Evaporative Fraction Estimation Research Articles

Surface flux equilibrium estimates of evaporative fraction and evapotranspiration at global scale: Accuracy evaluation and performance comparison

Accurate estimation of terrestrial evapotranspiration (ET) is crucial to understand the water cycle and partitioning of turbulent energy fluxes at the land surface. However, large-scale ET estimation is always difficult to achieve due to the complexity of controlling factors and the heterogeneity of landscapes. A recent model, referred to as the surface flux equilibrium (SFE) model, proposes that evaporative fraction (EF, the ratio of ET to available energy at the surface) can be estimated accurately from near-surface specific humidity and air temperature, which makes it applicable to common weather stations and climate reanalysis datasets. However, previous validation only focused on the evaluation of this model at site and basin scale for ET estimation, making the real performance of the SFE model for EF estimation heretofore unknown. There also exists a gap in the comparison between the SFE model and other existing products in the representation of global ET patterns and variation trend. Against this background, a comprehensive validation was performed in this study to evaluate the accuracy of the SFE model for both EF and ET estimation. The validation against 136 flux towers worldwide shows that the ground-based SFE model achieved EF estimation with a root-mean-square-error (RMSE) of 0.22 and 0.16 at daily and monthly scale, respectively. The corresponding RMSE of ET estimation was 0.94 mm/day and 16.37 mm/month. The application of the SFE model on a global scale was achieved by using two reanalysis products (ERA5-Land and GLDAS-CLSM) as inputs respectively. The results indicate that the SFE model did hold potential to reduce the RMSE of these two products for ET estimation, but the cost was the decrease in correlation coefficient (r). The comparison with five existing global ET products (BESS, GLEAM, AVHRR, MOD16, and SSEBop) shows that the SFE model performed on the basis of ERA5-Land product has achieved moderate accuracy among these products. Judged from r, mean absolute error, RMSE, and bias, its accuracy was ranked as fourth, fourth, second, and second, respectively. However, a further evaluation over different land cover types suggests that the ERA-based SFE model has significant superiority over cropland, relative superiority over forest and savannas, and significant inferiority over shrubland and grassland. This analysis is the first global validation of the SFE model for EF estimation as well as its comparison with existing global ET products. The validation analysis proves that this simple model has generally achieved comparable accuracy to existing complex models in the representation of global ET patterns and variation trend.

Integration of microwave satellite soil moisture products in the contextual surface temperature-vegetation index models for spatially continuous evapotranspiration estimation

The contextual surface temperature-vegetation index (TVX) models have been widely used for the retrieval of soil moisture (SM) and evapotranspiration (ET). One of the key premises for their application is to determine quantitatively the theoretical boundaries of this contextual TVX space. Usually these theoretical boundaries are determined based on land surface energy balance principle. Although sound in physical mechanism, the complex parameterization involved has hampered its remote sensing (RS) applications. Our recent studies show that ground-based SM observations can be used for a quick and continuous retrieval of these theoretical boundaries. The main disadvantage lies in its reliance on in-situ SM observations. Fortunately the development of microwave RS technology has made it possible to monitor SM at both regional and global scale. Under this background, a practical framework based on the contextual TVX models was proposed in this paper for continuous ET estimation through the combination of microwave-based SM products and MODIS (Moderate Resolution Imaging Spectroradiometer) products. Daily actual ET over the Southern Great Plains of the United States of America was calculated as the product of net radiation and evaporative fraction (EF). The focus of this framework was to investigate how optical and microwave RS can be combined to achieve a quick and continuous retrieval of EF independently of ground observations. Specifically, EF was retrieved from the contextual TVX models, the theoretical boundaries of which were calibrated at the annual scale based on an optimization scheme. Assuming the seasonal variation of the theoretical dry edge follows a cosine function of solar zenith, the essence of this optimization scheme is to search for the optimal amplitude parameter that maximizes the correlation between microwave-based SM observations and soil moisture index retrieved from the contextual TVX models. Two microwave-based SM products including SMAP (Soil Moisture Active Passive) and SMOS (Soil Moisture and Ocean Salinity) were adopted to demonstrate this optimization scheme. The correlation coefficient, root mean square error and bias of EF estimates calibrated by SMAP were 0.700, 0.146 and 0.047, and those achieved by SMOS were 0.663, 0.174 and 0.168, respectively. The optimization scheme has not only made it possible to conduct a continuous monitoring of ET based entirely on RS observations but also achieved the downscaling of microwave satellite SM products. Thus the combination of optical and microwave RS holds great potential for coupled and continuous estimation of SM and ET over large scale.

