Usage of Airborne Hyperspectral Imaging Data for Identifying Spatial Variability of Soil Nitrogen Content Article Swipe
Vilém Pechanec
,
Alexander Mráz
,
Ladislav Rozkošný
,
Pavel Vyvlečka
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/ijgi10060355
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/ijgi10060355
Soil is a significant natural resource composed of organic and inorganic material. Nitrogen, one of the essential elements, is traditionally measured using laboratory methods. The development of hyperspectral imaging enables the cost-effective acquisition of both spectral and spatial information for detecting physical, chemical, and biological attributes of the soil samples. The presented work evaluates the suitability of airborne hyperspectral imaging for determining soil nitrogen content and producing a soil nitrogen map on a pixel-wise basis. The measurement of spatial variability of the soil nitrogen content was taken at two fields located at Rudice, in northeast Brno, Czech Republic, using laboratory methods and a handheld spectrometer. The soil reflectance was also recorded using airborne-mounted imaging spectroscopy sensors. A partial least squares regression was used to develop a model for the calibration of the data collected with a portable spectrometer and to predict the total nitrogen in the soils based on hyperspectral images from airborne sensors. The determination factor for the PLSR model presented in this paper reached an R2 of 0.44. The model’s performance could be improved by using a handheld spectrometer with a wider spectral range, using the same acquisition period for field data collection and hyperspectral imaging, and enlarging the sample size.
Related Topics
Concepts
Hyperspectral imaging
Remote sensing
Imaging spectrometer
Environmental science
Partial least squares regression
Spectrometer
Soil test
Multispectral image
Imaging spectroscopy
Calibration
Soil water
Pixel
Soil science
Computer science
Geography
Mathematics
Artificial intelligence
Optics
Statistics
Physics
Machine learning
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/ijgi10060355
- https://www.mdpi.com/2220-9964/10/6/355/pdf?version=1621921810
- OA Status
- gold
- Cited By
- 23
- References
- 47
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3164892293
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3164892293Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/ijgi10060355Digital Object Identifier
- Title
-
Usage of Airborne Hyperspectral Imaging Data for Identifying Spatial Variability of Soil Nitrogen ContentWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-05-21Full publication date if available
- Authors
-
Vilém Pechanec, Alexander Mráz, Ladislav Rozkošný, Pavel VyvlečkaList of authors in order
- Landing page
-
https://doi.org/10.3390/ijgi10060355Publisher landing page
- PDF URL
-
https://www.mdpi.com/2220-9964/10/6/355/pdf?version=1621921810Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2220-9964/10/6/355/pdf?version=1621921810Direct OA link when available
- Concepts
-
Hyperspectral imaging, Remote sensing, Imaging spectrometer, Environmental science, Partial least squares regression, Spectrometer, Soil test, Multispectral image, Imaging spectroscopy, Calibration, Soil water, Pixel, Soil science, Computer science, Geography, Mathematics, Artificial intelligence, Optics, Statistics, Physics, Machine learningTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
23Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 6, 2024: 7, 2023: 2, 2022: 5, 2021: 3Per-year citation counts (last 5 years)
- References (count)
-
47Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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