objective of this work was to analyze the spectral behavior of some soil profiles from the region of Piracicaba, São Paulo State, using a laboratory spectroradiometer (400 to 2500 nm). The relations between the reflected electromagnetic energy and the soil physical, chemical and mineralogical attributes were analyzed, verifying the spectral variations of soil samples in depth along the profiles with their classification and discrimination. Sandy soil reflected more, presenting a spectral curve with an ascendant form, opposite to clayey soils. The 1900 nm band discriminated soil with 2:1 mineralogy from the 1:1 and oxidic soils. It was possible to detect the presence of kaolinite, gibbsite, hematite and goethite in the soils through the descriptive aspects of curves, absorption features and reflectance intensity. A relation exists between the weathering stage and spectral data. The evaluation of the superficial and subsuperficial horizon samples allowed characterizing and discriminating the analytical variability of the profile, helping to soil distinguishing and classification.Key words: soil reflectance, soil attributes, soil classification. Comportamento espectral de alguns perfis modais de solos do Estado de São Paulo, BrasilResumo O sensoriamento remoto representa um importante potencial na avaliação do ambiente, contudo, ainda existe a necessidade de entender melhor as relações entre atributos do solo e dados espectrais. O objetivo do trabalho foi analisar descritivamente o comportamento espectral de alguns perfis de solos da região de Piracicaba, Estado de São Paulo, utilizando o espectrorradiômetro de laboratório (400 a 2500 nm). Procurou-se ainda, avaliar as relações entre energia eletromagnética refletida com atributos químicos, físicos e mineralógicos dos solos, verificando as variações espectrais das amostras ao longo dos perfis e suas relações com classificação e discriminação dos solos. Observou-se que solos mais arenosos refletiram mais, com curvas espectrais de aspecto ascendente, ao contrário dos solos argilosos. A banda centrada em 1900 nm discriminou solos com mineralogia 2:1 dos de 1:1 e oxídicos. Foi possível detectar a presença de caulinita, gibbsita e dos óxidos de ferro (hematita e goethita) presentes nos solos pelos aspectos descritivos das curvas, feições de absorção e intensidade de reflectância; e que existe uma relação entre níveis de intemperismo e informações espectrais. A avaliação dos dados espectrais de amostras dos horizontes superficiais e subsuperficiais permitiu caracterizar e discernir a variabilidade analítica do perfil, auxiliando na discriminação e classificação dos solos.Palavras-chave: reflectância do solo, atributos do solo, classificação do solo.

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Near infrared reflectance spectroscopy for assessment of spatial soil variation in an agricultural field

Cited by 77 publications

References 24 publications

Visible and Near Infrared Spectroscopy in Soil Science

Visible and Near Infrared Spectroscopy in Soil Science

Prediction of Common Surface Soil Properties Based on Vis-NIR Airborne and Simulated EnMAP Imaging Spectroscopy Data: Prediction Accuracy and Influence of Spatial Resolution

Spectral behavior of some modal soil profiles from São Paulo State, Brazil

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