Instantaneous Evapotranspiration Research Articles

Improving time upscaling of instantaneous evapotranspiration based on machine learning models

ABSTRACT Evapotranspiration (ET) plays a crucial role in the global water and energy cycle. Upscaling instantaneous ET ( E T i ) to daily ET ( E T d ) is vital for thermal-based ET estimation. Conventional methods – such as the constant evaporative fraction method (ConEF), radiation-based method, and evaporative ratio method – often overlook environmental factors, leading to biased estimates of E T d from E T i . To resolve this issue, this study aimed to assess four machine learning (ML) algorithms—XGBoost, LightGBM, AdaBoost, and Random Forest—to integrate meteorological and remote sensing data for upscaling E T i across 88 global flux sites. Each ML model was tested with eight different variable combinations. Results indicated that XGBoost exhibited the best performance, with a root mean square error (RMSE) generally below 13 W m − 2 in estimating E T d from E T i . The best variable combination simultaneously considers evaporative fraction, available energy, meteorology factors, remote sensing albedo, normalized vegetation index, and leaf area index. Using this combination, the XGBoost model achieved an R 2 = 0.88 and an RMSE = 12.33 W m − 2 , outperforming the ConEF method ( R 2 = 0.71 and RMSE = 18.86 W m − 2 ) and its expansions. These findings support the application of ML models in ET upscaling, enabling ET estimation across large spatiotemporal scales.

Quantifying energy fluxes in Tarai region of India during post-monsoon season: Insights from METRIC model, ET station and remote sensing

Accurate evapotranspiration (ET) assessment is crucial for agricultural water management, encompassing crop water requirements, irrigation scheduling, water budgeting and drought monitoring. This study integrates remote sensing-based surface energy balance model with in-situ ET measurements to evaluate surface energy fluxes and ET in Pantnagar, Tarai region. The Mapping Evapotranspiration at high Resolution with Internalized Calibration (METRIC) model, using high-resolution remote sensing data, was validated against observations from an ET station equipped with large aperture scintillometer and micrometeorological tower, situated in sugarcane farm at Govind Ballabh Pant University of Agriculture & Technology (GBPUA&T), Pantnagar. On November 13, 2021, METRIC and Landsat-9 satellite data estimated an instantaneous ET of 7.39 mm day-1, closely aligned with the observed value of 6.72 mm day-1 recorded by the ET station. The findings confirm the METRIC model's high accuracy for spatial ET estimation and its associated micrometeorological variables. This study underscores the utility of the METRIC model, ET station and remote sensing in determining ET and energy flux which may be further utilised in the estimation of crop water requirement, energy fluxes and irrigation water management for sugarcane cultivation in the Tarai region.

A comparative spectral assessment approach of SEBAL and SEBS for actual evaporation estimation in Ardabil Province

Evapotranspiration is a crucial process in the Earth's water and climate cycle, responsible for transforming water from liquid to water vapor. This transformation plays a vital role in the global water cycle and has a significant impact on the climate, weather patterns, and precipitation in various regions. In this study, actual evapotranspiration in the Mughan plain of Ardabil province has been estimated using spectral data from the OLI and TIRS sensors of the Landsat 8 satellite. The SEBAL (Surface Energy Balance Algorithm for Land) and SEBS (Surface Energy Balance System) methods have been employed to calculate these phenomena. SEBAL is an energy balance algorithm designed for land surfaces, utilizing satellite data to estimate actual evapotranspiration accurately. Similarly, SEBS is a surface energy balance system that provides a more precise estimation of evapotranspiration and transpiration rates. Data from the OLI and TIRS sensors of Landsat 8 were collected from the study area for this research. By applying the SEBAL and SEBS methods to these data, actual evapotranspiration values in the Mughan plain were obtained. The results indicated that SEBAL showed a broader range of actual evapotranspiration values (0.74 to 5.8 mm) compared to SEBS (1.25 to 8.85 mm), highlighting its ability to distinguish different regions with varying evapotranspiration rates. However, SEBAL's implementation is more computationally demanding than SEBS, making the calculation process time-consuming. Overall, both algorithms demonstrated relatively high capabilities in calculating instantaneous evapotranspiration using spectral data. The findings of this study can be valuable for water resources and agricultural management in the research area, as well as for water resource planning and environmental studies.

