Digital soil mapping and its implications in the extrapolation of soil-landscape relationships in detailed scale

Wolski, Mario Sergio; Dalmolin, Ricardo Simão Diniz; Flores, Carlos A.; Moura-Bueno, Jean Michel; Caten, Alexandre ten; Kaiser, Douglas Rodrigo

doi:10.1590/s0100-204x2017000800009

Cited by 19 publications

(11 citation statements)

References 18 publications

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“…Camera et al (2017) achieved an OOB error of 8.6 %, which is comparable to the RF results in Dataset05. The results of OA are in the same range as those obtained by Taghizadeh-Mehrjardi et al (2012), Heung et al (2017), Wolski et al (2017), Pásztor et al (2018), andAfshar et al (2018), even though they used different approaches to build the dataset and process the data.…”

Section: Discussionmentioning

confidence: 58%

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Sample design effects on soil unit prediction with machine: randomness, uncertainty, and majority map

Carvalho¹,

Pereira²,

Filho³

et al. 2020

Revista Brasileira De Ciência Do Solo

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Notwithstanding the importance of soil surveys, advances in digital soil mapping have mainly focused on mapping soil attributes or properties rather than developing digital maps of soil units or soil classes. The purpose of this research was to develop digital soil unit maps based on primary soil data collection in areas without previously collected soil information. The covariate variability, the random effect across the data subset and the map outputs were the focuses of this study. We used five datasets with four models (Random Forest -RF, Gradient Boosted Machine -GBM, C5.0, and multinomial log-linear model -MLR). The covariates were grouped into five datasets, where four were grouped by Region Of Interest per Class (ROIC) and one was not grouped by ROIC. To evaluate the random effect to split the dataset, we ran each model 50 times and observed the overall accuracy (OA) and kappa index, and uncertainty, majority and variety maps. The OA of Dataset01 to 04 was lower than to Dataset05 accuracy. However, map outputs of RF and GBM for Dataset01 and Dataset05 had the same majority prediction. It seems that RF and GBM produce consistent results in map outputs according to this methodology and pedologist expertise. To evaluate the uncertainty and the consistency of soil unit prediction, we used the majority maps process. Random Forest, similar to GBM, presented the best results. The increase in the number of covariates was not a guarantee of improvement in the OA or in the quality of the map output. Geographic position and distance raster did not improve the map output according to expert evaluation. Because the variance between the ROICs, when the training and validation datasets were split based on it, the subsets are quite different in relation to the covariates, and this is the reason for the worse results of this model, comparing with the Dataset05. On the other hand, when considering one complete dataset not based on ROICs, the variance of training and validation subsets is lower and produced more accurate parameters of quality.

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Section: Discussionmentioning

confidence: 58%

“…The OA was obtained from the confusion matrix and represents the total success classification of the model when applied to the validation subset. Kappa is an association measure used to describe the concordance level of the map unit prediction (Wolski et al, 2017) over the validation subset.…”

Section: Computational Operation and Methodsmentioning

confidence: 99%

Sample design effects on soil unit prediction with machine: randomness, uncertainty, and majority map

Carvalho¹,

Pereira²,

Filho³

et al. 2020

Revista Brasileira De Ciência Do Solo

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“…There is evidence that the extrapolation of a mechanistic model to areas with similar characteristics and processes has more potential than the extrapolation of a statistical model, because the mechanistic model depends on cause–effect relationships, whereas the statistical model depends on associations. Several studies that used a statistical model for DSM explored the potential of extrapolation (Afshar, Ayoubi, & Jafari, 2018; Grinand, Arrouays, Laroche, & Martin, 2008; Wolski et al, 2017). However, the soil property predictions in these extrapolated areas were poor.…”

Section: Discussionmentioning

confidence: 99%

Introducing a mechanistic model in digital soil mapping to predict soil organic matter stocks in the Cantabrian region (Spain)

