Marcus Vinicius Sato

Universidade de São Paulo

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Co-Authors

José Alexandre Melo Demattê

Forest Science and Research Institute

Marilusa Pinto Coelho Lacerda

Universidade de Brasília

Bruna Cristina Gallo

Brazilian Center for Research in Energy and Materials

Arnaldo Barros e Souza

Universidade de São Paulo

Peterson Ricardo Fiorio

Universidade de São Paulo

Wanderson de Sousa Mendes

Food and Agriculture Organization of the United Nations

L. E. Vicente

Brazilian Agricultural Research Corporation

José Lucas Safanelli

Woodwell Climate Research Center

Danilo Jefferson Romero

Universidade de São Paulo

Rodnei Rizzo

Universidade de São Paulo

Cooperative Institutions

Universidade de São Paulo

Forest Science and Research Institute

Brazilian Agricultural Research Corporation

Universidade Federal de Viçosa

Universidade Federal Rural do Rio de Janeiro

Methodist University of Piracicaba

Secretaria de Agricultura e Abastecimento

Universidade Federal Rural de Pernambuco

Hospital Universitário da Universidade de São Paulo

Empresa de Pesquisa Agropecuária do Estado do Rio de Janeiro

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Internal soil standard method for the Brazilian soil spectral library: Performance and proximate analysis

Geoderma (2017)

Danilo Jefferson Romero Eyal Ben‐Dor José Alexandre Melo Demattê Arnaldo Barros e Souza L. E. Vicente

Soil test

10.1016/j.geoderma.2017.09.014

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Citations (56)

The Brazilian Soil Spectral Library (BSSL): A general view, application and challenges

Geoderma (2019)

José Alexandre Melo Demattê André Carnieletto Dotto Ariane F.S. Paiva Marcus Vinicius Sato Ricardo Simão Diniz Dalmolin

Soil test

Saturation (graph theory)

10.1016/j.geoderma.2019.05.043

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Citations (141)

Multi-Temporal Satellite Images on Topsoil Attribute Quantification and the Relationship with Soil Classes and Geology

Remote Sensing (2018)

Bruna Cristina Gallo José Alexandre Melo Demattê Rodnei Rizzo José Lucas Safanelli Wanderson de Sousa Mendes

The mapping of soil attributes provides support to agricultural planning and land use monitoring, which consequently aids the improvement of soil quality and food production. Landsat 5 Thematic Mapper (TM) images are often used to estimate a given soil attribute (i.e., clay), but have the potential to model many other attributes, providing input for soil mapping applications. In this paper, we aim to evaluate a Bare Soil Composite Image (BSCI) from the state of São Paulo, Brazil, calculated from a multi-temporal dataset, and study its relationship with topsoil properties, such as soil class and geology. The method presented detects bare soil in satellite images in a time series of 16 years, based on Landsat 5 TM observations. The compilation derived a BSCI for the agricultural sites (242,000 hectare area) characterized by very complex geology. Soil properties were analyzed to calibrate prediction models using 740 soil samples (0–20 cm) collected of the area. Partial least squares regression (PLSR) based on the BSCI spectral dataset was performed to quantify soil attributes. The method identified that a single image represents 7 to 20% of bare soil while the compilation of the multi-temporal dataset increases to 53%. Clay content had the best soil attribute prediction estimates (R2 = 0.75, root mean square error (RMSE) = 89.84 g kg−1, and accuracy = 74%). Soil organic matter, cation exchange capacity and sandy soils also achieved moderate predictions. The BSCI demonstrates a strong relationship with legacy geological maps detecting variations in soils. From a single composite image, it was possible to use spectroscopy to evaluate several environmental parameters. This technique could greatly improve soil mapping and consequently aid several applications, such as land use planning, environmental monitoring, and prevention of land degradation, updating legacy surveys and digital soil mapping.

Topsoil

Digital Soil Mapping

Thematic Mapper

10.3390/rs10101571

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Citations (106)

Tropical Texture Determination by Proximal Sensing Using a Regional Spectral Library and Its Relationship with Soil Classification

Remote Sensing (2016)

Marilusa Pinto Coelho Lacerda José Alexandre Melo Demattê Marcus Vinicius Sato Caio Troula Fongaro Bruna Cristina Gallo

The search for sustainable land use has increased in Brazil due to the important role that agriculture plays in the country. Soil detailed classification is related with texture attribute. How can one discriminate the same soil class with different textures using proximal soil sensing, as to reach surveys, land use planning and increase crop productivity? This study aims to evaluate soil texture using a regional spectral library and its usefulness on classification. We collected 3750 soil samples covering 3 million ha within strong soil class variations in São Paulo State. The spectral analyses of soil samples from topsoil and subsoil were measured in laboratory (400–2500 nm). The potential of a regional soil spectral library was evaluated on the discrimination of soil texture. We considered two types of soil texture systems, one related with soil classification and another with soil managements. The soil line technique was used to assess differentiation between soil textural groups. Soil spectra were summarized by principal component analysis (PCA) to select relevant information on the spectra. Partial least squares regression (PLSR) was used to predict texture. Spectral curves indicated different shapes according to soil texture and discriminated particle size classes from clayey to sandy soils. In the visible region, differences were small because of the organic matter, while the short wave infrared (SWIR) region showed more differences; thus, soil texture variation could be differentiated by quartz. Angulation differences are on a spectral curve from NIR to SWIR. The statistical models predicted clay and sand levels with R2 = 0.93 and 0.96, respectively. Indeed, we achieved a difference of 1.2% between laboratory and spectroscopy measurement for clay. The spectral information was useful to classify Ferralsols with different texture classification. In addition, the spectra differentiated Lixisols from Ferralsols and Arenosols. This work can help the development of computer programs that allow soil texture classification and subsequent digital soil mapping at detailed scales. In addition, it complies with requirements for sustainable land use and soil management.

Soil texture

Topsoil

Texture (cosmology)

Subsoil

Soil test

Digital Soil Mapping

10.3390/rs8090701

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Citations (61)