Two-source Model Research Articles

Precision modelling of leaf area index for enhanced surface temperature partitioning and improved evapotranspiration estimation

Remote Sensing-based two-source model is widely used to estimate crop evapotranspiration (ET), involving one key step of partitioning land surface temperature (LST) into canopy and soil temperatures (Tc and Ts). Leaf area index (LAI) plays a significant part in available energy allocation during this process. However, the asymptotic saturation problem makes the mismatch between vegetation index and LAI. In this study, two-stage LAI models were developed through the red-edge chlorophyll index (CIred-edge) considering the hysteresis between them. Considering the distinct characteristics, modeling LAI by one-degree linear equations for sunflower (C3), linear and exponential functions for maize (C4) were presented in the distinguished grow-up and senescence periods. The two-source energy balance (TSEB) and hybrid dual-source scheme and trapezoid framework-based evapotranspiration (HTEM) models were selected to estimate Tc, Ts, ET, and its components contrastively. The established LAI models and other modified parameters were then integrated into the two models to improve the estimation of Tc, Ts, and ET (named the R-TSEB and R-HTEM models, respectively). Results demonstrated that the partitioned Tc & Ts became closer to the measurements after utilizing the presented LAI models. For daily ET, the R-TSEB and R-HTEM models alleviated the overestimation and underestimation existing in the original two models, respectively. At monthly and seasonal scales compared to the water balance results (ETwb), the ET of R-TSEB model had significant promotion, including the determination coefficient (R2), mean relative error (RE), root mean square error (RMSE), and model agreement index (d) with values of 0.87, 6.54%, 11.65 mm, and 0.95, from the according values of 0.80, 12.85%, 17.60 mm, and 0.90 for the TSEB model, respectively. The estimated ET by the R-HTEM model was more consistent with ETwb than the HTEM model. These results indicate that the established LAI models can enhance ET estimation and further advance water cycle understanding.

A double two-sources energy-water balance model for improving evapotranspiration estimates and irrigation management in fruit trees fields

Improving the use of water in irrigated agriculture is meaningful in a period of increasing water scarcity conditions. A more accurate estimate of evapotranspiration (ET) and its components thus becomes fundamental to better quantify the irrigation water volumes. Many existing models, based on different remote sensing data, provide daily estimates of latent heat flux (LE) from correlation between net radiation and instantaneous estimates of LE, computed as residual component of the energy balance equation or from a correlation of land surface temperature (LST) with vegetation indices., However, they mainly lack of solutions which are continuous in time (e.g. hourly), independent from satellite LST availability and a simultaneous estimation of soil moisture. Addressing this gap, a double two-sources energy-water balance model (FEST‐2×2‐EWB) is developed based on the decoupling of both water and energy fluxes between the bare soil or grass interrow and the trees rows. This novel parameterization approach enables the differentiation of water uptake from the root zone, which varies between tall trees and grass, considering the dynamics of soil moisture (SM) in the superficial and deep layers. Additionally, it provides partitioned values for transpiration and evaporation. The new model has been evaluated in two irrigated trees fields in the North of Italy, a walnut trees field from 2019 to 2021 and a pear trees field, for the year 2022. Results of the study showcased a root-mean-square-error (RMSE) of about 55 W m− 2 and a bias of about 40 W m− 2 for hourly latent and sensible heat fluxes when compared to the eddy covariance stations located in the fields, while a RMSE of 2 °C (bias of 1.5 °C) for LST and of 0.04 for SM. The FEST‐2×2‐EWB model significantly enhances the accuracy of ET simulation in fruits trees areas in respect to the original one source and one-layer version of the same model. Finally, the application of an irrigation optimization strategy with this new model, allowed to demonstrate its potentiality in water saving (about 90 mm in a year) in respect to farmers applied irrigation, and with a difference of about 60 mm between using the double two-sources model and single source one.

