High-resolution digital soil mapping of multiple soil properties: an alternative to the traditional field survey?

The soil particle-size fractions (PSFs) are one of the most important attributes to influence soil physical (e.g., soil hydraulic properties) and chemical (e.g., cation exchange) processes. There is an increasing need, therefore, for high-resolution digital prediction of PSFs to improve our ability to manage agricultural land. Consequently, use of ancillary data to make cheaper high-resolution predictions of soil properties is becoming popular. This approach is known as “digital soil mapping.” However, most commonly employed techniques (e.g., multiple linear regression or MLR) do not consider the special requirements of a regionalized composition, namely PSF; (1) should be nonnegative (2) should sum to a constant at each location, and (3) estimation should be constrained to produce an unbiased estimation, to avoid false interpretation. Previous studies have shown that the use of the additive log-ratio transformation (ALR) is an appropriate technique to meet the requirements of a composition. In this study, we investigated the use of ancillary data (i.e., electromagnetic (EM), gamma-ray spectrometry, Landsat TM, and a digital elevation model to predict soil PSF using MLR and generalized additive models (GAM) in a standard form and with an ALR transformation applied to the optimal method (GAM-ALR). The results show that the use of ancillary data improved prediction precision by around 30% for clay, 30% for sand, and 7% for silt for all techniques (MLR, GAM, and GAM-ALR) when compared to ordinary kriging. However, the ALR technique had the advantage of adhering to the special requirements of a composition, with all predicted values nonnegative and PSFs summing to unity at each prediction point and giving more accurate textural prediction.

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

Digital Soil Mapping with Limited Data ◽

10.1007/978-1-4020-8592-5_33 ◽

2008 ◽

pp. 367-380 ◽

Cited By ~ 1

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

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Combining Proximal and Penetrating Soil Electrical Conductivity Sensors for High-Resolution Digital Soil Mapping

Proximal Soil Sensing ◽

10.1007/978-90-481-8859-8_19 ◽

2010 ◽

pp. 233-243 ◽

Cited By ~ 1

Author(s):

D.B. Myers ◽

N.R. Kitchen ◽

K.A. Sudduth ◽

S. Grunwald ◽

R.J. Miles ◽

...

Keyword(s):

Electrical Conductivity ◽

High Resolution ◽

Soil Mapping ◽

Digital Soil Mapping ◽

Soil Electrical Conductivity

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

Geoderma Regional ◽

10.1016/j.geodrs.2020.e00289 ◽

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

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

Geoderma ◽

10.1016/j.geoderma.2020.114253 ◽

2020 ◽

Vol 366 ◽

pp. 114253 ◽

Cited By ~ 2

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

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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

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).

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

Environmental Monitoring and Assessment ◽

10.1007/s10661-016-5204-8 ◽

2016 ◽

Vol 188 (3) ◽

Cited By ~ 30

Author(s):

Zohreh Mosleh ◽

Mohammad Hassan Salehi ◽

Azam Jafari ◽

Isa Esfandiarpoor Borujeni ◽

Abdolmohammad Mehnatkesh

Keyword(s):

Soil Properties ◽

Soil Mapping ◽

Digital Soil Mapping

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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).

10.5194/egusphere-egu2020-20873 ◽

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

One of the biggest challenges for digital soil mapping is the limited of&#160;field&#160;soil information (e.g., soil profile descriptions, soil sample analysis) for representing soil variability across scales. Global initiatives such as the Global Soil&#160;Partnership&#160;(GSP) and the development of a Global Soil Information System&#160;(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.&#160; 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.The main objective of this research was the rescue, synthesis and harmonization of legacy&#160;soil profile information&#160;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.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, &#160;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.The new database includes historical soil information that automatically converted a generic tabular database form (e.g., .csv) information.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),&#160; and the United Nations goals&#160; towards increasing soil carbon sequestration areas or decreasing land desertification trends.&#160; 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.&#160;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).This dataset could support new knowledge for more accurate environmental modelling and to support land use management decisions at the national scale.&#160;

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Oblique geographic coordinates as covariates for digital soil mapping

10.5194/soil-2019-83 ◽

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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