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

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

10.5194/egusphere-egu2020-18480 ◽

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

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 km2 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.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.

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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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Detailed soil mapping for large areas in Berne – putting well researched knowledge into practice

10.5194/egusphere-egu2020-7958 ◽

2020 ◽

Author(s):

Madlene Nussbaum ◽

Stéphane Burgos

Keyword(s):

Machine Learning ◽

Soil Properties ◽

Natural Hazard ◽

Soil Mapping ◽

Digital Soil Mapping ◽

Climate Mitigation ◽

Field Observations ◽

Soil Maps ◽

Hazard Management ◽

Swiss Canton

Spatial information on soil is crucial for many applications such as spatial planning, erosion reduction, climate mitigation and forest or natural hazard management. Many countries (e. g. Switzerland, France, Germany, Albania) still use conventional soil mapping approaches which are often very time consuming and costly. Methods to gain soil maps with geostatistics and supported with other digital technologies have reached a high level of maturity some time ago. Each single method has been well studied and transfer to practice took place in some countries. Nevertheless, we are not aware of a large soil mapping endeavor that sampled a considerable amount of new soil data by a practical and geostatistically sound sampling design and by integrating digital field tools, centralized soil data management, soil spectroscopy, digital soil mapping and subsequent soil function assessment all followed by quality assurance measures.&#160;In Switzerland, political pressure has recently risen to improve the basis for soil related decision making. The administration of the Swiss Canton of Berne aims to map agricultural and forest soils of the lowlands (210000 hectares) with high resolution to allow for decisions relevant to landownership. In the mountainous areas (240000 hectares) at least maps with medium detail are necessary, especially for natural hazard management. Currently, the project is in the phase of efficiency testing of each methodological element and establishing of interfaces between them. We present a concept that combines available state-of-the-art technologies and should allow to create the required detailed soil maps within the next 15 years. Only few legacy soil data are available, hence we planned for 5200 newly sampled profile pits and about 360000 auger holes. This large sampling effort is hierarchically structured with field observations based on classical pedological descriptions supported with laboratory and field spectroscopy. Iterative sampling is driven by the uncertainty of the maps up to the point where the required accuracy is reached. Intermediate and final soil maps are created with machine learning based digital soil mapping techniques. From the finally mapped soil properties soil functions and application products are derived by digital soil assessment approaches driven by the needs of the end users.&#160;Within this phase of the project we exploited the legacy soil maps available for the surroundings of some villages. As soil augerings were not recorded during map production, we generated &#8220;virtual soil samples&#8221; from the maps and used a machine learning based model averaging approach to predict soil properties for the nearby areas. Class width and multiple assignments of legend units per soil map polygon were considered by a non-parametric bootstrap approach to create predictive distributions and map the uncertainty. To avoid extrapolation into areas with different soil forming factors we have carefully chosen the target area for prediction based on a similarity analysis. The predictions have been successfully validated with legacy soil profiles and new field observations.

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Evaluating digital soil mapping approaches to predict topsoil exchangeable calcium and magnesium in a sugarcane field of Australia

10.5194/egusphere-egu2020-292 ◽

2020 ◽

Author(s):

Maryem Arshad

Keyword(s):

Sample Size ◽

Statistical Models ◽

Soil Mapping ◽

Digital Soil Mapping ◽

Soil Maps ◽

Environmental Sciences ◽

Exchangeable Calcium ◽

Sugarcane Field ◽

Calcium And Magnesium ◽

Digital Soil Maps

Evaluating digital soil mapping approaches to predict topsoil exchangeable calcium and magnesium in a sugarcane field of AustraliaMaryem Arshad1, Dongxue Zhao1, Tibet Khongnawang1 and John Triantafilis1*1School of Biological, Earth and Environmental Sciences, Faculty of Science, UNSW Sydney, Kensington, NSW, 2052, AustraliaCorresponding John Triantafilis, School of Biological, Earth and Environmental Sciences, Faculty of Science, UNSW Sydney, Kensington, NSW, 2052, AustraliaEmail: [email protected]AbstractKnowledge about spatial distribution of exchangeable (exch.) calcium (Ca) and magnesium (Mg) is needed to maintain sugarcane biomass in north Queensland, Australia. To create digital soil maps (DSM), herein, we evaluated three approaches, including; geostatistical (i.e. ordinary kriging [OK]), statistical and hybrid. We first determined the number of samples (10 &#8211; 120) required to compute variogram by calculating nugget to sill ratio (NSR) and sum of squared error (SSE). We then used this variogram with OK to predict topsoil (0 &#8211; 0.3 m) exch. Ca and Mg. For comparison, four statistical models, including; one linear regression (LR) and three machine learning (ML) models (i.e. Cubist, support vector machine [SVM] and random forest [RF]) were used. Doing so, usefulness of two digital data, including; gamma-ray (g-ray) and soil apparent electrical conductivity (ECa), either individual or combined, was tested. Regression residuals (RR) were then added to find out improvement in prediction performance (i.e. Lin&#8217;s) and in hybrid approach. Influence of varying sample size (10 &#8211; 120) was also determined on all three DSM approaches. Comparisons were then drawn with a traditional soil type map and by calculating the mean square prediction error (MSPE). Finally, Digital soil maps (DSM) of exch. Ca and Mg were developed. Results showed that 50 samples were enough to compute a good variogram for exch. Ca (NSR = 11%, SSE = 0.39) and Mg (NSR = 33%, SSE = 0.005). Considering OK, exch. Ca and Mg were predicted with moderate agreement (Lin&#8217;s = 0.65 &#8211; 0.80). Comparing statistical models and to predict exch. Ca, RF (0.64) and SVM (0.63) outperformed Cubist and LR (0.60) while to predict exch. Mg, SVM (0.79), RF and Cubist (0.74) outperformed LR (0.62). Combined and individual g-ray data performed best and equally well. Hybrid models i.e. RK and CubistRR improved prediction of exch. Ca (0.76) and Mg (0.81) using individual g-ray and ECa data, respectively. Considering sample size, OK and statistical models required 80 samples while hybrid models required only 30 samples to satisfactorily (Lin&#8217;s &#8805; 0.70) predict exch. Ca and Mg. Comparisons based on MSPE showed that to predict exch. Ca, hybrid (RK = 1.44) was the best approach followed by geostatistical (OK = 1.94), statistical (Cubist = 2.15) and then traditional soil map (2.64). Same was the case for exch. Mg. DSM of predicted exch. Ca and Mg were consistent with contour plots of measured data. However, some poor predictions were apparent across field edges or areas where small scale variation in digital or soil data was prevalent. &#160;

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