Post-processed data and graphical tools for a CONUS-wide eddy flux evapotranspiration dataset

Evapotranspiration (ET) is dynamic and influences water resource distribution. Sustainable management of water resources requires accurate estimations of the individual components that result in evapotranspiration, including the daily net radiation (DNR). Daily ET is more useful than the evaporative fraction (EF) provided by remote sensing ET models, and to account for daily variations, EF is usually combined with the DNR. DNR exhibits diurnal and spatiotemporal variations due to landscape heterogeneity. In the modified Two-Source Energy Balance (TSEB) approach by Zhuang and Wu, 2015, ecophysiological constraint functions of temperature and moisture of plants based on atmospheric moisture and vegetation indices were introduced, but the DNR was not spatially accounted for in the estimation of the daily ET. This research adopted a novel approach that accounts for spatiotemporal variations in estimated daily ET by incorporating the Bisht and Bras DNR model in the modified version of the TSEB model. Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite imagery over the Tono irrigation watershed within the Upper East Region of Ghana and Southern Burkina Faso were used. We estimated the energy fluxes of latent and sensible heat as well as the net radiation and soil heat fluxes from the satellite images and compared our results with ground-based measurements from an eddy covariance (EC) station established by the West African Science Service Center on Climate Change and Adapted Land Use (WASCAL) within the watershed area. We noticed a similarity between our model estimated fluxes and ET with the ground-based EC station measurements. Eight different land use/cover types were identified in the study area, and each of these contributed significantly to the overall ET variations between the two study periods: December 2009 and December 2017. For instance, due to a higher leaf area index (LAI) for all vegetation types in December 2009 than in December 2017, the ET for December 2017 was higher than that for December 2009. We also noticed that the land use/cover types within the footprint area of the EC station were only six out of the eight. Generally, all the surface energy fluxes increased from December 2009 to December 2017. Mean ET varied from 3.576 to 4.486 (mm/d) for December 2009 while from 4.502 to 5.280 (mm/d) for December 2017 across the different land use/cover classes. Knowledge of the dynamics of evapotranspiration and adoption of cost-effective methods to estimate its individual components in an effective and efficient way is critical to water resources management. Our findings provide a tool for all water stakeholders within watersheds to manage water resources in an engaging and cost-effective way.

According to 230 flux site observations, intra-day and diurnal ground heat flux (G) accounted for 19.2 % and 28.8 % of the corresponding net radiation, respectively. This indicates that G plays an important role in remote sensing (RS) evapotranspiration (ET) models. The accuracy of the five G simulation methods in the surface energy balance-based RS ET models was evaluated using half-hourly observations. The linear coefficient (LC) method and the methods embedded with the normalized difference vegetation index (NDVI) were able to accurately simulate a half-hourly G series at most sites. The mean and median Nash-Sutcliffe efficiency (NSE) values of all sites were generally higher than 0.50 in each half-hour period. However, the two methods embedded by fractional vegetation coverage showed poor performance in most half-hour periods, except the sunrise and sunset periods, with the mean and median NSE values of all sites below 0.20. The accuracy of each method varied significantly at different sites and at half-hour intervals. The highest accuracy was exhibited during sunrise periods (6:00–7:00), followed by sunset periods (17:00–18:00). There were 92 % (211/230) sites with an NSE of the LC method greater than 0.50 at 6:30. It showed a slightly higher accuracy during night periods than during daytime periods. The lowest accuracy was observed at noon periods (10:00–15:30). The sites with an NSE exceeding 0.50 only accounted for 51 % (118/230) and 43 % (100/230) at 10:30 and 13:30, respectively. The accuracy of Northern Hemisphere sites was generally higher than that of Southern Hemisphere sites. In general, the highest and lowest accuracies were observed at the high- and low-latitude sites, respectively. The performance of the LC method and the methods embedded with NDVI were generally satisfactory at the Eurasian and North American sites, with the NSE values of most sites exceeding 0.70. Conversely, it exhibited relatively poor performance at the African, South American, and Oceanian sites, especially the African sites. Both the temporal and spatial distributions of the accuracy of the G simulation were positively correlated with the correlation between G and the net radiation. Although the G simulation methods accurately simulated the G series at most sites and time periods, their performance was poor at some sites and time periods. The application of RS ET datasets covering these sites requires caution. The optimal parameter value for each method varied greatly at different sites. This indicates that the fixed parameter values in the G simulation methods are not appropriate. Further improvement of G simulations at these sites and time periods is recommended for the RS ET modelers.

Evapotranspiration (ET) is a significant consumer of irrigation water and precipitation on cropland. Global and regional interest in the sustainable management of limited freshwater supplies to meet the rapidly increasing population and food demands has resulted in advanced scientific research on ET measurement, rapid water accounting, and irrigation schedules in the NENA region. The primary goal of this paper is to compare actual daily evapotranspiration (ET) collected by a remote sensing model and validated by Energy Balance (EB) flux tower field measurements. The flux tower was installed in a wheat field in Sids Agricultural Research Station in Beni Suef Governorate. Through the integration of Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Sentinel-2 data, a new remote sensing-based ET model is built on two parties: Thermal condition factor (TCF) and vegetation condition fraction (VCF). The remote sensing-based ET estimation model was evaluated using ET field measurements from the Energy Balance flux tower. The land use and land cover maps were created to assist the interpretation of remotely sensed ET data. Field data for five categories were collected to test the accuracy of the land use and cover maps: Water bodies (93 points), urban areas (252 points), trees (104 points), other field crops (227 points), and wheat (249 points), for a total of 925 ground points. The Google Earth Engine (GEE) imported sentinel-2 datasets and filtered the necessary dates and regions. From 1 October 2020 to 30 May 2021, sentinel-2 data were processed and transformed into the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Normalized Difference Built-up Index (NDBI), which were then combined. The composite layer data were classified using the Random Forest (RF) method on the GEE platform, and the results showed an overall accuracy of 91 percent. The validation factors revealed good indices when RS-based ET results were compared to ground-measured ET. The Root Mean Square Error (RMSE) was 0.84 mm/day. The ‘r’ and ‘d’ values indicated satisfactory results, where ‘r’ yielded a value of 0.785, which indicates that the correlation between predicted and reference results is robust. The analysis of d values revealed a high degree of correlation between predicted (RS-based ET) and reference results (measured ET). The d value was found to be 0.872. Between 21 November 2020 and 30 April 2021, RS-based accumulated ET was 418 mm/season, while ground-measured ET was 376 mm/season. The new RS-based ET model produced acceptable daily and seasonal results.

One of the most important parts of the hydrological cycle is evapotranspiration (ET). Accurate estimates of ET in irrigated regions are critical to the planning, control, and regulation of agricultural natural resources. Accurate ET estimation is necessary for agricultural irrigation scheduling. ET is a nonlinear and complex process that cannot be calculated directly. Reference evapotranspiration (RET) and potential evapotranspiration (PET) are two primary forms of ET. The ideas, equations, and application areas for PET and RET are different. These two terms have been confused and used interchangeably by researchers. Therefore, terminology clarification is necessary to ensure their proper use. The research indicates that PET and RET concepts have a long and distinguished history. Thornthwaite devised the original PET idea, and it has been used ever since, although with several improvements. The development of RET, although initially confused with that of PET, was formally defined as a standard method. In this study, the Preferred Reporting Item for Systematic reviews and Meta-Analysis (PRISMA) was used. Equations for RET estimation were retrieved from 44 research articles, and equations for PET estimation were collected from 26 studies. Both the PET and RET equations were divided into three distinct categories: temperature-based, radiation-based, and combination-based. The results show that, among temperature-based equations for PET, Thornthwaite’s (1948) equation was mentioned in 12,117 publications, whereas among temperature-based equations for RET, Hargreaves and Samani’s (1985) equation was quoted in 3859 studies. Similarly, Priestley (1972) had the most highly cited equation in radiation-based PET equations (about 6379), whereas Ritchie (1972) had the most highly cited RET equations (around 2382) in radiation-based equations. Additionally, among combination-based PET equations, Penman and Monteith’s (1948) equations were cited in 9307 research studies, but the equations of Allen et al. (1998) were the subject of a significant number of citations from 23,000 publications. Based on application, PET is most often applied in the fields of hydrology, meteorology, and climatology, whereas RET is more frequently utilized in the fields of agronomy, agriculture, irrigation, and ecology. PET has been used to derive drought indices, whereas RET has been employed for single crop and dual crop coefficient approaches. This work examines and describes the ideas and methodologies, widely used equations, applications, and advanced approaches associated with PET and RET, and discusses future enhancements to increase the accuracy of ET calculation to attain accurate agricultural irrigation scheduling. The use of advanced tools such as remote sensing and satellite technologies, in addition to machine learning algorithms, will help to improve the accuracy of PET and RET estimates. Researchers will be able to distinguish between PET and RET in the future with the use of the study’s results.