Estimation of instantaneous, diurnal, and daily evaporative fraction using readily available inputs in the wetlands of South Florida, United States

ABSTRACT Evaporative fraction (EF) is the ratio of latent heat (LE in W m−2) to available energy at the land surface. It can aid in partitioning the Sun’s energy available at the Earth’s surface into surface energy fluxes to understand land–atmosphere interactions as well as monitoring Evapotranspiration and terrestrial drought conditions. In this study, the feasibility of estimating instantaneous, diurnal, and daily EF with minimal satellite and meteorological inputs based on the temperature difference-vegetation index framework is explored in South Florida, United States (US). The model performance is assessed against the ground observations collected at three flux stations between 2008 and 2011. It was found that the energy balance closure was improved by 9% at the long hydroperiod marsh site (i.e. US-Elm) and 6% at the short hydroperiod marsh site (i.e. US-Esm) after water heat storage (W) correction was applied on energy fluxes since W is more important than soil heat flux (G) for wetland ecosystems. The results further indicated that the model produced accurate EF estimates and performed the best on diurnal, then instantaneous, and lastly daily periods. Overall, the model can be used to produce spatially continuous EF and ET maps since the flux towers are no longer active.

A moisture-based triangle approach for estimating surface evaporative fraction with time-series of remotely sensed data

Evapotranspiration (ET) is a primary process for water and heat transfer between the land and atmosphere. The spatial contextual information-based surface temperature versus vegetation index triangle (temperature-based triangle) is one of the most famous and widely applied methods for regional ET estimation from remotely sensed data. However, the determination of temporally variable satellite scene-specific dry and wet edges in this traditional triangle is often largely biased due to the limited range of variability of surface soil moisture availability and fractional vegetation cover (or the nonexistence of end-member pixels), or must rely on the auxiliary ground-based measurements, leading to a large uncertainty or difficulty in regional evaporative fraction estimation. This short communication presents a practically operational moisture-based triangle to address the deficiency of the temperature-based triangle, where temporally universal dry and wet edges in this new triangle are determined by making use of the time-series slope of remotely sensed surface temperature versus vegetation index negative relationship over a small window centering the target pixel. Our validation results show that this new triangle outperformed the temperature-based triangle, reducing the root mean square error from 0.19 to 0.16 and increasing the coefficient of determination from 0.44 to 0.53, when the model-estimated evaporative fractions were validated against ground-based eddy covariance measurements at 34 sites across the globe.

Coupled and continuous estimation of soil moisture and evaporative fraction within the remotely sensed surface temperature-vegetation index framework

Numerous studies have focused on the retrieval of soil moisture (SM) and evaporative fraction (EF) based on the surface temperature-vegetation index (TVX) framework. However, few literatures have investigated whether these two variables can be retrieved simultaneously under the same TVX framework. Besides, most previous studies only demonstrated the application of the TVX framework under clear sky conditions. The estimation of SM and EF under partially cloudy conditions is still a challenging task. The main motivation for this study is to develop an observation-driven optimization framework combined with TVX method for coupled and continuous estimation of SM and EF. Specifically, the optimization scheme was designed to construct the constraints and objective functions of the TVX framework at seasonal scale. In-situ SM and EF measurements were utilized to calibrate the theoretical boundaries of the TVX framework, respectively. The applicability of this new scheme was demonstrated over the Southern Great Plains (SGP) of the United States of America in the year 2005 using Moderate Resolution Imaging Spectroradiometer (MODIS) products. The comparisons between present method and previous traditional TVX models indicate that the accuracy of the optimization scheme produced using only one site for calibration has reached a comparable level over the whole study domain. However, the new scheme holds unique advantages in simplicity and continuity. It has not only circumvented the complex parameterization scheme of theoretical TVX framework, but also achieved estimation of SM and EF under partially cloudy conditions. The validation results of SM estimates show that the absolute value of correlation coefficient (r) under clear and partially cloudy sky conditions was 0.51 and 0.43, respectively. The validation results of EF estimates indicate that the r and root mean square error (RMSE) under clear sky conditions was 0.70 and 0.163. In contrast, the r and RMSE under partially cloudy conditions was 0.66 and 0.158. In addition, the coupled estimation of SM and EF means that these two objective variables can be interconverted into each other within the optimization framework. Thus either SM or EF can be used to calibrate the theoretical dry edge that applies to the estimation of both variables.

