Updating the Australian digital soil texture mapping (Part 2

Malone, Brendan; Searle, Ross

doi:10.1071/sr20284

Cited by 19 publications

(3 citation statements)

References 42 publications

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“…Current approaches in pedometric modelling to generate nationwide soil information predominantly address the prediction of individual soil properties. Žížala et al and Gebauer et al (24,25) provide recent 2D applications, Malone and Searle and Reddy et al (26,27) 2.5D applications, and Padarian et al and Ma et al (28,29) 3D applications. However, the separate modelling of individual soil properties and their respective joint consideration as input to agricultural process models may result in constructed soil profile information which does not occur in reality and which may be unrealistic according to the underlying pedogenetic processes and dependencies between the properties.…”

Section: Introductionmentioning

confidence: 99%

Modelling the Agricultural Soil Landscape of Germany – A Data Science Approach Involving Spatially Allocated Functional Soil Process Units

Ließ¹

2022

Preprint

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The national-scale evaluation and modelling of the impact of agricultural management and cli-mate change on soils, crop growth, and the environment require soil information at a spatial res-olution addressing individual agricultural fields. This manuscript presents a data science ap-proach which agglomerates the soil parameter space into a limited number of functional soil pro-cess units (SPUs) which may be used to run agricultural process models. In fact, two unsupervised classification methods were developed to generate a multivariate 3D data product consisting of SPUs, each being defined by a multivariate parameter distribution along the depth profile from 0 to 100 cm. The two methods account for differences in variable types and distributions and in-volve genetic algorithm optimization to identify those SPUs with the lowest internal variability and maximum inter-unit difference with regards to both, their soil characteristics and landscape setting. The high potential of the methods was demonstrated by applying them to the agricultural German soil landscape. The resulting data product consists of twenty SPUs. It has a 100 m raster resolution in the 2D mapping space, and its resolution along the depth profile is 1 cm. It includes the soil properties texture, stone content, bulk density, hydromorphic properties, total organic carbon content, and pH.

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Section: Introductionmentioning

confidence: 99%

Modelling the Agricultural Soil Landscape of Germany – A Data Science Approach Involving Spatially Allocated Functional Soil Process Units

Ließ¹

2022

Preprint

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“…These digital soil maps will be part of the v2.SLGA for Australia, following the principles of transparency, reproducibility, and updatability as new data become available (Malone and Searle, 2021).…”

Section: Introductionmentioning

confidence: 99%

Mapping soil organic carbon fractions for Australia, their stocks and uncertainty

Dobarco

Wadoux

Malone

et al. 2022

Preprint

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Abstract. Soil organic carbon (SOC) is the largest terrestrial carbon pool. SOC is composed of a continuum set of compounds with different chemical composition, origin and susceptibilities to decomposition, that are commonly separated into pools characterised by different responses to anthropogenic and environmental disturbance. Here we map the contribution of three SOC fractions to the total SOC content of Australia’s soils. The three SOC fractions: mineral-associated organic carbon (MAOC), particulate organic carbon (POC) and pyrogenic organic carbon (PyOC), represent SOC composition with distinct turnover rates, chemistry, and pathway formation. Data for MAOC, POC, and PyOC were obtained with near- and mid-infrared spectral models calibrated with measured SOC fractions. We transformed the data using an isometric log-ratio transformation (ilr) to account for the closed compositional nature of SOC fractions. The resulting, back-transformed ilr components were mapped across Australia. SOC fraction stocks for the 0–30 cm were derived with maps of total organic carbon concentration, bulk density, coarse fragments and soil thickness. Mapping was done by quantile regression forest fitted with the ilr transformed data and a large set of environmental variables as predictors. The resulting maps along with the quantified uncertainty show the unique spatial pattern of SOC fractions in Australia. MAOC dominated the total SOC with an average of 59 % ± 17.5 %, whereas 28 % ± 17.5 % was PyOC and 13 % ± 11.1 % was POC. The allocation of TOC into the MAOC fractions increased with depth. SOC vulnerability (i.e., POC/[MAOC + PyOC]) was greater in areas with Mediterranean and temperate climate. TOC and the distribution among fractions were the most influential variables on SOC fraction uncertainty. Further, the diversity of climatic and pedological conditions suggests that different mechanisms will control SOC stabilisation and dynamics across the continent, as shown by the model covariates importance metric. We estimated the total SOC stocks (0–30 cm) to be 12.7 Pg MAOC, 2 Pg POC and 5.1 Pg PyOC, which is consistent with previous estimates. The maps of SOC fractions and their stocks can be used for modelling SOC dynamics and forecasting changes in SOC stocks as response to land use change, management, and climate change.

