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Thermal infrared (TIR) remote sensing of the land-surface temperature (LST) provides an invaluable diagnostic of surface fluxes and vegetation state, from plant and sub-field scales up to regional and global coverage. However, without proper consideration of the nuances of the remotely sensed LST signal, TIR imaging can give poor results for estimating sensible and latent heating. For example, sensor view angle, atmospheric impacts, and differential coupling of soil and canopy sub-pixel elements with the overlying atmosphere can affect the use of satellite-based LST retrievals in land-surface modeling systems. A concerted effort to address the value and perceived shortcomings of TIR-based modeling culminated in the Workshop on Thermal Remote Sensing of the Energy and Water Balance, held in La Londe les Maures, France in September of 1993. One of the outcomes of this workshop was the Two-Source Energy Balance (TSEB) model, which has fueled research and applications over a range of spatial scales. In this paper we provide some historical context for the development of TSEB and TSEB-based multi-scale modeling systems (ALEXI/DisALEXI) aimed at providing physically based, diagnostic estimates of latent heating (evapotranspiration, or ET, in mass units) and other surface energy fluxes. Applications for TSEB-based ET retrievals are discussed: in drought monitoring and yield estimation, water and forest management, and data assimilation into – and assessment of – prognostic modeling systems. New research focuses on augmenting temporal sampling afforded in the thermal bands by integrating cloud-tolerant, microwave-based LST information, as well as evaluating the capabilities of TSEB for separating ET estimates into evaporation and transpiration components. While the TSEB has demonstrated promise in supplying water use and water stress information down to sub-field scales, improved operational capabilities may be best realized in conjunction with ensemble modeling systems suc

Evapotranspiration (ET) is a crucial part of commercial grapevine production in California, and the partitioning of this quantity allows the separate assessment of soil and vine water and energy fluxes. This partitioning has an important role in agriculture since it is related to grapevine stress, yield quality, irrigation efficiency, and growth. Satellite remote sensing-based methods provide an opportunity for ET partitioning at a subfield scale. However, medium-resolution satellite imagery from platforms such as Landsat is often insufficient for precision agricultural management at the plant scale. Small, unmanned aerial systems (sUAS) such as the AggieAir platform from Utah State University enable ET estimation and its partitioning over vineyards via the two-source energy balance (TSEB) model. This study explores the assessment of ET and ET partitioning (i.e., soil water evaporation and plant transpiration), considering three different resistance models using ground-based information and aerial high-resolution imagery from the Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX). We developed a new method for temperature partitioning that incorporated a quantile technique separation (QTS) and high-resolution sUAS information. This new method, coupled with the TSEB model (called TSEB-2TQ), improved sensible heat flux (H) estimation, regarding the bias, with around 61% and 35% compared with the H from the TSEB-PT and TSEB-2T, respectively. Comparisons among ET partitioning estimates from three different methods (Modified Relaxed Eddy Accumulation—MREA; Flux Variance Similarity—FVS; and Conditional Eddy Covariance—CEC) based on EC flux tower data show that the transpiration estimates obtained from the FVS method are statistically different from the estimates from the MREA and the CEC methods, but the transpiration from the MREA and CEC methods are statistically the same. By using the transpiration from the CEC method to compare with th

Green urban areas are increasingly affected by water scarcity and climate change. The combination of warmer temperatures and increasing drought poses substantial challenges for water management of urban landscapes in the western U.S. A key component for water management, actual evapotranspiration (ETa) for landscape trees and turfgrass in arid regions is poorly documented as most rigorous evapotranspiration (ET) studies have focused on natural or agricultural areas. ET is a complex and non-linear process, and especially difficult to measure and estimate in urban landscapes due to the large spatial variability in land cover/land use and relatively small areas occupied by turfgrass in urban areas. Therefore, to understand water consumption processes in these landscapes, efforts using standard measurement techniques, such as the eddy covariance (EC) method as well as ET remote sensing-based modeling are necessary. While previous studies have evaluated the performance of the remote sensing-based two-source energy balance (TSEB) in natural and agricultural landscapes, the validation of this model in urban turfgrass remains unknown. In this study, EC flux measurements and hourly flux footprint models were used to validate the energy fluxes from the TSEB model in green urban areas at golf course near Roy, Utah, USA. High-spatial resolution multispectral and thermal imagery data at 5.4 cm were acquired from small Unmanned Aircraft Systems (sUAS) to model hourly ETa. A protocol to measure and estimate leaf area index (LAI) in turfgrass was developed using an empirical relationship between spectral vegetation indices (SVI) and observed LAI, which was used as an input variable within the TSEB model. In addition, factors such as sUAS flight time, shadows, and thermal band calibration were assessed for the creation of TSEB model inputs. The TSEB model was executed for five datasets collected in 2021 and 2022, and its performance was compared against EC measurements. For ETa to b

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 sur

Crop evapotranspiration (ET) is a crucial component of energy and water budgets. The accurate determination of ET is vital for agricultural water management. Several satellite-based ET models have been developed to map ET at the field to regional scales. The spatial resolution of the satellite observations, particularly thermal-infrared imagery, is insufficient to estimate ET for small (<1 ha) agricultural fields. With unmanned aerial vehicle (UAV) technology advancement, high spatial and temporal images can be acquired with UAVs to monitor ET for small fields or even at a canopy scale. The aim of this study was the evaluation of the two-source energy balance (TSEB) model to estimate daily and seasonal crop (bell pepper) ET (ETTSEB) using high-resolution visible and thermal UAV imagery. Also, the impact of using different pixel resolutions (40, 50, 60, and 70 cm) with TSEB is compared with ET values derived using a soil water budget approach (ETSWD) with a profile soil water content. The results of this study showed that there is a high correlation between ETTSEB and ETSWD values (R2 = 0.73 for daily, R2 = 0.98 for seasonal). The root mean square error values for daily and seasonal ETTSEB are 0.62 mm day − 1 and 11.46 mm season − 1, respectively. The sensitivity of TSEB output to the spatial resolution indicated that different pixel resolutions do not significantly impact ET estimates. We suggest that the TSEB model has a real potential for agricultural water management applications for small agricultural fields using high-resolution UAV multispectral and thermal images.