Comportamento espectral dos solos na paisagem a partir de dados coletados por sensores terrestre e orbital

Junior, José Geraldo de Sousa; Demattê, José Alexandre Melo; Genú, Aline Marques

doi:10.1590/s0100-06832008000200027

Cited by 25 publications

(14 citation statements)

References 11 publications

(10 reference statements)

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“…We observed a gradual decrease of reflectance intensity from surface to subsurface layers, mainly because of the increase of clay content in deeper soil layers, in agreement with results observed by Sousa Junior et al (2008). However, the spectral curve of the surface was significantly influenced by SOM, with consequent smoothing of the absorption features (Demattê and Garcia, 1999).…”

Section: Spectral Curves As Indicators For Soil Classificationsupporting

confidence: 90%

Multiple Geotechnological Tools Applied to Digital Mapping of Tropical Soils

Alves

Demattê

Barros

2015

Rev. Bras. Ciênc. Solo

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in recent years, geotechnologies as remote and proximal sensing and attributes derived from digital terrain elevation models indicated to be very useful for the description of soil variability. however, these information sources are rarely used together. therefore, a methodology for assessing and specialize soil classes using the information obtained from remote/proximal sensing, Gis and technical knowledge has been applied and evaluated. two areas of study, in the state of são Paulo, Brazil, totaling approximately 28.000 ha were used for this work. first, in an area (area 1), conventional pedological mapping was done and from the soil classes found patterns were obtained with the following information: a) spectral information (forms of features and absorption intensity of spectral curves with 350 wavelengths -2,500 nm) of soil samples collected at specific points in the area (according to each soil type); b) obtaining equations for determining chemical and physical properties of the soil from the relationship between the results obtained in the laboratory by the conventional method, the levels of chemical and physical attributes with the spectral data; c) supervised classification of Landsat TM 5 images, in order to detect changes in the size of the soil particles (soil texture); d) relationship between classes relief soils and attributes. subsequently, the obtained patterns were applied in area 2 obtain pedological classification of soils, but in GIS (ArcGIS). finally, we developed a conventional pedological mapping in area 2 to which was compared with a digital map, ie the one obtained only with pre certain standards. the proposed methodology had a 79 % accuracy in the first categorical level of Soil Classification System, 60 % accuracy in the second category level and became less useful in the categorical level 3 (37 % accuracy).

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Section: Spectral Curves As Indicators For Soil Classificationsupporting

confidence: 90%

Multiple Geotechnological Tools Applied to Digital Mapping of Tropical Soils

Alves

Demattê

Barros

2015

Rev. Bras. Ciênc. Solo

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“…This fact was related to the large soil area still exposed during data collection; in these phenological stages, the soil was only partially covered by the crambe canopy. The soil of the experimental area was rich in iron oxide, causing the red tonality, which may have contributed to this behaviour (Sousa Jr., Demattê, & Genú, 2008;Genú & Demattê, 2012).…”

Section: Harvestmentioning

confidence: 99%

Characterisation of the Spectral-Temporal Pattern of the Crambe Crop Using Hyperspectral Sensors

Viana¹,

Mercante²,

Felipetto³

et al. 2017

JAS

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Crambe is an oleaginous plant mainly cultivated in Brazil due to its oil characteristics that provide stability to oxidation, qualifying it for the use in a variety of products. Understanding the spectral-temporal pattern of the crambe crop is important to identify and quantify already cultivated areas via remote sensing. This study spectrally characterised the plant, seeking to relate the spectral pattern to the phenological stages of the crop throughout its development. The spectral information was obtained by passive terrestrial sensors in two harvests, thus generating a spectral-temporal pattern and the crambe temporal profile through the vegetation indices NDVI and SAVI. During the phenological stages of the seedling and the beginning of the vegetative growth, the red spectral band showed higher values of reflectance; this occurred because the crop had not yet completely covered the soil. Stages at the end of the vegetative growth and the beginning of the flowering, there was a higher reflectance in the near infrared and a lower reflectance in the mid-infrared. For the granulation and maturation stages, the reflectance in the mean and near infrared reduced due to leaf senescence and loss of cellular water content. The NDVI and SAVI temporal profiles demonstrate linear growth up to the vegetative peak, which occurs between the end of the phenological stage of the vegetative growth and the beginning of the flowering and highest amount of green biomass. At the beginning of grain formation and filling, yellowing of leaves and senescence, granulation and maturation stages, the values reduced.

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“…Even in conventional surveys where satellite images are used, such features as texture, colour and current use, and aspects relating to relief such as shadow, are commonly evaluated so as to separate landscape units that are subsequently checked in the field (SOUSA JÚNIOR; DEMATTÊ; GENU, 2008).…”

Section: Introductionmentioning

confidence: 99%

“…Most studies in soil science involving remote sensing, have to do with the spectral characterisation of soils and the relationship with its components (BROWN et al, 2006;COHEN et al, 2007;COZZOLINO;MORON, 2003;DEMATTÊ, 2009;NANNI;DEMATTÊ, 2006;VISCARRA ROSSEL et al, 2009), which has been helping in soil discrimination. In the same way, conventional surveys use information about soil components obtained from chemical and physical analyses and from morphological evaluations as a means of establishing the classes which are present, and their distribution in the landscape.…”

Section: Introductionmentioning

confidence: 99%