Evapotranspiration of Residential Lawns Across the United States

AbstractDespite interest in the contribution of evapotranspiration (ET) of residential turfgrass lawns to household and municipal water budgets across the United States, the spatial and temporal variability of residential lawn ET across large scales is highly uncertain. We measured instantaneous ET (ETinst) of lawns in 79 residential yards in six metropolitan areas: Baltimore, Boston, Miami, Minneapolis‐St. Paul (mesic climates), Los Angeles and Phoenix (arid climates). Each yard had one of four landscape types and management practices: traditional lawn‐dominated yards with high or low fertilizer input, yards with water‐conserving features, and yards with wildlife‐friendly features. We measured ETinst in situ during the growing season using portable chambers and identified environmental and anthropogenic factors controlling ET in residential lawns. For each household, we used ETinst to estimate daily ET of the lawn (ETdaily) and multiplied ETdaily by the lawn area to estimate the total volume of water lost through ET of the lawn (ETvol). ETdaily varied from 0.9 ± 0.4 mm d1 in mesic cities to 2.9 ± 0.7 mm d−1 in arid cities. Neither ETinst nor ETdaily was significantly influenced by yard landscape types and ETinst patterns indicated that lawns may be largely decoupled from regional rain‐driven climate patterns. ETvol ranged from ∼0 L d−1 to over 2,000 L d−1, proportionally increasing with lawn area. Current irrigation and lawn management practices did not necessarily result in different ETinst or ETdaily among traditional, water‐conserving, or wildlife‐friendly yards, but smaller lawn areas in water‐conserving and wildlife‐friendly yards resulted in lower ETvol.

Daily Evapotranspiration Estimations by Direct Calculation and Temporal Upscaling Based on Field and MODIS Data

Daily evapotranspiration (ET) integration is essential to various applications of agricultural water planning and management, ecohydrology, and energy balance studies. The constant reference evaporative fraction (EFr) temporal upscaling method has been proven to be efficient in extrapolating instantaneous ET to a daily timescale. Unlike upscaling methods, the direct calculation (DC) method developed in our previous study directly estimates daily ET without calculating instantaneous ET. The present study aimed to compare daily estimations of ET using the EFr and DC methods based on field and MODIS data at a site from the ChinaFLUX network. The estimation results were validated by eddy covariance (EC) ET both with and without the correction of energy imbalance. Based on field data, the results show that (i) the DC method performed with higher accuracy when compared to uncorrected EC measurements, while daily ET from both methods was overestimated; (ii) the DC method still performed better after the EC ET was corrected by the Residual Energy scheme, and the overestimations were significantly decreased; (iii) both methods performed best when compared with corrected ET by the Bowen Ratio scheme. The results from satellite data reveal that (i) the constant EFr method overestimated daily ET by a mean-bias-error (MBE) of 5.6 W/m2, and a root-mean-square error (RMSE) of 18.6 W/m2; and (ii) the DC method underestimated daily ET by a smaller MBE of −4.8 W/m2 and an RMSE of 22.5 W/m2. Therefore, the DC method has similar or better performance than the widely used constant EFr upscaling method and can estimate daily ET directly and effectively.

Assessing and modeling diurnal temperature buffering and evapotranspiration dynamics in forest restoration using ECOSTRESS thermal imaging

Restoration of temperate forests alters the diurnal dynamics of land surface temperature (LST) and evapotranspiration (ET) through increases in biomass, diversity, and complexity. The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) is the first space-based thermal imaging instrument that allows for a study of diurnal LST and ET dynamics at a moderate spatial resolution (70 × 70 m).We quantified, compared, and modeled the LST and ET dynamics of two groups of forest restoration sites (43 sites in total, between 2 and 75 ha) in Southern Ontario, Canada that were initially restored from mainly agriculture between 2007 and 2019. We included all useable LST (n = 29) and ET (n = 9) ECOSTRESS image products from a full growing season in 2020 (June 1st to September 30th). Forest restoration sites were compared by age and against agriculture (pre-restoration state), mature forest (post-restoration state), and suburban residential sites (competing land use).The ability of forest restoration sites to buffer temperatures was highest in the afternoon, around 14:00 local time (EDT). As predicted, restoration sites were significantly cooler (x̄ 4.4–7.4 °C) than residential and agricultural areas and significantly warmer (x̄1.6–2.9 °C) compared to mature reference forest sites at both groups of sites. Relative diurnal (24-h) LST variability of forest and restoration sites was also significantly lower (0.9–2.9 °C) compared to agriculture and residential sites (3.3–5.2 °C). Daytime LST decreased significantly by 0.1 °C (3.1%), per year since restoration for one of the groups of sites relative to nearby mature reference forest sites as per a linear mixed effects regression model. It would take these sites ~30 years to reach the same buffering as mature forests. In characterizing ET dynamics of a subset of sites, we found that more recently restored sites had a statistically significant higher overall ET than older ones and that daytime relative instantaneous ET decreased with years since restoration. The variation explained by the ET model was however low.Our study provides insights into how diurnal forest ecosystem energy conversion and storage dynamics changes over time after restoration. These diurnal thermal dynamics impact wildlife habitat as well as human wellbeing. The change in thermal buffering over time could be used to assess the pace and trajectory of restoration by managers. The thermal buffering provided by restored forest can also be quantified as an ecosystem service. As such, the study demonstrates the utility and also limitations of novel thermal remote sensing methods, using free and publicly available imagery from ECOSTRESS.

