Sensing Evapotranspiration Model Research Articles

Accuracy evaluation of ET and its components from three remote sensing ET models and one process based hydrological model using ground measured eddy covariance and sap flow

Evapotranspiration (ET) and its components, transpiration (T) and soil evaporation (Es) are crucial to understand hydrological cycle and effective water loss in water resource management. Remote sensing models and process based hydrological models are two kinds of effective methods for evaluating ET and its partitions. Although the ability of remote sensing models to simulate ET has been widely verified, there is limited verification of ET components, especially T, which is the major component of ET. This research used ET obtained from eddy covariance system and T obtained from sap flow observation in an apricot orchard of north China to evaluate the accuracy of ET and its components from three remote sensing models (PT-JPL, PML, and SWH) and a process-based hydrological model (SHAW). The results showed that after calibrations, all the four models satisfactorily simulated total ET particularly at annual level. Among them, the PML model had the best fit efficiency, with the most satisfied R2 and NSE values. The PT-JPL estimated average ET was the most accurate, with the slope nearest to 1.0. While for the simulation of T, SHAW model had a better performance owing to the fact that SHAW model simulate plant transpiration using changes in soil water content. The average T/ET of the models for the growing season was close to the observed value of 0.70, except that the T/ET of SWH is high due to the lack of Ei calculation. Furthermore, nearly all the three remote sensing models underestimated ET in the dry spring and overestimated ET in the rainy summer. In comparison with the process-based SHAW model, it is suggested that remote sensing ET relies more heavily on the seasonal change of meteorological factors such as precipitation but less capable in reflecting the gradually delaying process of soil moisture in the dry season. Therefore, it is indicated that for future improvement of remote sensing ET models, adjustment of soil water availability in a much longer term is suggested at least for semi-humid and semi-arid ecosystems.

The Modified Soil Moisture Constraint Scheme Significantly Enhances the Evapotranspiration Simulation Accuracy of the MOD16 Model

Remotely sensed (RS) evapotranspiration (ET) models can make full use of the land surface information retrieved using remote sensing and are therefore widely used in large-scale ET estimates. The MODIS Global Evapotranspiration model (MOD16) is one of the most commonly used remote sensing ET models. MOD16 parameterizes the moisture constraints on soil evaporation (Es) using atmospheric vapor pressure deficit (VPD) and relative humidity (RH). This moisture constraint algorithm has been criticized by many studies due to the weak correlation between soil moisture and VPD or RH over short timescales (e.g., hourly and daily). In this study, we introduce a modified moisture constraint algorithm of ET, based on the ratio of antecedent accumulated precipitation to soil equilibrium evaporation, in order to improve the ET simulation capabilities of the MOD16 model. The original and modified MOD16 models are evaluated at 14 ChinaFlux sites and 286 basins in China, using eddy covariance measurements and water-balance-based ET estimates. The results show that the modified MOD16 model outperforms the original MOD16 model at both the site and basin scales. Compared with the original model, the modified model increases the value of KGE by an average of 0.17 at the flux site scale and by 0.01 at the basin scale. Using soil moisture measurements from flux sites as a reference, we further found that the modified MOD16 model also has a better soil moisture simulation capacity than the original model. This study highlights the importance of reliable soil moisture constraints in remotely sensed ET models.

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.

Remote Sensing of Global Daily Evapotranspiration based on a Surface Energy Balance Method and Reanalysis Data

AbstractCurrently available evapotranspiration (ET) products have not provided us with an accurate estimation for global irrigated land area. Thermal energy balance (EB) model has the potential to solve this problem. The accurate estimation of aerodynamic resistances is a major complication in most remote sensing ET models. An EB model using a column canopy‐air turbulent heat diffusion method was developed to more realistically depict dynamic changes in aerodynamic resistance. In order to estimate global ET and land surface fluxes for all weather conditions, Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua and Terra land surface temperature fields were combined and a nearest‐evaporative‐fraction gap‐filling method was merged into the EB model. A global ET product covering the period 2003–2017 was produced using the EB model. By combining thermal and optional information from MODIS satellites and surface meteorological forcing data from ERA‐Interim reanalysis data, the EB model provides a 5 × 5 km resolution estimate of daily ET without spatio‐temporal gaps at the global scale. Assessment of the daily EB ET at 238 flux sites showed a mean bias of 0.05 mm/day and an RMSE of 1.56 mm/day. Analysis of global precipitation minus ET demonstrated that EB ET has a relatively higher potential for agriculture water resource management than currently available global ET products, such as Landflux, GLEAM, MOD16, GLDAS, and ERA‐Interim ET. In addition, the EB model developed in this study can be applied to both polar and geostationary satellite thermal sensors.

