Identification of soil profile classes using depth-weighted visible–near-infrared spectral reflectance

Xie, Xinglong; Li, Anbo

doi:10.1016/j.geoderma.2018.03.029

Cited by 27 publications

(23 citation statements)

References 47 publications

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“…Spectroscopy: This topic is related to proximal soil sensing covering different light wavelength sections, from microwave to infrared to gamma (Heggemann et al, 2017;Butler et al, 2018;Xie and Li, 2018).…”

Section: Methods (Nn Svm)mentioning

confidence: 99%

Machine learning and soil sciences: A review aided by machine learning tools

Padarian¹,

Minasny²,

McBratney³

2019

Preprint

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Abstract. The application of machine learning (ML) techniques in various fields of science has increased rapidly, especially in the last ten years. The increasing availability of soil data that can be efficiently acquired remotely and proximally, and freely available open-source algorithms, have led to an accelerated adoption of ML techniques to analyse soil data. Given the large number of publications, it is an impossible task to manually review all papers on the application of ML in soil science without narrowing down a narrative of ML application in a specific research question. This paper aims to provide a comprehensive review of the application of ML techniques in soil science aided by a ML algorithm (Latent Dirichlet Allocation) to find patterns in a large collection of text corpus. The objective is to gain insight into publications of ML applications in soil science and to discuss the research gaps in this topic. We found that: a) there is an increasing usage of ML methods in soil sciences, mostly concentrated in developed countries, b) the reviewed publication can be grouped into 12 topics, namely remote sensing, soil organic carbon, water, contamination, methods (ensembles), erosion and parent material, methods (NN, SVM), spectroscopy, modelling (classes), crops, physical and modelling (continuous), c) advanced ML methods usually perform better than simpler approaches thanks to their capability to capture non-linear relationships. From these findings, we found research gaps, in particular: about the precautions that should be taken (parsimony) to avoid overfitting, and that the interpretability of the ML models is an important aspect to consider when applying advanced ML methods in order to improve our knowledge and understanding of soil. We foresee that a large number of studies will focus on the latter topic.

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Section: Methods (Nn Svm)mentioning

confidence: 99%

Machine learning and soil sciences: A review aided by machine learning tools

Padarian¹,

Minasny²,

McBratney³

2019

Preprint

View full text Add to dashboard Cite

show abstract

“…Articles mostly focused on soil water content and its changes over time (Ahmad et al, 2010;Coopersmith et al, 2014;Greifeneder et al, 2018;Han et al, 2018). Other articles in this category are related to soil temperature and CO 2 fluxes (Xing et al, 2018;Oh et al, 2019;Warner et al, 2019;Zeynoddin et al, 2019). All these articles comprise measurements made by "stations".…”

Section: Main Topicsmentioning

confidence: 99%

Machine learning and soil sciences: a review aided by machine learning tools

Padarian

Minasny

McBratney

2020

SOIL

283

View full text Add to dashboard Cite

Abstract. The application of machine learning (ML) techniques in various fields of science has increased rapidly, especially in the last 10 years. The increasing availability of soil data that can be efficiently acquired remotely and proximally, and freely available open-source algorithms, have led to an accelerated adoption of ML techniques to analyse soil data. Given the large number of publications, it is an impossible task to manually review all papers on the application of ML in soil science without narrowing down a narrative of ML application in a specific research question. This paper aims to provide a comprehensive review of the application of ML techniques in soil science aided by a ML algorithm (latent Dirichlet allocation) to find patterns in a large collection of text corpora. The objective is to gain insight into publications of ML applications in soil science and to discuss the research gaps in this topic. We found that (a) there is an increasing usage of ML methods in soil sciences, mostly concentrated in developed countries, (b) the reviewed publications can be grouped into 12 topics, namely remote sensing, soil organic carbon, water, contamination, methods (ensembles), erosion and parent material, methods (NN, neural networks, SVM, support vector machines), spectroscopy, modelling (classes), crops, physical, and modelling (continuous), and (c) advanced ML methods usually perform better than simpler approaches thanks to their capability to capture non-linear relationships. From these findings, we found research gaps, in particular, about the precautions that should be taken (parsimony) to avoid overfitting, and that the interpretability of the ML models is an important aspect to consider when applying advanced ML methods in order to improve our knowledge and understanding of soil. We foresee that a large number of studies will focus on the latter topic.

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“…Fajardo et al [74] indicated that soil spectroscopy offers more information than the traditional description and avoids observer bias when describing a soil core. In addition, soil classes were identified or allocated with surface or profile spectral reflectance [76][77][78][79][80][81]. Since the use of soil spectral reflectance applied to soil science studies has seen exponential growth in the past decades, a new term "spectral pedology" was defined by Demattê and Terra [28].…”

Section: Reflectance Spectroscopy and Soil Genesismentioning

confidence: 99%

“…The study object of soil genesis should be the entire soil profile, not only topsoil or a certain horizon. Therefore, a critical issue when using this new methodology to characterize the entire soil profile is how to combine the spectroscopy of soil horizons for individual profiles [78]. At present, there are four methods to integrate spectral curves for the soil profile.…”

Section: Combination Of Horizontal Spectroscopymentioning

confidence: 99%

“…Therefore, this spectral surface can be used to classify soil classes, but a smaller sampling interval is needed to distinguish the genetic horizon. The last method is to integrate the spectra of a genetic horizon by depth-weighted averaging [78], similar to the method of Viscarra Rossel and Webster [73]. The above methods serve as efficient tools for integrating spectra of legacy soil samples.…”

Section: Combination Of Horizontal Spectroscopymentioning

confidence: 99%

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Visible and Near-Infrared Reflectance Spectroscopy Analysis of a Coastal Soil Chronosequence

et al. 2019

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The soil chronosequence is a useful method for investigating pedological theories. Soil chemical, physical and mineralogical properties in chronosequences change over time and exhibit systematic and time-dependent trends, which can be used to analyze the rates and directions of pedogenic changes. The potential of soil spectroscopy as an emerging, rapid and cost-effective technique for predicting soil properties has been widely accepted and has motivated the application of spectroscopic techniques to the analysis of soil chronosequence. We present a soil chronosequence derived from 1000-year-old calcareous marine sediments and examine changes in six soil properties over this period. We evaluated the utility of a soil spectroscopic method to detect soil property changes and to predict the pedogenic properties and soil ages of the chronosequence. The results show that some soil pedogenic processes, such as soil organic matter accumulation, CaCO3 leaching and clay migration, can be identified in the millennium chronosequence. Power chronofunctions are formulated for soil organic matter (SOM) and Logarithmic chronofunctions are fitted for clay, CaCO3 and pH. These pedogenic processes are identified in the reflectance intensity and absorption features of soil spectroscopy, and pedogenic properties can be calibrated via soil reflectance spectroscopy. Profile ages can also be predicted via pseudo multi-depth spectra of soil profiles, and soil spectral curves for 0–30 cm generated the best prediction results (RPD = 1.85). We conclude that soil properties, changing due to weathering and soil formation, act as a bridge linking spectroscopy and weathering levels/pedogenic processes. The results imply that applying spectroscopy techniques to chronosequence study and mapping the degree of soil development in certain areas should be possible.

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Identification of soil profile classes using depth-weighted visible–near-infrared spectral reflectance

Cited by 27 publications

References 47 publications

Machine learning and soil sciences: A review aided by machine learning tools

Machine learning and soil sciences: A review aided by machine learning tools

Machine learning and soil sciences: a review aided by machine learning tools

Visible and Near-Infrared Reflectance Spectroscopy Analysis of a Coastal Soil Chronosequence

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