A - Papers appearing in refereed journals
Crucil, G., Castaldi, F., Aldana-Jague, E., van Wesemael, B., Macdonald, A. J. and Van Oost, K. 2019. Assessing the performance of UAS-compatible multispectral and hyperspectral sensors for soil organic carbon prediction . Sustainability. 11 (7), p. 1889.
|Authors||Crucil, G., Castaldi, F., Aldana-Jague, E., van Wesemael, B., Macdonald, A. J. and Van Oost, K.|
Soil laboratory spectroscopy has proved its reliability for the estimation of soil organic carbon (SOC) by exploiting the relationship between electromagnetic radiation and key spectral features of organic matter located in the VIS-NIR-SWIR (350-2500 nm) region. It currently allows estimating soil variables at sampled points, however geo-statistical techniques have to be used to infer continuous spatial information on soil properties. In this regard, the use of proximal or remote sensing data could be very useful to provide detailed spectral sampling on soil spatial variability at the field or even regional scale. However, the factors affecting the quality of spectral acquisition in outdoor conditions need to be taken into account. In this perspective, we designed a study to investigate the capabilities of two portable hyperspectral sensors (STS-VIS and STS-NIR), and two multispectral cameras with narrow bands in the VIS-NIR region (Parrot Sequoia and Mini-MCA6), against a more sensitive reference hyper-spectral sensor (ASD Fieldspec-Pro 3) to provide data for SOC modelling from ground-based measurements. The aim of the comparison was to assess the performance of Partial Least Squares Regression (PLSR) models, when moving from laboratory to outdoor conditions, namely changing illumination, air conditions and sensor distance. Moreover, to verify the transferability of the prediction models between different measurement setups, we tested a methodology to align spectra acquired under different conditions (laboratory and outdoor) or by different instruments, by means of a calibration factor based on an internal soil standard.
|Keywords||Soil organic carbon; Proximal sensing; Hyperspectral sensors; Multispectral sensors; Precision agriculture|
|Year of Publication||2019|
|Journal citation||11 (7), p. 1889|
|Digital Object Identifier (DOI)||doi:10.3390/su11071889|
|Open access||Published as bronze (free) open access|
|Funder||Biotechnology and Biological Sciences Research Council|
|Funder project or code||The Rothamsted Long Term Experiments [2017-2022]|
|Online||29 Mar 2019|
|Publication process dates|
|Accepted||25 Mar 2019|
|Copyright license||CC BY|
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