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

This work was partially supported by the National Natural Science Foundation of China (41401042), National Key Basic Research Program of China (973 Program) (Grant No. 2015CB452701) and National Natural Science Foundation of China (Grant Nos. 41571019 and 41371043).Peer reviewedproo

Regional estimation of daily to annual regional evapotranspiration with MODIS data in the Yellow River Delta wetland

Evapotranspiration (ET) from the wetland of the Yellow River Delta (YRD) is one of the important components in the water cycle, which represents the water consumption by the plants and evaporation from the water and the non-vegetated surfaces. Reliable estimates of the total evapotranspiration from the wetland is useful information both for understanding the hydrological process and for water management to protect this natural environment. Due to the heterogeneity of the vegetation types and canopy density and of soil water content over the wetland (specifically over the natural reserve areas), it is difficult to estimate the regional evapotranspiration extrapolating measurements or calculations usually done locally for a specific land cover type. Remote sensing can provide observations of land surface conditions with high spatial and temporal resolution and coverage. In this study, a model based on the Energy Balance method was used to calculate daily evapotranspiration (ET) using instantaneous observations of land surface reflectance and temperature from MODIS when the data were available on clouds-free days. A time series analysis algorithm was then applied to generate a time series of daily ET over a year period by filling the gaps in the observation series due to clouds. A detailed vegetation classification map was used to help identifying areas of various wetland vegetation types in the YRD wetland. Such information was also used to improve the parameterizations in the energy balance model to improve the accuracy of ET estimates. This study showed that spatial variation of ET was significant over the same vegetation class at a given time and over different vegetation types in different seasons in the YRD wetlan

Methods to Evaluate Land-Atmosphere Exchanges in Amazonia Based on Satellite Imagery and Ground Measurements

During the last three decades, intensive campaigns and experiments have been conducted for acquiring micrometeorological data in the Amazonian ecosystems, which has increased our understanding of the variation, especially seasonally, of the total energy available for the atmospheric heating process by the surface, evapotranspiration and carbon exchanges. However, the measurements obtained by such experiments generally cover small areas and are not representative of the spatial variability of these processes. This chapter aims to discuss several algorithms developed to estimate surface energy and carbon fluxes combining satellite data and micrometeorological observations, highlighting the potentialities and limitations of such models for applications in the Amazon region. We show that the use of these models presents an important role in understanding the spatial and temporal patterns of biophysical surface parameters in a region where most of the information is local. Data generated may be used as inputs in earth system surface models allowing the evaluation of the impact, both in regional as well as global scales, caused by land-use and land-cover changes

A Review of Current Methodologies for Regional Evapotranspiration Estimation from Remotely Sensed Data

An overview of the commonly applied evapotranspiration (ET) models using remotely sensed data is given to provide insight into the estimation of ET on a regional scale from satellite data. Generally, these models vary greatly in inputs, main assumptions and accuracy of results, etc. Besides the generally used remotely sensed multi-spectral data from visible to thermal infrared bands, most remotely sensed ET models, from simplified equations models to the more complex physically based two-source energy balance models, must rely to a certain degree on ground-based auxiliary measurements in order to derive the turbulent heat fluxes on a regional scale. We discuss the main inputs, assumptions, theories, advantages and drawbacks of each model. Moreover, approaches to the extrapolation of instantaneous ET to the daily values are also briefly presented. In the final part, both associated problems and future trends regarding these remotely sensed ET models were analyzed to objectively show the limitations and promising aspects of the estimation of regional ET based on remotely sensed data and ground-based measurements

Incorporating an iterative energy restraint for the Surface Energy Balance System (SEBS)

© 2017 Elsevier Inc. The Surface Energy Balance System (SEBS) has proven itself as an effective remotely sensed estimator of actual evapotranspiration (ETa). However, it has several vulnerabilities associated with the partitioning of the available energy (AE) at the land surface. We introduce a two stage energy restraint process into the SEBS algorithm (SEBS-ER) to overcome these vulnerabilities. The first offsets the remotely sensed surface temperature to ensure the surface to air temperature difference reflects AE, while the second stage uses a domain based image search process to identify and adjust the proportions of sensible (H) and latent (λE) heat flux with respect to AE. We effectively implemented SEBS-ER over 61 acquisitions over two Landsat tiles (path 90 row 84 and path 91 row 85) in south-eastern Australia that feature heterogeneous land covers. Across the two areas we showed that the SEBS-ER algorithm has: greater resilience to perturbed errors in surface energy balance algorithm inputs; significantly improved accuracy (p < 0.05) at two eddy covariance flux towers in heavily forested (RMSE 62.3 W m− 2, R2 0.879) and sub-alpine grassland (RMSE 33.2 W m− 2, R2 0.939) land covers; and greater temporal stability across 52 daily actual evapotranspiration (ETa) estimates compared to a temporally stable and independent ETa dataset. The energy restraint within SEBS-ER has reduced exposure to the complex errors and uncertainties within remotely sensed, meteorological, and land type SEBS inputs, providing more reliable and accurate spatially distributed ETa products