Beamforming with modified steering vectors for jet noise source location

Prof. Krish Ahuja has a longstanding interest in jet noise source location. His work in this area is grounded in the idea that if the assumed source location is correct, then the sound should obey the inverse square law relative to that point and the phase should be constant along lines originating at that point. He applied this with, conceptually, one microphone in 1985 and two microphones in 1998. In 2006 he commissioned a beamforming system, Array 48, from OptiNav, Inc. His student, Nick Breen, used this to measure subsonic jet noise source location in detail. The NASA-Glenn Research Center also purchased an Array 48. In the current work, a jet noise data set measured by Gary Podboy using Glenn’s array in 2008 is revisited with a new beamforming algorithm, Robust Functional Beamforming, to further support Tam’s two-source model and Breen’s source location. Beamforming with modified steering vectors is performed to measure the parameters of the wavepacket source model from the far field. This process suggested replacing the wavepacket spatial length parameter with a temporal lifetime parameter. Another steering vector modification aimed to measure modes with odd spinning order. It seems to have found them at an apparent location 10 jet diameters removed from the jet, laterally. This is tentatively interpreted as a Mach radius phenomenon like one observed by Csaba Horvath at NASA-Glenn, also using Array 48, to study counter-rotating propeller noise. In an observation unrelated to beamforming, the excess noise measured at 40° from the jet axis as compared with the 90° angle, is fully contained in the first few cross spectral matrix eigenvalues, or Spectral Proper Orthogonal Decomposition modes, in some cases.

Perceptual echoes as travelling waves may arise from two discrete neuronal sources

Growing evidence suggests that travelling waves are functionally relevant for cognitive operations in the brain. Several electroencephalography (EEG) studies report on a perceptual alpha-echo, representing the brain response to a random visual flicker, propagating as a travelling wave across the cortical surface. In this study, we ask if the propagating activity of the alpha-echo is best explained by a set of discrete sources mixing at the sensor level rather than a cortical travelling wave. To this end, we presented participants with gratings modulated by random noise and simultaneously acquired the ongoing MEG. The perceptual alpha-echo was estimated using the temporal response function linking the visual input to the brain response. At the group level, we observed a spatial decay of the amplitude of the alpha-echo with respect to the sensor where the alpha-echo was the largest. Importantly, the propagation latencies consistently increased with the distance. Interestingly, the propagation of the alpha-echoes was predominantly centro-lateral, while EEG studies reported mainly posterior-frontal propagation. Moreover, the propagation speed of the alpha-echoes derived from the MEG data was around 10 m/s, which is higher compared to the 2 m/s reported in EEG studies. Using source modelling, we found an early component in the primary visual cortex and a phase-lagged late component in the parietal cortex, which may underlie the travelling alpha-echoes at the sensor level. We then simulated the alpha-echoes using realistic EEG and MEG forward models by placing two sources in the parietal and occipital cortices in accordance with our empirical findings. The two-source model could account for both the direction and speed of the observed alpha-echoes in the EEG and MEG data. Our results demonstrate that the propagation of the perceptual echoes observed in EEG and MEG data can be explained by two sources mixing at the scalp level equally well as by a cortical travelling wave. Importantly, these findings should not be directly extrapolated to intracortical recordings, where travelling waves gradually propagate at a sub-millimetre scale.

Time continuous two-source energy-water balance modelling of heterogeneous crops: FEST-2-EWB

