LAI estimation across California vineyards using sUAS multi-seasonal multi-spectral, thermal, and elevation information and machine learning
Date Issued
01 January 2022
Access level
metadata only access
Resource Type
journal article
Gao R.
Aboutalebi M.
White W.A.
Anderson M.
Kustas W.P.
Agam N.
Alsina M.M.
Alfieri J.
Hipps L.
Dokoozlian N.
Nieto H.
Gao F.
McKee L.G.
Prueger J.H.
Sanchez L.
Mcelrone A.J.
Bambach-Ortiz N.
Coopmans C.
Gowing I.
Utah State University
Springer Science and Business Media Deutschland GmbH
In agriculture, leaf area index (LAI) is an important variable that describes occurring biomass and relates to the distribution of energy fluxes and evapotranspiration components. Current LAI estimation methods at subfield scale are limited not only by the characteristics of the spatial data (pixel size and spectral information) but also by the empiricity of developed models, mostly based on vegetation indices, which do not necessarily scale spatiality (among different varieties or planting characteristics) or temporally (need for different LAI models for different phenological stages). Widely used machine learning (ML) algorithms and high-resolution small unmanned aerial system (sUAS) information provide an opportunity for spatial and temporal LAI estimation addressing the spatial and temporal limitations. In this study, considering both accuracy and efficiency, a point-cloud-based feature-extraction approach (Full Approach) and a raster-based feature-extraction approach (Fast Approach) using sUAS information were developed based on multiple growing seasons (2014–2019) to extract and generate vine-scale information for LAI estimation in commercial vineyards across California. Three known ML algorithms, Random Forest (RF), eXtreme Gradient Boosting (XGB), and Relevance Vector Machine (RVM), were considered, along with hybrid ML schemes based on those three algorithms, coupled with different feature-extraction approaches. Results showed that the hybrid ML technique using RF and RVM and the Fast Approach with 9 input variables, called RVM-RFFast model, performs better than others in a visual and statistical assessments of the generated LAI being also computationally efficient. Furthermore, using the generated LAI products in the quantification of energy balance using the two-source energy balance Priestley-Taylor version (TSEB-PT) model and EC tower data, the results indicated excellent estimation of net radiation (Rn) and latent heat flux (LE), good estimation of surface heat flux (G), and poor estimation of sensible heat flux (H). Additionally, TSEB-PT sensitivity analysis performed by regenerating LAI maps based on the generated LAI map (from − 15% of the original LAI map to + 15% with a 5% gap) showed that LAI uncertainty had a major impact on G, followed by evapotranspiration partitioning (T/ET), H, LE, and Rn. When considering the annual growth cycle of grapevines, the impact of LAI uncertainty on the T/ET in the veraison stage was larger than in the fruit set stage.
OCDE Knowledge area
Meteorología y ciencias atmosféricas Otras ciencias agrícolas
Scopus EID
Irrigation Science
ISSN of the container
This study was possible thanks to support from USDA-Agricultural Research Service, NASA Applied Sciences Water Resources Grant NNX17AF51G and the Utah Water Research Laboratory Student Fellowship. The authors are also grateful for the extraordinary support from the Utah State University AggieAir sUAS program staff and E&J Gallo scientific teams for data collection support and analysis. The authors would like to thank Dr. Ayman Nassar for his preliminary work in TSEB model and footprint-area calculation; Wasim Akram Khan for helping with the computer parallelization; and Carri Richards for editing the manuscript.
Sources of information: Directorio de Producción Científica Scopus