Development of Reflectance Spectral Libraries for Characterization of Soil Properties

Shepherd, Keith D.; Walsh, Markus

doi:10.2136/sssaj2002.0988

Cited by 358 publications

(425 citation statements)

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“…Principal component analysis showed that PC 2 and PC 4 explained soil variability well for CEC, Mg, O.M., K, and pH. Shepherd and Walsh [2002] used spectral library in estimating soil properties. Their work was based on the analysis of diffusion reflectance spectroscopy.…”

Section: Background and Objectivesmentioning

confidence: 99%

Evaluation of Landsat TM5 Multispectral Data for Automated Mapping of Surface Soil Texture and Organic Matter in GIS

Ahmed

Iqbal

2014

European Journal of Remote Sensing

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Mapping fine scale spatial variations of soil properties is important for site specific agriculture. The current study explores the potentials of remote sensing (RS) and geographical information system (GIS) techniques in studying the spatial variability of surface soil attributes. Around 170 surface (0-30 cm) soil samples collected from the soils of Shorkot Tehsil, Punjab, Pakistan were analyzed for surface soil texture and organic matter (O.M.). A multivariate linear regression (MLR) analysis technique was employed to relate surface soil variables with the spectral data from Landsat TM5 satellite. The MLR analysis showed significant (p<0.05) relationship of band 4 and band 6 with silt% (R 2 = 0.724) and clay% (R 2 = 0.509) while soil O.M. was best modeled using data from band 1, 6 and 7 (R 2 = 0.545). The resulting MLR equations were then used for the spatial modeling of these attributes for the entire study area. For developing surface soil texture map, the USDA textural triangle limits for clay% and silt% were used to develop a code in Visual Basic Language in ArcGIS environment. The results showed that 'sandy clay loam' was the most abundant textural class in the area followed by 'sandy loam' and 'clay loam' classes. Moreover, the status of O.M. in the entire study area soils was very poor (< 1%). The results indicate that RS and GIS techniques could be successfully used for fine scale mapping of soil texture and O.M. of a larger area.

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Section: Background and Objectivesmentioning

confidence: 99%

Evaluation of Landsat TM5 Multispectral Data for Automated Mapping of Surface Soil Texture and Organic Matter in GIS

Ahmed

Iqbal

2014

European Journal of Remote Sensing

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“…In particular, reflectance spectroscopy can be used for nondestructive assessment of soil and crop physical and biochemical properties Moron 2003, 2006;Chang et al 2001;Dunn et al 2002;Shepherd and Walsh 2002;Shepherd et al 2003). Although the NIR range (800-2,500 nm) is still the most widely used, midinfrared spectroscopy is becoming increasingly common due to the specificity of the absorbance bands in that spectral range (Stuart 1997).…”

Section: Soil Identificationmentioning

confidence: 99%

“…Using technique of Infrared spectroscopy research on air-dried ground soil samples has shown predictions of soil clay content with R 2 values ranging from 0.56 to 0.91 and RMSE ranging from 23 to 11 g kg -1 (Ben- Banin 1990, 1995;Janik et al 1998;Shepherd and Walsh 2002;Islam et al 2003;Sorensen and Dalsgaard 2005;Brown et al 2006). An infrared spectra data based calibration model for determination of clay in soil was developed and tested in practice.…”

Section: Soil Claysmentioning

confidence: 99%

Evaluation of soil fertility using infrared spectroscopy: a review

Zhang

2008

Environ Chem Lett

101

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“…A complimentary approach that retains a focus on soil properties is use of visible/near infrared reflectance spectroscopy (VNIRS) for rapid, low-cost, accurate inference of soil properties. The research literature increasingly supports the conclusion that VNIRS spectra (high resolution diffuse reflectance signatures in the visible [350-750 nm] and near infrared [750-2500 nm] regions; Johnston and Aochi, 1996) can be used to acquire substantial information about mineralogical, physical and chemical properties of plant and soil samples (Ben Dor and Banin, 1995;Reeves et al, 1999;Chang et al, 2001;Shepherd and Walsh, 2002;Dunn et al, 2002;Cozzolino and Moron, 2003). The ability to accurately infer material properties from reflectance signatures has the multiple benefits of allowing tremendously accelerated sample throughput and reduced per sample cost, both of which are significant considerations for large area soil-based diagnosis of ecological condition.…”

Section: Reflectance Spectra As Diagnostic Indicatorsmentioning

confidence: 99%

Evaluating Ecological Condition Using Soil Biogeochemical Parameters and Near Infrared Reflectance Spectra

Cohen

Dabral

Graham

et al. 2006

Environ Monit Assess

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Rapid, repeatable assessment of ecological condition is critical for quantitative ecosystem monitoring. Soils provide a sensitive, integrative indicator for which sampling and analysis techniques are well defined. We evaluated soil properties as indicators of ecological condition (subjectively classified into minimally/moderately/severely degraded based on vegetative, hydrologic and edaphic cues) at 526 sites within Ft. Benning military installation (Georgia, USA). For each sample, we measured 17 biogeochemical parameters, and collected high-resolution diffuse reflectance spectra using visible/near infrared reflectance spectroscopy (VNIRS). VNIR spectra have been related to numerous soil attributes - we examine them here for diagnosing integrated response (i.e., ecological condition). We used ordinal logistic regression (OLR) and classification trees (CT) to discriminate between condition categories using both sets of predictors (biogeochemistry and spectra). Sixteen biogeochemical parameters were significantly different across condition categories; however, multivariate models greatly improved discrimination ([calibration, validation] accuracy of [69%, 66%] and [96%, 73%] for OLT and CT models, respectively). Important predictors included total C, total P, and Mehlich K/Ca/Mg. VNIR spectra further improved discrimination ([calibration, validation] accuracy of [74%, 70%] and [96%, 75%] for OLR and CT models, respectively). While spectra were comparably effective at discriminating minimally degraded sites, they were significantly more effective at discriminating severely degraded sites. Error rates across confounding factors suggest that watershed of origin and landscape position were the only important confounders, likely due to imbalanced sampling. We conclude that multivariate diagnosis improves accuracy, and that VNIR spectroscopy, which yields substantial cost and logistical improvements over conventional analyses, provides an effective tool for rapid condition diagnosis.

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Development of Reflectance Spectral Libraries for Characterization of Soil Properties

Cited by 358 publications

References 0 publications

Evaluation of Landsat TM5 Multispectral Data for Automated Mapping of Surface Soil Texture and Organic Matter in GIS

Evaluation of Landsat TM5 Multispectral Data for Automated Mapping of Surface Soil Texture and Organic Matter in GIS

Evaluation of soil fertility using infrared spectroscopy: a review

Evaluating Ecological Condition Using Soil Biogeochemical Parameters and Near Infrared Reflectance Spectra

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