Multiple sources of uncertainties in satellite retrieval of terrestrial actual evapotranspiration

Since evapotranspiration (ET) is the intrinsic link between global energy and water cycle, remote sensing-based models have been developed for regional and global scale ET on heterogeneous land surface over the past four decades. In view of the significantly different physical mechanisms and mathematical expressions among remote sensing ET models as well as data availability and quality control process among the model input products, it is necessary to investigate the uncertainties of the multiple sources in actual ET estimation. Here, three remote sensing ET models, including the PT-DTsR model, the PM-mod model and the PML model, were simultaneously driven by three meteorological reanalysis products, resulting in nine calculation schemes to analyze the combined effect of the models and the input datasets. The Sobol’ sensitivity method was also adopted for identifying the influential model parameters and in turn understanding the model process. The results indicated that estimates from nine calculation schemes showed great differences in the magnitude and temporal variation, explaining 20–50% of ET variability over all sites. Additionally, schemes compared with both uncorrected and corrected energy balance observations, as well as schemes using meteorological variables from three reanalysis products and Eddy Covariance tower observations, verified that the uncertainties in latent heat flux data observations caused by the energy budget mis-closure problem and spatial scale mismatch have propagated into the ET estimation. Our study is a beneficial reference for the uncertainties in remote sensing-based methods, and thus can provide guidance for the future development of ET models.

Evapotranspiration in the Tono Reservoir Catchment in Upper East Region of Ghana Estimated by a Novel TSEB Approach from ASTER Imagery

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.

Incorporation of Unmanned Aerial Vehicle (UAV) Point Cloud Products into Remote Sensing Evapotranspiration Models.

In recent years, the deployment of satellites and unmanned aerial vehicles (UAVs) has led to production of enormous amounts of data and to novel data processing and analysis techniques for monitoring crop conditions. One overlooked data source amid these efforts, however, is incorporation of 3D information derived from multi-spectral imagery and photogrammetry algorithms into crop monitoring algorithms. Few studies and algorithms have taken advantage of 3D UAV information in monitoring and assessment of plant conditions. In this study, different aspects of UAV point cloud information for enhancing remote sensing evapotranspiration (ET) models, particularly the Two-Source Energy Balance Model (TSEB), over a commercial vineyard located in California are presented. Toward this end, an innovative algorithm called Vegetation Structural-Spectral Information eXtraction Algorithm (VSSIXA) has been developed. This algorithm is able to accurately estimate height, volume, surface area, and projected surface area of the plant canopy solely based on point cloud information. In addition to biomass information, it can add multi-spectral UAV information to point clouds and provide spectral-structural canopy properties. The biomass information is used to assess its relationship with in situ Leaf Area Index (LAI), which is a crucial input for ET models. In addition, instead of using nominal field values of plant parameters, spatial information of fractional cover, canopy height, and canopy width are input to the TSEB model. Therefore, the two main objectives for incorporating point cloud information into remote sensing ET models for this study are to (1) evaluate the possible improvement in the estimation of LAI and biomass parameters from point cloud information in order to create robust LAI maps at the model resolution and (2) assess the sensitivity of the TSEB model to using average/nominal values versus spatially-distributed canopy fractional cover, height, and width information derived from point cloud data. The proposed algorithm is tested on imagery from the Utah State University AggieAir sUAS Program as part of the ARS-USDA GRAPEX Project (Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment) collected since 2014 over multiple vineyards located in California. The results indicate a robust relationship between in situ LAI measurements and estimated biomass parameters from the point cloud data, and improvement in the agreement between TSEB model output of ET with tower measurements when employing LAI and spatially-distributed canopy structure parameters derived from the point cloud data.

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.

