Estimation of Some Chemical Properties of an Agricultural Soil by Spectroradiometric Measurements

Jarmer, Thomas; Vohland, Michael; Lilienthal, Holger; Schnug, Ewald

doi:10.1016/s1002-0160(08)60004-1

Cited by 29 publications

(16 citation statements)

References 19 publications

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“…Viscarra Rossel et al [26] were unable to achieve such a good result (R 2 = 0.72) with the same regression method when using fewer samples and a lower observed data range. Similar results were also presented by Jarmer et al [27], who performed PLSR analyses with ASD FieldSpec-II spectra and obtained an R 2 value of 0.62.…”

Section: Introductionsupporting

confidence: 87%

Regionalization of Uncovered Agricultural Soils Based on Organic Carbon and Soil Texture Estimations

2016

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Abstract:The determination of soil texture and organic carbon across agricultural areas provides important information to derive soil condition. Precise digital soil maps can help to till agricultural fields with more accuracy, greater cost-efficiency and better environmental protection. In the present study, the laboratory analysis of sand, silt, clay and soil organic carbon (SOC) content was combined with hyperspectral image data to estimate the distribution of soil texture and SOC across an agricultural area. The aim was to identify regions with similar soil properties and derive uniform soil regions based on this information. Soil parameter data and corresponding laboratory spectra were used to calibrate cross-validated (leave-one-out) partial least squares regression (PLSR) models, resulting in robust models for sand (R 2 = 0.77, root-mean-square error (RMSE) = 5.37) and SOC (R 2 = 0.89, RMSE = 0.27), as well as moderate models for silt (R 2 = 0.62, RMSE = 5.46) and clay (R 2 = 0.53, RMSE = 2.39). The regression models were applied to Airborne Imaging Spectrometer for Applications DUAL (aisaDUAL) hyperspectral image data to spatially estimate the concentration of these parameters. Afterwards, a decision tree, based on the Food and Agriculture Organization (FAO) soil texture classification scheme, was developed to determine the soil texture for each pixel of the hyperspectral airborne data. These soil texture regions were further refined with the spatial SOC estimations. The developed method is useful to identify spatial regions with similar soil properties, which can provide a vital information source for an adapted treatment of agricultural fields in terms of the necessary amount of fertilizers or water. The approach can also be adapted to wider regions with a larger sample size to create detailed digital soil maps (DSMs). Further, the presented method should be applied to future hyperspectral satellite missions like Environmental Mapping and Analysis Program (EnMap) and Hyperspectral Infrared Imager (HyspIRI) to cover larger areas in shorter time intervals. Updated DSMs on a regular basis could particularly support precision farming aspects.

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Section: Introductionsupporting

confidence: 87%

Regionalization of Uncovered Agricultural Soils Based on Organic Carbon and Soil Texture Estimations

2016

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“…There is no evidence of interaction between iron oxides and spectral data in mid-IR and its lower second-order modeling for the training step was possible because its correlation with SiO 2 (r: 0.67) and Al 2 O 3 (r: 0.76). Comparing R 2 and RMSE, vis-NIR modeling of Fe 2 O 3 content was less effective than those obtained by Fernandes et al (2004) and Richter et al (2009) but similar or higher than the results reached by Ben-Dor and Banin (1995), Dunn et al (2002), Sorensen and Dalsgaard (2005), Jarmer et al (2008), and Summers et al (2011). Also, prediction quality for iron oxide content in mid-IR was equivalent to that obtained by Bertrand et al (2002).…”

Section: Mineralogical Predictionsmentioning

confidence: 44%

Spectral libraries for quantitative analyses of tropical Brazilian soils: Comparing vis–NIR and mid-IR reflectance data

2015

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“…Knowledge of the quantity and the spatial distribution of harvest residues are important for the determination of surface properties. Hyperspectral (HS) remote sensing is used in agriculture, mapping crop characteristics (Jarmer, 2013;Thenkabail, 2000), the assessment of crop residues (Daughtry, 2004) and the assessment of soil properties (Jarmer et al, 2008;Barnes and Baker, 2000). The captured mixed signal in case of existing harvest residues can be interpreted and considered in further analysis.…”

Section: Introductionmentioning

confidence: 99%

Radiometric Correction of Terrestrial LiDAR Data for Mapping of Harvest Residues Density

Koenig

Höfle

Müller

et al. 2013

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:In precision agriculture detailed geoinformation on plant and soil properties plays an important role. Laser scanning already has been used to describe in-field variations of plant growth in 3D and over time and can serve as valuable complementary topographic data set for remote sensing, such as deriving soil properties from hyperspectral sensors. In this study full-waveform laser scanning data acquired with a Riegl VZ-400 instrument is used to classify 3D point clouds into post-harvest straw residues and bare soil. A workflow for point cloud based classification is presented using radiometric and geometric point features. A radiometric correction is performed by using a range-correction function f(r), which is derived from lab experiments with a reference target of known reflectance. Thereafter, the corrected signal amplitude and local height features are explored with respect to the target classes. The following procedure includes feature calculation, decision tree analysis, point cloud classification and finally result validation using detailed classified reference RGB images. The classification tree separates the classes of harvest residues and bare soil with an accuracy of 96% by using geometric and radiometric features. The LiDAR-derived harvest residue coverage value of 75% lies in accordance with the image-based reference (coverage of 68%). The results indicate the high potential of radiometric features for natural surface classification, particularly in combination with geometric features.

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Estimation of Some Chemical Properties of an Agricultural Soil by Spectroradiometric Measurements

Cited by 29 publications

References 19 publications

Regionalization of Uncovered Agricultural Soils Based on Organic Carbon and Soil Texture Estimations

Regionalization of Uncovered Agricultural Soils Based on Organic Carbon and Soil Texture Estimations

Spectral libraries for quantitative analyses of tropical Brazilian soils: Comparing vis–NIR and mid-IR reflectance data

Radiometric Correction of Terrestrial LiDAR Data for Mapping of Harvest Residues Density

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