Sensitivity of METRIC-based tree crop evapotranspiration estimation to meteorology, land surface parameters and domain size

Evapotranspiration (ET), a key component of crop water balance, is essential for growers to manage agricultural irrigation. METRIC model is increasingly used to estimate and map ET with satellite observations, especially at daily and field scales. However, the analysis of its uncertainties on tree crop ET estimation is very limited. We used METRIC model to estimate daily ET over the almond and pistachio orchards throughout the entire growing season in the San Joaquin Valley, California, and evaluated the impacts of variations in meteorological variables, land surface parameters and different study sizes on ET estimation. The results showed that the METRIC-based daily ET estimates of the almond and pistachio trees were most sensitive to net radiation (Rn), followed by instantaneous reference ET (ETo), land surface temperature (LST), and near-surface temperature difference (dT). When a 5% error was added to Rn, the uncertainty in daily ET estimates was up to 8.44 ± 1.24%, corresponding to a mean increase of 0.45 ± 0.02 mm/day throughout the entire growing season. A 5% or 0.5 K error in instantaneous ETo, LST, and dT also caused changes in daily ET estimates by 4.15 ± 3.75%, 2.97 ± 0.14%, and 2.31 ± 1.20%, respectively. ET estimates with METRIC were relatively much less sensitive to other variables. The results from different study sizes indicated that daily ET estimates in Size-2 were 0.33 ± 0.08 mm/day overestimation compared with those from Size-1 in the almond site. Similarly, a mean overestimation of 0.30 ± 0.14 mm/day was found between Size-1 and Size-3 in the pistachio sites throughout the entire growing season. This study demonstrates the potential to further improve the accuracy of remote sensing based ET estimation for complex tree crops with METRIC model, by optimizing key parameters and choosing a proper domain size for study area.

Comparison of evapotranspiration upscaling methods from instantaneous to daytime scale for tea and wheat in southeast China

Accurately converting instantaneous evapotranspiration (λETi) at satellite over-passing time into daily evapotranspiration (λETd) is a key issue of applying remotely sensed data to estimate regional evapotranspiration (λET) from remote sensing satellites, which plays an important role for effective water resource management. The scaling methods that take advantage of the relationship between λET and other environmental factors and can be used to convert λETi into λETd. In this study, five scaling methods of converting λETi into λETd, including the evaporative fraction method (Eva-f method), revised evaporative fraction method (R-Eva-f method), crop coefficient method (Kc-ET0 method), revised crop coefficient method (R-Kc-ET0 method) and direct canopy resistance method (Direct-rc method), were evaluated based on the detailed meteorological data measured from 2016 to 2018 in a tea field and 2018 to 2020 in a wheat field in southeast China. The estimated λETd was compared with the measured λETd by the Bowen ratio energy balance (BREB) method. The results indicated that the Eva-f and R-Eva-f methods with the mean root mean square error (RMSE) and coefficient of efficiency (ε) equaled 9.02 W m−2 and 0.92; 12.06 W m−2 and 0.89, respectively, were superior to the Kc-ET0, R-Kc-ET0 and Direct-rc method; the Kc-ET0 method with the mean RMSE and ε equaled 20.62 W m−2 and 0.79 was also a good option for simulating the λETd of tea and wheat; while the R-Kc-ET0 method simulated the λETd well for the wheat with mean RMSE and ε equaled 36.29 W m−2 and 0.71, but significantly overestimated the tea λETd with the mean RMSE and ε values of 39.61 W m−2 and 0.59 for tea; the Direct-rc method overestimated λETd of tea and wheat for the most of intervals with the mean RMSE and ε of 39.58 W m−2 and 0.62, and was not recommended to use in the present study areas.