An accurate knowledge of the water and energy exchanges in the soil-crop-atmosphere system is critical for a precise and aware control of all agricultural operations, from field management to irrigation and harvest. This concern is more complex to address in heterogeneous systems, with the presence of bare soil or simple grass in between the main crop. In this work, a two-source energy-water balance model is developed, joining the advantages of the single-source FEST-EWB model (internal computation of surface temperature and time-continuous modelling) and those of two-source approaches (employing two surface temperatures to accurately characterize the intra-pixel heterogeneity). In order to validate the model results, a laboratory experiment has been designed over a lysimeter, with the target of obtaining partitioned estimates of Evaporation and Transpiration over a heterogeneous grass crop. These partitioned fluxes were supported by a safe error margin in the scaling procedure (less than ± 1 mm/d). The FEST-EWB and FEST-2-EWB models were first calibrated using surface temperature obtained from thermal camera observations (with roughly 1.7°C of average bias). Then, they were validated against the partitioned estimated. The single-source modelling showed a consistent overestimation of the estimated Transpiration, by an amount roughly 20% of the average irrigation event. On the other hand, the two-source model shows much lower (6%) errors, portraying more accurately the Transpired water volume. Such an improvement in crop water consumption estimation can provide a successful reference to optimize the use of the irrigation water resource, with consistent water savings.

Comparison of remote sensing evapotranspiration models: Consistency, merits, and pitfalls

Various models are available for large-scale evapotranspiration (ET) estimation. The performance of these models generally differs due to their differences in forcing data, model structure (mathematical representations of the ET process), and parameter estimation. Model comparison is the most straightforward way to identify the strengths, weaknesses, and uncertainty sources of a model. In this study, three widely used remote sensing ET models were considered: the air-relative-humidity-based two-source (ARTS) model, Priestley-Taylor Jet Propulsion Laboratory (PT-JPL) model, and Penman-Monteith-Leuning (PML) model. The three models were evaluated based on measurements from 12 eddy-covariance (EC) towers and water-balance-based ET estimates from 286 basins. The evaluation results indicate that the PML model performs the best on both the site and basin scales. The advantage of the PML model may be due to (i) the land-cover-based parameter configuration and (ii) the consideration of differences in the responses of soil evaporation and transpiration to soil water deficit. We also investigated the consistencies and differences between the models in simulating the spatiotemporal variation and component partitioning of ET, that is, interception loss (Ei), soil evaporation (Es), and transpiration (Et). The three models show high consistency in estimating nationwide multi-year average ET (416.3–438.2 mm/yr) and its spatial pattern but large discrepancies in ET trends (0.10–0.98 mm/yr2) and component partitioning. The PT-JPL model considerably overestimates the ratio of Ei/ET, thereby underestimating the ratio of Es/ET because of the negative correlation between Ei and Es. The ARTS model presents better applicability in grasslands than in croplands or forestlands. This may be because its parameter value (constant for all biomes) and the water constraint scheme are more suitable for grasslands. Finally, we proposed specific modifications to address the potential issues of each model.

Water Budget, Biological Water Use, and the Soil Hydrological Cycle across Typical Ecosystems of the Heihe River Basin

Quantification of the water budget of an arid inland river ecosystem is essential but still a challenge for the sustainable development of water resources. In situ observed data were used to analyze the monthly and annual water budgets and the soil hydrological cycle for six typical ecosystems in the Heihe River Basin (HRB). The two-source model was used to partition evapotranspiration (ET) into transpiration (T) and evaporation, after which the validated model was applied to quantitatively analyze the biological water use fraction [T/Ecosystem Water Supply (WS)] for different ecosystems. There were differences in the water budgets of the different ecosystems due to differences in climate, vegetation, soil, and external inputs. Precipitation in the HRB decreased from upstream to downstream, whereas there was a gradual increase in ET. External sources of water (e.g., natural runoff from upstream, irrigation in the middle reaches, and groundwater recharge in the lower reaches) to soil layers played an important role in regulating the water budgets of HRB ecosystems. Cropland obtained the maximum biological water use fraction (0.50), followed by Populus euphratica (0.49), alpine meadow (0.49), alpine swamp meadow (0.44), Tamarix ramosissima (0.42), and Kalidium foliatum (0.4). The soil water residence time (at a depth of 40 cm) varied from 14 d to 97 d (average of 60 d). The order of plant species in terms of soil water residence time was: K. foliatum (88 d) > T. ramosissima (72 d) > alpine meadow (68 d) > alpine swamp meadow (63 d) > cropland (53 d) > P. euphratica forest (20 d). Differences in the biological water use fraction and soil water residence time could be attributed to the characteristics of the water budget for each ecosystem. This study quantified the water budget, biological water use, and soil hydrological cycle across typical ecosystems in HRB, and can act as a reference for ecosystem management of the arid inland river basin.

