Unifying regional soil maps at different scales to generate a national soil map for the United Arab Emirates applying digital soil mapping techniques

Since different pedologists will draw different soil maps of a same area, it is important to compare the differences between mapping by specialists and mapping techniques, as for example currently intensively discussed Digital Soil Mapping. Four detailed soil maps (scale 1:10.000) of a 182-ha sugarcane farm in the county of Rafard, São Paulo State, Brazil, were compared. The area has a large variation of soil formation factors. The maps were drawn independently by four soil scientists and compared with a fifth map obtained by a digital soil mapping technique. All pedologists were given the same set of information. As many field expeditions and soil pits as required by each surveyor were provided to define the mapping units (MUs). For the Digital Soil Map (DSM), spectral data were extracted from Landsat 5 Thematic Mapper (TM) imagery as well as six terrain attributes from the topographic map of the area. These data were summarized by principal component analysis to generate the map designs of groups through Fuzzy K-means clustering. Field observations were made to identify the soils in the MUs and classify them according to the Brazilian Soil Classification System (BSCS). To compare the conventional and digital (DSM) soil maps, they were crossed pairwise to generate confusion matrices that were mapped. The categorical analysis at each classification level of the BSCS showed that the agreement between the maps decreased towards the lower levels of classification and the great influence of the surveyor on both the mapping and definition of MUs in the soil map. The average correspondence between the conventional and DSM maps was similar. Therefore, the method used to obtain the DSM yielded similar results to those obtained by the conventional technique, while providing additional information about the landscape of each soil, useful for applications in future surveys of similar areas.

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Spatial distribution of selected soil features in Hajdú-Bihar county represented by digital soil maps

Landscape & Environment ◽

10.21120/le/10/3-4/14 ◽

2016 ◽

Vol 10 (3-4) ◽

pp. 203-213 ◽

Cited By ~ 3

Author(s):

László Pásztor ◽

Annamária Laborczi ◽

Katalin Takács ◽

Gábor Szatmári ◽

Gábor Illés ◽

...

Keyword(s):

Spatial Distribution ◽

Spatial Data ◽

Soil Mapping ◽

Digital Soil Mapping ◽

Soil Maps ◽

Information Requirements ◽

Soil Features ◽

Soil Information ◽

Mapping Techniques ◽

Digital Soil Maps

With the ongoing DOSoReMI.hu project we aimed to significantly extend the potential, how soil information requirements could be satisfied in Hungary. We started to compile digital soil maps, which fulfil optimally general as well as specific national and international demands from the aspect of thematic, spatial and temporal accuracy. In addition to relevant and available auxiliary, spatial data themes related to soil forming factors and/or to indicative environmental elements we heavily lean on the various national soil databases. The set of the applied digital soil mapping techniques is gradually broadened. In our paper we present some results in the form of brand new soil maps focusing on the territory of Hajdú-Bihar county.

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Digital soil mapping: strategy for data pre-processing

Revista Brasileira de Ciência do Solo ◽

10.1590/s0100-06832012000400003 ◽

2012 ◽

Vol 36 (4) ◽

pp. 1083-1092 ◽

Cited By ~ 7

Author(s):

Alexandre ten Caten ◽

Ricardo Simão Diniz Dalmolin ◽

Luis Fernando Chimelo Ruiz

Keyword(s):

Decision Tree ◽

Predictive Performance ◽

Soil Mapping ◽

Digital Soil Mapping ◽

Soil Maps ◽

Soil Map ◽

Mapping Strategy

The region of greatest variability on soil maps is along the edge of their polygons, causing disagreement among pedologists about the appropriate description of soil classes at these locations. The objective of this work was to propose a strategy for data pre-processing applied to digital soil mapping (DSM). Soil polygons on a training map were shrunk by 100 and 160 m. This strategy prevented the use of covariates located near the edge of the soil classes for the Decision Tree (DT) models. Three DT models derived from eight predictive covariates, related to relief and organism factors sampled on the original polygons of a soil map and on polygons shrunk by 100 and 160 m were used to predict soil classes. The DT model derived from observations 160 m away from the edge of the polygons on the original map is less complex and has a better predictive performance.

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Updating a Physiography-Based Soil Map Using Digital Soil Mapping Techniques

Geopedology ◽

10.1007/978-3-319-19159-1_18 ◽

2016 ◽

pp. 305-319

Author(s):

D. J. Bedendo ◽

G. A. Schulz ◽

G. F. Olmedo ◽

D. M. Rodríguez ◽

M. E. Angelini

Keyword(s):

Soil Mapping ◽

Digital Soil Mapping ◽

Soil Map ◽

Mapping Techniques

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Progress in soil geography I: Reinvigoration

Progress in Physical Geography Earth and Environment ◽

10.1177/0309133319889048 ◽

2019 ◽

Vol 43 (6) ◽

pp. 827-854 ◽

Cited By ~ 3

Author(s):

Bradley A Miller ◽

Eric C Brevik ◽

Paulo Pereira ◽

Randall J Schaetzl

Keyword(s):

Land Use Planning ◽

Precision Agriculture ◽

Critical Role ◽

Soil Mapping ◽

Digital Soil Mapping ◽

Soil Science ◽

Accurate Information ◽

Soil Maps ◽

Geospatial Technologies ◽

Soil Geography

The geography of soil is more important today than ever before. Models of environmental systems and myriad direct field applications depend on accurate information about soil properties and their spatial distribution. Many of these applications play a critical role in managing and preparing for issues of food security, water supply, and climate change. The capability to deliver soil maps with the accuracy and resolution needed by land use planning, precision agriculture, as well as hydrologic and meteorologic models is, fortunately, on the horizon due to advances in the geospatial revolution. Digital soil mapping, which utilizes spatial statistics and data provided by modern geospatial technologies, has now become an established area of study for soil scientists. Over 100 articles on digital soil mapping were published in 2018. The first and second generations of soil mapping thrived from collaborations between Earth scientists and geographers. As we enter the dawn of the third generation of soil maps, those collaborations remain essential. To that end, we review the historical connections between soil science and geography, examine the recent disconnect between those disciplines, and draw attention to opportunities for the reinvigoration of the long-standing field of soil geography. Finally, we emphasize the importance of this reinvigoration to geographers.