Assessing geeSEBAL automated calibration and meteorological reanalysis uncertainties to estimate evapotranspiration in subtropical humid climates

The application of energy balance models for estimation of evapotranspiration (ET) still has challenges to be addressed for large scale applications. The algorithm for automated calibration using inverse modeling at extreme conditions (CIMEC) is based on the definition of endmembers that represent the extreme conditions of the ET spectrum, between hot (dry and sparse vegetation) and cold (wet and dense vegetation) surfaces, with pre-defined quantiles for the endmember selection. The main goal was to assess geeSEBAL algorithm uncertainties related to the (i) automated calibration, including the use of additional filters (land cover, homogeneity, and domain area) and (ii) the use of a global climate grid as input data. Based on a sensitivity analysis, we defined new set of quantiles to increase the accuracy of ET estimates in subtropical humid climates, since the default quantiles were adjusted to semiarid climates with dry summers. To validate our ET estimates we used eddy covariance measurements from five flux towers located in the South of Brazil. Processing 132 Landsat cloud free images and using adjusted quantiles, we found a root mean square error (RMSE) of 0.91 mm d − 1 and a coefficient of determination (R²) of 0.82 with geeSEBAL driven by meteorological measurements. Using the pre-defined quantiles, we found an RMSE of 1.16 mm d − 1 (27% higher) and R² of 0.75. The upscaling instantaneous ET to daily ET resulted in an underestimation of the daily ET using the pre-defined quantiles, while the optimized quantiles corrected the daily estimates. Furthermore, our results suggested a low sensitivity of geeSEBAL to meteorological inputs, since RMSE slightly increased to 1.04 mm d − 1 (14.3% higher) and R² decreased to 0.76 (8.5% smaller) when driven by global climate data. For data scarce areas, geeSEBAL is a feasible alternative for cropland ET estimation and water resources management in subtropical humid climates.

The accuracy of temporal upscaling of instantaneous evapotranspiration to daily values with seven upscaling methods

Abstract. This study evaluated the accuracy of seven upscaling methods in simulating daily latent heat flux (LE) from instantaneous values using observations from 148 global sites under all sky conditions and at different times during the day. Daily atmospheric transmissivity (τ) was used to represent the sky conditions. The results showed that all seven methods could accurately simulate daily LE from instantaneous values. The mean and median of Nash–Sutcliffe efficiency were 0.80 and 0.85, respectively, and the corresponding determination coefficients were 0.87 and 0.90, respectively. The sine and Gaussian function methods simulated mean values with relatively higher accuracy, with relative errors generally within ±10 %. The evaporative fraction (EF) methods, which use potential evapotranspiration and incoming shortwave radiation, performed relatively better than the other methods in simulating daily series. Overall, the EF method using potential evapotranspiration had the highest accuracy. However, the sine function and the EF method using extraterrestrial solar irradiance are recommended in upscaling applications because of the relatively minimal data requirements of these methods and their comparable or relatively higher accuracy. The intra-day distribution of the LE showed greater consistency with the Gaussian function than the sine function. However, the accuracy of simulated daily LE series using the Gaussian function method did not improve significantly compared with the sine function method. The simulation accuracy showed a minor difference when using the same type of method, for example, the same type of mathematical function or EF method. In any upscaling scheme, the simulation accuracy from multi-time values was significantly higher than that from a single-time value. Therefore, when multi-time data are available, multi-time values should be used in evapotranspiration upscaling. The upscaling methods show the ability to accurately simulate daily LE from instantaneous values from 09:00 to 15:00, particularly for instantaneous values between 11:00 and 14:00. However, outside of this time range the upscaling methods performed poorly. These methods can simulate daily LE series with high accuracy at τ > 0.6; when τ < 0.6, simulation accuracy is significantly affected by sky conditions and is generally positively related to daily atmospheric transmissivity. Although every upscaling scheme can accurately simulate daily LE from instantaneous values at most sites, this ability is lost at tropical rainforest and tropical monsoon sites.

Assessing Daily Evapotranspiration Methodologies from One-Time-of-Day sUAS and EC Information in the GRAPEX Project.