An observation-driven optimization method for continuous estimation of evaporative fraction over large heterogeneous areas

Ground-based evaporative fraction (EF) observations have been used widely for validation purposes in previous remote sensing-based EF models. Few studies have investigated whether such measurements can be utilized for calibration use. In this paper, an observation-driven optimization method is proposed to quantify EF over a large heterogeneous area within the surface temperature-vegetation index framework. It is designed at both daily scale and seasonal scale with MODIS products and in-situ EF observations over the Southern Great Plains in the US. The goal is to search for the optimal dry edge within the allowable range that minimizes the difference between the estimated and observed EF of a given site. Results show that the accuracy produced using only one site for calibration has reached a level comparable to those produced by traditional triangle methods. Compared with the daily-scale optimization method, the seasonal-scale optimization method has not only demonstrated its superiority in accuracy but also held distinctive advantages over the traditional triangle methods. Specifically, the dry edge produced by our optimization method holds true under both clear sky and partially cloudy conditions. This has not only bypassed the repetitive work of previous triangle methods but also made it possible to conduct a continuous monitoring of EF. Besides, the optimization method is characterized by its simplicity in algorithm, stability in accuracy and extensibility in parameterization, which makes it a suitable tool for providing a quick and reasonable estimation of EF over large heterogeneous areas from a limited number of in-situ EF observations.

Feasibility of Estimating Turbulent Heat Fluxes via Variational Assimilation of Reference-Level Air Temperature and Specific Humidity Observations

This study investigated the feasibility of partitioning the available energy between sensible (H) and latent (LE) heat fluxes via variational assimilation of reference-level air temperature and specific humidity. For this purpose, sequences of reference-level air temperature and specific humidity were assimilated into an atmospheric boundary layer model (ABL) within a variational data assimilation (VDA) framework to estimate H and LE. The VDA approach was tested at six sites (namely, Arou, Audubon, Bondville, Brookings, Desert, and Willow Creek) with contrasting climatic and vegetative conditions. The unknowns of the VDA system were the neutral bulk heat transfer coefficient (CHN) and evaporative fraction (EF). EF estimates were found to agree well with observations in terms of magnitude and day-to-day fluctuations in wet/densely vegetated sites but degraded in dry/sparsely vegetated sites. Similarly, in wet/densely vegetated sites, the variations in the CHN estimates were found to be consistent with those of the leaf area index (LAI) while this consistency deteriorated in dry/sparely vegetated sites. The root mean square errors (RMSEs) of daily H and LE estimates at the Arou site (wet) were 25.43 (Wm−2) and 55.81 (Wm−2), which are respectively 57.6% and 45.4% smaller than those of 60.00 (Wm−2) and 102.21 (Wm−2) at the Desert site (dry). Overall, the results show that the VDA system performs well at wet/densely vegetated sites (e.g., Arou and Willow Creek), but its performance degrades at dry/slightly vegetated sites (e.g., Desert and Audubon). These outcomes show that the sequences of reference-level air temperature and specific humidity have more information on the partitioning of available energy between the sensible and latent heat fluxes in wet/densely vegetated sites than dry/slightly vegetated sites.

