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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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Abstract

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

Coverage

Spatial

Coordinate System/Geographic Projection:
WGS 84 EPSG:4326
Coordinate Units:
Decimal degrees
Place/Area Name:
California vineyards
North Latitude
38.5354°
East Longitude
-119.9697°
South Latitude
36.7692°
West Longitude
-123.2468°

Temporal

Start Date:
End Date:

Content

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.

Related Resources

Credits

Funding Agencies

This resource was created using funding from the following sources:
Agency Name Award Title Award Number
NASA NNX17AF51G
Utah Water Research Laboratory Student Fellowship

Contributors

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, https://doi.org/10.4211/hs.923cf9a7a3bb49369a4e65d48237002b

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