Fusion of Soil and Remote Sensing Data to Model Soil Properties

Grunwald, Sabine; Vasques, Gustavo M.; Rivero, Rosanna G.

doi:10.1016/bs.agron.2014.12.004

Cited by 85 publications

(47 citation statements)

References 263 publications

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“…Especially the reflectance spectral information of minerals in soils plays an important role in predicting toxic metals because the spectral assignments' position of minerals can change with their chemical composition and surface activity [27,[29][30][31][32]. This indirect mechanism allows the relationships between spectral information and soil toxic metals to be deduced since the information of Fe-oxides, clays and organic matter in soils or vegetation cover can be extracted from the RS spectra [33][34][35]. Therefore, RS technology is able to map the spatial distribution of toxic metals in soils.…”

Section: Introductionmentioning

confidence: 99%

Digital Mapping of Toxic Metals in Qatari Soils Using Remote Sensing and Ancillary Data

Kheir

Adhikari

et al. 2016

Remote Sensing

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After decades of mining and industrialization in Qatar, it is important to estimate their impact on soil pollution with toxic metals. The study utilized 300 topsoil (0-30 cm) samples, multi-spectral images (Landsat 8), spectral indices and environmental variables to model and map the spatial distribution of arsenic (As), chromium (Cr), nickel (Ni), copper (Cu), lead (Pb) and zinc (Zn) in Qatari soils. The prediction model used condition-based rules generated in the Cubist tool. In terms of R 2 and the ratio of performance to interquartile distance (RPIQ), the models showed good predictive capabilities for all elements. Of all of the prediction results, Cu had the highest R 2 = 0.74, followed by As > Pb > Cr > Zn > Ni. This study found that all of the models only chose images from January and February as predictors, which indicates that images from these two months are important for soil toxic metals' monitoring in arid soils, due to the climate and the vegetation cover during this season. Topsoil maps of the six toxic metals were generated. The maps can be used to prioritize the choice of remediation measures and can be applied to other arid areas of similar environmental/socio-economic conditions and pollution causes.

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Section: Introductionmentioning

confidence: 99%

Digital Mapping of Toxic Metals in Qatari Soils Using Remote Sensing and Ancillary Data

Kheir

Adhikari

et al. 2016

Remote Sensing

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“…One of its variants -regression kriging (RK) -has received special attention as a means to incorporate the variation of soil-forming environmental factors (so-called environmental covariates) into the variation of the target soil property (Knotters et al, 1995;Kravchenko & Robertson, 2007;Vasques et al, 2010). Regression kriging integrates remote, field, and laboratory data, and statistical methods in a quantitative estimation framework for soil property or class mappingdigital soil mapping -, with applications ranging from precision agriculture (Grunwald et al, 2015) to global mapping (Hengl et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Mapping soil carbon, particle-size fractions, and water retention in tropical dry forest in Brazil

Vasques

Coelho

Dart

et al. 2016

Pesq. agropec. bras.

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-The objective of this work was to compare ordinary kriging with regression kriging to map soil properties at different depths in a tropical dry forest area in Brazil. The 11 soil properties evaluated were: organic carbon content and stock; bulk density; clay, sand, and silt contents; cation exchange capacity; pH; water retention at field capacity and at permanent wilting point; and available water. Samples were taken from 327 sites at 0.0-0.10, 0.10-0.20, and 0.20-0.40-m depths, in a tropical dry forest area of 102 km 2 .Stepwise linear regression models for particle-size fractions and water retention properties had the best fit. Relief and parent material covariates were selected in 31 of the 33 models (11 properties at three depths) and vegetation covariates in 29 models. Based on external validation, ordinary kriging obtained higher accuracy for 21 out of 33 property x depth combinations, indicating that the inclusion of a linear trend model before kriging does not necessarily improve predictions. Therefore, for similar studies, the geostatistical methods employed should be compared on a case-by-case basis.Index terms: caatinga, digital soil mapping, gamma radiometric survey, geostatistics, pedometrics. Mapeamento de carbono, frações granulométricas e água do solo em Floresta Tropical Seca no BrasilResumo -O objetivo deste trabalho foi comparar krigagem ordinária com regressão-krigagem para mapear atributos do solo, em diferentes profundidades, em área de Floresta Tropical Seca no Brasil. Os 11 atributos do solo avaliados foram: conteúdo e estoque de carbono orgânico; densidade do solo; conteúdos de argila, areia e silte; capacidade de troca catiônica; pH; retenção de água na capacidade de campo e no ponto de murcha permanente; e água disponível. As amostras foram retiradas de 327 locais a 0,0-0,10, 0,10-0,20 e 0,20-0,40 m de profundidade, em área de Floresta Tropical Seca de 102 km 2 . Os modelos de regressão linear "stepwise" tiveram o melhor ajuste para as frações granulométricas e as propriedades de retenção de água. Foram selecionadas covariáveis de relevo e material de origem em 31 dos 33 modelos (11 atributos em três profundidades) e de vegetação em 29 modelos. Com base na validação externa, a krigagem ordinária obteve maior acurácia para 21 das 33 combinações atributo vs. profundidade, o que é indicativo de que a inclusão de um modelo linear de tendência antes da krigagem não necessariamente melhora as predições. Portanto, para estudos semelhantes, os métodos geoestatísticos empregados devem ser comparados caso a caso.Termos para indexação: caatinga, mapeamento digital de solos, levantamento gamarradiométrico, geoestatística, pedometria.