A Statistical Analysis of the Remotely Sensed Land Surface Temperature–Vegetation Index Method for the Retrieval of Evaporative Fraction Over Grasslands in the Southern Great Plains

The triangle method based on the spatial relationship between remotely sensed (RS) land surface temperature and vegetation index (TVX) has been widely used for the estimation of evaporative fraction (EF). Combing moderate resolution imaging spectroradiometer and in situ EF observations, the physical constraint and theoretical accuracy of the TVX method is investigated in this paper through statistical analysis. In theory, the physical constraint of the TVX method is mathematically solvable by using EF measurements as inputs. However, our research suggests that nearly 90% of its solutions are meaningless to retrieve the spatial distribution of EF. Instead, we propose an optimization method to retrieve the spatial distribution of EF over a large heterogeneous area from a limited number of in situ EF observations. Results show that the new method using just one site for calibration has an accuracy comparable with those produced by the traditional TVX method. Its robustness is also demonstrated by its applicability under partially cloudy sky conditions. The accuracy of this optimization method increases with the number of sites used for calibration, but there is an upper limit. Besides, when all sites are used for calibration, the accuracy produced represents the upper limit of the accuracy of the TVX method. Thus, the proposed method also provides a perspective to evaluate the theoretical accuracy of RS-based EF models.

Estimating Evapotranspiration From Satellite Using Easily Obtainable Variables: A case study over the Southern Great Plains, U.S.A.

Evapotranspiration (ET) is a critical component of the Earth's water budget, a critical modulator of land-atmosphere (L-A) interactions, and also plays a crucial role in managing the Earth's energy balance. In this study, the feasibility of generating spatially-continuous daily evaporative fraction (EF) and ET from minimal remotely-sensed and meteorological inputs in a trapezoidal framework is demonstrated. A total of four variables, Normalized Difference Vegetation Index (NDVI), Land surface temperature (Ts ), gridded daily average temperature (Ta ) and elevation (z) are required to estimate EF. Then, ET can be estimated with the available soil heat flux (G) and net radiation (Rn) data. Firstly, the crucial model variable, Ts - Ta , is examined how well it characterizes the variation in EF using in situ data recorded at two eddy correlation flux towers in Southern Great Plains, U.S.A in 2011. Next, accuracy of satellite-based Ts are compared to ground-based Ts . Finally, EF and ET estimates are validated. The results reveal that the model performed satisfactorily in modeling EF and ET variation at winter wheat and deciduous forest during the high evaporative months. Even though the model works best with the observed MODIS-Ts as opposed to temporally interpolated Ts , results obtained from interpolated Ts are able to close the gaps with reasonable accuracy. Due to the fact that Ts - Ta , is not a good indicator of EF outside the growing season when deciduous forest is dormant, potential improvements to the model are proposed to improve accuracy in EF and ET estimates at the expense of adding more variables.

A Universal Ts‐VI Triangle Method for the Continuous Retrieval of Evaporative Fraction From MODIS Products

AbstractThe triangle method based on the spatial relationship between remotely sensed land surface temperature (Ts) and vegetation index (VI) has been widely used for the estimates of evaporative fraction (EF). In the present study, a universal triangle method was proposed by transforming the Ts‐VI feature space from a regional scale to a pixel scale. The retrieval of EF is only related to the boundary conditions at pixel scale, regardless of the Ts‐VI configuration over the spatial domain. The boundary conditions of each pixel are composed of the theoretical dry edge determined by the surface energy balance principle and the wet edge determined by the average air temperature of open water. The universal triangle method was validated using the EF observations collected by the Energy Balance Bowen Ratio systems in the Southern Great Plains of the United States of America (USA). Two parameterization schemes of EF were used to demonstrate their applicability with Terra Moderate Resolution Imaging Spectroradiometer (MODIS) products over the whole year 2004. The results of this study show that the accuracy produced by both of these two parameterization schemes is comparable to that produced by the traditional triangle method, although the universal triangle method seems specifically suited to the parameterization scheme proposed in our previous research. The independence of the universal triangle method from the Ts‐VI feature space makes it possible to conduct a continuous monitoring of evapotranspiration and soil moisture. That is just the ability the traditional triangle method does not possess.