Evaporation is the process whereby liquid water is converted to water vapor and removed from the evaporating surface. In hydrological practice, the estimation of evaporation can be achieved by direct or indirect methods. Direct method is based on the field measurements. Evaporation pan have also been used and compared to estimate evaporation by researchers (Choudhury, 1999; McKenzie and Craig, 2001; Vallet-Coulomb et al., 2001). The Class A evaporation pan is one of the most widely used instruments for the measurements of evaporation from a free water surface. The pan evaporation (PE) is widely used to estimate the evaporation of lakes and reservoirs (Finch, 2001). Many researchers have tried to estimate the evaporation through the indirect methods using the climatic variables, but some of these techniques require the data which cannot be easily obtained (Rosenberry et al., 2007). Evapotranspiration (ET) is the sum of volume of water used by vegetation, evaporated from the soil, and intercepted precipitation (Singh, 1988). ET plays an important role in our environment at global, regional, and local scales. ET is observed using a lysimeter directly or can be calculated using the water balance method or the climatic variables indirectly. Because the measurements of ET using a lysimeter directly, however, requires much unnecessary time and needs correct and careful experience, it is not always possible in field measurements. Thus, an empirical approach based on the climatic variables is generally used to calculate ET (Penman, 1948; Allen et al., 1989). In the early 1970s, the Food and Agricultural Organization of the United Nations (FAO), Rome, developed practical procedures to calculate the crop water requirements (Doorenbos & Pruitt, 1977), which have become the widely accepted standard for irrigation studies. A common practice for estimating ET from a well-watered agricultural crop is to calculate the reference crop ET such as the grass reference ET (ETo) or the alfalfa reference ET (ETr) from a standard surface and to apply an appropriate empirical crop coefficient, which accounts for the difference between the standard surface and the crop ET. Recently, the outstanding results using the neural networks model in the fields of PE and ET modeling have been obtained (Bruton et al., 2000; Sudheer et al., 2003; Trajkovic et al., 2003; Trajkovic, 2005; Keskin and Terzi, 2006; Kisi, 2006; Kisi, 2007; Zanetti et al., 2007; Jain et al., 2008; Kumar et al., 2008; Landeras et al., 2008; Kisi, 2009; Kumar et al., 2009; Tabari et al.,

Optimization of a remote sensing energy balance method over different canopy applied at global scale

One of the major production areas in Europe for Hardy Ornamental Nursery Stocks (HONS) is located around Pistoia (Tuscany, Italy). In this area, pot ornamental crops show low water use efficiency (WUE). Main reasons of poor irrigation efficiency are both the inaccurate irrigation scheduling, which is normally based on grower’s personal experience, and the choice to group different plant species together in the same irrigation plot in order to simplify irrigation and to reduce growing costs. An efficiency-oriented approach for the management of HONS requires the use of soil/substrate moisture sensors (SMS) and/or crop evapotranspiration (ET) model to regulate irrigation frequency. In order to compare different methods for irrigation scheduling in container beds hosting four different HONS species, two experiments were carried out in the spring-summer season of 2009 and 2010 at ‘Centro Speri-mentale per il Vivaismo’ (CESPEVI), Pistoia. In particular, we tested two irrigation control methods based on SMS or ET model as compared to conventional timer scheduling. The WET sensor was used for SMS control while the ET model consisted of a composite regression model of crop ET (as function of plant height and potential evapotranspiration) along with a specific algorithm implemented in the software of an irrigation controller prototype. Scheduling irrigation by means of the WET sensor (irrigation was activated when the matric potential of substrate was less than -5.0 kPa) or applying ET model reduced seasonal water use respectively by 40% and 45%, and water runoff by 74% and 81% in comparison with timer control strategy, without significant effects on plant ET, growth, and commercial value at the end of growing season. Both new irrigation scheduling are useful for growers, but for a large use of RZS method, a low cost RZS sensor is required. The use ET-model is less expensive, but it requires the knowledge of the Kc for the hundred of different species grown in the nurseries.

