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Features extraction from the LAI2200C Plant Canopy Analyzer

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Created: Oct 31, 2021 at 5:45 p.m.
Last updated: Nov 03, 2021 at 7:39 p.m.
DOI: 10.4211/hs.6d0c4a14289742d0951ba5ab9eca7dc0
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Sharing Status: Published
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Leaf area index (LAI) plays an important role in land-surface models to describe the energy, carbon, and water fluxes between the soil and canopy vegetation. Indirect ground LAI measurements, such as using the LAI2200C Plant Canopy Analyzer (PCA), can not only increase the measurement efficiency but also protect the vegetation compared with the direct and destructive ground LAI measurement. Additionally, indirect measurements provide opportunities for remote-sensing-based LAI monitoring. This project focuses on the extraction of several features observed using the LAI2200C PCA because the extracted features can help to explore the relationship between the ground measurements and remote sensing data. Although FV2200 software can provide convenient data calculation, data visualization, etc., it cannot generate features such as time, coordinates, and LAI from the data log for deeper exploration, especially when facing a large amount of collected data that needs to process. In order to increase efficiency, this project developed a simple python script for feature extraction, and demo data are provided.

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Coordinate System/Geographic Projection:
WGS 84 EPSG:4326
Coordinate Units:
Decimal degrees
Place/Area Name:


Start Date:
End Date:




People or Organizations that contributed technically, materially, financially, or provided general support for the creation of the resource's content but are not considered authors.

Name Organization Address Phone Author Identifiers
Carri Richards Utah State University 1600 Canyon Rd.

How to Cite

Gao, R., A. F. Torres-Rua (2021). Features extraction from the LAI2200C Plant Canopy Analyzer, HydroShare,

This resource is shared under the Creative Commons Attribution CC BY.


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