Multivariate analysis of soil moisture data

Autores/as

  • Any Sena
  • Josiclêda Domiciano Galvíncio Universidade Federal de Pernambuco/Departamento de Ciências Geográficas/Recife/Pernambuco/Brasil
  • Valeria Costa
  • Rodrigo Miranda
  • Maria do Socorro Araujo
  • Magna Soelma

DOI:

https://doi.org/10.29150/jhrs.v7.7.p432-438

Palabras clave:

multivariate statistics, Ward method, soil moisture

Resumen

Soil water content is an important variable in the understanding of hydrology in agricultural and environmental systems in a region. It is known that soil moisture is related to soil characteristics, porosity, depth, hydraulic conductivity, among others, that is, characteristics that define its typology. Studies related to soil moisture are still very precarious in Brazil. Recently, the Europe Space Agency has provided soil moisture data estimated worldwide with satellite data. This availability made possible the spatial and temporal assessment of soil magna.moura@embrapa.br moisture for different studies in the world, even though we did not know the accuracy of these data. Many studies have used multivariate analysis to find groups that have similar characteristics that can be analyzed and managed with the same actions. Therefore, this study sought to analyze the similarities and dissimilarities between soil types when considering the characteristics of soil moisture, precipitation, soil elevation and soil depth. After applying the statistical methods it was possible to perceive that the soil moisture does not depend strongly on the precipitation and to suggest caution in the analysis of the relations between the humidity factor and the others scored.

 

 

 

Biografía del autor/a

Josiclêda Domiciano Galvíncio, Universidade Federal de Pernambuco/Departamento de Ciências Geográficas/Recife/Pernambuco/Brasil

Departamento de Ciências Geográficas/área de sensoriamento remoto/climatologia/hidrologia/estatistica.

Citas

EMBRAPA. Empresa Brasileira de Pesquisa Agropecuária, 2006. Centro Nacional de Pesquisa de Solos. Sistema Brasileiro de Classificação de Solos. 2. ed. EMBRAPA-SPI, Rio de Janeiro.

Fechine, J.A.L., Galvíncio, J.D., 2008. Agrupamento da precipitação mensal da bacia hidrográfica do rio Brigida-PE, através da multivariada. Revista Brasileira de Geografia Física 1, 39-46.

Galvíncio, J.D., L.M.A., 2014. Impact of the hydric reposition in soil on the agriculture in semi arid region. Journal of Hyperspectral Remote Sensing 4, 134-152.

Hair, J.F., Anderson, R.E., Tatham, R.L., Black, W.C., 2005. Análise Multivariada de Dados. 5. ed. Bookman, Porto Alegre.

Sartori, S.D., 2008. Aplicações de técnicas de análise multivariada em experimentos agropecuários usando o software R. Dissertação (Mestrado). Piracicaba, ESALQ.

White, R.E., 2005. Principles and Practice of Soil Science: the soil as a natural resource. 4th ed. Blackwell, Oxford.

Whiting, M.L., Ustin, S.L., Zarco-Tejad, P., Palacios-Orueta, A., Vanderbilt, V.C., 2006. Hyperspectral mapping of crop and soils for precision agriculture. Remote Sensing and Modeling of Ecosystems for Sustainability III, 62980B. doi: 10.1117/12.681289

Descargas

Publicado

2018-05-17

Cómo citar

Sena, A., Galvíncio, J. D., Costa, V., Miranda, R., Araujo, M. do S., & Soelma, M. (2018). Multivariate analysis of soil moisture data. Journal of Hyperspectral Remote Sensing, 7(7), 432–438. https://doi.org/10.29150/jhrs.v7.7.p432-438

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