Prediction of soil texture classes through different wavelength regions of reflectance spectroscopy at various soil depths

Coblinski, João Augusto; Giasson, Élvio; Demattê, José A. M.; Dotto, André Carnieletto; Costa, José Janderson Ferreira; Vašát, Radim

doi:10.1016/j.catena.2020.104485

Cited by 54 publications

(35 citation statements)

References 59 publications

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“…The clay fraction showed a negative correlation at the beginning of the visible spectra and the end of the SWIR. In contrast, the sand fraction obtained a positive correlation in these wavelengths, agreeing with most of the literature results obtained using these bands to predict soil texture [2,[6][7][8]12,14,37]. The HCA successfully grouped the 42 soil samples based on texture content.…”

Section: Discussionsupporting

confidence: 89%

“…FRro-I, FRro-II, FRha-I, FRha-II, PTpt-I, PTpt-II, RGdy-I, RGdy-II, and CMdy showed similar results described in [10]. The OM content tends to reduce the reflectance factor [4,14,44]. Conversely, smaller soil particles increase the reflectance factor [6].…”

Section: Soils Spectral Behaviorsupporting

confidence: 64%

“…Previous studies revealed that soil color is a function of mineralogical composition, mainly iron oxides and OM content [2,11]. Soils with high levels of quartz and low levels of OM and iron oxides are related to lighter colors [6,8,14,44]. Hematite and goethite are the most common iron oxides in tropical regions [10].…”

Section: Soil Classificationmentioning

confidence: 99%

“…Therefore, there is a need for efficient soil mapping techniques at detailed scales [4]. A number of researchers have been attempting to test and develop technologies capable of gathering data and mapping them remotely and efficiently [1,[4][5][6][7][8][9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

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Digital Soil Mapping Using Multispectral Modeling with Landsat Time Series Cloud Computing Based

et al. 2021

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Geotechnologies allow natural resources to be surveyed more quickly and cheaply than traditional methods. This paper aimed to produce a digital soil map (DSM) based on Landsat time series data. The study area, located in the eastern part of the Brazilian Federal District (Rio Preto hydrographic basin), comprises a representative basin of the Central Brazil plateau in terms of pedodiversity. A spectral library was produced based on the soil spectroscopy (from the visible to shortwave infrared spectral range) of 42 soil samples from 0–15 cm depth using the Fieldspec Pro equipment in a laboratory. Pearson’s correlation and principal component analysis of the soil attributes revealed that the dataset could be grouped based on the texture content. Hierarchical clustering analysis allowed for the extraction of 13 reference spectra. We interpreted the spectra morphologically and resampled them to the Landsat 5 Thematic Mapper satellite bands. Afterward, we elaborated a synthetic soil/rock image (SySI) and a soil frequency image (number of times the bare soil was captured) from the Landsat time series (1984–2020) in the Google Earth Engine platform. Multiple Endmember Spectral Mixture Analysis (MESMA) was used to model the SySI, using the endmembers as the input and generating a DSM, which was validated by the Kappa index and the confusion matrix. MESMA successfully modeled 9 of the 13 endmembers: Dystric Rhodic Ferralsol (clayic); Dystric Rhodic Ferralsol (very clayic); Dystric Haplic Ferralsol (loam-clayic); Dystric Haplic Ferralsol (clayic); Dystric Petric Plinthosol (clayic); Dystric Petric Plinthosol (very clayic); Dystric Regosol (clayic); Dystric Regosol (very clayic); and Dystric, Haplic Cambisol (clayic). The root mean squared error (RMSE) varied from 0 to 1.3%. The accuracy of DSM achieved a Kappa index of 0.74, describing the methodology’s effectiveness to differentiate the studied soils.

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Section: Discussionsupporting

confidence: 89%

Section: Soils Spectral Behaviorsupporting

confidence: 64%

Section: Soil Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Digital Soil Mapping Using Multispectral Modeling with Landsat Time Series Cloud Computing Based

et al. 2021

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“…The overtones and combinations produced by C‐H, O‐H and N‐H bonds is the analytical mechanism of Vis‐NIR technology on soil properties (Shepherd & Walsh, 2002). Recently, Vis‐NIR technology has been successfully applied in the laboratory or field using its capacity for predicting soil properties, including soil organic matter (SOM), soil nitrogen, phosphorus (Abdi et al., 2016), cation exchange capacity, water content, texture, and other properties (Coblinski et al., 2020; Wang et al., 2015; Zheng et al., 2016). This new soil analysis approach has been considered as a promising potential tool to improve conventional soil analysis mainly conducted both in the laboratory (Minasny & McBratney, 2008) and field application in the future.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral estimation of soil organic matter and clay content in loess plateau of China

Chao

Qiao

et al. 2021

Agronomy Journal

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Visible and near-infrared reflectance (Vis-NIR) spectroscopy is considered a promising tool for the estimation of soil properties. Soil clay content and soil organic matter (SOM) are main components affecting soil spectra. Accurate assessment of clay content and SOM is essential before achieving accurate prediction for other soil properties. Selecting the proper spectral transformation technique and optimal calibration method are important processes to improve model performance. In this study, a total of 240 soil samples were collected from the main area of winter wheat (Triticum aestivum L.) fields in the Southwest region of Shanxi province, northern China. Six spectral pre-treatments and three multivariate methods were utilized to realize the estimation of clay content and SOM. Finally, the important spectral wavelengths were identified

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Soil texture and organic matter prediction using Mehlich‐3 extractable nutrients

Drescher,

Slaton,

Roberts

et al. 2024

Agrosystems Geosci & Env

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Soil organic matter (SOM) and texture are key properties influencing soil nutrient and water dynamics but are time‐consuming procedures for analytical laboratories. Our objective was to evaluate SOM and soil texture predictions using Mehlich‐3 nutrients and pH in Arkansas soils. Particle size was determined by the hydrometer method (2‐ and 8‐h readings) and SOM by loss on ignition. Two datasets were used to calibrate clay and sand (n = 409) and SOM (n = 1019) prediction models using simple and multiple regression. Estimated cation exchange capacity was highly correlated with clay, resulting in significant prediction models alone or combined with phosphorus (P); pH and copper (Cu); or pH, sodium (Na), and Cu (R2 = 0.84, 0.88, 0.89, and 0.90; p < 0.0001, respectively). Soil nutrients were weakly correlated with sand, resulting in a prediction model with moderate accuracy when using Mehlich‐3 P, calcium (Ca), Na, iron (Fe), and manganese (Mn) (R2 = 0.49; p < 0.0001). Clay and sand prediction models presented comparable accuracy when validated on a new dataset (n = 103). Predicted sand and clay showed good accuracy in grouping soils into medium (65%) and fine (96%) textural categories but had limited ability to define the coarse‐textural group (9%). SOM had moderate goodness‐of‐fit statistics for calibration and validation datasets using pH, P, Ca, Na, Mn, and zinc (R2 = 0.65 and 0.70, respectively; p < 0.0001). Mehlich‐3 nutrients can be used to estimate soil texture and assist with crop management decisions, but further research is needed to improve SOM prediction.

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Prediction of soil texture classes through different wavelength regions of reflectance spectroscopy at various soil depths

Cited by 54 publications

References 59 publications

Digital Soil Mapping Using Multispectral Modeling with Landsat Time Series Cloud Computing Based

Digital Soil Mapping Using Multispectral Modeling with Landsat Time Series Cloud Computing Based

Hyperspectral estimation of soil organic matter and clay content in loess plateau of China

Soil texture and organic matter prediction using Mehlich‐3 extractable nutrients

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