Problems of modern soil mapping and ways to solve them

Nikiforova, A. S.; Fleis, Maria E.; Nyrtsov, M.V.; Kazantsev, N. N.; Kim, Klim V.; Belyonova, Nina K.; Kim, Julia K.

doi:10.1016/j.catena.2020.104885

Cited by 9 publications

(3 citation statements)

References 49 publications

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“…Modern soil mapping combines traditional/conventional (visual-expert) and digital methods. In recent decades, the worldwide growing interest in digital soil maps/mapping (DSM) has been accompanied by a downward trend in the use of conventional maps/mapping (CSM) [1]. Comparative assessments of CSM and DSM are few, while the "real data" of digital mapping and the "conceptual assumptions" of conventional ones, of course, are not compared in favor of the latter [2].…”

Section: Introductionmentioning

confidence: 99%

“…Comparative assessments of CSM and DSM are few, while the "real data" of digital mapping and the "conceptual assumptions" of conventional ones, of course, are not compared in favor of the latter [2]. Today's mood and tendency in soil mapping suppose that DSM preferences are mainly related to: (1) the decrease in subjective (expert) opinion, making maps more objective and reproducible [3]; (2) the reduction of the cost of the mapping process [4][5][6][7][8]; (3) increasing the informativeness of soil maps [9,10]; (4) creating a global DSM [1,11].…”

Section: Introductionmentioning

confidence: 99%

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Comparative Assessment of Digital and Conventional Soil Mapping: A Case Study of the Southern Cis-Ural Region, Russia

et al. 2022

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Digital mapping was applied for a key site located at the Southern Cis-Ural region near Ufa city (the Republic of Bashkortostan, Russia). The digital soil map (DSM) was created using the open-source GIS software packages and compared to a conventional (CSM) one. As input parameters, we used standard morphometric values of the topography and field descriptions of soils, including the authors’ data. The DSM was created at the same scale (1:25,000) as the CSM, and soils of different classes were grouped according to the principle of genetic homogeneity and regional agroecological value. Comparing DSM and CSM showed several significant differences in the position, areas, and boundaries of hydromorphic soils and chernozems. The DSM has advantages over CSM at estimating smaller soil areas (areals) and their boundaries, in particular, on elevated topography elements (hills and steep slopes) and upper links of the erosion network (small dry valleys, hollows, and gullies). On the other hand, fluvial soils are mapped rather poorly by the digital approach, and CSM is more appropriate for such soils’ areals. The highest discrepancy is confined to the areas of eroded soils and fluvisols (15% and 12% of total area, respectively) due to significant differences in DSM and CSM approaches for such soil groups. We suppose that the digital method is effective and suitable for the Cis-Ural region, despite 57% soil taxa (types) prediction accuracy and the complexity of the territory by its ruggedness, erosion, and suffusion processes. The implementation and further use of digital mapping methods increase the quality of work, reduce its cost and terms in the region.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparative Assessment of Digital and Conventional Soil Mapping: A Case Study of the Southern Cis-Ural Region, Russia

et al. 2022

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“…Despite the tremendous progress made in the field of pedometrics and the development of predictive soil mapping tools, conceptual challenges with predictive soil mapping have been identified. Specifically, there is a lack of understanding of the relationship between soil mapping and conceptual soil classification, and a lack of consistent mapping theory used in predictive soil mapping studies (Nikiforova et al 2020). Additionally, other researchers have noted that conflicting information needs to be addressed as part soil mapping, monitoring, and management (Baruck et al 2016).To ensure that predictive mapping results can be interpreted within pedological frameworks, both established scientific frameworks in pedology and links between soil classification and pedology need to be incorporated into predictive soil mapping processes.…”

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confidence: 99%

Predictive soil mapping in the Boreal Plains of Northern Alberta by using multi-temporal remote sensing data and terrain derivatives

Sorenson

Kiss

Anna³

et al. 2022

Can. J. Soil. Sci.

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As Canada’s vast Boreal Plains are extensively managed, predictive soil mapping could be used as an effective tool to generate high-resolution soil information for the region to inform sustainable resource management. This study aimed to investigate the use of multi-temporal remote sensing data and terrain derivatives to map soil types in the region. A method of constraining sub-group and great-group soil type predictions based on the predictions at higher-order levels (great-group and order, respectively) was tested. Sentinel time series median values obtained using Google Earth Engine were tested in combination with first- and second-order digital elevation model derivatives for use as predictor variables in the predictive models. A recursive feature selection process was implemented to reduce the number of predictor variables used in model training. Soil classes were predicted at the order, great group, and subgroup levels and two approaches were tested. In the first approach, models were unconstrained based on previous predictions. In the second approach, models were constrained to predict only soil great group classes that occur within the predicted soil order for a given location and similarly predict only soil subgroup classes that occur within the predicted soil great group for a given location. Determined through independent validation testing, the most probable predicted soil maps had overall accuracies ranging from 42 to 68% and Kappa scores ranging from 0.33 to 0.48. Overall, the constrained models had the best performance of the approaches tested.

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Spatial prediction of soil properties using random forest, k-nearest neighbors and cubist approaches in the foothills of the Ural Mountains, Russia

Suleymanov

Tuktarova

Belan

et al. 2023

Model. Earth Syst. Environ.

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Problems of modern soil mapping and ways to solve them

Cited by 9 publications

References 49 publications

Comparative Assessment of Digital and Conventional Soil Mapping: A Case Study of the Southern Cis-Ural Region, Russia

Comparative Assessment of Digital and Conventional Soil Mapping: A Case Study of the Southern Cis-Ural Region, Russia

Predictive soil mapping in the Boreal Plains of Northern Alberta by using multi-temporal remote sensing data and terrain derivatives

Spatial prediction of soil properties using random forest, k-nearest neighbors and cubist approaches in the foothills of the Ural Mountains, Russia

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