Integration of remote sensing evapotranspiration (ET) model and hydrologic model for mapping daily ET time series at river basin scale

Hydrological models and remote sensing evapotranspiration (ET) models usually are used to estimate regional ET. This study aims to integrate the advantages of both the models to simulate the daily ET processes. A compromise between these two methodologies is represented by improving the optimization of the hydrological model on the basis of a new probability optimal ET series, which is produced by a data assimilation scheme combining sparse remote estimates and continuous modeling of regional ETs. The distributed time-variant gain hydrological model (DTVGM) and a two-layer remote sensing ET model are chosen. First, the DTVGM is optimized by maximizing the Nash–Sutcliffe efficiency of daily streamflow in the Shahe River basin, and simulates the daily hydrological processes of 1999–2007. For improving the accuracy of continuous ET simulation, the DTVGM is further optimized by dual objective functions composed of the assimilated ETs and observed outlet discharge. The results show that the accuracy of the DTVGM-based daily ETs is improved after the dual optimization, and the mean absolute percentage error between the DTVGM-based ETs and the measured ETs in the study area is reduced by 5.84%. The integrated method is proved better, and improves the hydrology modeling accuracy.

Comparison of three dual‐source remote sensing evapotranspiration models during the MUSOEXE‐12 campaign: Revisit of model physics

AbstractVarious remote sensing‐based terrestrial evapotranspiration (ET) models have been developed during the past four decades. These models vary in conceptual and mathematical representations of the physics, consequently leading to different performances. Examination of uncertainties associated with limitations in model physics will be useful for model selection and improvement. Here, three dual‐source remote sensing ET models (i.e., the Hybrid dual‐source scheme and Trapezoid framework‐based ET Model (HTEM), the Two‐Source Energy Balance (TSEB) model, and the MOD16 ET algorithm) using ASTER images were compared during the MUSOEXE‐12 campaign in the Heihe River Basin in Northwest China, aiming to better understand the differences in model physics that potentially lead to differences in model performance. Model results were first compared against observations from a dense network of eddy covariance towers and isotope‐based evaporation (E) and transpiration (T) partitioning. Results show that HTEM outperformed the other two models in simulating ET and its partitioning, whereas MOD16 performed worst (i.e., ET root‐mean‐square errors are 42.3 W/m2 (HTEM), 49.8 W/m2 (TSEB), and 95.3 W/m2 (MOD16)). On to model limitations, HTEM tends to underestimate ET under high advection due mostly to the underestimation of temperatures for the wet edge in its trapezoidal space. For TSEB, large uncertainties occur in determining the initial Priestley‐Taylor coefficient and the iteration procedure for ET partitioning, leading to overestimation/underestimation of T/E in most cases, particularly over sparse vegetation. Primary use of meteorological data for MOD16 does not effectively capture the soil moisture restriction on ET, and therefore results in unreasonable spatial ET patterns.

Quantitative estimation of land surface evapotranspiration in Taiwan based on MODIS data

Land surface evapotranspiration (ET) determines the local and regional water-heat balances. Accurate estimation of regional surface ET provides a scientific basis for the formulation and implementation of water conservation programs. This study set up a table of the momentum roughness length and zero-plane displacement related with land cover and an empirical relationship between land surface temperature and air temperature. A revised quantitative remote sensing ET model, the SEBS-Taiwan model, was developed. Based on Moderate Resolution Imaging Spectroradiometer (MODIS) data, SEBS-Taiwan was used to simulate and evaluate the typical actual daily ET values in different seasons of 2002 and 2003 in Taiwan. SEBS-Taiwan generally performed well and could accurately simulate the actual daily ET. The simulated daily ET values matched the observed values satisfactorily. The results indicate that the net regional solar radiation, evaporation ratio, and surface ET values for the whole area of Taiwan are larger in summer than in spring, and larger in autumn than in winter. The results also show that the regional average daily ET values of 2002 are a little higher than those of 2003. Through analysis of the ET values from different types of land cover, we found that forest has the largest ET value, while water areas, bare land, and urban areas have the lowest ET values. Generally, the Northern Taiwan area, including Ilan County, Nantou County, and Hualien County, has higher ET values, while other cities, such as Chiayi, Taichung, and Tainan, have lower ET values.