Daily evapotranspiration (ETd) plays a key role in irrigation water management and is particularly important in drought-stricken areas, such as California and high-value crops. Remote sensing allows for the cost-effective estimation of spatial evapotranspiration (ET), and the advent of small unmanned aerial systems (sUAS) technology has made it possible to estimate instantaneous high-resolution ET at the plant, row, and subfield scales. sUAS estimates ET using “instantaneous” remote sensing measurements with half-hourly/hourly forcing micrometeorological data, yielding hourly fluxes in W/m2 that are then translated to a daily scale (mm/day) under two assumptions: (a) relative rates, such as the ratios of ET-to-net radiation (Rn) or ET-to-solar radiation (Rs), are assumed to be constant rather than absolute, and (b) nighttime evaporation (E) and transpiration (T) contributions are negligible. While assumption (a) may be reasonable for unstressed, full cover crops (no exposed soil), the E and T rates may significantly vary over the course of the day for partially vegetated cover conditions due to diurnal variations of soil and crop temperatures and interactions between soil and vegetation elements in agricultural environments, such as vineyards and orchards. In this study, five existing extrapolation approaches that compute the daily ET from the “instantaneous” remotely sensed sUAS ET estimates and the eddy covariance (EC) flux tower measurements were evaluated under different weather, grapevine variety, and trellis designs. Per assumption (b), the nighttime ET contribution was ignored. Each extrapolation technique (evaporative fraction (EF), solar radiation (Rs), net radiation-to-solar radiation (Rn/Rs) ratio, Gaussian (GA), and Sine) makes use of clear skies and quasi-sinusoidal diurnal variations of hourly ET and other meteorological parameters. The sUAS ET estimates and EC ET measurements were collected over multiple years and times from different vineyard sites in California as part of the USDA Agricultural Research Service Grape Remote Sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX). Optical and thermal sUAS imagery data at 10 cm and 60 cm, respectively, were collected by the Utah State University AggieAir sUAS Program and used in the Two-Source Energy Balance (TSEB) model to estimate the instantaneous or hourly sUAS ET at overpass time. The hourly ET from the EC measurements was also used to validate the extrapolation techniques. Overall, the analysis using EC measurements indicates that the Rs, EF, and GA approaches presented the best goodness-of-fit statistics for a window of time between 1030 and 1330 PST (Pacific Standard Time), with the Rs approach yielding better agreement with the EC measurements. Similar results were found using TSEB and sUAS data. The 1030–1330 time window also provided the greatest agreement between the actual daily EC ET and the extrapolated TSEB daily ET, with the Rs approach again yielding better agreement with the ground measurements. The expected accuracy of the upscaled TSEB daily ET estimates across all vineyard sites in California is below 0.5 mm/day, (EC extrapolation accuracy was found to be 0.34 mm/day), making the daily scale results from TSEB reliable and suitable for day-to-day water management applications.

Mapping Very-High-Resolution Evapotranspiration from Unmanned Aerial Vehicle (UAV) Imagery

There is a growing concern about water scarcity and the associated decline in Australia’s agricultural production. Efficient water use as a natural resource requires more precise and adequate monitoring of crop water use and irrigation scheduling. Therefore, accurate estimations of evapotranspiration (ET) at proper spatial–temporal scales are critical to understand the crop water demand and uptake and to enable optimal irrigation scheduling. Remote sensing (RS)-based ET estimation has been adopted as a method for large-scale applications when the detailed spatial representation of ET is required. This research aimed to estimate instantaneous ET using very-high-resolution (VHR) multispectral and thermal imagery (GSD < 8 cm) collected using a single flight of a UAV over a high-density peach orchard with a discontinuous canopy. The energy balance component estimation was based on the high-resolution mapping of evapotranspiration (HRMET) model. A tree-by-tree ET map was produced using the canopy surface temperature and the leaf area index (LAI) resampled at the corresponding scale via a systematic feature segmentation method based on pure canopy extraction. Results showed a strong linear relationship between the estimated ET and the leaf transpiration (n = 42) measured using a gas exchange sensor, with a coefficient of determination (R2) of 0.89. Daily ET (5.5 mm d−1) derived from the instantaneous ET map was comparable with daily crop ET (6.4 mm d−1) determined by the meteorological approach over the study site. The proposed approach has important implications for mapping tree-by-tree ET over horticultural fields using VHR imagery.

