scholarly journals Relative prediction intervals reveal larger uncertainty in 3D approaches to predictive digital soil mapping of soil properties with legacy data

Geoderma ◽  
2019 ◽  
Vol 347 ◽  
pp. 170-184 ◽  
Author(s):  
Travis W. Nauman ◽  
Michael C. Duniway
Keyword(s):  
Soil Properties ◽  
Soil Mapping ◽  
Prediction Intervals ◽  
Digital Soil Mapping ◽  
Legacy Data

Creation of detailed soil properties maps of the Czech Republic based on national legacy data and digital soil mapping

2020 ◽  
Author(s):  
Robert Minařík ◽  
Daniel Žížala ◽  
Anna Juřicová
Keyword(s):  
Czech Republic ◽  
Soil Properties ◽  
Large Scale ◽  
Agricultural Land ◽  
Prediction Models ◽  
External Validation ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
The Czech Republic ◽  
Legacy Data

<p>Legacy soil data arising from traditional soil surveys are an important resource for digital soil mapping. In the Czech Republic, a large-scale (1:10 000) mapping of agricultural land was completed in 1970 after a decade of field investigation mapping. It represents a worldwide unique database of soil samples by its national extent and detail. This study aimed to create a detailed map of soil properties (organic carbon, ph, texture, soil unit) by using state-of-the-art digital soil mapping (DSM) methods. For this purpose we chose four geomorphologically different areas (2440 km<sup>2</sup> in total). A selected ensemble machine learning techniques based on bagging, boosting and stacking with random hyperparameters tuning were used to model each soil property. In addition to soil sample data, a DEM and its derivatives were used as common covariate layers. The models were evaluated using both internal repeated cross-validation and external validation. The best model was used for prediction of soil properties. The accuracy of prediction models is comparable with other studies. The resulting maps were also compared with the available original soil maps of the Czech Republic. The new maps reveal more spatial detail and natural variability of soil properties resulting from the use of DEM. This combination of high detailed legacy data with DSM results in the production of more spatially detailed and accurate maps, which may be particularly beneficial in supporting the decision-making of stakeholders.</p><p>The research has been supported by the project no. QK1820389 " Production of actual detailed maps of soil properties in the Czech Republic based on database of Large-scale Mapping of Agricultural Soils in Czechoslovakia and application of digital soil mapping" funding by Ministry of Agriculture of the Czech Republic.</p>

Digital Soil Mapping of Soil Properties in Honduras Using Readily Available Biophysical Datasets and Gaussian Processes

2008 ◽  
pp. 367-380 ◽  
Author(s):  
Juan Pablo Gonzalez ◽  
Andy Jarvis ◽  
Simon E. Cook ◽  
Thomas Oberthür ◽  
Mauricio Rincon-Romero ◽  
...  
Keyword(s):  
Soil Properties ◽  
Gaussian Processes ◽  
Soil Mapping ◽  
Digital Soil Mapping

Predicting spatial variability of selected soil properties using digital soil mapping in a rainfed vineyard of central Chile

2020 ◽  
Vol 22 ◽  
pp. e00289
Author(s):  
Lwando Mashalaba ◽  
Mauricio Galleguillos ◽  
Oscar Seguel ◽  
Javiera Poblete-Olivares
Keyword(s):  
Spatial Variability ◽  
Soil Properties ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Central Chile

Three-dimensional digital soil mapping of multiple soil properties at a field-scale using regression kriging

Geoderma ◽  
2020 ◽  
Vol 366 ◽  
pp. 114253 ◽  
Author(s):  
Yakun Zhang ◽  
Wenjun Ji ◽  
Daniel D. Saurette ◽  
Tahmid Huq Easher ◽  
Hongyi Li ◽  
...  
Keyword(s):  
Soil Properties ◽  
Three Dimensional ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Regression Kriging ◽  
Field Scale

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

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>

The effectiveness of digital soil mapping to predict soil properties over low-relief areas

2016 ◽  
Vol 188 (3) ◽  
Author(s):  
Zohreh Mosleh ◽  
Mohammad Hassan Salehi ◽  
Azam Jafari ◽  
Isa Esfandiarpoor Borujeni ◽  
Abdolmohammad Mehnatkesh
Keyword(s):  
Soil Properties ◽  
Soil Mapping ◽  
Digital Soil Mapping

Digital soil mapping: the challenge to obtain the best soil dataset and create a precise environmental model to support land use management at a national level (Ecuador).

