Author
Peterson Ricardo Fiorio
Other affiliations: State University of West Paraná, Escola Superior de Agricultura Luiz de Queiroz
Bio: Peterson Ricardo Fiorio is an academic researcher from University of São Paulo. The author has contributed to research in topics: Soil test & Soil classification. The author has an hindex of 15, co-authored 42 publications receiving 694 citations. Previous affiliations of Peterson Ricardo Fiorio include State University of West Paraná & Escola Superior de Agricultura Luiz de Queiroz.
Topics: Soil test, Soil classification, Soil texture, Soil water, Soil map
Papers
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TL;DR: In this paper, a spectral reflectance (SR)-based methodology was developed to evaluate soil types and soil tillage systems, which can be used as a methodology to assist soil surveys.
215 citations
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University of São Paulo1, Universidade Federal de Santa Maria2, Federal University of Pernambuco3, Universidade Estadual de Maringá4, Universidade Federal de Santa Catarina5, Amazon.com6, University of Brasília7, Empresa Brasileira de Pesquisa Agropecuária8, Universidade Federal de Viçosa9, Federal University of Rio Grande do Norte10, IAC11, Federal Rural University of Amazonia12, Universidade Federal de Mato Grosso13, Universidade Federal Rural do Rio de Janeiro14, University of Florida15, Sao Paulo State University16, Universidade Federal de Sergipe17, Federal Fluminense University18, Federal University of Piauí19, Federal University of Amazonas20, Universidade Federal Rural de Pernambuco21, Universidade Federal de Rondônia22
TL;DR: The Brazilian Soil Spectral Library (BSSL) as mentioned in this paper was developed in a joint partnership with the Brazilian pedometrics community to standardize and evaluate spectra within the 350-2500nm range of Brazilian soils.
78 citations
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TL;DR: In this paper, the authors evaluated spectral data of wet and dry tropical Brazilian soils with different hydration and determined a method to identify soil mineralogy, to evaluate clay minerals at different moisture stages and their relationship with soil minerals, and to determine a model to estimate soil moisture using spectral data measured in the laboratory by a spectroradiometer.
73 citations
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TL;DR: In this paper, the authors compared the spectral intensity of each combination between spectrometers and protocols by ANOVA module and the clay prediction capacity by PLSR with cross-validation, before and after the internal soil standard (ISS) method application.
39 citations
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TL;DR: In this article, the spectral reflectance of ground area reflectance data from the TM-Landsat-5 image was used to estimate soil attributes by labora- tory and orbital sensors and compare these results with soil classification.
Abstract: Wet chemistry methods to extract soil properties such as Fe2O3, TiO2, MnO and clay are cost effective, time consuming and environmental polluter. Moreover, a large set of samples has to be collected for precise spatial mapping. Ordinary surface soil mapping is a problematic method. Accordingly, non destructive technologies, such as remote sens- ing methods can provide important vantages. The objective of the present work was to estimate soil attributes by labora- tory and orbital sensors and compare these results with soil classification. The study area is a 473 ha bare soil field located in the region of Barra Bonita, Brazil. A sampling grid of 100 by 100 m was established and the exact position of each point was georeferenced, and sent to traditional (wet) laboratory analyses. The soil samples reflectance were also acquired by a laboratory sensor using artificial illumination (450 to 2500 nm). Over the same selected ground area reflectance data were extracted from the TM-Landsat-5 image. Prediction equations between the satellite and laboratory reflectance data and the wet chemistry were generated for each attribute. Most of the generated equations presented high and significant R 2 such as for the Fe2O3 with 0.82 for laboratory and 0.67 for the orbital reflectance data. The comparison between reflec- tance estimates and laboratory wet measurements for iron presented 92.2% success for the laboratory and 91.3% for the orbital sensors. The comparison for the texture intervals, showed 65% and 50% success for laboratory and orbital data re- spectively. The iron contents obtained by the sensors allowed to better remotely classify soil classes. Soil extractions to determine these attributes can be substitute by spectral reflectance models based on the present methodology.
33 citations
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TL;DR: A review on the state of soil visible-near infrared (vis-NIR) spectroscopy is provided in this article, focusing on important soil attributes such as soil organic matter (SOM), minerals, texture, nutrients, water, pH, and heavy metals.