Evaporation from cultivated and semi-wild Sudanian Savanna in west Africa

Abstract. Rain-fed farming is the primary livelihood of semi-arid west Africa. Changes in land cover have the potential to affect precipitation, the critical resource for production. Turbulent flux measurements from two eddy-covariance towers and additional observations from a dense network of small, wireless meteorological stations combine to relate land cover (savanna forest and agriculture) to evaporation in a small (3.5 km2) catchment in Burkina Faso, west Africa. We observe larger sensible and latent heat fluxes over the savanna forest in the headwater area relative to the agricultural section of the watershed all year. Higher fluxes above the savanna forest are attributed to the greater number of exposed rocks and trees and the higher productivity of the forest compared to rain-fed, hand-farmed agricultural fields. Vegetation cover and soil moisture are found to be primary controls of the evaporative fraction. Satellite-derived vegetation index (NDVI) and soil moisture are determined to be good predictors of evaporative fraction, as indicators of the physical basis of evaporation. Our measurements provide an estimator that can be used to derive evaporative fraction when only NDVI is available. Such large-scale estimates of evaporative fraction from remotely sensed data are valuable where ground-based measurements are lacking, which is the case across the African continent and many other semi-arid areas. Evaporative fraction estimates can be combined, for example, with sensible heat from measurements of temperature variance, to provide an estimate of evaporation when only minimal meteorological measurements are available in remote regions of the world. These findings reinforce local cultural beliefs of the importance of forest fragments for climate regulation and may provide support to local decision makers and rural farmers in the maintenance of the forest areas.

On-orbit evaporative fraction estimations using a histogram-based triangle method from Terra MODIS

A triangle method has been frequently used for a regional evapotranspiration (ET) or evaporative fraction (EF) estimation using the relationship between a vegetation index and surface temperature. A simple histogram based on an on-orbit EF estimation algorithm is developed to determine hot and cold edges in the triangle distribution using only remotely sensed data. A new histogram based on an EF estimation algorithm is developed and compared with different options of existing hot and cold edge algorithms. The selected algorithms are applied to 5-year Terra MODIS data products, and the on-orbit EF estimations are validated using ground truth data from southern Great Plains. Using numerous iterations among the tested EF algorithms with the triangle method, the “histogram”-based hot edge and newly proposed cold edge algorithm combinations provide an excellent performance between the ground truth and on-orbit EF estimations. It is shown that the histogram-based EF algorithm has enhanced correlation and mean bias values of 0.74 and 0.087, respectively, providing excellent accuracy for the regional on-orbit EF estimations.

Evapotranspiration estimation using Landsat-8 data with a two-layer framework

Evapotranspiration (ET) plays an important role in hydrological cycle by linking land surface and atmosphere through water and energy transfers. Based on the data from the Landsat-8 satellite for typical days with clear sky condition from 2013 to 2016, a two-layer daily ET remote sensing framework was built, which includes four compartments: surface feature parameter estimation, evaporative fraction estimation, daily net radiation estimation, and daily ET extension. Based on the model, evaporation, transpiration, and daily ET in Shahe River Basin were estimated. The estimated daily ET showed a mean absolute percentage error of 8.7% in the plain areas, and 12.1% in the mountainous areas, compared to observations using large aperture scintillometer and eddy covariance system. The method gave higher accuracy than other remote sensing models applied in the same area previously, including the surface energy balance system and the ETWatch. By analyzing the relationship between land use types and surface water/heat fluxes, it was found that the surface energy balance components in the basin have prominent spatial-temporal features, and the soil component’s features are more obvious. It indicated that the proposed two-layer approach is superior to others in terms of simulation accuracy, and applicable to daily scale ET estimations on complex terrains.

Simplified triangle method for estimating evaporative fraction over soybean crops

Accurate estimates are emerging with technological advances in remote sensing, and the triangle method has demonstrated to be a useful tool for the estimation of evaporative fraction (EF). The purpose of this study was to estimate the EF using the triangle method at the regional level. We used data from the Moderate Resolution Imaging Spectroradiometer orbital sensor, referring to indices of surface temperature and vegetation index for a 10-year period (2002/2003 to 2011/2012) of cropping seasons in the state of Parana, Brazil. The triangle method has shown considerable results for the EF, and the validation of the estimates, as compared to observed data of climatological water balance, showed values >0.8 for modified “d” of Wilmott and R2 values between 0.6 and 0.7 for some counties. The errors were low for all years analyzed, and the test showed that the estimated data are very close to the observed data. Based on statistical validation, we can say that the triangle method is a consistent tool, is useful as it uses only images of remote sensing as variables, and can provide support for monitoring large-scale agroclimatic, specially for countries of great territorial dimensions, such as Brazil, which lacks a more dense network of meteorological ground stations, i.e., the country does not appear to cover a large field for data.

