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 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 LAI and spatially-distributed canopy structure parameters derived from the point cloud data.
Highlights
Evapotranspiration (ET) is one of the key components in water and energy cycles, and its quantification is essential to increasing crop water use efficiency [1]
The Two-Source Energy Balance model (TSEB) model was developed by Norman et al [5] to compute surface energy fluxes using a single measurement of remotely-sensed surface temperature to overcome the difficulties associated with characterizing the impact of canopy structure, fractional cover, sensor view, and sun zenith angle on the radiometric brightness temperature and its relationship to surface aerodynamic temperature
The vegetation structure information is executed for three categories: (1) vine canopy, (2) cover crop, and (3) vegetation
Summary
Evapotranspiration (ET) is one of the key components in water and energy cycles, and its quantification is essential to increasing crop water use efficiency [1]. Estimation of ET using physically-based models is not a straightforward process due to input requirements and model complexity [2]. The degree of complexity increases with non-homogeneous landscapes where both soil and vegetation contribute to radiometric temperature and surface energy fluxes [3]. One ET model that has been successful in estimating spatially distributed surface energy fluxes from aerial imagery over different landscapes is the Two-Source Energy Balance model (TSEB) [4]. The TSEB model was developed by Norman et al [5] to compute surface energy fluxes using a single measurement of remotely-sensed surface temperature (at one view angle) to overcome the difficulties associated with characterizing the impact of canopy structure, fractional cover, sensor view, and sun zenith angle on the radiometric brightness temperature and its relationship to surface aerodynamic temperature. Numerous studies have evaluated the performance of TSEB-based models at different spatial scales, climates, and landscape heterogeneity
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