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Feature extraction approaches for leaf area index estimation in California vineyards via machine learning algorithms

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Created: Oct 25, 2021 at 4:35 p.m.
Last updated: Oct 27, 2021 at 12:45 a.m.
DOI: 10.4211/hs.923cf9a7a3bb49369a4e65d48237002b
Citation: See how to cite this resource
Content types: Geographic Feature Content  Geographic Raster Content 
Sharing Status: Published
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Accurate leaf area index (LAI) estimation through machine learning (ML) algorithms is a channel for better understanding and monitoring the existing biomass and it relates to the distribution of energy fluxes and evapotranspiration partitioning. In order to support the ML algorithm for accurate LAI estimation, the supporting data (or features) gained from the sUAS platform are challenging in terms of variety, quantity, and quality. This project provides two types of feature-extraction approaches and the demo data to show how a variety of features are generated based on the sUAS platform via the python language. This project is also part of our pending paperwork. Other researchers can also use this project based on their sUAS platform to gain the features for estimation of their interested parameters, such as biomass and leaf water potential.

Subject Keywords



Coordinate System/Geographic Projection:
WGS 84 EPSG:4326
Coordinate Units:
Decimal degrees
Place/Area Name:
California vineyards
North Latitude
East Longitude
South Latitude
West Longitude


Start Date:
End Date:


Data Services

The following web services are available for data contained in this resource. Geospatial Feature and Raster data are made available via Open Geospatial Consortium Web Services. The provided links can be copied and pasted into GIS software to access these data. Multidimensional NetCDF data are made available via a THREDDS Data Server using remote data access protocols such as OPeNDAP. Other data services may be made available in the future to support additional data types.

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Funding Agencies

This resource was created using funding from the following sources:
Agency Name Award Title Award Number
Utah Water Research Laboratory Student Fellowship


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
Wasim Akram Khan Utah State University

How to Cite

Gao, R., A. F. Torres-Rua, M. Aboutalebi, W. A. White, M. Anderson, W. P. Kustas, N. Agam, M. M. Alsina, J. Alfieri, L. Hipps, N. Dokoozlian, H. Nieto, F. Gao, L. McKee, J. H. Prueger, L. Sanchez, A. J. Mcelrone, N. B. Ortiz, I. Gowing, C. Coopmans (2021). Feature extraction approaches for leaf area index estimation in California vineyards via machine learning algorithms, HydroShare,

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


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