Influence of Model Grid Size on the Estimation of Surface Fluxes Using the Two Source Energy Balance Model and sUAS Imagery in Vineyards

Abstract

Evapotranspiration (ET) is a key variable for hydrology and irrigation water management, with significant importance in drought-stricken regions of the western US. This is particularly true for California, which grows much of the high-value perennial crops in the US. The advent of small Unmanned Aerial System (sUAS) with sensor technology similar to satellite platforms allows for the estimation of high-resolution ET at plant spacing scale for individual fields. However, while multiple efforts have been made to estimate ET from sUAS products, the sensitivity of ET models to different model grid size/resolution in complex canopies, such as vineyards, is still unknown. The variability of row spacing, canopy structure, and distance between fields makes this information necessary because additional complexity processing individual fields. Therefore, processing the entire image at a fixed resolution that is potentially larger than the plant-row separation is more efficient. From a computational perspective, there would be an advantage to running models at much coarser resolutions than the very fine native pixel size from sUAS imagery for operational applications. In this study, the Two-Source Energy Balance with a dual temperature (TSEB2T) model, which uses remotely sensed soil/substrate and canopy temperature from sUAS imagery, was used to estimate ET and identify the impact of spatial domain scale under different vine phenological conditions. The analysis relies upon high-resolution imagery collected during multiple years and times by the Utah State University AggieAirTM sUAS program over a commercial vineyard located near Lodi, California. This project is part of the USDA-Agricultural Research Service Grape Remote Sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX). Original spectral and thermal imagery data from sUAS were at 10 cm and 60 cm per pixel, respectively, and multiple spatial domain scales (3.6, 7.2, 14.4, and 30 m) were evaluated and compared against eddy covariance (EC) measurements. Results indicated that the TSEB2T model is only slightly affected in the estimation of the net radiation (Rn) and the soil heat flux (G) at different spatial resolutions, while the sensible and latent heat fluxes (H and LE, respectively) are significantly affected by coarse grid sizes. The results indicated overestimation of H and underestimation of LE values, particularly at Landsat scale (30 m). This refers to the non-linear relationship between the land surface temperature (LST) and the normalized difference vegetation index (NDVI) at coarse model resolution. Another predominant reason for LE reduction in TSEB2T was the decrease in the aerodynamic resistance (Ra), which is a function of the friction velocity ( u * ) that varies with mean canopy height and roughness length. While a small increase in grid size can be implemented, this increase should be limited to less than twice the smallest row spacing present in the sUAS imagery. The results also indicated that the mean LE at field scale is reduced by 10% to 20% at coarser resolutions, while the with-in field variability in LE values decreased significantly at the larger grid sizes and ranged between approximately 15% and 45%. This implies that, while the field-scale values of LE are fairly reliable at larger grid sizes, the with-in field variability limits its use for precision agriculture applications.

UAVs for Vegetation Monitoring: Overview and Recent Scientific Contributions

“…It is also common to use thermal sensors since water stress causes stomatal closure that reduces transpiration and evaporative cooling and increases temperature [23]. In this way, data from thermal sensors were combined with RGB [21] or multispectral sensors [24,25] to take advantage of the information provided in different areas of the spectrum and the higher spatial resolution of such sensors.…”

“…Lower altitudes result in images with finer spatial resolution, but require longer flights and demand higher computational times for image processing [35]. Therefore, evaluation of flight altitude and spatial resolution was a central point in some investigations of this SI [16,24,27,29,36], being a usual objective to resample UAV images to spatial resolutions similar to those of common satellites, e.g., 5 m of Rapideye, 10 m of Sentinel-2, and 30 m of LANDSAT, in order to study the potential of using such satellite platforms instead of UAV for specific agro-forestry goals. Thus, Iizuka et al (2019) [16] discovered that CC estimation was affected by spatial resolution and coarser resolutions showed stronger correlation with manually delineated data in an RGB orthomosaic, concluding that the satellite images could be feasible for this purpose.…”

“…These authors explained that variability decreases when finer data is aggregated to a larger unit, thus increasing correlations. However, Nassar et al (2020) [24] demonstrated that a very high spatial resolution is necessary to use the Two-Source Energy Balance with a dual temperature (TSEB2T) model for successfully estimating evapotranspiration (ET). The authors argued that the selected spatial resolution should be fine enough to discriminate plant canopy and soil temperature, as they evaluated multiple spatial domain scales (3.6, 7.2, 14.4, and 30 m), leading them to conclude that the current satellites were not suitable for this application, especially those with spatial resolution of 30 m or broader, e.g., Landsat.…”

A Decade of Unmanned Aerial Systems in Irrigated Agriculture in the Western U.S.

“…A selected list of efforts are mentioned here: a) Landsat-UAV data harmonization (Aboutalebi et al, 2018b) to evaluate potential biases on UAV information and direct comparison to Landsat satellite products; b) Atmospheric impact on UAS thermal information (Torres-Rua, 2017), to address atmospheric conditions with the advent of stronger UAVs (e.g. BVLOS); c) UAV optical and thermal spectral and spatial uncertainty impact (McKee et al, 2018) to evaluate potential issues caused by spectral and location biases towards estimation of evapotranspiration; d) Shadow impact on UAS optical and thermal products (Aboutalebi et al, 2019a), to evaluate shadow effect in orchards and vineyards on vegetation indices, to biomass and surface energy balance; e) Estimation of energy balance fluxes for vineyards crops using UAS (Nieto et al, 2015;Nieto et al, 2019), an adaptation of the TSEB approach to the uniqueness of vine orchards; f) Soil water estimation using UAS (Hassan-Esfahani et al, 2015), application of machine learning approaches for soil water content; g)Yield and biomass estimation using UAS (Aboutalebi et al, 2018a); h) use of point cloud in estimation of evapotranspiration (Aboutalebi et al, 2020); and i) Pixel size impact on the estimation of ET using UAV (Nassar et al, 2020), to assess the changes in ET estimation accuracy for energy balance and ET with fine and coarser pixels. These studies, along with other researchers (Kustas et al, 2018), provide the necessary support for additional UAV development such as use of beyond line of sight UAVs and drone swarm, real-time agricultural applications, and integration of UAV and satellite information for agriculture.…”

sUAS Remote Sensing of Vineyard Evapotranspiration Quantifies Spatiotemporal Uncertainty in Satellite-Borne ET Estimates

“…Small Unmanned Aerial Systems (sUAS) operated under the Federal Aviation Administration (FAA) Part 107 licensing outfitted with optical sensors are increasingly used in vegetation remote sensing [6] and precision agriculture applications [7]. More recently, however, comparative studies of EOS and sUAS observations of crop water stress have shown significant spatiotemporal uncertainty in coarser EOS data [8,9].…”