Upscaling evapotranspiration from the instantaneous to the daily time scale: Assessing six methods including an optimized coefficient based on worldwide eddy covariance flux network

Remote sensing plays an irreplaceable role in mapping evapotranspiration (ET) at the regional scale. Instantaneous ET can be derived by multiple ET models based on various remotely sensed data at the time of satellite overpass. Therefore, a reliable temporal upscaling method from instantaneous ET to daily ET is of great importance. In this study, six widely used upscaling methods for estimating daily ET from instantaneous ET were compared under different ecosystems and meteorological conditions based on worldwide eddy covariance flux network. In total, 139 flux sites based on eddy ccovariance systems at the global scale were used, and the 9:00–15:00 overpass times of the main growing season (May to September in the Northern Hemisphere and November to March in the Southern Hemisphere) were chosen as the study period. Five assessment metrics (coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), index of agreement (d), and relative error (RE)) were selected to evaluate the accuracy and applicability of the six upscaling methods. The results showed that the modified evaporative fraction (MEF) method and the sine method (only at specific times) performed best for most ecosystems, but the optimal instantaneous time was quite different for different ecosystems. The MEF method worked best at 12:00 for forests and shrublands and attained the smallest MAE at 13:00 for grasslands and savannas. For wetlands and croplands, the MEF method performed best at 14:00. More attention should be paid to the selected instantaneous time when using the sine method because of its high sensitivity to time of day. The modified coefficient of the MEF method, which varied with instantaneous time and ecosystem, ranging from 1.04 (grassland) to 1.36 (wetland), was also examined. The comprehensive results of this study could be useful for mapping daily ET from instantaneous ET.

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%.

Modeling Evapotranspiration of Winter Wheat Using Contextual and Pixel-Based Surface Energy Balance Models

HighlightsThree contextual-based (CB) and two pixel-based (PB) models were evaluated to estimate ET of rainfed winter wheat.Instantaneous available energy estimation and ET upscaling impacted model performance.The CB models performed better at instantaneous and daily scales compared to the PB models.ET estimation biases increased during low vegetation and drier conditions, especially for the PB models.Abstract. Surface energy balance (SEB) models based on thermal remote sensing data are widely used in research applications to map evapotranspiration (ET) across various landscapes. However, their ability to capture ET from winter wheat remains underexplored, especially in practical applications such as integrated resource management and drought preparedness. Investigating winter wheat ET dynamics is important in agricultural regions such as the Southern Great Plains of the U.S., where winter wheat is extensively cultivated. The goal of this study was to evaluate the performance of five fully automated SEB models, three contextual-based (CB) and two pixel-based (PB), in estimating instantaneous and daily ET of winter wheat by comparing the model results with flux tower observations. The CB models included Surface Energy Balance Algorithm for Land (SEBAL), Mapping Evapotranspiration at high Resolution with Internalized Calibration (METRIC), and Triangular Vegetation Temperature (TVT). The PB models included Surface Energy Balance System (SEBS) and Two-Source Energy Balance (TSEB). Model evaluation during two winter wheat growing seasons (2016-2018) using 28 Landsat images showed that the instantaneous ET estimates from METRIC and TSEB had the smallest (RMSE = 0.14 mm h-1) and largest (RMSE = 0.27 mm h-1) errors, respectively. At the daily scale, SEBAL was the best performing model (RMSE = 1.0 mm d-1), followed by TVT (RMSE = 1.1 mm d-1), METRIC (RMSE = 1.2 mm d-1), SEBS (RMSE = 1.3 mm d-1), and TSEB (RMSE = 1.5 mm d-1). Overall, the CB models provided smaller errors than the PB models. Larger errors in daily ET estimation were observed during low vegetation and drier conditions, especially for the PB models. Keywords: Flux tower, Landsat, Southern Great Plains, Water use.

Estimating instantaneous evapotranspiration based on dual-angle thermal infrared observations from Sentinel-3 satellite

Estimating instantaneous evapotranspiration based on dual-angle thermal infrared observations from Sentinel-3 satellite