Remote sensing-based evapotranspiration determination: A review of single-source energy balance models

Remote Sensing evapotranspiration models are critical in order to understand the cycling of water in the environment. Initially, an outline of the concepts related to evapotranspiration, as well as the shortcomings of land-based methods, is presented. The aim of the study was based on reviewing remote sensing evapotranspiration models which provide an alternative data source. These models have proved to be a cheaper alternative to mapping and estimating spatiotemporal evapotranspiration measurements across local and regional scales. This paper reviews the single-source energy balance model, which differs from the two-source model, for estimating spatiotemporal measurements of evapotranspiration. The single-source energy balance model is underpinned by mathematical equations which differentiate the various single-source evapotranspiration models (Surface Energy Balance Systems, Simplified Surface Energy Systems, Surface Energy Balance Algorithm, and Mapping Evapotranspiration at high Resolution and with Internalised Calibration). The soil surface and forest canopy components were observed to be the major difference between the single and dual-source models. Further advice was discussed on the implementation of the OpenET tool, which provides an open and accessible satellite-based estimation of evapotranspiration for improved water management.

Novel chaos game optimization tuned-fractional-order PID fractional-order PI controller for load\u2013frequency control of interconnected power systems

In this work, chaos game optimization (CGO), a robust optimization approach, is employed for efficient design of a novel cascade controller for four test systems with interconnected power systems (IPSs) to tackle load–frequency control (LFC) difficulties. The CGO method is based on chaos theory principles, in which the structure of fractals is seen via the chaotic game principle and the fractals’ self-similarity characteristics are considered. CGO is applied in LFC studies as a novel application, which reveals further research gaps to be filled. For practical implementation, it is also highly desirable to keep the controller structure simple. Accordingly, in this paper, a CGO-based controller of fractional-order (FO) proportional–integral–derivative–FO proportional–integral (FOPID–FOPI) controller is proposed, and the integral time multiplied absolute error performance function is used. Initially, the proposed CGO-based FOPID–FOPI controller is tested with and without the nonlinearity of the governor dead band for a two-area two-source model of a non-reheat unit. This is a common test system in the literature. A two-area multi-unit system with reheater–hydro–gas in both areas is implemented. To further generalize the advantages of the proposed scheme, a model of a three-area hydrothermal IPS including generation rate constraint nonlinearity is employed. For each test system, comparisons with relevant existing studies are performed. These demonstrate the superiority of the proposed scheme in reducing settling time, and frequency and tie-line power deviations.

Building a UAV Based System to Acquire High Spatial Resolution Thermal Imagery for Energy Balance Modelling

High spatial resolution and geolocation accuracy canopy evapotranspiration (ET) maps are well suited tools for evaluation of small plot field trials. While creating such a map by use of an energy balance model is routinely performed, the acquisition of the necessary imagery at a suitable quality is still challenging. An UAV based thermal/RGB integrated imaging system was built using the RaspberryPi (RPi) microcomputer as a central unit. The imagery served as input to the two-source energy balance model pyTSEB to derive the ET map. The setup’s flexibility and modularity are based on the multiple interfaces provided by the RPi and the software development kit (SDK) provided for the thermal camera. The SDK was installed on the RPi and used to trigger cameras, retrieve and store images and geolocation information from an onboard GNSS rover for PPK processing. The system allows acquisition of 8 cm spatial resolution thermal imagery from a 60 m height of flight and less than 7 cm geolocation accuracy of the mosaicked RGB imagery. Modelled latent heat flux data have been validated against latent heat fluxes measured by eddy covariance stations at two locations with RMSE of 75 W/m2 over a two-year study period.