Identifying an Appropriate and Sustainable Irrigation Method Using Some Remotely Sensed Parameters for the Crop Cultivation in Vavuniya District

Over consumption of water resources due to unplanned and inappropriate irrigation methods would be a threat to the ecosystem health and to the sustainable agriculture in Vavuniya District. The objective of this study was to identify the most appropriate irrigation method using remotely sensed or derived hydro geological and meteorological information. The impact of different types of irrigation methods on water use efficiency, water consumption, socio economic aspects of irrigation and soil condition were considered to fulfil the objective of this study. Evaporative fraction, soil moisture content, hydrological parameters and the meteorological parameters were derived using satellite imageries namely the products of Moderate Resolution Imaging Spectro-radiameter (MODIS), MOD 09 A1 (Solar Zenith Angle), MOD 11 A1 (Land Surface Temperature), MOD 13 A1 (Normalized Different Vegetation Index) and MOD 43 B3 (Surface Albedo) and the Shuttle Radar Topography Mission (SRTM) DEM (90m resolution) associated with field reference data during January 2014 to June 2014. Estimation of evaporative fraction and soil moisture content were collectively done with the aid of Surface Energy Balance Algorithm (SEBAL) methodin Ilwis 3.7 environment. Appropriate geometric corrections and image processing operations were applied. Parameterization of hydrological and meteorological parameters was done using automated operations associated with auxiliary information of respective imageries. Information on socio economic aspects of irrigation methods were gathered from semi structured interviews and secondary data sources. This study revealed that integral of hydrological, topographical, meteorological and socio economic factors signifying the importance of micro irrigation methods as viable and effective irrigation method in agriculture. Encouraging the farmers to use micro irrigation methods by providing the credit facilities and the subsidy to install micro irrigation systems is vital for the water resource conservation and thereby the sustainable agriculture.

Assessment of two temporal-information-based methods for estimating evaporative fraction over the Southern Great Plains

Evaporative fraction (EF) is an important index for partitioning surface available energy. Temporal information derived from multi-times observations of remote sensing can reduce the uncertainty of EF estimates caused by the retrieval error of land surface parameters. Two temporal-information-based EF methods were assessed using in situ measurements from the Energy Balance Bowen Ratio system and the outputs from the second phase of the North American Land Data Assimilation System (NLDAS-2) over the Southern Great Plains (SGP). One is a newly developed method for estimating daily EF using a semi-empirical parameterization based on the day–night differences of land surface temperature (LST), air temperature, and incoming solar radiation; the other is the triangular feature space method constructed by the day–night difference of LST and fractional vegetation cover. The results showed that the two methods could reasonably estimate EF over the SGP region in spatial distribution. The EF estimated from the newly developed method was closer to the in situ measurements with a bias of −0.028 and a root mean square error (RMSE) of 0.194, and it was also closer to the outputs of the NLDAS-2 with an RMSE of ~0.13 compared with the results from the feature space method. A strong relationship between the results from the two methods was found with a coefficient of determination up to 0.824 and an RMSE of 0.143. The RMSE for the EF estimates caused by different vegetation indexes in the feature space could reach 0.15, and larger bias mainly occurred on surfaces with low evapotranspiration surface.

Evapotranspiration estimation over agricultural plains using MODIS data for all sky conditions

Agricultural, water resource, and forest management require evapotranspiration (ET) data over a range of spatial and temporal scales. Several methods, with reasonable accuracy, exist for computing ET for clear-sky conditions using satellite remote sensing data. However, computation of ET for cloudy pixels remains a challenge. Here, we have developed an ET estimation algorithm for all sky conditions using primarily remote sensing data. The approach is based on an extension of the Priestley–Taylor model with contextual interpretation of remotely sensed surface temperature and vegetation index. It requires estimation of evaporative fraction, soil heat flux, and net radiation that can be derived from the Moderate Resolution Imaging Spectroradiometer (MODIS). For cloudy pixels, a regression-based algorithm utilizes MODIS cloud data product for cloud-top temperature, cloud fraction, cloud emissivity, cloud optical thickness, and land-surface temperature (LST) to calculate net radiation and thereafter ET. The algorithm was tested and validated using data from the South Great Plains in the USA with reasonable accuracy of ranging between 54.5% and 69.6% and an average root mean square error of 40.3 . The methodology was tested to estimate ET over the agricultural plains of the Ganges Basin in India. The proposed ET algorithm has capabilities to estimate ET over a range of space and time scales in data-scarce regions.

