Assessing the performance of UAS-compatible multispectral and hyperspectral sensors for soil organic carbon prediction

Autor: Kristof Van Oost, Bas van Wesemael, Emilien Aldana-Jague, Giacomo Crucil, Fabio Castaldi, Andy Macdonald
Přispěvatelé: UCL - SST/ELI/ELIC - Earth & Climate
Předmět:
010504 meteorology & atmospheric sciences
Monitoring
Geography
Planning and Development
Multispectral image
lcsh:TJ807-830
lcsh:Renewable energy sources
Management
Monitoring
Policy and Law
01 natural sciences
Calibration
Renewable Energy
Spatial analysis
Multispectral sensors
lcsh:Environmental sciences
0105 earth and related environmental sciences
Remote sensing
lcsh:GE1-350
Planning and Development
Topsoil
Sustainability and the Environment
Geography
Policy and Law
Precision agriculture
Renewable Energy
Sustainability and the Environment
Soil organic carbon
lcsh:Environmental effects of industries and plants
Hyperspectral imaging
Sampling (statistics)
04 agricultural and veterinary sciences
Building and Construction
Management
Hyperspectral sensors
lcsh:TD194-195
040103 agronomy & agriculture
0401 agriculture
forestry
and fisheries
Environmental science
Proximal sensing
Interpolation
Zdroj: Sustainability
Volume 11
Issue 7
Sustainability (Online), Vol. 11, no.7, p. 1889 (2019)
Sustainability, Vol 11, Iss 7, p 1889 (2019)
DOI: 10.3390/su11071889
Popis: Laboratory spectroscopy has proved its reliability for estimating soil organic carbon (SOC) by exploiting the relationship between electromagnetic radiation and key spectral features of organic carbon located in the VIS-NIR-SWIR (350&ndash
2500 nm) region. While this approach provides SOC estimates at specific sampling points, geo-statistical or interpolation techniques are required to infer continuous spatial information. UAS-based proximal or remote sensing has the potential to provide detailed and spatially explicit spectral sampling of the topsoil at the field or even watershed scale. However, the factors affecting the quality of spectral acquisition under outdoor conditions need to be considered. In this study, we investigate the capabilities of two portable hyperspectral sensors (STS-VIS and STS-NIR), and two small-form multispectral cameras with narrow bands in the VIS-NIR region (Parrot Sequoia and Mini-MCA6), to predict SOC content. We collected spectral data under both controlled laboratory and outdoor conditions, with the latter being affected by variable illumination and atmospheric conditions and sensor-sample distance. We also analysed the transferability of the prediction models between different measurement setups by aligning spectra acquired under different conditions (laboratory and outdoor) or by different instruments. Our results indicate that UAS-compatible small-form sensors can be used to reliably estimate SOC. The results show that: (i) the best performance for SOC estimation under outdoor conditions was obtained using the VIS-NIR range, while the addition of the SWIR region decreased the prediction accuracy
(ii) prediction models using only the narrow bands of multispectral cameras gave similar or better performances than those using continuous spectra from the STS hyperspectral sensors
and (iii) when used in outdoor conditions, the micro hyperspectral sensors substantially benefitted from a laboratory model calibration followed by a spectral transfer using an internal soil standard. Based on this analysis, we recommend VIS-NIR portable instruments for estimating spatially distributed SOC data. The integration of these sensors in UAS-mapping devices could represent a cost-effective solution for soil research and precision farming applications when high resolution data are required.
Databáze: OpenAIRE
Externí odkaz: