Two Alternatives to the Two‐Source Energy Balance Evapotranspiration Model

Evapotranspiration (ET) is a typical biological environmental process to influence leaf temperature, crop water requirement, and greenhouse microclimate. Affecting factors of ET include air temperature, air relative humidity, wind speed, solar radiation, longwave radiation, soil moisture, CO2 concentration, and crop state. In this study, two ET models of indoor cultivation commonly adopted in literature were selected to evaluate the effect of the performance of sensors on the model uncertainty. The method of the International Organization for Standardization, Guides to the expression of Uncertainty in Measurement (ISO GUM) was adopted. The result indicated that the performance of leaf area index (LAI) and solar radiation (Is) sensors were primary sources of uncertainty. The uncertainty of ET models due to sensor performance needs to be considered. To ensure the predictive ability for applying the ET model for crops irrigation management and greenhouse environmental control, the improvements in the measurement of environmental variables for calculating ET would be of particular importance. The method of this study can be used for evaluating the uncertainty of ET models that calculate ET based on environmental variables measured by meteorological sensors or the remote sensing technique.

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.

Remote sensing tools are becoming increasingly important for providing spatial information on water use by different ecosystems. Despite significant advances in remote sensing based evapotranspiration (ET) models in recent years, important information gaps still exist on the accuracy of the models particularly in arid and semi-arid environments. In this study, we evaluated the Penman-Monteith based MOD16 and the modified Priestley-Taylor (PT-JPL) models at the daily time step against three measured ET datasets. We used data from two summer and one winter rainfall sites in South Africa. One site was dominated by native broad leaf and the other by fine leafed deciduous savanna tree species and C4 grasses. The third site was in the winter rainfall Cape region and had shrubby fynbos vegetation. Actual ET was measured using open-path eddy covariance systems at the summer rainfall sites while a surface energy balance system utilizing the large aperture boundary layer scintillometer was used in the Cape. Model performance varied between sites and between years with the worst estimates (R2<0.50 and RMSE>0.80 mm/d) observed during years with prolonged mid-summer dry spells in the summer rainfall areas. Sensitivity tests on MOD16 showed that the leaf area index, surface conductance and radiation budget parameters had the largest effect on simulated ET. MOD16 ET predictions were improved by: (1) reformulating the emissivity expressions in the net radiation equation; (2) incorporating representative surface conductance values; and (3) including a soil moisture stress function in the transpiration sub-model. Implementing these changes increased the accuracy of MOD16 daily ET predictions at all sites. However, similar adjustments to the PT-JPL model yielded minimal improvements. We conclude that the MOD16 ET model has the potential to accurately predict water use in arid environments provided soil water stress and accurate biome-specific parameters are incorporated.

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

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.

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.

Improving estimation of evapotranspiration during soil freeze-thaw cycles by incorporating a freezing stress index and a coupled heat and water transfer model into the FAO Penman-Monteith model

Wildfires represent a significant threat to both ecosystems and human assets in Mediterranean countries, where fire occurrence is frequent and often devastating. Accurate assessments of the initial fire severity are required for management and mitigation efforts of the negative impacts of fire. Evapotranspiration (ET) is a crucial hydrological process that links vegetation health and water availability, making it a valuable indicator for understanding fire dynamics and ecosystem recovery after wildfires. This study uses the Mapping Evapotranspiration at High Resolution with Internalized Calibration (eeMETRIC) and Operational Simplified Surface Energy Balance (SSEBop) ET models based on Landsat imagery to estimate fire severity in five large forest fires that occurred in Spain and Portugal in 2022 from two perspectives: uni- and bi-temporal (post/pre-fire ratio). Using-fine-spatial resolution ET is particularly relevant for heterogeneous Mediterranean landscapes with different vegetation types and water availability. ET was significantly affected by fire severity according to eeMETRIC (F &gt; 431.35; p-value &lt; 0.001) and SSEBop (F &gt; 373.83; p-value &lt; 0.001) metrics, with reductions of 61.46% and 63.92%, respectively, after the wildfire event. A Random Forest machine learning algorithm was used to predict fire severity. We achieved higher accuracy (0.60 &lt; Kappa &lt; 0.67) when employing both ET models (eeMETRIC and SSEBop) as predictors compared to utilizing the conventional differenced Normalized Burn Ratio (dNBR) index, which resulted in a Kappa value of 0.46. We conclude that both fine resolution ET models are valid to be used as indicators of fire severity in Mediterranean countries. This research highlights the importance of Landsat-based ET models as accurate tools to improve the initial analysis of fire severity in Mediterranean countries.