Estimation of turbulent fluxes over almond orchards using high-resolution aerial imagery with one and two-source energy balance models

Estimation of actual crop evapotranspiration (ETc) using high-resolution aerial remote sensing data is important to detect water stress, map, and manage water resources in precision agriculture. High-resolution ETc can be estimated using land surface energy balance models and remotely sensed land surface temperatures (LST) obtained using manned or unmanned aerial vehicles equipped with thermal cameras. In this study, three remote sensing ETc models were compared, i.e., Two-Source Energy Balance (TSEB) model, Mapping Evapotranspiration with Internalized Calibration (METRIC), and Surface Energy Balance Algorithm for Land (SEBAL) models. Thermal images were obtained using an airplane flying daily over almond orchards in California during the 2020 growing season. The LST images were obtained at 0.5 m spatial resolution. Model comparisons indicated that all three models produced latent heat fluxes and net radiation estimates that agreed with eddy covariance measurements in the order of TSEB (R2 of 0.89 for LE and 0.88 for Rn), METRIC (R2 of 0.81 for LE and 0.86 for Rn), and SEBAL (R2 of 0.81 for LE and 0.83 for Rn). However, METRIC and SEBAL overestimated the latent heat fluxes while underestimating the sensible heat fluxes as compared to the two-source model (TSEB). The root mean square error (RMSE) of the instantaneous latent and sensible heat fluxes were less than 38 W m−2 for TSEB, and were within 58 W m−2 for METRIC and SEBAL models. The TSEB model’s good performance can be attributed to its partitioning of surface temperature between soil, crop cover in the inter rows, and almond tree canopies. Overall, the results suggest that both one and two-source surface energy models originally developed for satellite imagery are able to estimate instantaneous turbulent fluxes and spatial variability in ETc using high-resolution imagery. In addition, systematic variations in LST due to variable rate irrigation scheduling as depicted in the high spatial resolution imagery provided confidence in the spatially distributed latent heat flux maps estimated by the energy balance models. This study shows that high-resolution aerial imagery combined with energy balance models originally developed for satellite remote sensing can be used to accurately estimate site specific ETc that is critical to achieving precision irrigation management in almond orchards and other crops.

Exposure to source-specific air pollution and risk for type 2 diabetes: a nationwide study covering Denmark.

Only few epidemiological studies have investigated whether chronic exposure to air pollution from different sources have different impacts on risk of diabetes. We aimed to investigate associations between air pollution from traffic versus non-traffic sources and risk of type 2 diabetes in the Danish population. We estimated long-term exposure to traffic and non-traffic contributions of particulate matter with a diameter <2.5 µg (PM2.5), elemental carbon (EC), ultrafine particles (UFP) and nitrogen dioxide (NO2) for all persons living in Denmark for the period 2005-17. In total, 2.6 million persons aged >35 years were included, of whom 148 020 developed type 2 diabetes during follow-up. We applied Cox proportional hazards models for analyses, using 5-year time-weighted running means of air pollution and adjustment for individual- and area-level demographic and socioeconomic covariates. We found that 5-year exposure to all particle measures (PM2.5, UFP and EC) and NO2 were associated with higher type 2 diabetes risk. We observed that for UFP, EC and potentially PM2.5, the pollution originating from traffic was associated with higher risks than the non-traffic contributions, whereas for NO2 similar hazard ratios (HR) were observed. For example, in two-source models, hazard ratios (HRs) per interquartile change in traffic UFP, EC and PM2.5 were 1.025, 1.045 and 1.036, respectively, whereas for non-traffic UFP, EC and PM2.5, the HRs were 1.013, 1.018 and 1.001, respectively. Our finding of stronger associations with particulate matter from traffic compared with non-traffic sources implies that prevention strategies should focus on limiting traffic-related particulate matter air pollution.

