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

**Michael Kalua 1,2 , Anna M. Rallings 1,2 , Lorenzo Booth 1,3, Josué Medellín-Azuara 1,2 , Stefano Carpin 1,3 and Joshua H. Viers 1,2,\***


Received: 21 August 2020; Accepted: 25 September 2020; Published: 7 October 2020

**Abstract:** Small Unmanned Aerial Systems (sUAS) show promise in being able to collect high resolution spatiotemporal data over small extents. Use of such remote sensing platforms also show promise for quantifying uncertainty in more ubiquitous Earth Observation System (EOS) data, such as evapotranspiration and consumptive use of water in agricultural systems. This study compares measurements of evapotranspiration (ET) from a commercial vineyard in California using data collected from sUAS and EOS sources for 10 events over a growing season using multiple ET estimation methods. Results indicate that sUAS ET estimates that include non-canopy pixels are generally lower on average than EOS methods by >0.5 mm day−<sup>1</sup> . sUAS ET estimates that mask out non-canopy pixels are generally higher than EOS methods by <0.5 mm day−<sup>1</sup> . Masked sUAS ET estimates are less variable than unmasked sUAS and EOS ET estimates. This study indicates that limited deployment of sUAS can provide important estimates of uncertainty in EOS ET estimations for larger areas and to also improve irrigation management at a local scale.

**Keywords:** evapotranspiration; variability; uncertainty; unmanned aerial system; sUAS; multispectral; remote sensing; viticulture; water resources management; California

## **1. Introduction**

Global environmental change and anthropogenic activity have long stressed Earth's hydrological cycle [1,2]. Evapotranspiration remains the least certain quantified component of the hydrological cycle [3], with implications for not only water resources planning and management, but also for human livelihoods and supporting ecosystems [4]. Globally, irrigated agriculture represents 70% of water withdrawn from surface and ground water supplies but estimates of actual consumptive loss (the largest component of the water balance in an agricultural region) through evapotranspiration (ET) remain coarse [5]. New advances in satellite remote sensing of ET via Earth Observation Systems (EOS) show promise in providing consistent and reliable quantitative estimates with global coverage and reasonable repeat cycles. However, the spatiotemporal resolution of EOS platforms and sensors may obscure finer spatial resolution phenomena or weather-related aberrations observed at finer time scales. 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].

#### *1.1. Evapotranspiration in Water Management*

Globally, freshwater withdrawals have increased five-fold over the past century [10]. While rain-fed agriculture is most widespread, representing approximately 78% of water use in agriculture, irrigated agriculture is most prevalent in arid and semi-arid parts of the globe, such as the Mediterranean biome that is characterized by cool, wet winters and dry, hot summers [11]. This region, extending beyond the Mediterranean basin to include portions of Australia, Chile, South Africa, and California (USA) is also characterized by expansive urbanization and high intensity agriculture [11]. The productivity of agriculture in such places, especially in California, is achieved in part because of the ideal growing conditions, resulting in hundreds of different agricultural commodities, but also because of expansive water development infrastructure built primarily to meet irrigation demand [12]. California is the most agriculturally productive region in the USA [13], but also one of the world's most water stressed [14,15]. Like much of the Mediterranean biome, California is characterized by pronounced wet and dry periods, both intra- and inter-annually. More recently, however, extreme variation in precipitation has resulted in exceptionally wet events and prolonged dry droughts [16,17]. Managing California's water has thus become more challenging [18], and therefore a better understanding agricultural consumptive water use (i.e., ET) is a critical need.

In a recent comprehensive study by Medellín-Azuara et al. [19], seven different ET calculation methods involving remotely sensed data, satellite imagery and ground level meteorological stations were evaluated for their performance in quantifying ET for the heavily cultivated Sacramento-San Joaquin Delta region of California over a period of two growing seasons (2015–2016). While there was general consensus between these seven methods, model results were only within 20% of the median estimate for total consumptive use. This high level of uncertainty has implications not only for broad water resources management decisions across California, but also limits local decision-making by individual growers if they are unable to know how much water is needed when and where. Improving reliability of coarse ET estimates, therefore, would improve water management decision-making more broadly by balancing demand with supply [20] and also could accelerate adoption of irrigation methods that leverage real-time information on crop water demand [21]. When coupled with localized monitoring, such as with sUAS remote sensing of evapotranspiration, these technologies have the potential to integrate other ecologically-friendly practices [22].

