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Minimizing soil moisture variations in multi-temporal airborne imaging spectrometer data for digital soil mapping


Diek, Sanne; Chabrillat, Sabine; Nocita, Marco; Schaepman, Michael E; de Jong, Rogier (2019). Minimizing soil moisture variations in multi-temporal airborne imaging spectrometer data for digital soil mapping. Geoderma, 337:607-621.

Abstract

Bare soil surfaces vary spatially and temporally as a result of – among other - meteorological conditions and land
management practices. Literature shows that most of the existing soil surface corrections are dependent on laboratory measurements. These measurements are time consuming and therefore unsuitable for large amounts of airborne or satellite spectroscopy data. Soil moisture and soil surface roughness present main challenges for in-field and remote sensing application of soil spectroscopy and soil moisture has substantial influence on surface reflectance. Here, we present a straightforward method for soil moisture reduction applicable to large amounts of airborne or satellite spectroscopy data.
Multi-temporal airborne imaging spectrometer data was collected with the Airborne Prism Experiment (APEX) in September 2013, May 2014 and May 2015 on the same pilot site. A new soil moisture index was developed to quantify the soil moisture of the airborne imaging spectroscopy data (R2 of 0.59). The index appeared to be closely related to the existing NINSOL index. A, laboratory based, independent soil moisture spectral dataset was used to quantify the correction factor per wavelength based on the wavelength dependent relative differences.
After minimizing the effect of soil moisture variability, the airborne spectrometer data better approximate the reference dry-soil spectra (up to 21.3%). Then, the corrected data were tested for soil properties prediction (soil organic matter (SOM), sand silt and clay). The soil moisture corrected spectra performed marginally better for SOM (RMSE from 3.13 to 3.10) and silt (RMSE from 5.74 to 5.72), although not for clay (RMSE from 9.14 to 9.33) and sand (RMSE from 5.98 to 6.31). The methodology shows that field spectra can be corrected to better match laboratory conditions, focusing on soil moisture as main varying factor. Next step should include the various types of soil surface variation and their interactions. A better understanding of these interactions is needed and can be achieved by field-scale experiments and by modelling the different processes.

Abstract

Bare soil surfaces vary spatially and temporally as a result of – among other - meteorological conditions and land
management practices. Literature shows that most of the existing soil surface corrections are dependent on laboratory measurements. These measurements are time consuming and therefore unsuitable for large amounts of airborne or satellite spectroscopy data. Soil moisture and soil surface roughness present main challenges for in-field and remote sensing application of soil spectroscopy and soil moisture has substantial influence on surface reflectance. Here, we present a straightforward method for soil moisture reduction applicable to large amounts of airborne or satellite spectroscopy data.
Multi-temporal airborne imaging spectrometer data was collected with the Airborne Prism Experiment (APEX) in September 2013, May 2014 and May 2015 on the same pilot site. A new soil moisture index was developed to quantify the soil moisture of the airborne imaging spectroscopy data (R2 of 0.59). The index appeared to be closely related to the existing NINSOL index. A, laboratory based, independent soil moisture spectral dataset was used to quantify the correction factor per wavelength based on the wavelength dependent relative differences.
After minimizing the effect of soil moisture variability, the airborne spectrometer data better approximate the reference dry-soil spectra (up to 21.3%). Then, the corrected data were tested for soil properties prediction (soil organic matter (SOM), sand silt and clay). The soil moisture corrected spectra performed marginally better for SOM (RMSE from 3.13 to 3.10) and silt (RMSE from 5.74 to 5.72), although not for clay (RMSE from 9.14 to 9.33) and sand (RMSE from 5.98 to 6.31). The methodology shows that field spectra can be corrected to better match laboratory conditions, focusing on soil moisture as main varying factor. Next step should include the various types of soil surface variation and their interactions. A better understanding of these interactions is needed and can be achieved by field-scale experiments and by modelling the different processes.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Geography
Dewey Decimal Classification:910 Geography & travel
Uncontrolled Keywords:Soil Science
Language:English
Date:1 March 2019
Deposited On:04 Jan 2019 17:16
Last Modified:17 Feb 2019 06:53
Publisher:Elsevier
ISSN:0016-7061
OA Status:Closed
Publisher DOI:https://doi.org/10.1016/j.geoderma.2018.09.052

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