An enhanced shuttleworth-wallace model for simulation of evapotranspiration and its components

The Shuttleworth-Wallace two-source (S-W) model has been widely applied to estimate evapotranspiration (ET) and its components in a variety of vegetation-covered surface conditions. However, significant uncertainties occur in calculation of vegetation canopy resistance (rsc) and soil surface resistance (rss). In this study, an enhanced version of the S-W model is proposed to simulate and partition ET. The deep learning (DL) approach which combined soil, vegetation, and meteorological observation data, is employed to simulate rsc. A two-objective Monte Carlo-based Bayesian parameter optimization (TOMCBP) is developed to determine the empirical parameters for the rss calculation. The enhanced S-W model was verified based on the three years of eddy correlation (EC) and stable water isotope observations in an urban forest land located in Tianjin, China. Results suggest the machine learning-based rsc parametric scheme effectively improved the performance of the S-W model for the ET simulation. The TOMCBP-based rss parameterization scheme can improve the performance of the S-W model for ET partition. Furthermore, this study used the Bayesian model evidence (BME) to evaluate the performance of different models. BME is shown to balance the model complexity and fitting accuracy when compared with traditional statistical parameters, thus showing superiority in model evaluation and selection. This study improves the physical mechanism and performance of the S-W model and proposes a new method for rsc and ET components simulation based on machine learning. The enhanced S-W model is more precise and provides guidance for irrigation measures in urban woodland areas.

A one – source model to estimate sensible heat flux in agricultural landscapes

A one-source model type that combines surface renewal (SR) and similarity theories to estimate the sensible heat flux (H) involving the land surface temperature (LST) and low-frequency measurements, HSR-LST, was tested over agricultural surfaces with fractions of ground canopy cover that ranged from 39% up to 97% and canopy heights ranging from 0.2 to 1.8 m. The performance was tested under clear sky conditions and for stable and unstable cases during the day. The sensible heat flux was estimated using in situ LST measurements over a homogenous canopy (wheat) under stable conditions. Unstable conditions were met for two growing crops (rain-fed soybean and corn) where HSR-LST was tested using LST retrieved from Landsat 5 to 7. The sensible heat flux taken as a reference for soybean and corn was determined using the eddy covariance (EC) method and for wheat it was determined using the residual method (the evapotranspiration was measured using a large weighing lysimeter). For wheat HSR-LST performed slightly better than a calibrated one-source model. For soybean and corn, it performed similarly to one-source and two-source model types. The one-source models included two types, one requiring calibration and the other one self-calibrating, using ‘hot’ and ‘cold’ spots in the surroundings. Further testing for sparse canopies is suggested prior to the development of a two-source model in the framework of SR-LST because HSR-LST performed similarly to the other models. For corn, HSR-LST had the closest performance to HEC and for soybean the two-source model (the best) was slightly closer to HEC than HSR-LST. The root mean square differences (RMSD) comparing HSR-LST against the reference were in the range 26 (corn) – 46 (wheat) Wm−2. Therefore, HSR-LST is an alternative to consider for estimating H because it was competitive and simpler to apply than other thermal remote sensing-based methods.

Estimation of Evapotranspiration in Sparse Vegetation Areas by Applying an Optimized Two-Source Model