Remote sensing of evapotranspiration over cotton using the TSEB and METRIC energy balance models

Remote sensing of evapotranspiration (ET) can help detect, map and provide guidance for crop water needs in irrigated lands. Two remote sensing ET models based on thermal infrared (TIR), the Two-Source Energy Balance (TSEB) and the Satellite-Based Energy Balance for Mapping Evapotranspiration with Internalized Calibration (METRIC), were tested for accuracy, and bias at fine (1m) and moderate (30–120m) spatial scales. Airborne and Landsat data were collected over Maricopa, Arizona in 2009 and 2011 as part of a cotton irrigation scheduling study. Based on soil moisture observations at 112 locations across 4.9ha and image data spanning two growing seasons, TSEB and METRIC were found similarly accurate at both fine and moderate scales with average discrepancies no more than 1.9mm/day. Tests at 1-m scales showed that TSEB and METRIC model sensitivities were seasonally correlated, with greater sensitivity modeled by METRIC in early growth and slightly greater sensitivity by TSEB at maturity. Time integration of flux estimates was done by assuming constant evaporative fraction and was also tested for 2011 data using ground-based TIR radiometers; this latter approach improved daily ET estimates by 0.8mm/day or better in two cases. Time-series assessment of the utility of using evaporative fraction as a water-stress indicator was tested using Landsat data and both TSEB and METRIC. Two early season water depletion events were detected and none in mid-season. The impact of overpass frequency upon ET estimates was tested for the field as a whole and found that cumulative ET estimates were significantly affected, up to 200mm out of ~1000mm consumed. Results from this study showed that for ET accuracy, TSEB and METRIC perform similarly. METRIC is preferred when model ancillary data are sparse, while TSEB is preferred when support data are plentiful. Future ET modeling should consider implementing both to take advantage of their seasonally dependent sensitivities.

Sensitivity Analysis of the Surface Energy Balance Algorithm for Land (SEBAL)

New versions of evapotranspiration (ET) algorithms based on the Surface Energy Balance Algorithm for Land (SEBAL) are being published, each containing slightly different equations to calculate the energy balance. It is difficult to determine what impact changing one or more of the equations or coefficients in the series of equations of SEBAL has on the final calculation of ET. The objective of this article is to conduct a sensitivity analysis of ET estimates in SEBAL to identify the most sensitive variables and equations. A remote sensing ET model based on SEBAL was programmed and validated against eddy-covariance data. A sensitivity analysis was conducted for three contrasting land surface conditions: full, half, and sparse canopy cover in pecan orchards. Results were most sensitive to the selection (according to temperature) of the dry (~zero ET) reference pixel and to c (the estimated ratio of soil heat flux to net solar radiation). At all the three degrees of canopy cover, estimated ET changed by 40% to 270% (1 to 2 mm d-1) when either variable changed from its baseline value by ±50% of the permissible range. Estimated ET was also sensitive to the selection of the wet (full ET) reference pixel and to dT (aerodynamic difference of air and land temperatures). Changes in ET estimates were 47% to 72% (1.3 to 3.7 mm d-1) at both the full and half canopy areas under changes from baseline values equal to 50% of the permissible range for either variable. In addition, ET was sensitive to the roughness length in areas of half canopy cover (ET changed by 61% [1.5 mm d-1]) and to the value of the normalized difference vegetation index (NDVI) in areas of sparse canopy cover (ET changed by 118% [0.35 mm d-1]). Future research on ET algorithm improvement should focus on the above variables and relative equations. The selection of the wet- and dry-spots should be automated to avoid subjective errors from manual selection.

Satellite-Based Energy Balance to Assess Within-Population Variance of Crop Coefficient Curves

Quantifying evapotranspiration (ET) from agricultural fields is important for field water management, water resources plan- ning, and water regulation. Traditionally, ET from agricultural fields has been estimated by multiplying the weather-based reference ET by crop coefficients sKcd determined according to the crop type and the crop growth stage. Recent development of satellite remote sensing ET models has enabled us to estimate ET and Kc for large populations of fields. This study evaluated the distribution of Kc over space and time for a large number of individual fields by crop type using ET maps created by a satellite based energy balance(EB) model. Variation of Kc curves was found to be substantially larger than that for the normalized difference vegetation index because of the impacts of random wetting events on Kc, especially during initial and development growth stages. Two traditional Kc curves that are widely used in Idaho for crop management and water rights regulation were compared against the satellite-derived Kc curves. Simple adjustment of the traditional Kc curves by shifting dates for emergence, effective full cover, and termination enabled the traditional curves to better fit Kc curves as determined by the EB model. Applicability of the presented techniques in humid regions having higher chances of cloudy dates was discussed.