Digital soil mapping and environmental modelling Research Papers

The efficiency of different digital and conventional soil mapping approaches to produce categorical maps of soil types is determined by cost, sample size, accuracy and the selected taxonomic level. The efficiency of digital and... more

The efficiency of different digital and conventional soil mapping approaches to produce categorical maps of soil types is determined by cost, sample size, accuracy and the selected taxonomic level. The efficiency of digital and conventional soil mapping approaches was examined in the semi-arid region of Borujen, central Iran. This research aimed to (i) compare two digital soil mapping approaches including Multinomial logistic regression and random forest, with the conventional soil mapping approach at four soil taxonomic levels (order, suborder, great group and subgroup levels), (ii) validate the predicted soil maps by the same validation data set to determine the best method for producing the soil maps, and (iii) select the best soil taxonomic level by different approaches at three sample sizes (100, 80, and 60 point observations), in two scenarios with and without a geomorphology map as a spatial covariate. In most predicted maps, using both digital soil mapping approaches, the best results were obtained using the combination of terrain attributes and the geomorphology map, although differences between the scenarios with and without the geomorphology map were not significant. Employing the geomorphology map increased map purity and the Kappa index, and led to a decrease in the 'noisiness' of soil maps. Multinomial logistic regression had better performance at higher taxonomic levels (order and subor-der levels); however, random forest showed better performance at lower taxonomic levels (great group and subgroup levels). Multinomial logistic regression was less sensitive than random forest to a decrease in the number of training observations. The conventional soil mapping method produced a map with larger minimum polygon size because of traditional cartographic criteria used to make the geological map 1:100,000 (on which the conventional soil mapping map was largely based). Likewise, conventional soil mapping map had also a larger average polygon size that resulted in a lower level of detail. Multinomial logistic regression at the order level (map purity of 0.80), random forest at the suborder (map purity of 0.72) and great group level (map purity of 0.60), and conventional soil mapping at the subgroup level (map purity of 0.48) produced the most accurate maps in the study area. The multinomial logistic regression method was identified as the most effective approach based on a combined index of map purity, map information content, and map production cost. The combined index also showed that smaller sample size led to a preference for the order level, while a larger sample size led to a preference for the great group level.

Traditional manual methods for determining properties geomorphometrics in topographic maps, consist of measurements tend to be complex and more time consuming, so that elements classified landform require highly trained personnel.... more

Rainfall erosivity considers the effects of rainfall amount and intensity on soil detachment. Rainfall erosivity is most commonly expressed as the R-factor in the Universal Soil Loss Equation (USLE) and its revised version, RUSLE. Several... more

This article presents the steps taken to ensure the consistency and usability of the geographic information system of soil resources of Romania SIGSTAR-200 within INSPIRE, the Infrastructure for Spatial Information of the European... more

This article presents the steps taken to ensure the consistency and usability of the geographic information system of soil resources of Romania SIGSTAR-200 within INSPIRE, the Infrastructure for Spatial Information of the European Community. At present, SIGSTAR-200 contains tens of thousands of polygons characterized by three attributes collected from legacy paper maps, namely (i) soil mapping unit (SMU), including soil association, (ii) topsoil texture class and (iii) skeleton class, all defined as stated by the national methodologies. In addition, each soil is characterized concerning the risk of land degradation (by water erosion, wind erosion, salinization, alkalization, gleyzation and waterlogging) through attributes inferred by expert system rules built on pedogenesis. To achieve the compatibility and usability within INSPIRE, SIGSTAR-200 has been transformed in accordance with the common Implementing Rules in force. To this end, first, the SMUs have been correlated at the dominant soil type level with the international soil classification system World Reference Base of Soil Resources (WRB) to ensure the semantic interoperability. Second, the SMUs, modeled as INSPIRE feature type SoilBody, have been populated with new attributes. This step was performed for SIGSTAR-200 dataset in three coordinate reference systems (CRSs): EPSG:3844 (CRS of Romania), EPSG:3035 and EPSG: 4258 (the last two CRSs being required or recommended by INSPIRE). Finally, the data transformed in INSPIRE-compliant GML have been checked by the INSPIRE Validator (Executable Test Framework), passing all the tests currently available for soil datasets, i.e., regarding (i) data consistency, (ii) INSPIRE GML application schemas, (iii) information accessibility, and (iv) reference systems. The next steps of the work, synchronized with the availability of the Executable Test Suites for the themes defined in Annex III of the Directive, aim to fully validate SIGSTAR-200 GML and to conclude its first integration into INSPIRE (Accession Number: WOS: 000526176700012).