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Comparing digital soil mapping techniques for organic carbon and clay content: Case study in Burundi's central plateaus

CATENA ◽

10.1016/j.catena.2017.04.003 ◽

2017 ◽

Vol 156 ◽

pp. 161-175 ◽

Cited By ~ 13

Author(s):

Anicet Sindayihebura ◽

Sam Ottoy ◽

Stefaan Dondeyne ◽

Marc Van Meirvenne ◽

Jos Van Orshoven

Keyword(s):

Organic Carbon ◽

Clay Content ◽

Soil Mapping ◽

Digital Soil Mapping ◽

Mapping Techniques

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Efficiency Comparison of Conventional and Digital Soil Mapping for Updating Soil Maps

Soil Science Society of America Journal ◽

10.2136/sssaj2011.0424 ◽

2012 ◽

Vol 76 (6) ◽

pp. 2097-2115 ◽

Cited By ~ 52

Author(s):

Bas Kempen ◽

Dick J. Brus ◽

Jetse J. Stoorvogel ◽

Gerard B.M. Heuvelink ◽

Folkert de Vries

Keyword(s):

Soil Mapping ◽

Digital Soil Mapping ◽

Soil Maps ◽

Efficiency Comparison

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Covariates selection assessment for field scale digital soil mapping in the context of precision fertilization management

10.5194/egusphere-egu2020-4904 ◽

2020 ◽

Author(s):

Nada Mzid ◽

Stefano Pignatti ◽

Irina Veretelnikova ◽

Raffaele Casa

Keyword(s):

Soil Properties ◽

Precision Agriculture ◽

Prediction Models ◽

Central Italy ◽

Soil Mapping ◽

Digital Soil Mapping ◽

Soil Maps ◽

Field Scale ◽

Umbria Region ◽

Digital Soil Maps

<p>The application of digital soil mapping in precision agriculture is extremely important, since an assessment of the spatial variability of soil properties within cultivated fields is essential in order to optimize agronomic practices such as fertilization, sowing, irrigation and tillage. In this context, it is necessary to develop methods which rely on information that can be obtained rapidly and at low cost. In the present work, an assessment is carried out of what are the most useful covariates to include in the digital soil mapping of field-scale properties of agronomic interest such as texture (clay, sand, silt), soil organic matter and pH in different farms of the Umbria Region in Central Italy. In each farm a proximal sensing-based mapping of the apparent soil electrical resistivity was carried out using the EMAS (Electro-Magnetic Agro Scanner) sensor. Soil sampling and subsequent analysis in the laboratory were carried out in each field. Different covariates were then used in the development of digital soil maps: apparent resistivity, high resolution Digital Elevation Model (DEM) from Lidar data, and bare soil and/or vegetation indices derived from Sentinel-2 images of the experimental fields. The approach followed two steps: (i) estimation of the variables using a Multiple Linear Regression (MLR) model, (ii) spatial interpolation via prediction models (including regression kriging and block kriging). The validity of the digital soil maps results was assessed both in terms of the accuracy in the estimation of soil properties and in terms of their impact on the fertilization prescription maps for nitrogen (N), phosphorus (P) and potassium (K).</p>

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DIGITAL SOIL MAPPING USING GEOMORPHOMETRIC ANALYSIS AND CASE-BASED FUZZY LOGIC APPROACH

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-4-w18-863-2019 ◽

2019 ◽

Vol XLII-4/W18 ◽

pp. 863-866

Author(s):

A. Rahmani ◽

F. Sarmadian ◽

S. R. Mousavi ◽

S. E. Khamoshi

Keyword(s):

Fuzzy Logic ◽

Soil Mapping ◽

Digital Soil Mapping ◽

Kappa Index ◽

Valley Bottom ◽

Error Matrix ◽

Soil Map ◽

Expert’S Opinion ◽

Fuzzy Logic Approach ◽

Landscape Units

Abstract. In low relief region such as plains, applied digital soil mapping has a controvertible issue, therefore, this study was aimed to digital mapping of soil classes at family levels by appropriate Geomorphometric variables along with fuzzy logic with area of 16,600 hectares in Qazvin Plain. Based on the geomorphologic map, the plain and pen plain are dominant landscape units. In this regards, 61 soil profiles were dogged. According to the expert’s opinion, covariates including diffuse insolation, standardized height, catchment area, valley depth and multiresolution valley bottom flatness (MrVBF) had the most important in order to generating soil map. Also, 19 fuzzy soil class maps were generated through using sample-based in ArcSIE software. Validation were carried out using achieved overall accuracy (OA) and Kappa index through error matrix. Subsequently, both ignorance and exaggerating uncertainty of hardened soil map were also done. The results showed that 19 soil families class were found. Accordingly, OA and the Kappa index were 54% and 46% respectively. The uncertainty of ignorance and exaggeration were obtained from 0 to 0.64 and 0 to 1, respectively. Moreover, the results indicated that exaggerated uncertainty was the highest in the northern and the lowest in the southern regions. Generally, applied geomorphometric parameters had the specific importance in the low relief areas for mapping of soils that have not been assessed properly so far.

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