From Profile Morphometrics to Digital Soil Mapping

Demattê, José Alexandre Melo

doi:10.1007/978-3-319-28295-4_24

Cited by 3 publications

(2 citation statements)

References 31 publications

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“…These characteristics of the soil surface, revealed through the soil surface mapping technique described in this study, were not considered in any previous study relating greenhouse gas fluxes and emissions to soil physical properties (e.g., porosity, bulk density), as far as we know. There are even more potential applications of the developed procedure beyond studying the behavior of earthworms and exchange of gases at the soil surface, for example, estimating surface roughness for features such as a biological soil crust, with implications for eolian additions to desert soils (Williams et al, 2012) and defining pore vs. exterior space in CT scans in studies of soil particles with compound structure (Demattê, 2016).…”

Section: Resultsmentioning

confidence: 99%

A High‐Resolution Surface Mapping Procedure for Soil Cores Using Computed Tomography Scanning Data

Han

Whalen

Dutilleul

2017

Vadose Zone Journal

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High-resolution mapping of the soil surface provides insight regarding gas and water transport at the soil-atmosphere interface and biogeochemical reactions occurring in soil pores connected to the atmosphere. A new procedure for high-resolution soil mapping was tested in undisturbed cores (15-cm diameter) with images constructed from X-ray computed tomography (CT) scanning. Our procedure includes detecting boundaries at the soil-air interface, edge cleaning, and removal of artifacts by use of a moving window with optimized size. We tested the procedure on three undisturbed soil cores from an agricultural field: (i) without introduced earthworms vs. in which earthworms of (ii) smaller (Aporrectodea turgida Eisen) or (iii) larger (Lumbricus terrestris L.) size were introduced and resided for 28 d. Soil surface maps were produced at a scale of 0.35-by 0.35-mm pixels, with gray tones reflecting lower to higher elevation. This work has implications for describing pore connectivity and interpreting greenhouse gas fluxes from the soil. Citation: Han, L., J.K. Whalen, and P. Dutilleul. 2017. A high-resolution surface mapping procedure for soil cores using computed tomography scanning data. Vadose Zone J. 16(6).

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Section: Resultsmentioning

confidence: 99%

A High‐Resolution Surface Mapping Procedure for Soil Cores Using Computed Tomography Scanning Data

Han

Whalen

Dutilleul

2017

Vadose Zone Journal

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“…The Digital Soil Mapping (DSM) is an easy and feasible approach to improve the understanding of soil attributes and properties. The DSM has been applied to predict soil classes (Silva et al, 2016;Triantafilis et al, 2009;, morphology (Demattê, 2016;, parent materials , and attributes, such as clay (Loiseau et al, 2019), SOM , pH , among others , contributing to the improvement of management practices Tajik et al, 2020). The DSM basis was formalised in the scorpan model by and it takes into account the model of soil formation established by .…”

Section: Introductionmentioning

confidence: 99%

Geotechnologies applied in digital soil mapping

Mendes¹

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Geotechnologies applied in digital soil mappingThe civilisation lives in a world of maps and soil maps are vital at regional and farm levels to achieve best management agricultural practices. Soil is the substrate for plant growth and vital to the fulfilment of the food demand. However, the cartographic scale of those soil maps, which for the best management agricultural practice (BMAP) have to be the most detailed as possible and they are scarce. The Digital Soil Mapping (DSM) became the easiest and feasible approach to achieve such demand. Despite previous studies have tried to better characterise soil depths, there is space for improvements on its dynamics and mapping. Looking at this goal, Remote Sensing (RS) technologies have proven to be a great power on this task. Nevertheless, some aspects of that approach still need to be tested using another hybrid, stochastic, and deterministic models for the predictions of magnetic susceptibility (MS) and soil attributes at surface and subsurface. Therefore, chapter 1 presents the evaluation of nine machine learning algorithms (MLAs) to predict the free iron content at the soil surface (e.g. 0 -20 cm) using the DSM framework. Based on the best performance of those nine MLAs, we selected five MLAs. Chapter 2 shows the use of those five MLAs with usual and new environmental variables (e.g. DEM, drainage network, and soil spectroscopy) to predict the MS and soil attributes up to 100 cm depth. Attempts on quantifying soil mineral consist of having an observation measured using traditional laboratory soil analysis. However, developments in interpreting and analysing the visible and near-infrared (VNIR) diffuse reflectance have allowed quantifying some soil minerals. In chapter 3, it implements a novel framework using VNIR spectroscopy to quantify the main soil minerals and evaluates the application of digital soil mapping framework to spatialise those soil minerals. Last but not least, the chapter 4 presents the novelty of using all predict soil components as predictors of the soil mapping units in the region of Piracicaba-SP at farm scale (1:20,000), generating the first detailed digital soil map of the region. Additionally in this chapter, it was created the digital yield environmental map for sugarcane production. Thus, this thesis presents a new integrative framework to achieve detail soil maps for the BMAP and serves as a guide for future soil surveys across the world.

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