土地利用/覆被变化扎龙湿地蒸散发量及生态需水量的遥感估算

地表蒸散发量是影响湿地水热平衡的主要因素，也是水分损失的主要途径，对湿地生态需水量的合理确定和水资源的有效管理具有重要意义。借助遥感和GIS技术，利用2002、2010、2016年34景Landsat影像，基于时序NDVI数据对湿地下垫面物候特征的定量表征，准确获取了扎龙湿地保护区3个时期土地利用/覆被的动态变化信息。选用物理基础较好且应用广泛的SEBAL模型，估算了湿地的瞬时蒸散发量，并结合站点气象数据，实现了湿地蒸散发量在时间尺度上的扩展，分别得到日、月、年尺度的湿地蒸散发量，深入探究了扎龙湿地蒸散发的时空分布特征；最后从湿地湖泡需水量、植物需水量及生物栖息地需水量3个方面，定量估算出扎龙湿地3个时期的现状生态需水量。研究发现：扎龙湿地的土地覆被类型主要以芦苇沼泽、草地和耕地为主，其中芦苇沼泽分布占绝对优势，且2002-2016年持续增加了205.82 km²；草地、耕地呈持续减少态势，分别减少了119.35 km²和95.96km²，表明2002-2016年扎龙湿地生态系统呈恢复态势；湿地保护区蒸散发量年内均大致呈单峰型分布，符合夏季 > 春季 > 秋季 > 冬季的规律，在年际上呈现明显的递增趋势，年蒸散发量由2002年的518.87mm增加到2016年的625.98mm，增加了20.64%。为满足湿地内的生态消耗，总体上2002-2016年湿地的生态需水量也相应的增加，保护区适宜生态需水量的变动范围为5.40亿-7.08亿m³，可以维持湿地湖泊、植被、动植物栖息地的健康态势。维持核心区健康状态的最小生态需水量变化范围为2.71亿-3.32亿m³。随着遥感数据时空分辨率的提高，基于蒸散发量反演的湿地生态需水量估算将更加实用和准确，为湿地保护区制定科学合理的补水方案提供有效的技术支撑。;Surface evapotranspiration is a main factor influencing wetland water-heat balance and a main way of water loss. It has important significance for the reasonable determination of ecological water demand in wetlands and the effective management of water resources. With the help of remote sensing and GIS technology, the quantitative characterization of the phenological characteristics of the underlying surface of Zhalong wetland based on temporal NDVI data was made by using Landsat images of 34 scenes in 2002, 2010 and 2016. At the same time, the dynamic change information of land use/cover in three periods of the wetland reserve was obtained accurately. The SEBAL model with good physical foundation and wide application was selected to estimate the instantaneous evapotranspiration of the wetland. By combining with the meteorological data of nearby hydrographic stations, the temporal expansion of evapotranspiration of the wetland was realized. The daily, monthly and annual evapotranspiration of Zhalong wetland was obtained, and the spatio-temporal distribution characteristics of evapotranspiration in Zhalong wetland were deeply investigated. At last, the present ecological water demand of Zhalong wetland in three periods was quantitatively estimated from three aspects:water demand of wetland lake bubble, water demand of plants, and water demand of biological habitat. The results indicated that the land cover types of Zhalong wetland were mainly reed marshes, grasslands and cultivated land, among which reed marshes occupied the absolute dominance, increased by 205.82 km² continuously from 2002 to 2016. Grassland and cultivated land decreased by 119.35 km² and 95.96 km², respectively, indicating that Zhalong wetland ecosystem showed a recovery trend from 2002 to 2016. The annual evapotranspiration of the wetland reserve presented a roughly single-peak distribution in the three years, which was consistent with the summer > spring > autumn > winter. The annual evapotranspiration increased significantly from 518.87 mm in 2002 to 625.98 mm in 2016, by an increase of 20.64%. In order to meet the ecological consumption in the wetland, the total ecological water demand of the wetland increased correspondingly from 2002 to 2016. The range of the appropriate ecological water demand of the wetland reserve was 540 million m³ -708 million m³, which could maintain the health of the lakes, vegetation, animal and plant habitats in the wetland. The minimum ecological water demand for maintaining the health of the core area varied from 271 million m³ to 332 million m³. With the improvement of spatial and temporal resolution of remote sensing data, the estimation of wetland ecological water demand based on evapotranspiration inversion will be more practical and accurate, providing effectively technical support for the formulation of scientific and reasonable water rehydration schemes in wetland reserves.

A Scheme to Estimate Diurnal Cycle of Evapotranspiration from Geostationary Meteorological Satellite Observations

The diurnal cycle of evapotranspiration (ET) is significant in studying the dynamics of land–atmosphere interactions. The diurnal ET cycle can be considered as an indicator of dry/wet surface conditions. However, the accuracy of current models in estimating the diurnal ET cycle is generally low. This study developed an improved scheme to estimate the diurnal cycle of ET by solving the surface energy balance equation combined with simplified parameterization, with daily ET as the constraint. Meteosat Second Generation (MSG) land surface temperature, and longwave and shortwave radiation products were the primary inputs. Daily ET was from the remote sensing-based ETMonitor model. The estimated instantaneous (30 min) ET from the improved scheme outperformed the official MSG instantaneous ET product when compared with in situ half-hourly measurements at 35 flux sites from the FLUXNET2015 dataset, and was also comparable with European Center for Medium-Range Weather Forecasts (ECMWF) ERA5 ET data, with an R2 of 0.617 and root mean square error (RMSE) of 65.8 W/m2 for the improved scheme. Results were largely improved compared with those without daily ET as the constraint. The improved method was stable for the estimation of ET’s diurnal cycle at the similar atmospheric conditions and the accuracy was comparative at different land cover surfaces. Errors in the input variables and the simplification of surface heat flux parameterization affected surface energy balance closure, which can lead to instability of the solution of constants in the simplified parameterization and further to the uncertainty of ET’s diurnal cycle estimation. Measurement errors, different source areas in measured variables, and inconsistent spatial representativeness between remote sensing and site measurements also impacted the evaluation.