The status and spatial variability of soil properties across agrarian communities in Yakurr Local Government Area, Southeast Nigeria were assessed and soil management strategies suggested for limiting soil properties. Nine communities... more

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- by Kingsley John and +2
  Victoria Ediene
  John Kingsley
- •
- 8
  Soil Science, Environmental Soil Science, Soil Survey and Land Evaluation, Soil Fertility

Actualmente se desarrollan técnicas de cartografía digital de suelos innovadoras y eficientes, basadas en el modelo suelo-paisaje y en métodos estadísticos y geoestadísticos. El objetivo del estudio fue explorar la distribución espacial... more

This study was conducted to characterize the soils of alluvial parent material in derived savannah ecology in Edo State, Southern Nigeria. Three mapping units were identified and a profile pit was dug in each mapping unit. The soils were... more

It is critical to produce more crop per drop in an environment where water availability is decreasing and competition for water is increasing. In order to build such agricultural production systems, well parameterized crop growth models... more

It is critical to produce more crop per drop in an environment where water availability is decreasing and competition for water is increasing. In order to build such agricultural production systems, well parameterized crop growth models are essential. While in most crop growth modeling research, focus is on gathering model inputs such as climate data, less emphasis is paid to collecting the critical soil hydraulic properties (SHPs) data needed to operate crop growth models. Collection of SHPs data for the Zambezi River Basin (ZRB) is extremely labor-intensive and expensive, thus alternate technologies such as digital soil mapping (DSM) must be explored. We evaluated ﬁve types of DSM models to establish the best spatially explicit estimates of the soil water content at pF0.0 (saturation), pF2.0 (ﬁeld capacity), and pF4.2 (wilting point), and of the saturated hydraulic conductivity (Ksat) across the ZRB by using estimates of locally calibrated pedotransfer functions of 1481 locations for training and testing the DSM models, as well as a reference dataset of measurements from 174 locations for validating the DSM models. We produced coverages of environmental covariates from various source datasets, including climate variables, soil and land use maps, parent materials and lithologic units, derivatives of a digital elevation model (DEM), and Landsat imagery with a spatial resolution of 90 m. The ﬁve types of models included multiple linear regression and four machine learning techniques: artiﬁcial neural network, gradient boosted regression trees, random forest, and support vector machine. Where the residuals of the initial DSM models were spatially autocorrelated, the models were extended/complemented with residual kriging (RK). Spatial autocorrelation in the model residuals was observed for all ﬁve models of each of the three water contents, but not for Ksat. On average for the water content, the R 2 ranged from 0.40 to 0.80 in training and test datasets before adding kriged model residuals and ranged from 0.80 to 0.95 after adding model residuals. Overall, the best prediction method consisted of random forest as the deterministic model, complemented with RK, whereby soil texture followed by climate and topographic elevation variables were the most important covariates. The resulting maps are a ready-to-use resource for hydrologists and crop modelers to aliment and calibrate their hydrological and crop growth models.

Soil organic carbon plays a vital role in the arid and semiarid regions. This study aimed to predict and map soil organic carbon content at soil depth intervals of 0-0.3 m, 0.3-0.6 m, 0.6-0.9 m, and 0.9-1.2 m in alluvium soils along Blue... more

O uso cada vez mais intensivo do solo e sua ocupação desordenada resultam em sua degradação. Existe uma grande carência de informações mais detalhadas de solos e seus levantamentos. Por apresentarem informações básicas sobre esse recurso... more

Digital soil mapping (DSM) approaches provide soil information by utilising the relationship between soil properties and environmental variables. Calibration of DSM models requires measurements that may often have substantial measurement... more

O aumento populacional e das práticas agrícolas, cada vez mais intensivas, faz crescer a necessidade de levantamentos de solos mais detalhados no Brasil e no mundo, visando o melhor planejamento e monitoramento da exploração dos recursos... more

O aumento populacional e das práticas agrícolas, cada vez mais intensivas, faz crescer a necessidade de levantamentos de solos mais detalhados no Brasil e no mundo, visando o melhor planejamento e monitoramento da exploração dos recursos naturais, a fim de se reduzir os impactos ambientais produzidos pelas diversas atividades desenvolvidas. No Brasil, entre os anos 1970 e 1980, os levantamentos de solos tiveram maior ênfase, porém, dada às limitações metodológicas da época, foram elaborados em escalas de pouco detalhamento. Deste então, novas técnicas vêm sendo desenvolvidas para potencializar as atividades de mapeamentos pedológicos convencionais. Metodologias envolvendo sensoriamento remoto e técnicas quantitativas para predição da distribuição espacial das classes pedológicas, bem como de seus atributos, têm colocado o mapeamento digital de solos em posição de destaque na realização desta atividade. A principal fonte de informações pedológicas do Distrito Federal (DF) foi elaborada em escala de 1:100.000, pela Embrapa em 1978. Diante das dificuldades apresentadas por material cartográfico realizados em escalas menores, o qual a escassez de detalhes provoca generalizações indevidas sobre a utilização dos solos da região, levantamentos de solos com maior riqueza de informações são imprescindíveis. Este panorama reflete o objetivo geral deste trabalho, que foi mapear classes de solos da microbacia do Ribeirão Extrema (MBRE), no DF, por meio de técnicas de sensoriamento remoto, por meio de extrapolação de biblioteca espectral de solos (BES) disponível em uma microbacia contígua à proposta para esse estudo. Atividades de campo permitiram a avaliação da distribuição dos solos, por meio de relações pedomorfogeológicas, na microbacia detentora da biblioteca espectral de solos e da microbacia deste estudo. Em função da semelhança das feições geomorfológicas e ocorrência de unidades geológicas, a distribuição de solos da MBRE foi considerada muito similar à da microbacia com BES. Análises morfológicas, físicas, químicas e espectroscópicas foram realizadas para as classes de solos de ocorrência em ambas as microbacias e somadas às demais classes de ocorrência restrita na MBRE. As curvas espectrais foram associadas gerando a BES
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da MBRE, DF. Para o mapeamento digital dos solos foi utilizada uma imagem composta elaborada a partir das imagens do sensor ASTER de solos expostos de uma série multitemporal entre anos 2001, 2004 e 2006. Mediante a utilização do algoritmo Multiple Endmember Spectral Mixture Analysis – MESMA, a Biblioteca Espectral gerada, juntamente com a imagem composta dos solos expostos da área estudada, foi submetida à processamentos digitais com utilização de técnicas de análises espectrais, gerando-se o mapa digital de solos da microbacia do Ribeirão Extrema, DF, o qual foi avaliado pelo índice Kappa como de boa qualidade.

