Incorporation of unmanned aerial vehicle (UAV) point cloud product into remote sensing evapotranspiration models

Authors: ABOUTALEBI, M. - Utah State University; TORRES, A. - Utah State University; MCKEE, M. - Utah State University; Kustas, William - Bill; NIETO, H. - University Of Alcala; ALSANA, M. - E & J Gallo Winery; White, William - Alex; Prueger, John; McKee, Lynn; Alfieri, Joseph; HIPPS, L.E. - Utah State University; COOPMANS, C. - Utah State University; DOKOOZLIAN, N. - E & J Gallo Winery

Submitted to: Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/6/2019
Publication Date: 12/20/2019
Citation: Aboutalebi, M., Torres, A., McKee, M., Kustas, W.P., Nieto, H., Alsana, M., White, W.A., Prueger, J.H., McKee, L.G., Alfieri, J.G., Hipps, L., Coopmans, C., Dokoozlian, N. 2019. Incorporation of unmanned aerial vehicle (UAV) point cloud product into remote sensing evapotranspiration models. Remote Sensing. 12(1):50. https://doi.org/10.3390/rs12010050.
DOI: https://doi.org/10.3390/rs12010050

Interpretive Summary: The deployment of satellites and unmanned aerial vehicles (UAVs) has led to production of enormous amounts of data and to novel data processing and analysis techniques for monitoring crop water use and stress conditions. However, the incorporation of 3D information derived from multi-spectral imagery and photogrammetry algorithms has not been fully exploited. In this study, UAV point cloud information for enhancing remote sensing evapotranspiration (ET) models, particularly the Two-Source Energy Balance (TSEB) model, over a commercial vineyard located in California is developed and evaluated. The results indicate a strong relationship exists between in-situ LAI measurements and estimated biomass parameters from the point-cloud data. The use of point cloud data from UAVs has potential in providing strong crop ET and biomass information for improving satellite retrievals of crop conditions at field and landscape scales.

Technical Abstract: In recent years, the deployment of satellites and unmanned aerial vehicles (UAVs) has led to production of enormous amounts of data and to novel data processing and analysis techniques for monitoring crop conditions. One overlooked data source amid these efforts, however, is incorporation of 3D information derived from multi-spectral imagery and photogrammetry algorithms into crop monitoring algorithms. Few studies and algorithms have taken advantage of 3D UAV information in monitoring and assessment of plant conditions. In this study, different aspects of UAV point cloud information for enhancing remote sensing evapotranspiration (ET) models, particularly the Two-Source Energy Balance Model (TSEB), over a commercial vineyard located in California are presented. Toward this end, an innovative algorithm called Vegetation Structural-Spectral Information eXtraction Algorithm (VSSIXA) has been developed. This algorithm is able to accurately estimate height, volume, surface area, and projected surface area of the plant canopy solely based on point cloud information. In addition to biomass information, it can add multi-spectral UAV information to point clouds and provide spectral-structural canopy properties. The biomass information is used to assess its relationship with in-situ Leaf Area Index (LAI), which is a crucial input for ET models. In addition, instead of using nominal field values of plant parameters, spatial information of fractional cover, canopy height, and canopy width are input to the TSEB model. Therefore, the two main objectives for incorporating point cloud information into remote sensing ET models for this study are to (1) evaluate the possible improvement in the estimation of LAI and biomass parameters from point cloud information in order to create robust LAI maps at the model resolution and (2) assess the sensitivity of the TSEB model to using average/nominal values versus spatially-distributed canopy fractional cover, height, and width information derived from point cloud data. The proposed algorithm is tested on imagery from the Utah State University AggieAir sUAS Program as part of the ARS-USDA GRAPEX Project (Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment) collected since 2014 over multiple vineyards located in California. The results indicate a robust relationship exists between in-situ LAI measurements and estimated biomass parameters from the point-cloud data, and improvement in the agreement between TSEB model output of ET with tower measurements when employing soil and canopy temperatures derived from the point cloud data.