Empirical equation for preliminary assessment of soil texture

Jović, Branislav; Ćirić, Vladimir; Kovačević, Marko; Šeremešić, Srđan; Kordić, Branko

doi:10.1016/j.saa.2018.08.039

Cited by 13 publications

(7 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The greater importance of MIR to explain the variation in physical‐hydric properties compared with Vis–NIR is attributed to its high sensitivity to identify quartz, with well‐defined features and peaks, throughout almost the entire spectrum (Janik et al, 1998; Jović et al, 2019; Viscarra Rossel et al, 2006). On the other hand, the Vis–NIR region does not present specific quartz features (Viscarra Rossel et al, 2006) and showed a lower capacity of explaining the WRC in sandy soils.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Sand fractions micromorphometry detected by Vis‐NIR‐MIR and its impact on water retention

Raimo

Couto

Demattê

et al. 2022

European J Soil Science

View full text Add to dashboard Cite

The expansion of agricultural frontiers has increased the use of sandy soils as cropping areas; however, the water-retention capacity (WRC) of the soil is a crucial factor. Thus, fast and accurate methodologies, such as spectroscopy, have gained importance to identify physical-hydric and other attributes related to the WRC. Some theories indicate the micromorphometry of particles is an important factor in sandy soils. This study (I) investigated the WRC of sandy soils from distinct geologic formations and the influence of particle micro-

show abstract

Section: Discussionmentioning

confidence: 99%

“…A gold plate was used for equipment calibration after every four samples. To reduce sampling noise, reflectance values between 500 and 400 cm −1 were removed (Jović et al, 2019).…”

Section: Methodsmentioning

confidence: 99%

Sand fractions micromorphometry detected by Vis‐NIR‐MIR and its impact on water retention

Raimo

Couto

Demattê

et al. 2022

European J Soil Science

View full text Add to dashboard Cite

show abstract

“…The soil used for the model of the sample system was selected from soil samples that had been kept in the laboratory for 12 years, in which the percentage of organic matter was low (about 0.5%). To examine and influence the soil type and obtain a more reliable model, three basic soil types were used for five model system mixtures: pure Arenosol and pure Vertisol, as well as a mixture of the five most common soil types from northern Serbia 25 (Arenosol, Vertisol, Solonec, Chernozem, and Fluvisol). Each of the tested soil model mixtures was made by adding certain proportions of starch and nicotinamide and humic acids to a certain basic soil.…”

Section: Methodsmentioning

confidence: 99%

Mid-Infrared Variable Selection for Soil Organic Matter Fractions Based on Soil Model Systems and Permutation Importance Algorithm

Jović,

Panić,

Pavlović

et al. 2023

Appl Spectrosc

View full text Add to dashboard Cite

In this research, an attempt was made to classify soil samples according to the different fractions of soil organic matter (SOM) using model systems in which the ratio of the fractions of SOM is chemically mimicked. A mixture of starch and nicotinamide was used for the labile organic matter model, while a standard of humic acid was used for the stabile organic matter. Changing the threshold value in the selected ranges after a permutation importance algorithm is conducted using train models and test data set, a list of selected important wavelengths and their importance scores were obtained. Three regions for the classification of soil fractions within the estimated probability density function are most prominent: 800–1200 cm–1, 0.48–0.55; 1800–2000 cm–1, 0.52–0.62; and 2500–3200 cm–1, 0.48–0.62, where the first component represents the spectral range while the second component covers the range of the importance score. Obtained wavelength ranges indicate the importance of the aliphatic stretching and bending vibration region, as well as the total soil reflectance (mineral content) for the characterization of organic matter fractions. A comparative evaluation with literature data found that the obtained wavelengths have a potential for application in methods of proximal and remote detection/calibration of existing and development of new sensors for Advanced Spaceborne Thermal Emission and Reflection Radiometer satellites, specifically in the shortwave infrared and thermal infrared ranges.

show abstract

“…The OSSL is made up of several SSLs that present a data-sharing policy. The latest version (v1.2) includes the United States Department of Agriculture (USDA) Natural Resources Conservation Service (NRCS) National Cooperative Soil Survey (NCSS) - Kellogg Soil Survey Laboratory (KSSL) MIR [19–21] and VisNIR libraries, labeled , with the VisNIR dataset being sourced from the Rapid Carbon Assessment Project (RaCA) project from USDA-NRCS [22, 23]; World Agroforestry Centre (ICRAF) MIR and VisNIR libraries, developed in cooperation with the International Soil Reference and Information Centre (ISRIC) [24], labeled ; two MIR libraries of the Africa Soil Information Service (AfSIS) [25, 26], labeled as and ; the VisNIR library from the European Data Centre (ESDAC) — Joint Research Centre (JRC) as part of the Land Use/Cover Area frame statistical Survey (LUCAS soil of 2009, 2012, and 2015) [27], labeled ; the Central African MIR SSL developed by ETH Zurich in the Congo Basin [28], labeled ; a MIR library of New Zealand forest soils from researchers of Scion Research, NZ [29], labeled ; a MIR library of boreal soils from researchers of the University of Zurich [30], labeled ; and a subset of the Serbian SSL from the University of Novi Sad [31], labeled .…”

Section: Methodsmentioning

confidence: 99%

Open Soil Spectral Library (OSSL): Building reproducible soil calibration models through open development and community engagement

Safanelli,

Hengl,

Parente

et al. 2023

Preprint

View full text Add to dashboard Cite

Soil spectroscopy is a widely used method for estimating soil properties that are important to environmental and agricultural monitoring. However, a bottleneck to its more widespread adoption is the need for establishing large reference datasets for training machine learning (ML) models, which are called soil spectral libraries (SSLs). Similarly, the prediction capacity of new samples is also subject to the number and diversity of soil types and conditions represented in the SSLs. To help bridge this gap and enable hundreds of stakeholders to collect more affordable soil data by leveraging a centralized open resource, the Soil Spectroscopy for Global Good has created the Open Soil Spectral Library (OSSL). In this paper, we describe the procedures for collecting and harmonizing several SSLs that are incorporated into the OSSL, followed by exploratory analysis and predictive modeling. The results of 10-fold cross-validation with refitting show that, in general, mid-infrared (MIR)-based models are significantly more accurate than visible and near-infrared (VisNIR) or near-infrared (NIR) models. From independent model evaluation, we found that Cubist comes out as the best-performing ML algorithm for the calibration and delivery of reliable outputs (prediction uncertainty and representation flag). Although many soil properties are well predicted, total sulfur, extractable sodium, and electrical conductivity performed poorly in all spectral regions, with some other extractable nutrients and physical soil properties also performing poorly in one or two spectral regions (VisNIR or Neospectra NIR). Hence, the use of predictive models based solely on spectral variations has limitations. This study also presents and discusses several other open resources that were developed from the OSSL, aspects of opening data, current limitations, and future development. With this genuinely open science project, we hope that OSSL becomes the driver of the soil spectroscopy community to accelerate the pace of scientific discovery and innovation.

show abstract

Empirical equation for preliminary assessment of soil texture

Cited by 13 publications

References 20 publications

Sand fractions micromorphometry detected by Vis‐NIR‐MIR and its impact on water retention

Sand fractions micromorphometry detected by Vis‐NIR‐MIR and its impact on water retention

Mid-Infrared Variable Selection for Soil Organic Matter Fractions Based on Soil Model Systems and Permutation Importance Algorithm

Open Soil Spectral Library (OSSL): Building reproducible soil calibration models through open development and community engagement

Contact Info

Product

Resources

About