2020 ◽  
Author(s):  
Daphne Armas ◽  
Mário Guevara ◽  
Fernando Bezares ◽  
Rodrigo Vargas ◽  
Pilar Durante ◽  
...  
Keyword(s):  
Land Use ◽  
Information System ◽  
Soil Properties ◽  
Soil Profile ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Land Use Management ◽  
National Scale ◽  
Global Soil ◽  
Soil Information

<p>One of the biggest challenges for digital soil mapping is the limited of field soil information (e.g., soil profile descriptions, soil sample analysis) for representing soil variability across scales. Global initiatives such as the Global Soil Partnership (GSP) and the development of a <strong>Global Soil Information System</strong> (GloSIS), World Soil Information Service (WoSis) or SoilGrids250m for global pedometric mapping highlight new opportunities but the crescent need of new and better soil datasets across the world. Soil datasets are increasingly required for the development of soil monitoring baselines, soil protection and sustainable land use strategies, and to better understand the response of soils to global environmental change.  However, soil surveys are a very challenging task due to their high acquisition costs such data and operational complexity. The use of legacy soil data can reduce these sampling efforts.</p><p>The main objective of this research was the rescue, synthesis and harmonization of legacy soil profile information collected between 2009 and 2015 for different purposes (e.g., soil or natural resources inventory) across Ecuador. This project will support the creation of a soil information system at the national scale following international standards for archiving and sharing soil information (e.g., GPS or the GlobalSoilMap.net project). This new information could be useful to increase the accuracy of current digital soil information across the country and the future development of digital soil properties maps.</p><p>We provided an integrated framework combining multiple data analytic tools (e.g., python libraries, pandas, openpyxl or pdftools) for the automatic conversion of text in paper format (e.g., pdf, jpg) legacy soil information, as much the qualitative soil description as analytical data,  to usable digital soil mapping inputs (e.g., spatial datasets) across Ecuador. For the conversion, we used text data mining techniques to automatically extract the information. We based on regular expressions using consecutive sequences algorithms of common patterns not only to search for terms, but also relationships between terms. Following this approach, we rescued information of 13.696 profiles in .pdf, .jpg format and compiled a database consisting of 10 soil-related variables.</p><p>The new database includes historical soil information that automatically converted a generic tabular database form (e.g., .csv) information.</p><p>As a result, we substantially improved the representation of soil information in Ecuador that can be used to support current soil information initiatives such as the WoSis, Batjes et al. 2019, with only 94 pedons available for Ecuador, the Latin American Soil Information System (SISLAC, http://54.229.242.119/sislac/es),  and the United Nations goals  towards increasing soil carbon sequestration areas or decreasing land desertification trends.  In our database there are almost 13.696 soil profiles at the national scale, with soil-related (e.g., depth, organic carbon, salinity, texture) with positive implications for digital soil properties mapping. </p><p>With this work we increased opportunities for digital soil mapping across Ecuador. This contribution could be used to generate spatial indicators of land degradation at a national scale (e.g., salinity, erosion).</p><p>This dataset could support new knowledge for more accurate environmental modelling and to support land use management decisions at the national scale.</p><p> </p>

scholarly journals Oblique geographic coordinates as covariates for digital soil mapping

2019 ◽  
Author(s):  
Anders Bjørn Møller ◽  
Amélie Marie Beucher ◽  
Nastaran Pouladi ◽  
Mogens Humlekrog Greve
Keyword(s):  
Organic Matter ◽  
Soil Properties ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Alternative Methods ◽  
Agricultural Field ◽  
Geographic Space ◽  
Promising Tool ◽  
Tree Algorithms ◽  
Realistic Prediction

Abstract. Decision tree algorithms such as Random Forest have become a widely adapted method for mapping soil properties in geographic space. However, implementing explicit geographic relationships into these methods has proven problematic. Using x- and y-coordinates as covariates gives orthogonal artefacts in the maps, and alternative methods using distances as covariates can be inflexible and difficult to interpret. We propose instead the use of coordinates along several axes tilted at oblique angles to provide an easily interpretable method for obtaining a realistic prediction surface. We test the method for mapping topsoil organic matter contents in an agricultural field in Denmark. The results show that the method provides accuracies on par with the most reliable alternative methods, namely kriging and the use of buffer distances to the training points. Furthermore, the proposed method is highly flexible, scalable and easily interpretable. This makes it a promising tool for mapping soil properties with complex spatial variation. We believe that the method will be highly useful for mapping soil properties in larger areas, and testing it for this purpose is a logical next step.

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