Environmental models are sensitive to inaccuracies in their approximation algorithms, which can bias model simulations and even lead to incorrect concepts. We present two alternatives to the two‐source evapotranspiration (ET) model, which utilize a more accurate approximation of the Clausius‐Clapeyron relation. Model performance was evaluated through a comparison with observed half‐hourly eddy covariance fluxes. Modeled representations of sensible heat dynamics and the limiting behaviors were also evaluated to identify the causes of model inaccuracies. Our analysis shows that the new exponential approximation used here significantly reduces errors that stem from solutions to the energy balance equations. The proposed parallel and series models are both more accurate than the Shuttleworth‐Wallace (SW) model under the conditions evaluated here, but we cannot conclude that the new models are consistently more accurate over a broader range of conditions. The new models can correctly reproduce several theoretical limiting behaviors, whereas the SW model generates conceptually incorrect results for several important limiting cases in which some key conductances approach both zero and infinity. Importantly, these models seriously underestimate the observed latent heat fluxes at night; the major causes of the nighttime model inadequacies are thought to be uncertainties of the estimated conductances and forcing data errors. The central concepts and processes related to new models are described via plots of temperature versus specific humidity to explore the theory behind an ET model. Through these efforts, we anticipate making more reliable ET predictions that will lead to a complete understanding of vegetation–atmosphere interactions.

Satellite data-driven modeling of field scale evapotranspiration in croplands using the MOD16 algorithm framework

Evapotranspiration (ET) is one of the main fluxes in the global water cycle. As the Brazilian Pampa biome carries a rich biodiversity, accurate information on the ET dynamics is essential to support its proper monitoring and establish conservation strategies. In this context, we assessed an operational methodology based on the Simplified Surface Energy Balance Index (S-SEBI) model to estimate energy fluxes over the natural grasslands of the Pampa between 2014 and 2019. The S-SEBI is an ET model that requires a minimum of meteorological inputs and has demonstrated reasonable accuracy worldwide. Therefore, we investigated the model performance considering radiation data from both ERA5 reanalysis and Eddy Covariance measurements from a flux tower. Furthermore, comparisons from satellite-based estimates with in situ measurements were performed with and without energy balance closure (EBC). Results indicated that the meteorological inputs have low sensitivity on daily ET estimates from the S-SEBI model. In contrast, the instantaneous energy balance components are more affected. The strong seasonality impacts the evaporative fraction, which is more evident in late summer and autumn and may compromise the performance of the model in the biome. The effects in the daily ET are lower when in situ data without EBC are considered as ground truth. However, they are less correlated with the remote sensing-based estimates. These insights are useful to monitor water and energy fluxes from local to regional scale and provide the opportunity to capture ET trends over the natural grasslands of the Pampa.

Using evapotranspiration to assess drought sensitivity on a subfield scale with HRMET, a high resolution surface energy balance model

An ensemble data assimilation approach to improve farm-scale actual evapotranspiration estimation

Long-term monitoring of evapotranspiration using the SEBAL algorithm and Google Earth Engine cloud computing

Core Ideas Increased bias in ET due to various data sources where generally utilized standard weather data poses similar biases. Increased understanding of the complex relationship among the various weather input and vegetation indices. Results showed local weather station data closely represented site condition indicating the difficulties and challenges in ET modeling using other commonly available and widely adopted data sources. ABSTRACTComplex interactions among meteorological data and vegetation indices are incompletely understood in relation to evapotranspiration (ET) calculations for larger spatial domains with higher spatial and temporal resolution. Objectives of this study were to evaluate contributions of inputs to uncertainty in ET calculations and to enhance understanding of interactions among weather data, vegetative indices, and resistances utilized in biophysical ET model. We evaluated individual and combined effects of weather variables and vegetation indices using BAITSSS (Backward‐Averaged Iterative Two‐Source Surface temperature and energy balance Solution). Local weather station (LWS) data at a lysimeter site were obtained for irrigated corn (Zea mays L.) during the growing season (May to September, 2016) at Bushland, Texas. Gridded meteorological data were obtained from North American Land Data Assimilation System (NLDAS) (∼ 12.5 km) and remotely‐sensed vegetation indices (Landsat 30 m). Standard weather station (SWS) data were obtained from a grass reference near lysimeter site. The r2 and RMSE of ET simulated using LWS data and measured vegetation indices were 0.90 and 0.85 mm for daily ET, and 0.90 and 0.10 mm, for hourly ET, compared to lysimeter ET (less than 4% cumulative error). However, r2 and RMSE were 0.74 and 1.64 mm for daily ET, and 0.81 and 0.14 mm for hourly ET using gridded data, with positive bias (∼ 25% from NLDAS data). Simulated ET from SWS data exhibited similar behavior to gridded data with increased ET up to 21%. Results quantify difficulties in ET modeling using commonly available and widely adopted data sources.

Multi-scale evaluation of global evapotranspiration products derived from remote sensing images: Accuracy and uncertainty