Validation and scale dependencies of the triangle method for the evaporative fraction estimation over heterogeneous areas

Remote sensing has proved to be a consistent tool for monitoring water fluxes at regional scales. The triangle method, in particular, estimates the evaporative fraction (EF), defined as the ratio of latent heat flux (LE) to available energy, based on the relationship between satellite observations of land surface temperature and a vegetation index. Among other methodologies, this approach has been commonly used as an approximation to estimate LE, mainly over large semi-arid areas with uniform landscape features. In this study, an interpretation of the triangular space has been applied over a heterogeneous area in central Spain, using Landsat5-TM, Envisat-AATSR/MERIS and MSG-SEVIRI images. Some aspects affecting the model performance such as spatial resolution, terrain conditions, vegetation index applied and method for deriving the triangle edges have been assessed. The derived EF estimations have been validated against ground measurements obtained with scintillometer on a winter crop field during 2010–2011. When working with large spatial windows, removing areas with different topographic characteristics (altitude and slope) improved the performance of the methods. In addition, replacing the typically used NDVI with Leaf Area Index enhances the performance of the triangle method allowing a better characterization of the wet edge. Finally, results showed a relatively good performance for the EF estimates, with an RMSE of 0.11, 0.15 and 0.23 and R2 of 0.77, 0.41, and 0.24 for Landsat, Envisat and MSG satellites respectively, showing a scale dependency on the accuracy.

Evaporative Fraction as an Indicator of Moisture Condition and Water Stress Status in Semi-Arid Rangeland Ecosystems

Rangeland monitoring services require the capability to investigate vegetation condition and to assess biomass production, especially in areas where local livelihood depends on rangeland status. Remote sensing solutions are strongly recommended, where the systematic acquisition of field data is not feasible and does not guarantee properly describing the spatio-temporal dynamics of wide areas. Recent research on semi-arid rangelands has focused its attention on the evaporative fraction (EF), a key factor to estimate evapotranspiration (ET) in the energy balance (EB) algorithm. EF is strongly linked to the vegetation water status, and works conducted on eddy covariance towers used this parameter to increase the performances of satellite-based biomass estimation. In this work, a method to estimate EF from MODIS products, originally developed for evapotranspiration estimation, is tested and evaluated. Results show that the EF estimation from low spatial resolution over wide semi-arid area is feasible. Estimated EF resulted in being well correlated to field ET measurements, and the spatial patterns of EF maps are in agreement with the well-known climatic and landscape Sahelian features. The preliminary test on rangeland biomass production shows that satellite-retrieved EF as a water availability factor significantly increased the capacity of a remote sensing operational product to detect the variability of the field biomass measurements.

Daily Evaporative Fraction Parameterization Scheme Driven by Day–Night Differences in Surface Parameters: Improvement and Validation

In a previous study, a daily evaporative fraction (EF) parameterization scheme was derived based on day–night differences in surface temperature, air temperature, and net radiation. Considering the advantage that incoming solar radiation can be readily retrieved from remotely sensed data in comparison with surface net radiation, this study simplified the daily EF parameterization scheme using incoming solar radiation as an input. Daily EF estimates from the simplified scheme were nearly equivalent to the results from the original scheme. In situ measurements from six Ameriflux sites with different land covers were used to validate the new simplified EF parameterization scheme. Results showed that daily EF estimates for clear skies were consistent with the in situ EF corrected by the residual energy method, showing a coefficient of determination of 0.586 and a root mean square error of 0.152. Similar results were also obtained for partly clear sky conditions. The non-closure of the measured energy and heat fluxes and the uncertainty in determining fractional vegetation cover were likely to cause discrepancies in estimated daily EF and measured counterparts. The daily EF estimates of different land covers indicate that the constant coefficients in the simplified EF parameterization scheme are not strongly site-specific.