National digital soil map of organic matter in topsoil and its associated uncertainty in 1980's China

Liang, Zhao; Chen, Songchao; Yang, Yuanyuan; Zhao, Ruihong; Shi, Zhou; Rossel, Raphael A. Viscarra

doi:10.1016/j.geoderma.2018.08.011

Cited by 90 publications

(36 citation statements)

References 32 publications

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“…In soil sciences, ML algorithms are usually trained using the traditional train-validation split or cross-validation (Keskin et al, 2019;Liang et al, 2019), or even no validation (Feng et al, 2019), except for some studies based on DL or with an engineering background (e.g. Reale et al, 2018), including some of our publications on the use of DL for DSM (Padarian et al, 2019c) or soil spectroscopy (Padarian et al, 2019b, a), which use a train-validation-test split.…”

Section: New Good Practicesmentioning

confidence: 99%

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

Padarian

Minasny

McBratney

2020

SOIL

261

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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: New Good Practicesmentioning

confidence: 99%

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

Padarian

Minasny

McBratney

2020

SOIL

261

View full text Add to dashboard Cite

show abstract

“…The training dataset is used to learn the parameters, the validation dataset to compare models fitted with different hyper-parameters in order to find the optimal combination, and the test dataset as the independent, unseen data. In soil sciences, ML algorithms are usually trained using the traditional train/validation split or cross-validation (Keskin et al, 2019;Liang et al, 2019), or even no validation (Feng et al, 2019), except for some studies based on DL or with engineering background (e.g. Reale et al (2018)), including some of our publications on the use of DL for DSM (Padarian et al, 2019c) or soil spectroscopy (Padarian et al, 2019b, a), which use a train/validation/test split.…”

Section: New Good Practicesmentioning

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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“…The amount of importance in assessing the uncertainty is almost as great as that in making a prediction map [58], uncertainty map helped researchers to identify the source of the uncertainty and propose solutions [59,60]. To obtain a very reliable prediction, we can control the uncertainty of soil TN map from two aspects.…”

Section: Uncertainty In Soil Tn Predictionmentioning

confidence: 99%

Fine-Resolution Mapping of Soil Total Nitrogen across China Based on Weighted Model Averaging

et al. 2019

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Accurate estimates of the spatial distribution of total nitrogen (TN) in soil are fundamental for soil quality assessment, decision making in land management, and global nitrogen cycle modeling. In China, current maps are limited to individual regions or are of coarse resolution. In this study, we compiled a new 90-m resolution map of soil TN in China by the weighted summation of random forest and extreme gradient boosting. After harmonizing soil data from 4022 soil profiles into a fixed soil depth (0–20 cm) by equal area spline, 18 environmental covariates were employed to characterize the spatial pattern of soil TN in topsoil across China. The accuracy assessments from independent validation data showed that the weighted model averaging gave the best predictions with an acceptable R2 (0.41). The prediction map showed that high-value areas of soil TN were mainly distributed in the eastern Tibetan Plateau, central Qilian Mountains and the north of the Greater Khingan Range. Climate factors had a considerable influence on the variation of the soil TN, and land-use types played a pivotal part in each climate zone. This high-resolution and high-quality soil TN data set in China can be very useful for future inventories of soil nitrogen, assessments of soil nutrient status, and management of arable land.

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National digital soil map of organic matter in topsoil and its associated uncertainty in 1980's China

Cited by 90 publications

References 32 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

Fine-Resolution Mapping of Soil Total Nitrogen across China Based on Weighted Model Averaging

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