Estimating Soil Texture from a Limited Region of the Visible/Near-Infrared Spectrum

Silva, Elisângela Benedet da; Caten, Alexandre ten; Dalmolin, Ricardo Simão Diniz; Dotto, André Carnieletto; Silva, Walquiria Chaves; Giasson, Élvio

doi:10.1007/978-3-319-28295-4_6

Cited by 6 publications

(5 citation statements)

References 18 publications

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“…While Chang et al (2001) and Shepherd and Walsh (2002) worked on the Vis-NIR spectrum, obtaining slightly better values, of 0.67 and 0.78. It is possible to predict clay in the NIR region using PLSR according to Silva et al (2016). Viscarra Rossel et al 2006modeled clay and found R² values of 0.43 (Vis), 0.60 (NIR), 0.67 (MIR), and 0.67 (Vis-NIR-MIR); while Kania and Gruba (2016) tested clay prediction by NIR spectra and found R² values of 0.57 and 0.21.…”

Section: Resultsmentioning

confidence: 99%

Digital Soil Mapping of Soil Properties in the “Mar de Morros” Environment Using Spectral Data

Campbell

Filho

Francelino

et al. 2019

Rev. Bras. Ciênc. Solo

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Quantification of soil properties is essential for better understanding of the environment and better soil management. The conventional techniques of laboratory analysis are sometimes costly and detrimental to the environment. Thus, development of new techniques for soil analysis that do not generate residues, such as spectroscopy, is increasingly necessary as a viable way to estimate a wide range of soil properties. The objective of this study was to predict the levels of organic carbon (OC), clay, and extractable phosphorus (P), from the spectral responses of soil samples in the visible and near infrared (Vis-NIR), medium infrared (MIR), and Vis-NIR-MIR using different preprocessing methods combined with five prediction models. Soil samples were collected in Iconha, Espírito Santo State, Brazil, in the Ribeirão Inhaúma basin. A total of 184 samples were collected from 92 sites at two depths (0.00-0.10 and 0.10-0.30 m). Physical, chemical, and spectral analyses were performed according to routine soil laboratory methods. Random selection was made of 70 % of total samples for training and 30 % for validation of the models. The coefficient of determination (R 2) and root mean square error (RMSE) were calculated in order to assess model performance. The standardized indexes of prediction error RPD and RPIQ were also calculated. For clay and OC, the best R 2 was found in the MIR spectrum, at 0.69 and 0.65, respectively, and for P, it was 0.57 in Vis-NIR. The MSC (Multiplicative Scatter Correction), CR (Continuum removal), and SNV (Standard Normal Variate) preprocesses were most efficient for predicting clay, OC, and P, respectively, while the PLSR-Partial Least Squares Regression (OC and P) and SVM-Support Vector Machine (clay) gave the best predictions and are therefore recommended for modeling these properties in the study area. The models identified in this study can be used to discriminate soils according to a critical test value for clay, OC, and P.

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Section: Resultsmentioning

confidence: 99%

Digital Soil Mapping of Soil Properties in the “Mar de Morros” Environment Using Spectral Data

Campbell

Filho

Francelino

et al. 2019

Rev. Bras. Ciênc. Solo

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“…e Shepherd e Walsh (2002) trabalharam no espectro Vis-NIR, obtendo valores ligeiramente melhores de 0,67 e 0,78. Silva et al (2016) mostraram a possibilidade de se predizer argila na região do infravermelho próximo utilizando PLSR.…”

Section: Resultsunclassified

“…Nesse trabalho, para o mapeamento utilizando a lógica fuzzy, foi utilizado o ArcSIE (SHI et al, 2009), que consiste em uma extensão ArcGIS que utiliza lógica fuzzy para identificar os locais dentro da área de interesse que corresponde a condição ambiental de cada classe de solo (SILVA et al 2016).…”

