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Remote Sens. 2016, 8(11), 906;

Creating Multi-Temporal Composites of Airborne Imaging Spectroscopy Data in Support of Digital Soil Mapping

Remote Sensing Laboratories (RSL), University of Zürich, Winterthurerstrasse 190, Zürich 8057, Switzerland
Author to whom correspondence should be addressed.
Academic Editors: José A.M. Demattê, Lenio Soares Galvao and Prasad S. Thenkabail
Received: 19 July 2016 / Revised: 5 October 2016 / Accepted: 27 October 2016 / Published: 2 November 2016
(This article belongs to the Special Issue Remote Sensing Applied to Soils: From Ground to Space)
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An increasing demand for full spatio-temporal coverage of soil information drives the growing use of soil spectroscopy. Soil spectroscopy application performed under laboratory conditions or in-field studies in semi-arid areas have shown promising results. However, when acquiring data in temperate zones, limitations by vegetation-free coverage, variation in soil moisture and management are driving coherent spatio-temporal data collection. This study explores the use of multi-temporal imaging spectroscopy data to increase the total mapping area of bare soils in a heterogeneous agricultural landscape. Spectrally and spatially high-resolution data from the Airborne Prism Experiment (APEX) were collected in September 2013, April 2014 and April 2015. Bare soils in all acquisitions were identified. To eliminate short-term differences in soil moisture and soil surface roughness, the empirical line method was used to calibrate the reflectance values of the singular images (2013 and 2015) towards the singular image with most bare soil pixels (2014). Difference indicators show that the calibration was successful (decrease in root mean square difference and angle difference, increase in R2 and gain and offset close to one and zero). Finally, the multi-temporal composite image contained more than double the amount of bare soil pixels as compared to a singular acquisition. Summary statistics show that reflectance values of the multi-temporal composite approximate the single image data of 2014 (mean and standard deviation of 2014: 24.2 ± 8.9 vs. 24.0 ± 9.5 for the multi-temporal composite of 2013, 2014 and 2015). This indicates that global differences in soil moisture and land management have been corrected for. As a result, an improved spatial representation of soil parameters can be retrieved from the composite data. Spatial distribution of the correction factors and analysis of the spatial variability of all images, however, indicate that non-linear, short-term differences like variation in soil moisture and land management largely influence the result of the multi-temporal composite. Quantification and attribution of those factors will be required in the future to allow correcting for them. View Full-Text
Keywords: imaging spectroscopy; multi-temporal composite; digital soil mapping; APEX; empirical line method imaging spectroscopy; multi-temporal composite; digital soil mapping; APEX; empirical line method

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Diek, S.; Schaepman, M.E.; de Jong, R. Creating Multi-Temporal Composites of Airborne Imaging Spectroscopy Data in Support of Digital Soil Mapping. Remote Sens. 2016, 8, 906.

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