Mapping Leaf Area Index With a Smartphone and Gaussian Processes Article Swipe
Manuel Campos‐Taberner
,
Francisco Javier Garcı́a-Haro
,
Álvaro Moreno‐Martínez
,
María Amparo Gilabert Navarro
,
Sergio Sánchez-Ruíz
,
Beatriz Martínez
,
Gustau Camps‐Valls
·
YOU?
·
· 2015
· Open Access
·
· DOI: https://doi.org/10.1109/lgrs.2015.2488682
YOU?
·
· 2015
· Open Access
·
· DOI: https://doi.org/10.1109/lgrs.2015.2488682
Leaf area index (LAI) is a key biophysical parameter used to determine\nfoliage cover and crop growth in environmental studies. Smartphones are\nnowadays ubiquitous sensor devices with high computational power, moderate\ncost, and high-quality sensors. A smartphone app, called PocketLAI, was\nrecently presented and tested for acquiring ground LAI estimates. In this\nletter, we explore the use of state-of-the-art nonlinear Gaussian process\nregression (GPR) to derive spatially explicit LAI estimates over rice using\nground data from PocketLAI and Landsat 8 imagery. GPR has gained popularity in\nrecent years because of their solid Bayesian foundations that offers not only\nhigh accuracy but also confidence intervals for the retrievals. We show the\nfirst LAI maps obtained with ground data from a smartphone combined with\nadvanced machine learning. This work compares LAI predictions and confidence\nintervals of the retrievals obtained with PocketLAI to those obtained with\nclassical instruments, such as digital hemispheric photography (DHP) and LI-COR\nLAI-2000. This letter shows that all three instruments got comparable result\nbut the PocketLAI is far cheaper. The proposed methodology hence opens a wide\nrange of possible applications at moderate cost.\n
Related Topics
Concepts
Leaf area index
Remote sensing
Computer science
Ground-penetrating radar
Gaussian process
Kriging
Range (aeronautics)
Bayesian probability
Bayesian optimization
Gaussian
Environmental science
Artificial intelligence
Machine learning
Radar
Geography
Telecommunications
Biology
Physics
Ecology
Composite material
Materials science
Quantum mechanics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/lgrs.2015.2488682
- OA Status
- green
- Cited By
- 30
- References
- 22
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2010423750
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2010423750Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/lgrs.2015.2488682Digital Object Identifier
- Title
-
Mapping Leaf Area Index With a Smartphone and Gaussian ProcessesWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2015Year of publication
- Publication date
-
2015-10-30Full publication date if available
- Authors
-
Manuel Campos‐Taberner, Francisco Javier Garcı́a-Haro, Álvaro Moreno‐Martínez, María Amparo Gilabert Navarro, Sergio Sánchez-Ruíz, Beatriz Martínez, Gustau Camps‐VallsList of authors in order
- Landing page
-
https://doi.org/10.1109/lgrs.2015.2488682Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2012.04596Direct OA link when available
- Concepts
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Leaf area index, Remote sensing, Computer science, Ground-penetrating radar, Gaussian process, Kriging, Range (aeronautics), Bayesian probability, Bayesian optimization, Gaussian, Environmental science, Artificial intelligence, Machine learning, Radar, Geography, Telecommunications, Biology, Physics, Ecology, Composite material, Materials science, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
30Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2022: 4, 2021: 3, 2020: 3, 2019: 1Per-year citation counts (last 5 years)
- References (count)
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22Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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