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“…The RF modeling approach has been extensively used for DSM works . We decided to use this algorithm because it has proven to be an efficient method to predict soil attributes in different ranges of data, scale, soil variability, and also to extract the relationships between soil and the landscape (Heung et al, 2014;Loiseau et al, 2019;Malone and Searle, 2021;Santra et al, 2017).…”

Section: Random Forest and Resampling Methodsmentioning

confidence: 99%

Remote sensing technologies for digital soil mapping: applications for agriculture and environmental planning

Mello¹

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Remote sensing technologies for digital soil mapping: applications for agriculture and environmental planning / Fellipe Alcantara de Oliveira Mello. --Piracicaba, 2022. 105 p. Tese (Doutorado) --USP / Escola Superior de Agricultura "Luiz de Queiroz". 1. Mapeamento digital de solos 2. Sensoriamento remoto 3. Redes de drenagem 4. Solos hidromórficos I. Título DEDICATÓRIA À Ana, Nilton, Ivanilza e Luccas Por todo suporte, incentivo e apoio incondicional Dedico. 4 AGRADECIMENTOS Primeiramente agradeço a Deus pelas incontáveis bençãos que recebo em minha vida todos os dias. A fé depositada em Jesus Cristo me trouxe para o desafio da pós-graduação e me sustentou durante estes cinco anos e meio. Sem esta certeza não seria possível superar os obstáculos e por isso continuo dedicando minha fé naquele que fortalece. Todos os dias deste doutorado foram compartilhados com minha querida esposa Ana. Os sorrisos, lágrimas e ansiedades eram primeiro recebidos por ela. Sem seu suporte e apoio essa conquista não seria possível e, por isso, sou profundamente grato. Por meu pai Nilton que abriu mão de finais de semana para investir em meus estudos e sempre acreditou no meu potencial. A minha mãe Ivanilza que chorou quando saí de casa, mas que se orgulha muito dessa conquista. Sem vocês eu não estaria aqui. Ao meu irmão Luccas, agradeço por entender essa mudança, deixar de jogar basquete nos finais de semana e me apoiar nas lutas e dificuldades. Não seria o mesmo sem minha família. Agradeço a meu sogro e sogra, Nivaldo e Hilda, por me receberem como um filho desde 2017 e pela amizade e confiança. Também a Pedro, Sara e Taís por toda amizade e parceria durante uma fase tão importante em nossas vidas (casamentos, mudanças, sonhos...). Aos professores da ESALQ/USP de quem recebi ensinamentos e conhecimento incalculáveis. Ao Departamento de Ciência do Solo e do PPG de Solos e Nutrição de Plantas por toda estrutura e apoio necessários. Em especial ao professor e orientador José Alexandre Melo Demattê, que me recebeu para o mestrado e confiou em meu trabalho para o doutorado. A oportunidade e o crescimento pessoal e profissional adquiridos neste período são muito maiores do que poderia imaginar. Obrigado por todo suporte e parceria ao longo destes anos. Aos professores Tiago Osório Ferreira, Benito Roberto Bonfatti e Eliângela Benedet da Silva por todas as sugestões e correções na qualificação. Ao professor Pablo Vidal-Torrado e sua disciplina de Gênese e Classificação de Solos, que foi o diferencial para meu trabalho como pesquisador em pedologia. Ao professor Igo Fernando Lepsch, com o qual tive o prazer e honra de conviver e trabalhar no levantamento de solos.

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Updating the Australian digital soil texture mapping (Part 2

Cited by 19 publications

References 42 publications

Modelling the Agricultural Soil Landscape of Germany – A Data Science Approach Involving Spatially Allocated Functional Soil Process Units

Modelling the Agricultural Soil Landscape of Germany – A Data Science Approach Involving Spatially Allocated Functional Soil Process Units

Mapping soil organic carbon fractions for Australia, their stocks and uncertainty

Remote sensing technologies for digital soil mapping: applications for agriculture and environmental planning

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