Evapotranspiration (ET) is an important part of the water, carbon, and energy cycles in ecosystems, especially in the drylands. However, due to the particularity of sparse vegetation, the estimation accuracy of ET has been relatively low in the drylands. Therefore, based on the dry climate and sparse vegetation distribution characteristics of the drylands, this study optimized the core algorithms (canopy boundary resistance, aerodynamic resistance, and sparse vegetation coverage) and explored an ET estimation method in the Shuttleworth–Wallace two-layer model (SW model). Then, the Beijing–Tianjin sandstorm source region (BTSSR) was used as the study area to evaluate the applicability of the improved model in the drylands. Results show that: (1) The R2 value of the improved model results was increased by 1.4 and the RMSE was reduced by 1.9 mm, especially in extreme value regions of ET (maximum or minimum). (2) Regardless of the spatial distribution and seasonal changes of the ET (63–790 mm), the improved ET estimation model could accurately capture the differences. Furtherly, the different vegetation regions could stand for the different climate regions to a certain extent. The accuracy of the optimized model was higher in the semi-arid region (R2 = 0.92 and 0.93), while the improved model had the best improvement effect in the arid region, with R2 increasing by 0.12. (3) Precipitation was the decisive factor affecting vegetation transpiration and ET, with R2 value for both exceeding 0.9. The effect of vegetation coverage (VC) was less. This method is expected to provide a more accurate and adaptable model for the estimation of ET in the drylands.

Evaluation of alternative two-source remote sensing models in partitioning of land evapotranspiration

Remote sensing (RS)-based two-source models have been widely used to separate soil evaporation (E) and vegetation transpiration (T) in ecosystems. Alternative model assumptions and physical mechanisms employed in different two-source models cause the difference in model performance. Understanding of the characteristics and limitations of alternative two-source models is critical to ensure the most effective applications in various climatic zones and underlying surfaces. This study investigated two types of land surface temperature (LST) decomposition based two-source models, namely Two-Source Energy Balance Model (TSEB) and Modified Pixel Component Arranging and Comparing Algorithm (M-PCACA); as well as a meteorological factors-based Penman-Monteith type model (PM-MU) in the partitioning of E and T. The three models were compared at 21 flux stations across China, which included seven types of underlying surfaces and one isotope station. The study demonstrated that all three models were applicable in a large scale study for latent heat flux (LE) estimation. Overall, M-PCACA performed the best with average root-mean-square errors (RMSE) of 38.7 W/m2, while PM-MU performs the worst with average RMSE of 61.4 W/m2. For ET partitioning, the two models based on LST decomposition (TSEB and M-PCACA) gave more reliable estimates compared with stable water isotope observations. In addition, verification results for the seven types of underlying surfaces showed that the TSEB gave the best accuracy in the areas with high leaf area index (LAI) and vegetation height (e.g. forest), while the PM-MU had an advantage in the areas without vegetation (e.g. deserts and water bodies). Due to the characteristics of the land surface temperature-vegetation coverage (LST-fc) trapezoidal framework, the M-PCACA model had the most stable model performance for various underlying surfaces and gave the best estimates in grassland, wetland, and farmland areas. Using a thorough discussion, this study identified the sources of discrepancies induced by different physical mechanisms of three remote sensing-based two-source models. This investigation provided insights to better understand the alternative two-source models and provides guidance for model application.

Evaluation of Penman-Monteith Model Based on Sentinel-2 Data for the Estimation of Actual Evapotranspiration in Vineyards

Water scarcity is one of the most important problems of agroecosystems in Mediterranean and semiarid areas, especially for species such as vineyards that largely depend on irrigation. Actual evapotranspiration (ET) is a variable that represents water consumption of a crop, integrating climate and biophysical variables. Actual evapotranspiration models based on remote sensing data from visible bands of Sentinel-2, including Penman-Monteith–Stewart (RS-PMS) and Penman-Monteith–Leuning (RS-PML), were evaluated at different temporal scales in a Cabernet Sauvignon vineyard (Vitis vinifera L.) located in central Chile, and their performance compared with independent ET measurements from an eddy covariance system (EC) and outputs from models based on thermal infrared data from Landsat 7 and Landsat 8, such as Mapping EvapoTranspiration with high Resolution and Internalized Calibration (METRIC) and Priestley–Taylor Two-Source Model (TSEB-PT). The RS-PMS model showed the best goodness of fit for all temporal scales evaluated, especially at instantaneous and daily ET, with root mean squared error (RMSE) of 28.9 Wm−2 and 0.52 mm day−1, respectively, and Willmott agreement index (d1) values of 0.77 at instantaneous scale and 0.7 at daily scale. Additionally, both approaches of RS-PM model were evaluated incorporating a soil evaporation estimation method, one considering the soil water content (fSWC) and the other hand, using the ratio of accumulated precipitation and equivalent evaporation (fZhang), achieving the best fit at instantaneous scale for RS-PMS fSWC method with relative root mean squared error (%RMSE) of 15.2% in comparison to 58.8% of fZhang. Finally, the relevance of the RS-PMS model was highlighted in the assessment and monitoring of vineyard drip irrigation in terms of crop coefficient (Kc) estimation, which is one of the methods commonly used in irrigation planning, yielding a comparable Kc to the one obtained by the EC tower with a bias around 9%.