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- by Jean J Novais and +1
  jean novais
- •
- 7
  Soil Science, Pedology (Soil), Thematic Cartography, Geopedology and Digital Soil Mapping

The Geographic Information System of Soil Resources of Romania „SIGSTAR-200” has been developed based on the information contained in the fifty tiles constituting “The Soil Map of Romania at the scale 1:200,000”, published from 1963 to... more

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- by Ruxandra VINTILA
- •
- 25
  Soil Science, Remote Sensing, Soil, Sustainable agriculture

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY

The reported work is part of a study that aims at exploiting validated data sets to predict soil properties of the top layer on large areas and to generate maps of international relevance at medium scales, in a more cost-effective way.... more

Digital soil mapping (DSM) approaches provide soil information by utilising the relationship between soil properties and environmental variables. Calibration of DSM models requires measurements that may often have substantial measurement... more

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Soil is the largest carbon (C) reservoir in terrestrial ecosystems with twice the amount of atmospheric C and three times the amount in terrestrial vegetation. Carbon related ecosystem services include retention of water and nutrients,... more

It is critical to produce more crop per drop in an environment where water availability is decreasing and competition for water is increasing. In order to build such agricultural production systems, well parameterized crop growth models... more

Background Legacy data are unique occasions for estimating soil organic carbon (SOC) concentration changes and spatial variability, but their use showed limitations due to the sampling schemes adopted and improvements may be needed in the... more

The Global Soil Map (GSM) is an initiative of the International Union of Soil Sciences intending to use novel technologies to predict soil properties. The final goal is to produce, by collaboration, a harmonized global soil map with a... more

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The reported work is part of a study that aims at exploiting validated data sets to predict soil properties of the top layer on large areas and to generate maps of international relevance at medium scales, in a more cost-effective way.... more

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The Global Soil Map (GSM) is an initiative of the International Union of Soil Sciences intending to use novel technologies to predict soil properties. The final goal is to produce, by collaboration, a harmonized global soil map with a... more

The Global Soil Map (GSM) is an initiative of the International Union of Soil Sciences intending to use novel technologies to predict soil properties. The final goal is to produce, by collaboration, a harmonized global soil map with a grid cell of 100m x 100m. Romanian authorities are interested in this initiative to improve decision-making concerning climate change, environmental degradation, and food production. This paper shows results obtained in an ongoing study to produce an up-to-date map of the topsoil organic carbon (OC) content of Romania compliant with the GSM Initiative. These results are mainly based on the LUCAS topsoil geo-referenced samples from the EUROSTAT 2012 survey collected across Romania. The samples were analyzed in a certified laboratory to measure the topsoil OC content and other key properties. The LUCAS dataset was chosen as a reference baseline because the methodologies of soil sampling and analyzing are harmonized at European level. To extrapolate the LUCAS topsoil data spatially, firstly some key environmental covariates were mapped for Romania, namely the elevation and derivatives (from the SRTM 3 arc-seconds dataset), land cover (from the CORINE2006 dataset at 100 m resolution), vegetation (from MERIS satellite images at full resolution), and climate (from the CRU TS3.22 dataset). Secondly, the water bodies and the GSM grid cells with more than 50% soil sealing were masked. Finally, in each unmasked GSM grid cell containing a topsoil survey point, the following environmental covariates were estimated: elevation, slope, aspect, monthly temperature and precipitation, and the vegetation state in two contrasting seasons, expressed in reflectance values and vegetation indexes. Some issues occurred because of incoherencies among these datasets coming from several sources. It also emerged that the radiometric richness of the MERIS superspectral data may have been overestimated as respects the correlation to the topsoil organic carbon. Other covariates – unavailable until now - should be added to the models for spatial prediction, such as the parent material. The study will continue to develop these models for each major land cover type and finally to produce a GSM-compliant map of the topsoil organic carbon of Romania.

A presentation is uploaded with the same title in the Conference Section.

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- by Ruxandra VINTILA and +1
  Alex Visan
- •
- 29
  Soil Science, Environmental Studies, Geoinformatics, Remote Sensing (Earth Sciences)

Digital soil mapping and environmental modelling

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