A high resolution map of soil types and physical properties for Cyprus: A digital soil mapping optimization

Camera, Corrado; Zomeni, Zomenia; Noller, Jay S.; Zissimos, Andreas M.; Christoforou, Irene C.; Bruggeman, Adriana

doi:10.1016/j.geoderma.2016.09.019

Cited by 119 publications

(60 citation statements)

References 56 publications

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“…The classification accuracy of MLR models was remarkable (63 80% depending on the models). This approach has been compared with RF for classifying soil types and exhibited either higher (Bernhardt-Barry et al, 2018) or lower performances (Camera et al, 2017). In a more similar context, (O'Farrell et al, 2019) obtained accuracy values comparable to ours while trying to classify Cyanobacteria into different ecological strategies.…”

Section: Drivers Of Dominance In Phytoplankton Communities From the Pmentioning

confidence: 53%

Drivers and ecological consequences of dominance in periurban phytoplankton communities using networks approaches

et al. 2019

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HAL is a multidisciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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Section: Drivers Of Dominance In Phytoplankton Communities From the Pmentioning

confidence: 53%

Drivers and ecological consequences of dominance in periurban phytoplankton communities using networks approaches

et al. 2019

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“…Modelling (classes): Articles focused on the modelling, especially mapping, of categorical soil properties based on their relationship with environmental covariates (Mansuy et al, 2014;Camera et al, 2017;Dharumarajan et al, 2017;Massawe et al, 2018). In this category is also possible to find articles related to the use of conventional soil maps, especially spatial disaggregation of polygons (Subburayalu et al, 2014;Vincent et al, 2018;Flynn et al, 2019).…”

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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“…It is able to model non-linear relationships between predictors and the response variable to handle noise data (observations with missing covariate data) and other situations in which a small dataset is associated with a large number of covariates (Collard et al, 2014). Although RF has shown better performance for soil class mapping when compared to a set of other classifiers (Pahlavan-Rad et al, 2016;Heung et al, 2017), the studies performed by Collard et al (2014), Taghizadeh-Mehrjardi et al (2015), and Camera et al (2017) showed that MLR performed better than RF.…”

Section: Random Forest (Rf)mentioning

confidence: 99%

“…Some studies addressed the comparison of data mining approaches for predicting soil classes by different predictive models. Nevertheless, despite the broad success of DSM techniques using tree-based model and logistic regression, few studies have compared the performance of promising classifiers for soil class mapping, such as logistic regression and Random Forest (Hengl et al, 2007;Collard et al, 2014;Taghizadeh-Mehrjardi et al, 2015;Pahlavan-Rad et al, 2016;Camera et al, 2017;Heung et al, 2017). Under similar conditions of western Haiti, this kind of studies is rarer.…”

Section: Introductionmentioning

confidence: 99%

Multinomial Logistic Regression and Random Forest Classifiers in Digital Mapping of Soil Classes in Western Haiti

Jeune

Francelino

Souza

et al. 2018

Rev. Bras. Ciênc. Solo

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Digital soil mapping (DSM) has been increasingly used to provide quick and accurate spatial information to support decision-makers in agricultural and environmental planning programs. In this study, we used a DSM approach to map soils in western Haiti and compare the performance of the Multinomial Logistic Regression (MLR) with Random Forest (RF) to classify the soils. The study area of 4,300 km 2 is mostly composed of diverse limestone rocks, alluvial deposits, and, to a lesser extent, basalt. A soil survey was conducted whereby soils were described and classified at 258 sites. Soil samples were collected and subjected to physical and chemical analyses. Recursive Feature Elimination (RFE) was used to select the most important covariates from auxiliary data, such as climate, lithology, and morphometric properties to describe the soil-landscape relationship. Mapping performance was assessed by the Kappa index and overall accuracy derived from a confusion matrix generated using a 5-fold cross validation process. In addition, an external mapping validation was carried out using an independent soil dataset. Accordingly, the soil dataset was split into 80 % and 20 % for training and validation of the models, respectively. No significant statistical difference (Z = 0.56< |1.96|) was found between maps generated with both classifiers (Kappa index 0.45 for MLR and 0.42 for RF). Based on the Kappa values, the classification performance can be characterized as moderate for both algorithms. Surprisingly, the RF classifier outperformed MLR in the validation process (Kappa values of 0.55 and 0.33, respectively). These results suggest a higher generalization ability of RF. However, no significant statistical difference (Z = 1.83< |1.96|) was observed.The soil map derived from RF indicated the occurrence of Leptosols (48.5 %), Gleysols (19.6 %), Chernozems (8 %), and Fluvisols (6.6 %) in most of the study area. The DSM approaches proved suitable for mapping soils in western Haiti and could be used in other parts of the country, thereby closing information gaps with regard to Haitian soils.

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A high resolution map of soil types and physical properties for Cyprus: A digital soil mapping optimization

Cited by 119 publications

References 56 publications

Drivers and ecological consequences of dominance in periurban phytoplankton communities using networks approaches

Drivers and ecological consequences of dominance in periurban phytoplankton communities using networks approaches

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

Multinomial Logistic Regression and Random Forest Classifiers in Digital Mapping of Soil Classes in Western Haiti

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