Abstract. It remains unclear how the selection of a spatial domain affects the accuracy of evapotranspiration (ET) estimates from contextual remote sensing based surface energy balance (SEB) models, particularly at large spatial scales. We thus tested the effect of spatial domain on four widely implemented contextual remote sensing based SEB models: Surface Energy Balance Algorithm for Land (SEBAL), Mapping ET with Internalized Calibration (METRIC), Simplified Surface Energy Balance Index (S-SEBI), and Triangular ET models. We applied these models on 44 near cloud-free Moderate Resolution Imaging Spectroradiometer (MODIS) thermal images across all of India from 2004 to 2006. Four spatial domains were considered: all of India, agro-ecological regions, 300 km × 300 km grids, and 600 km × 600 km grids and we compared hourly ET estimates from the models against observed ET data at four Bowen Ratio sites in India. Model performance varied across all models and spatial domains. ET values of neighboring pixels across spatial domains formed sharp edges along the boundaries of agro-ecological regions, 300 km × 300 km grids, and 600 km × 600 km grids suggesting that all ET models are highly sensitive to the selection of spatial domain. No single spatial domain was found to be optimal for all models and hence potential uncertainties associated with the selection of spatial domain should be taken into consideration when implementing these models at regional scales. The results from this study provide guidance for future regional-scale implementation of ET models and potential approaches to overcome these challenges.

Comparison of satellite-based evapotranspiration models over terrestrial ecosystems in China

Prediction model for daily reference crop evapotranspiration based on hybrid algorithm and principal components analysis in Southwest China

&lt;p class="MsoNormal" style="text-align: justify; text-justify: inter-ideograph; line-height: 120%; mso-pagination: none; layout-grid-mode: char; mso-layout-grid-align: none; punctuation-wrap: simple; text-autospace: none; mso-line-break-override: restrictions; margin: 12.0pt 0cm 6.0pt 0cm;"&gt;&lt;span lang="EN-US" style="font-size: 10.0pt; line-height: 120%; font-family: 'Times New Roman',serif; layout-grid-mode: line;"&gt;Evapotranspiration (ET) modeling plays a vital role in water resource management, agriculture, and climate adaptation. Accurate ET prediction is essential for effective irrigation planning and crop management. However, traditional methods often struggle to capture the complex relationships between environmental factors, resulting in less reliable forecasts. To address this, we implemented and optimized the Long Short-Term Memory (LSTM) network model to predict ET with improved accuracy of 98.8%, achieving a Mean Squared Error (MSE) of 0.12. Our approach incorporates SHapley Additive exPlanations (SHAP) to enhance model interpretability, offering insights into how key factors like solar radiation, wind speed, air temperature, and relative humidity impact ET predictions. The results showed that solar radiation had the highest impact on ET, followed by wind speed and air temperature. This improved understanding of key factors can help farmers and water managers make better decisions about irrigation, ensuring efficient water use and supporting sustainable agriculture. This provides a reliable and interpretable solution for ET prediction, aiding smarter irrigation strategies, improving resource efficiency, and supporting sustainable agricultural practices.&lt;/span&gt;&lt;/p&gt;

Large sample datasets of in situ evapotranspiration (ET) measurements with well documented data provenance and quality assurance are critical for water management and many fields of earth science research. We present a post-processed ET oriented dataset at daily and monthly timesteps, from 161 stations, including 148 eddy covariance flux towers, that were chosen based on their data quality from nearly 350 stations across the contiguous United States. In addition to ET, the data includes energy and heat fluxes, meteorological measurements, and reference ET downloaded from gridMET for each flux station. Data processing techniques were conducted in a reproducible manner using open-source software. Most data initially came from the public AmeriFlux network, however, several different networks (e.g., the USDA-Agricultural Research Service) and university partners provided data that was not yet public. Initial half-hourly energy balance data were gap-filled and aggregated to daily frequency, and turbulent fluxes were corrected for energy balance closure error using the FLUXNET2015/ONEFlux energy balance ratio approach. Metadata, diagnostics of energy balance, and interactive graphs of time series data are included for each station. Although the dataset was developed primarily to benchmark satellite-based remote sensing ET models of the OpenET initiative, there are many other potential uses, such as validation for a range of regional hydrologic and atmospheric models.

Abstract. We present an evapotranspiration (ET) model developed in the framework of the EUMETSAT "Satellite Application Facility" (SAF) on Land Surface Analysis (LSA). The model is a simplified Soil-Vegetation-Atmosphere Transfer (SVAT) scheme that uses as input a combination of remote sensed data and atmospheric model outputs. The inputs based on remote sensing are LSA-SAF products: the Albedo (AL), the Downwelling Surface Shortwave Flux (DSSF) and the Downwelling Surface Longwave Flux (DSLF). They are available with the spatial resolution of the MSG SEVIRI instrument. ET maps covering the whole MSG field of view are produced from the model every 30 min, in near-real-time, for all weather conditions. This paper presents the adopted methodology and a set of validation results. The model quality is evaluated in two ways. First, ET results are compared with ground observations (from CarboEurope and national weather services), for different land cover types, over a full vegetation cycle in the Northern Hemisphere in 2007. This validation shows that the model is able to reproduce the observed ET temporal evolution from the diurnal to annual time scales for the temperate climate zones: the mean bias is less than 0.02 mm h−1 and the root-mean square error is between 0.06 and 0.10 mm h−1. Then, ET model outputs are compared with those from the European Centre for Medium-Range Weather Forecasts (ECMWF) and the Global Land Data Assimilation System (GLDAS). From this comparison, a high spatial correlation is noted, between 80 to 90%, around midday. Nevertheless, some discrepancies are also observed and are due to the different input variables and parameterisations used.