Abstract: This chapter provides a review on the state of soil visible–near infrared (vis–NIR) spectroscopy Our intention is for the review to serve as a source of up-to-date information on the past and current role of vis–NIR spectroscopy in soil science It should also provide critical discussion on issues surrounding the use of vis–NIR for soil analysis and on future directions To this end, we describe the fundamentals of visible and infrared diffuse reflectance spectroscopy and spectroscopic multivariate calibrations A review of the past and current role of vis–NIR spectroscopy in soil analysis is provided, focusing on important soil attributes such as soil organic matter (SOM), minerals, texture, nutrients, water, pH, and heavy metals We then discuss the performance and generalization capacity of vis–NIR calibrations, with particular attention on sample pretratments, covariations in data sets, and mathematical data preprocessing Field analyses and strategies for the practical use of vis–NIR are considered We conclude that the technique is useful to measure soil water and mineral composition and to derive robust calibrations for SOM and clay content Many studies show that we also can predict properties such as pH and nutrients, although their robustness may be questioned For future work we recommend that research should focus on: (i) moving forward with more theoretical calibrations, (ii) better understanding of the complexity of soil and the physical basis for soil reflection, and (iii) applications and the use of spectra for soil mapping and monitoring, and for making inferences about soils quality, fertility and function To do this, research in soil spectroscopy needs to be more collaborative and strategic The development of the Global Soil Spectral Library might be a step in the right direction
1,063 citations
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TL;DR: In this article, the use of optical and microwave remote sensing data for soil and terrain mapping with emphasis on applications at regional and coarser scales is reviewed. But, most studies so far have been performed on a local scale and only few on regional or smaller map scale.
635 citations
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Forschungszentrum Jülich1, University of California, Davis2, Université catholique de Louvain3, ETH Zurich4, University of Southampton5, University of Texas at Austin6, University of Bonn7, James Hutton Institute8, University of California, Irvine9, Université Paris-Saclay10, Desert Research Institute11, Ghent University12, Washington State University13, Katholieke Universiteit Leuven14, University of Aberdeen15, Institut national de la recherche agronomique16, Polish Academy of Sciences17, University of Vienna18, University of Sydney19, University of Stuttgart20, Agricultural Research Service21, University of Naples Federico II22, University of California, Riverside23, Netherlands Environmental Assessment Agency24, Monash University25, University of Tübingen26, University of New England (Australia)27
TL;DR: Key challenges in modeling soil processes are identified, including the systematic incorporation of heterogeneity and uncertainty, the integration of data and models, and strategies for effective integration of knowledge on physical, chemical, and biological soil processes.
Abstract: The remarkable complexity of soil and its importance to a wide range of ecosystem services presents major challenges to the modeling of soil processes. Although major progress in soil models has occurred in the last decades, models of soil processes remain disjointed between disciplines or ecosystem services, with considerable uncertainty remaining in the quality of predictions and several challenges that remain yet to be addressed. First, there is a need to improve exchange of knowledge and experience among the different disciplines in soil science and to reach out to other Earth science communities. Second, the community needs to develop a new generation of soil models based on a systemic approach comprising relevant physical, chemical, and biological processes to address critical knowledge gaps in our understanding of soil processes and their interactions. Overcoming these challenges will facilitate exchanges between soil modeling and climate, plant, and social science modeling communities. It will allow us to contribute to preserve and improve our assessment of ecosystem services and advance our understanding of climate-change feedback mechanisms, among others, thereby facilitating and strengthening communication among scientific disciplines and society. We review the role of modeling soil processes in quantifying key soil processes that shape ecosystem services, with a focus on provisioning and regulating services. We then identify key challenges in modeling soil processes, including the systematic incorporation of heterogeneity and uncertainty, the integration of data and models, and strategies for effective integration of knowledge on physical, chemical, and biological soil processes. We discuss how the soil modeling community could best interface with modern modeling activities in other disciplines, such as climate, ecology, and plant research, and how to weave novel observation and measurement techniques into soil models. We propose the establishment of an international soil modeling consortium to coherently advance soil modeling activities and foster communication with other Earth science disciplines. Such a consortium should promote soil modeling platforms and data repository for model development, calibration and intercomparison essential for addressing contemporary challenges.
542 citations
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Commonwealth Scientific and Industrial Research Organisation1, University of Tübingen2, Tel Aviv University3, Washington State University4, University of São Paulo5, World Agroforestry Centre6, Zhejiang University7, Swedish University of Agricultural Sciences8, Université catholique de Louvain9, McGill University10, SupAgro11, Wageningen University and Research Centre12, Karlsruhe Institute of Technology13, Finnish Environment Institute14, Czech University of Life Sciences Prague15, Chinese Academy of Sciences16, Agrocampus Ouest17, Gembloux Agro-Bio Tech18, University of Florida19, Universidad Miguel Hernández de Elche20, Landcare Research21, Aarhus University22, International Trademark Association23, University of Northern British Columbia24, University of Molise25, Agricultural Research Service26, British Geological Survey27, Rural Development Administration28
TL;DR: In this article, the authors developed and analyzed a global soil visible-near infrared (vis-NIR) spectral library, which is currently the largest and most diverse database of its kind, and showed that the information encoded in the spectra can describe soil composition and be associated to land cover and its global geographic distribution, which acts as a surrogate for global climate variability.
535 citations
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TL;DR: In this paper, the authors provide an up-to-date overview of some of the case studies that have used IS technology for soil science applications, including soil degradation (salinity, erosion, and deposition), soil mapping and classification, soil genesis and formation, soil contamination, soil water content, and soil swelling.
448 citations