Team Contingent or Sport Native? A Bayesian Analysis of Home Field Advantage in Professional Soccer

ABSTRACT Besides confirming the existence of home advantage (HA) in professional sports competition, this work intends to breakdown HA into sub-components and trace the specific sources of HA. Using scoring performance data from ESPN FC, we fit a Bayesian multilevel-nested model to the parameters in our proposed hierarchical model of HA, allowing information obtained from the season level to inform the inferences about scoring capabilities at the upper team, league, and sport levels. Our analysis reveals that much of HA is attributed to the nature of the sport of interest as well as teams playing the sport. The results seem to endorse the view that home advantage is mainly characteristic of the sport and participating teams, while league grouping can be safely ignored as a credible contributing source. Finally, we discuss the implications of our proposed two-source model of HA for future research at the inter-sport level.

Review of approaches for the estimation of sensible heat flux in remote sensing-based evapotranspiration models

We recapitulate the approaches of sensible heat flux (H) estimation, which is a critical parameter in the remote sensing (RS)-based evapotranspiration (ET) models. We propose a classification scheme for the ET models considering their distinctions in approaches for the estimation of H. Adhering to the proposed classification scheme, the theoretical backgrounds of H estimation in the single-source and two-source RS-based ET models are discussed in brief, along with their unique characteristics. We addressed the role of critical parameters that influenced the H computation under each model and presented the related progress in the research. The importance of data assimilation techniques, as well as the application of unmanned aerial vehicles for the uninterrupted estimation of turbulent heat flux, are discussed in the context of single-source and two-source models. The influence of scale on the validation of the models and the impact of the aggregation methods are discussed. We compared the performance of the popular ET models for the estimation of H, utilizing the information obtained from peer-reviewed articles. The limitations related to the RS datasets in terms of spatial and temporal resolution and the scope of alleviating the shortcomings using the future satellite missions are discussed. We conclude by pointing toward the current challenges and the prospective domain of research, which needs to be addressed critically in the future.

Characteristic spectral analysis of turbulent-flow-induced noise in rivers

This talk describes the characteristic spectra resulting from turbulent water flow in multiple sections of the Provo and Colorado Rivers in Utah. For reference these rivers are characterized as medium-sized bedrock rivers, with Strahler orders of 5 and 7, respectively. Larson Davis 831C sound level meters were used to obtain two-second, one-third octave time histories. The spectra appear to be the superposition of two characteristic spectral curves possibly resulting from two different interactions within the turbulent section of the river. This two-source model for characterizing turbulent flow induced acoustics is similar to that observed in both the characteristic spectra for jet aircraft and for turbulent plasma flow. The higher frequency curve peaks between 700 and 1100 Hz and is almost certainly due to the Minnaert Frequency associated with resonating bubbles in the stream. This process and spectral curve appear similar to the fine spectral curve associated with Jet aircraft noise. The second curve is lower in frequency and peaks around 150 Hz and is most likely a result of interaction between the turbulent water and the ground.