#### *1.2. Remote Sensing of Evapotranspiration*

Evapotranspiration has long been the focus of hydrological and agrometeorological studies [23,24]. Zhang et al. [25] reviewed the state of science in remote sensing of evapotranspiration (RSE), described its use to estimate and map ET at regional to continental scales, and highlighted existing major RSE estimation methods. Because so many studies and reviews have been conducted on ET measurements, modeling and RSE methods of retrieval, they are not repeated here. Rather, we focus on the fact that RSE from EOS provides relatively frequent, consistent, and spatially contiguous measurements for global estimation, monitoring, and mapping of ET flux; however, due to the relatively coarse granularity of such data at the field level, we explore how high spatiotemporal resolution data from sUAS could quantify inherent variability in such estimates.

In almost all cases, RSE relies on energy balance methods. Most RSE methods focused on energy balance are rooted in Surface Energy Balance Algorithm for Land (SEBAL) [26–28] an image-processing model to quantify surface energy balance components, at both local and regional scales using empirical relationships and physical parameterization. SEBAL requires digital imagery data collected by any satellite sensor measuring visible, near-infrared, and thermal infrared radiation, and their derivative products including surface temperature, normalized difference vegetation index (NDVI), and albedo. SEBAL was a methodological precursor to METRIC (Mapping EvapoTranspiration at high Resolution

with Internalized Calibration), developed by Allen et al. [29], which is now a standard remote sensing estimation approach using Landsat 5, 7 and 8 EOS imagery (including thermal bands), local weather station data, and calibrated from either nearby alfalfa or pasture, and bare soil land cover types. Landsat-based METRIC, and other similar approaches are described in Irmak et al. [30].

More recently, a number of open-source methods have emerged as Google Earth Engine-based collaborative [31]. One such method includes EEFlux, a version of the METRIC model as developed by Morton et al. [31] employing algorithms by Irmak et al. [30]; EEFlux has been used because of expansive access to EOS imagery and computational assets, including built-in calibration using reference ET from the North America Land Data Assimilation System [32,33] and GridMET [34] for the contiguous United States. In implementation, however, RSE methods such as these are explicitly reliant upon thermal infrared sensors (*λ* = 10,500–12,510 nm) onboard EOS platforms, which largely prohibits inclusion of field-deployed sUAS RSE methods of comparison because of the limited performance and calibration difficulty experienced by small thermal infrared sensors [35,36]. Full-scale incorporation of sUAS thermal-based RSE remains elusive to most practitioners due to the fact that transmissivity and atmospheric radiance vary dramatically throughout the day [37] and that most sUAS data collection missions can last several hours.

A more direct comparison of RSE between EOS and sUAS approaches, due in part to the prevalence of low-cost commercial multispectral sensors for sUAS, would provide practitioners a means by which to evaluate coarse EOS RSE estimates to finer resolution RSE estimates from local collections. For cross-platform RSE comparison, without inclusion of thermal measurements, it is necessary then to focus on methods that use photosynthetic productivity as a proxy for ET using available spectral response in the 400–900 nm domain (Figure 1). This transfer function approach to RSE is increasingly driven by studies of robust empirical relation between image-derived estimates of vegetation greenness (i.e., NDVI) and ET [38], wherein cellular light energy in red wavelengths is absorbed by chlorophylls and near-infrared light energy is reflected by plant lignin and cellulose. It should be noted here that robust comparisons between EOS data products requires harmonization techniques [39] not employed here. For the purposes of this study, we also explore the utility of OpenET NDVI-ET and SSEBop estimates as described in more detail below.

**Figure 1.** Comparison of wavelength channel position and widths by sensor platform (RE = RedEdge; L8 = Landsat 8; S2 = Sentinel 2), showing a typical percent reflectance spectral response signature for a grapevine (black).