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“…Many soil scientists have attempted the development of index systems to evaluate soil quality that meets the criteria mentioned above, but this has been difficult due to the heterogeneous characteristics of soil in space and time (Ross et al, 2013). Some proxy techniques and their fusion techniques, including geospatial modeling/simulations and soil spectroscopy modeling, have been developed and applied to predict soil attributes (Grunwald et al, 2015). However, each technique has limitations that can be costly and laborious over time (Shepherd and Walsh, 2002), or the focus can be too narrow.…”

Section: Criteria For Indicator/index Developmentmentioning

confidence: 99%

“…Importantly, SCORPAN and STEP-AWBH models have the potential to infer on soilrelated In/Ix. Grunwald et al (2015) discussed the benefits and constraints of sophisticated/complex and simple/parsimonious quantification techniques, the latter allowing operationalizing soil/land management and protecting these resources.…”

Section: Constraints In Current Indicator/index Quantification Researmentioning

confidence: 99%

“…Some scientists also posited that multiple perspectives integrated into a coherent framework need to be considered to address soil security. Using questions recognizes stakeholders' multiple standpoints, which prevents overlooking conspicuous and inconspicuous roles of soil resources (Grunwald, 2014;Grunwald et al, 2015). Bouma (2005) adopted a communication model adapted from Habermas (1984), which was focused on soil science communication to provide different views on the "real soil" from the perspective of different groups: (i) soil scientists among themselves, (ii) soil scientists and their colleagues, and (iii) citizens at large.…”

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confidence: 99%

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New Soil Index Development and Integration with Econometric Theory

Mizuta

Grunwald

Phillips

2018

Soil Science Soc of Amer J

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Soil scientists have worked on the conceptualization and contextualization of soil-related notions, such as soil quality, soil health, and soil security, over the past few decades. We reviewed the massive amount of literature regarding those major concepts, and summarized definitions, visions, and constraints. Indicators (In) and indices (Ix) are well suited to aggregate soil and environmental data to assess soil quality, health, and security quantitatively. Our literature review showed that (i) more sophisticated quantification methods are necessary; (ii) often only a single soil property and/or class is modeled rather than more complex soil functions, risks, or services; (iii) there is a lack of harmonization, standardization, and reference frameworks that allow soil comparisons across regions and time; and (iv) methods frequently used to calculate soil In/Ix, such as ordination and factor analysis, do not consider rigorous axiomatic criteria of scientific sound indication systems. In summary, the complex soil concepts stand in sharp contrast to the applied indication methods in the soil science discipline. We investigated the potential to apply econometrical methods to assess soil quality, health, and security that serve as alternatives to more traditional In/Ix in soil science. A case study demonstrated the profound transformative potential of linking econometricssoil-environmental sciences.

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Fusion of Soil and Remote Sensing Data to Model Soil Properties

Cited by 85 publications

References 263 publications

Digital Mapping of Toxic Metals in Qatari Soils Using Remote Sensing and Ancillary Data

Digital Mapping of Toxic Metals in Qatari Soils Using Remote Sensing and Ancillary Data

Mapping soil carbon, particle-size fractions, and water retention in tropical dry forest in Brazil

New Soil Index Development and Integration with Econometric Theory

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