Estimating temporal changes in soil pH in the black soil region of Northeast China using remote sensing

Zhang, Yue; Sui, Biao; Shen, Haiou; Wang, Zongming

doi:10.1016/j.compag.2018.09.005

Cited by 33 publications

(16 citation statements)

References 46 publications

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“…Remote sensing. Articles heavily based on remote sensing (Grunwald et al, 2015;Xu et al, 2017;Zhang et al, 2018b). Articles related to salinity were also assigned to this group since most of them use remote sensing techniques (Khadim et al, 2019;Zhang et al, 2019).…”

Section: Main Topicsmentioning

confidence: 99%

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

Padarian

Minasny

McBratney

2020

SOIL

281

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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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Section: Main Topicsmentioning

confidence: 99%

“…The studies model two or more time steps independently followed by a change analysis. For instance, Schillaci et al (2017a) and Zhang et al (2018b) subtracted the maps of the modelled properties from 2 different years to compute the change in SOC concentration and pH, respectively.…”

Section: Space-time Modellingmentioning

confidence: 99%

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

Padarian

Minasny

McBratney

2020

SOIL

281

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show abstract

“…Remote sensing: Articles heavily based on remote sensing (Grunwald et al, 2015;Xu et al, 2017;Zhang et al, 2018b).…”

Section: Main Topicsmentioning

confidence: 99%

“…Subtraction: The studies model two or more time-steps independently followed by a change analysis. For instance , Schillaci et al (2017a) and Zhang et al (2018b) subtracted the maps of the modelled properties from two different years to compute the change in SOC concentration and pH, respectively.…”

Section: Space-time Modellingmentioning

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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“…While there is great potential in ML approaches for soil erosion prediction and management (Vu Dinh et al, 2021), there also exists the risk of equifinality, gaining plausible results for the wrong reason (Padarian et al, 2020). While these techniques offer new possibilities they so far show most useful on regional 320 scales with large data availability (Zhang et al, 2018b) and up to now have not found their way in improving process based soil erosion models.…”

Section: Machine Learningmentioning

confidence: 99%

Response to Pedro Batista

Epple

2021

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Estimating temporal changes in soil pH in the black soil region of Northeast China using remote sensing

Cited by 33 publications

References 46 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

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