Relationships between soil properties of the abandoned fields and spectral data derived from the advanced spaceborne thermal emission and reflection radiometer (ASTER)

Details Relationships between soil properties of the abandoned fields and spectral data derived from the advanced spaceborne thermal emission and reflection radiometer (ASTER) Relationships between soil properties of the abandoned fields and spectral data derived from the advanced spaceborne thermal emission and reflection radiometer (ASTER)

Publishing date

Jan 2012

URL

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2012adspr..49..280p&link_type=abstract

Journals

Advances in Space Research, Volume 49, Issue 2, p. 280-291.

Science

Mathematics

Field

Logic

Type

Scientific paper

Abstract

The structural transformation of Polish agriculture after 1989 has been leading to significant changes in land use. As a result a large part of formerly ploughed fields lies abandoned and they occur across considerable variability in soil typological units. Accurate information about soil conditions within the abandoned fields facilitates proper management in the new socio-economic situation. Such information can be collected using satellite images since the structure and condition of the vegetation growing on the abandoned fields reflects soil properties. The objective of this study is to evaluate the relationships between physical and chemical attributes of soil within the abandoned fields and spectral reflectance patterns recorded by ASTER sensors onboard Terra Satellite. Soil samples were collected at five abandoned fields which have not been ploughed since 2000 and analyzed in the laboratory to determine their physical and chemical properties. Nine ASTER nearly cloud-free pictures were used for this study in order to derive the remote-sensing attributes of the abandoned fields. In order to evaluate the relationships between soil fertility and remotely sensed data, partial least-squares (PLS) and a multiple linear regression (MLR) analysis between these two datasets were carried out. In the regression analysis, only soil TEB (total exchangeable bases) stock in the whole profile displayed the highest correlation with remotely sensed data acquired in April and May and the best predictors were NDVI and LSWI vegetation indices.

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