Estimation of farmland evapotranspiration based on SEBAL model in Kai-Kong River Basin, Xinjiang

Evapotranspiration (ET) is an important reference for scientific management of farmland irrigation, accurate estimation of crop yields, and optimal allocation of water resources. The purpose of this paper is to estimate ET by taking the farmland of Kai-Kong River Basin in Xinjiang province as an example. The method was based on the SEBAL model and the Landsat data; it estimated the net radiation flux, soil heat flux using the land-surface parameters first, and the instantaneous evapotranspiration was then calculated using the energy residual method and the latent heat flux. The sinusoidal function method was used to extend the time scale of the instantaneous evapotranspiration to the daily evapotranspiration and the daily evapotranspiration were verified using the FAO56 Penman-Monteith formula. The compared results shown that the error of daily evapotranspiration between SEBAL and FAO56 Penman-Monteith is within 1mm, and except for the estimation accuracy of final growth stage is about 80%, the estimation accuracy of other growth stages are more than 80%. The SEBAL model, coupled with the Landsat remote sensing data, can offer an efficient and adequate alternative to estimate ET at catchment scale.

Evolution of evapotranspiration models using thermal and shortwave remote sensing data

Evapotranspiration (ET) from the land surface is an important component of the terrestrial hydrological cycle. Since the advent of Earth observation by satellites, various models have been developed to use thermal and shortwave remote sensing data for ET estimation. In this review, we provide a brief account of the key milestones in the history of remote sensing ET model development in two categories: temperature-based and conductance-based models. Temperature-based ET models utilize land surface temperature (LST) observed through thermal remote sensing to calculate the sensible heat flux from which ET is estimated as a residual of the surface energy balance or to estimate the evaporative fraction from which ET is derived from the available energy. Models of various complexities have been developed to estimate ET from surfaces of different vegetation fractions. One-source models combine soil and vegetation into a composite surface for ET estimation, while two-source models estimate ET of soil and vegetation components separately. Image contexture-based triangular and trapezoid models are simple and effective temperature-based ET models based on spatial and/or temporal variation patterns of LST. Several effective temporal scaling schemes are available for extending instantaneous temperature-based ET estimation to daily or longer time periods. Conductance-based ET models usually use the Penman-Monteith (P-M) equation to estimate ET with shortwave remote sensing data. A key put to these models is canopy conductance to water vapor, which depends on canopy structure and leaf stomatal conductance. Shortwave remote sensing data are used to determine canopy structural parameters, and stomatal conductance can be estimated in different ways. Based on the principle of the coupling between carbon and water cycles, stomatal conductance can be reliably derived from the plant photosynthesis rate. Three types of photosynthesis models are available for deriving stomatal or canopy conductance: (1) big-leaf models for the total canopy conductance, (2) two-big-leaf models for canopy conductances for sunlit and shaded leaf groups, and (3) two-leaf models for stomatal conductances for the average sunlit and shaded leaves separately. Correspondingly, there are also big-leaf, two-big-leaf and two-leaf ET models based on these conductances. The main difference among them is the level of aggregation of conductances before the P-M equation is used for ET estimation, with big-leaf models having the highest aggregation. Since the relationship between ET and conductance is nonlinear, this aggregation causes negative bias errors, with the big-leaf models having the largest bias. It is apparent from the existing literature that two-leaf conductance-based ET models have the least bias in comparison with flux measurements. Based on this review, we make the following recommendations for future work: (1) improving key remote sensing products needed for ET mapping purposes, including soil moisture, foliage clumping index, and leaf carboxylation rate, (2) combining temperature-based and conductance-based models for regional ET estimation, (3) refining methodologies for tight coupling between carbon and water cycles, (4) fully utilizing vegetation structural and biochemical parameters that can now be reliably retrieved from shortwave remote sensing, and (5) to improve regional and global ET monitoring capacity.