Hendriks

Stoorvogel

Álvarez‐Martínez

et al. 2020

European J Soil Science

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Digital soil mapping (DSM) is an effective mapping technique that supports the increased need for quantitative soil data. In DSM, soil properties are correlated with environmental characteristics using statistical models such as regression. However, many of these relationships are explicitly described in mechanistic simulation models. Therefore, the mechanistic relationships can, in theory, replace the statistical relationships in DSM. This study aims to develop a mechanistic model to predict soil organic matter (SOM) stocks in Natura2000 areas of the Cantabria region (Spain). The mechanistic model is established in four steps: (a) identify major processes that influence SOM stocks, (b) review existing models describing the major processes and the respective environmental data that they require, (c) establish a database with the required input data, and (d) calibrate the model with field observations. The SOM stocks map resulting from the mechanistic model had a mean error (ME) of −2 t SOM ha−1 and a root mean square error (RMSE) of 66 t SOM ha−1. The Lin's concordance correlation coefficient was 0.47 and the amount of variance explained (AVE) was 0.21. The results of the mechanistic model were compared to the results of a statistical model. It turned out that the correlation coefficient between the two SOM stock maps was 0.8. This study illustrated that mechanistic soil models can be used for DSM, which brings new opportunities. Mechanistic models for DSM should be considered for mapping soil characteristics that are difficult to predict by statistical models, and for extrapolation purposes. Highlights Theoretically, mechanistic models can replace the statistical relationships in digital soil mapping. Mechanistic soil models were used to develop a mechanistic model for digital soil mapping that predicted SOM stocks. The applicability of the mechanistic approach needs to be explored for different soil properties and regions.

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“…Of the 334 studies, 263 presented cartographic scale and spatial resolution (pixel size) information used (Table 3); 38 studies were found incompatible with the planimetric PEC-PCD, since their pixel size is higher than that indicated at the cartographic scale. (Giasson et al, 2006) 2008 (Figueiredo et al, 2008) 2009 (Crivelenti et al, 2009) 2010 (Chagas et al, 2010); (Coelho and Giasson, 2010) (Höfig et al, 2014); (Teske et al, 2014) 2015 (Bagatini et al, 2015); (Giasson et al, 2015); (Teske et al, 2015a); (Teske et al, 2015b); (Vasques et al, 2015) 2016 (Arruda et al, 2016); (Bagatini et al, 2016); (Demattê et al, 2016); (Dias et al, 2016); (Henrique et al, 2016); (Pelegrino et al, 2016) 2017 (Chagas et al, 2017); (Wolski et al, 2017) 2018 (Costa et al, 2018); (Meier et al, 2018) 2019 (Campos et al, 2019a); (Campos et al, 2019b); (Moura-Bueno et al, 2019); (Silva et al, 2019); (Silvero et al, 2019) All learning algorithms were assigned to a type of classifier such as Artificial Neural Network (ANN), Bayes classifiers, Decision Tree (DT), Logistic Regression (LR) and Support Vector Machine (SVM). Approximately 95 % of the studies used DT, ANN and LR classifiers (Table 4).…”

Section: Descriptive Statistics Of the Data Extracted From The Studiesmentioning

confidence: 99%

Digital soil class mapping in Brazil: a systematic review

Coelho

Giasson

Campos

et al. 2021

Sci. agric. (Piracicaba, Braz.)

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In Brazil several digital soil class mapping studies were carried out from 2006 onwards to maximize the use of existing maps and information and to provide estimates for wider areas. However, there is no consensus on which methods have produced superior results in the predictive value of soil maps. This study conducts a systematic review of digital soil class mapping in Brazil and aims to analyze the factors which can improve the accuracy of digital soil class maps. Data from 334 digital soil class mapping studies were grouped and analyzed by Student's t-test, Wilcoxon-Mann-Whitney test and Kruskal-Wallis test. When conventional maps were used for validation, the studies showed average values of 63 % and when field samples were used, 56 % for Overall Accuracy. Studies compatible with the Planimetric Cartographic Accuracy Standard for Digital Cartographic Products (PEC-PCD) averaged between 4 % and 15 % higher accuracy than those of the incompatible group. There seems to be no evidence that increasing the number of variables and samples results in more accurate soil map prediction, but studies using variables related to four soil-forming factors enhanced accuracy. From a density of 0.08 MU km-2 and upwards, it became more difficult for studies to obtain greater accuracy. Artificial neural network classifiers and Decision Tree models seem to be producing more accurate digital soil class maps.

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Digital soil mapping and its implications in the extrapolation of soil-landscape relationships in detailed scale

Cited by 19 publications

References 18 publications

Sample design effects on soil unit prediction with machine: randomness, uncertainty, and majority map

Sample design effects on soil unit prediction with machine: randomness, uncertainty, and majority map

Introducing a mechanistic model in digital soil mapping to predict soil organic matter stocks in the Cantabrian region (Spain)

Digital soil class mapping in Brazil: a systematic review

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