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GIS-fuzzy logic technique in modeling soil depth classes: Using parts of the Clay Belt and Hornepayne region in Ontario, Canada as a case study

et al. 2016

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“…Coblentz, 1906) and its applications in geology are well established (Lyon & Burns, 1963). More recently, near-infrared remote sensing has been applied in soil sciences for multiple purposes, including: mapping proportions of sand, silt, and clay content (Saleh, Belal & Arafat, 2013;Silva et al, 2016); determination of soil salinity (Feyziyev et al, 2016); monitoring soil moisture and capacity of water absorption (Ben-Dor et al, 2002;Whiting, Li & Ustin, 2004); estimate organic carbon content (Hu, Chau & Si, 2015;Viscarra Rossel & Hicks, 2015); differentiate types of clay minerals (Surech, Sreenivas & Sivasamy, 2014); and assessing soil contamination and detection of heavy metals (Mohamed et al, 2016(Mohamed et al, , 2018). Yet, until now, machine learning approaches in geospatial paleontology tended to achieve good results but as "black-boxes" that did not reveal how exactly they were interpreting a "fossiliferous signal", and thus the relative importance of specific spectral bands for remote fossil site detection has not previously been demonstrated.…”

Section: Ground-truthingmentioning

confidence: 99%

Unsupervised learning of satellite images enhances discovery of late Miocene fossil sites in the Urema Rift, Gorongosa, Mozambique

Coelho

Anemone

Carvalho

2021

PeerJ

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Background Paleoanthropological research focus still devotes most resources to areas generally known to be fossil rich instead of a strategy that first maps and identifies possible fossil sites in a given region. This leads to the paradoxical task of planning paleontological campaigns without knowing the true extent and likely potential of each fossil site and, hence, how to optimize the investment of time and resources. Yet to answer key questions in hominin evolution, paleoanthropologists must engage in fieldwork that targets substantial temporal and geographical gaps in the fossil record. How can the risk of potentially unsuccessful surveys be minimized, while maximizing the potential for successful surveys? Methods Here we present a simple and effective solution for finding fossil sites based on clustering by unsupervised learning of satellite images with the k-means algorithm and pioneer its testing in the Urema Rift, the southern termination of the East African Rift System (EARS). We focus on a relatively unknown time period critical for understanding African apes and early hominin evolution, the early part of the late Miocene, in an overlooked area of southeastern Africa, in Gorongosa National Park, Mozambique. This clustering approach highlighted priority targets for prospecting that represented only 4.49% of the total area analysed. Results Applying this method, four new fossil sites were discovered in the area, and results show an 85% accuracy in a binary classification. This indicates the high potential of a remote sensing tool for exploratory paleontological surveys by enhancing the discovery of productive fossiliferous deposits. The relative importance of spectral bands for clustering was also determined using the random forest algorithm, and near-infrared was the most important variable for fossil site detection, followed by other infrared variables. Bands in the visible spectrum performed the worst and are not likely indicators of fossil sites. Discussion We show that unsupervised learning is a useful tool for locating new fossil sites in relatively unexplored regions. Additionally, it can be used to target specific gaps in the fossil record and to increase the sample of fossil sites. In Gorongosa, the discovery of the first estuarine coastal forests of the EARS fills an important paleobiogeographic gap of Africa. These new sites will be key for testing hypotheses of primate evolution in such environmental settings.

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Estimating Soil Texture from a Limited Region of the Visible/Near-Infrared Spectrum

Cited by 6 publications

References 18 publications

Digital Soil Mapping of Soil Properties in the “Mar de Morros” Environment Using Spectral Data

Digital Soil Mapping of Soil Properties in the “Mar de Morros” Environment Using Spectral Data

GIS-fuzzy logic technique in modeling soil depth classes: Using parts of the Clay Belt and Hornepayne region in Ontario, Canada as a case study

Unsupervised learning of satellite images enhances discovery of late Miocene fossil sites in the Urema Rift, Gorongosa, Mozambique

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