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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

Prediction of soil properties using imaging spectroscopy: Considering fractional vegetation cover to improve accuracy

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Autor(es):
Franceschini, M. H. D. [1, 2] ; Dematte, J. A. M. [1] ; Terra, F. da Silva [1] ; Vicente, L. E. [3, 4] ; Bartholomeus, H. [2] ; de Souza Filho, C. R. [5]
Número total de Autores: 6
Afiliação do(s) autor(es):
[1] Univ Sao Paulo, Luiz De Queiroz Coll Agr, Dept Soil Sci, BR-13418260 Piracicaba, SP - Brazil
[2] Wageningen Univ, Lab Geoinformat Sci & Remote Sensing, NL-6700 AA Wageningen - Netherlands
[3] Fed Univ Jequitinhonha & Mucuri Valleys, Inst Agr Sci, BR-38610000 Unai, MG - Brazil
[4] Brazilian Agr Res Corp, BR-13070115 Campinas, SP - Brazil
[5] Univ Estadual Campinas, Inst Geosci, BR-13083970 Campinas, SP - Brazil
Número total de Afiliações: 5
Tipo de documento: Artigo Científico
Fonte: International Journal of Applied Earth Observation and Geoinformation; v. 38, p. 358-370, JUN 2015.
Citações Web of Science: 22
Resumo

Spectroscopic techniques have become attractive to assess soil properties because they are fast, require little labor and may reduce the amount of laboratory waste produced when compared to conventional methods. Imaging spectroscopy (IS) can have further advantages compared to laboratory or field proximal spectroscopic approaches such as providing spatially continuous information with a high density. However, the accuracy of IS derived predictions decreases when the spectral mixture of soil with other targets occurs. This paper evaluates the use of spectral data obtained by an airborne hyperspectral sensor (ProSpecTIR-VS - Aisa dual sensor) for prediction of physical and chemical properties of Brazilian highly weathered soils (i.e., Oxisols). A methodology to assess the soil spectral mixture is adapted and a progressive spectral dataset selection procedure, based on bare soil fractional cover, is proposed and tested. Satisfactory performances are obtained specially for the quantification of clay, sand and CEC using airborne sensor data (R-2 of 0.77, 0.79 and 0.54; RPD of 2.14, 2.22 and 1.50, respectively), after spectral data selection is performed; although results obtained for laboratory data are more accurate (R-2 of 0.92, 0.85 and 0.75; RPD of 3.52, 2.62 and 2.04, for clay, sand and CEC, respectively). Most importantly, predictions based on airborne-derived spectra for which the bare soil fractional cover is not taken into account show considerable lower accuracy, for example for clay, sand and CEC (RPD of 1.52, 1.64 and 1.16, respectively). Therefore, hyperspectral remotely sensed data can be used to predict topsoil properties of highly weathered soils, although spectral mixture of bare soil with vegetation must be considered in order to achieve an improved prediction accuracy. (C) 2015 Elsevier B.V. All rights reserved. (AU)

Processo FAPESP: 11/04232-8 - Sensoriamento remoto hiperespectral nos níveis laboratório, campo e aéreo como ferramentas auxiliares no manejo do solo
Beneficiário:Marston Héracles Domingues Franceschini
Linha de fomento: Bolsas no Brasil - Mestrado