A multiple soil properties oriented representative sampling strategy for digital soil mapping

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

South African Journal of Plant and Soil ◽

10.1080/02571862.2019.1570566 ◽

2019 ◽

Vol 36 (4) ◽

pp. 237-247

Author(s):

Trevan Flynn ◽

Willem de Clercq ◽

Andrei Rozanov ◽

Cathy Clarke

Keyword(s):

High Resolution ◽

Soil Properties ◽

Field Survey ◽

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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On the role of parent material for predictive mapping of soil properties in mountain forests

10.5194/egusphere-egu21-9374 ◽

2021 ◽

Author(s):

Alois Simon ◽

Marcus Wilhelmy ◽

Ralf Klosterhuber ◽

Clemens Geitner ◽

Klaus Katzensteiner

Keyword(s):

Soil Properties ◽

Storage Capacity ◽

Water Storage ◽

Parent Material ◽

Soil Mapping ◽

Digital Soil Mapping ◽

Water Storage Capacity ◽

Plant Available Water ◽

Geological Substrates ◽

Geological Substrate

Parent material is widely recognised as an important factor for soil formation. Thus, quantitative information on the lithogenetic, geochemical, and physical characteristics of the subsolum geological substrates (SSGS) are essential input parameters for digital soil mapping (DSM). Forming the interface between bedrock &#8211; the domain of geologists, and soil &#8211; the domain of soil scientists, spatial information on SSGS is however scarce. Recognising these shortcomings, a novel geochemical-physical classification system for subsolum geological substrates has been developed, in order to support DSM at a regional scale. The units of the classification system reflect the properties of the SSGS also considering multilayering structure of quaternary deposits. The basis for the classification are mineral component groups, namely dolomite, calcite, and felsic, mafic, and clay minerals. In order to test the relevance of SSGS for the prediction of spatially continuous physical and chemical soil properties, Generalized Additive Models (GAMs) were applied to the forested area of Tyrol, Austria. The plant-available water storage capacity, as a physical soil property, was predicted with r&#178; = 0.56. The Ellenberg&#180;s mean soil reaction indicator value for vegetation turned out to be a suitable proxy for soil pH value and was predicted with r&#178; = 0.75. Topography and associated morphometric terrain features are formative characteristics of mountain areas and, due to its various effects on redistribution processes as well as on water and energy budget of forest sites, are considered as the most essential soil forming factors. Thus, variables derived from digital terrain models, which are available in high spatial resolution, are assumed to be one of the most important predictors for digital soil mapping. In our study we could show however, that SSGS information is the most relevant predictor for both investigated soil properties. In the plant-available water storage capacity model, the predictor variables related to SSGS account for around 76% of the variance explained. Accordingly, a special focus should be placed on the predictive relevance of parent material and the frequently unlocked potential of quantitative geological substrate information. Thus, the newly developed subsolum geological substrate information could stimulate further developments in digital soil mapping, especially in mountain environments.

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Evaluation of statistical and geostatistical models of digital soil properties mapping in tropical mountain regions

Revista Brasileira de Ciência do Solo ◽

10.1590/s0100-06832014000300003 ◽

2014 ◽

Vol 38 (3) ◽

pp. 706-717 ◽

Cited By ~ 5

Author(s):

Waldir de Carvalho Junior ◽

Cesar da Silva Chagas ◽

Philippe Lagacherie ◽

Braz Calderano Filho ◽

Silvio Barge Bhering

Keyword(s):

Soil Properties ◽

Predictive Models ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Soil Mapping ◽

Digital Soil Mapping ◽

Coefficient Of Determination ◽

Band Ratio ◽

Data Set ◽

Environmental Covariates

Soil properties have an enormous impact on economic and environmental aspects of agricultural production. Quantitative relationships between soil properties and the factors that influence their variability are the basis of digital soil mapping. The predictive models of soil properties evaluated in this work are statistical (multiple linear regression-MLR) and geostatistical (ordinary kriging and co-kriging). The study was conducted in the municipality of Bom Jardim, RJ, using a soil database with 208 sampling points. Predictive models were evaluated for sand, silt and clay fractions, pH in water and organic carbon at six depths according to the specifications of the consortium of digital soil mapping at the global level (GlobalSoilMap). Continuous covariates and categorical predictors were used and their contributions to the model assessed. Only the environmental covariates elevation, aspect, stream power index (SPI), soil wetness index (SWI), normalized difference vegetation index (NDVI), and b3/b2 band ratio were significantly correlated with soil properties. The predictive models had a mean coefficient of determination of 0.21. Best results were obtained with the geostatistical predictive models, where the highest coefficient of determination 0.43 was associated with sand properties between 60 to 100 cm deep. The use of a sparse data set of soil properties for digital mapping can explain only part of the spatial variation of these properties. The results may be related to the sampling density and the quantity and quality of the environmental covariates and predictive models used.

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