Calibration of the AquaCrop model for winter wheat using MODIS LAI images

In semi-arid environments vegetation density and distribution is of considerable importance for the hydrological water balance. A number of hydrological models exploit Leaf Area Index (LAI) maps retrievedby remote sensing as a measure of the vegetation cover, in order to enhance the evaluation of evapotran-spiration and interception losses. On the other hand, actual evapotranspiration and vegetation development can be derived through crop growth models, such as AquaCrop, developed by FAO (Food and Agricultural Organization), which allows the simulation of the canopy development of the main field crops. We used MODIS LAI images to calibrate AquaCrop according to the canopy cover development of winter wheat. With this aim we exploited an empirical relationship between LAI and canopy cover. In detail Aquacrop was calibrated with MODIS LAI maps collected between 2008 and 2011, and validated with reference to MODIS LAI maps of 2013-2014 in Rocchetta Sant'Antonio and Sant'Agata, two test sites in the Carapelle watershed, Southern Italy. Results, in terms of evaluation of canopy cover, provided improvements. For example, for Rocchetta Sant'Antonio, the statistical indexes vary from r = 0.40, ER = 0.22, RMSE = 17.28 and KGE = 0.31 (using the model without calibration), to r = 0.86, ER = 0.08, RMSE = 6.01 and KGE 0.85 (after calibration). © 2015 Elsevier B.V

Improved Modeling of Evapotranspiration using Satellite Remote Sensing at Varying Spatial and Temporal Scales

The overall objective of the dissertation was to improve the spatial and temporal representation and retrieval accuracy of evapotranspiration (ET) using satellite imagery. Specifically, (1) aiming at improving the spatial representation of daily net radiation (Rn,24) under rugged terrains, a new algorithm, which accounts for terrain effects on available shortwave radiation throughout a day and utilizes four observations of Moderate-resolution Imaging Spectroradiometer (MODIS)-based land surface temperature retrievals to simulate daily net longwave radiation, was developed. The algorithm appears to be capable of capturing heterogeneity in Rn,24 at watershed scales. (2) Most satellite-based ET models are constrained to work under cloud-free conditions. To address this deficiency, an approach of integrating a satellite-based model with a large-scale feedback model was proposed to generate ET time series for all days. Results show that the ET time series estimates can exhibit complementary features between the potential ET and the actual ET at watershed scales. (3) For improving the operability of Two-source Energy Balance (TSEB) which requires computing resistance networks and tuning the Priestley-Taylor parameter involved, a new Two-source Trapezoid Model for ET (TTME) based on deriving theoretical boundaries of evaporative fraction (EF) and the concept of soil surface moisture availability isopleths was developed. It was applied to the Soil Moisture and Atmosphere Coupling Experiment (SMACEX) site in central Iowa, U.S., on three Landsat TM/ETM imagery acquisition dates in 2002. Results show the EF and latent heat flux (LE) estimates with a mean absolute percentage difference (MAPD) of 6.7 percent and 8.7 percent, respectively, relative to eddy covariance tower-based measurements after forcing closure by the Bowen ratio technique. (4) The domain and resolution dependencies of the Surface Energy Balance Algorithm for Land (SEBAL) and the triangle model were systematically investigated. Derivation of theoretical boundaries of EF for the two models could effectively constrain errors/uncertainties arising from these dependencies. (5) A Modified SEBAL (M-SEBAL) was consequently proposed, in which subjectivity involved in the selection of extreme pixels by the operator is eliminated. The performance of M-SEBAL at the SMACEX site is reasonably well, showing EF and LE estimates with an MAPD of 6.3 percent and 8.9 percent, respectively

Bayesian multimodel estimation of global terrestrial latent heat flux from eddy covariance, meteorological, and satellite observations

Accurate estimation of the satellite-based global terrestrial latent heat flux (LE) at high spatial and temporal scales remains a major challenge. In this study, we introduce a Bayesian model averaging (BMA) method to improve satellite-based global terrestrial LE estimation by merging five process-based algorithms. These are the Moderate Resolution Imaging Spectroradiometer (MODIS) LE product algorithm, the revised remote-sensing-based Penman-Monteith LE algorithm, the Priestley-Taylor-based LE algorithm, the modified satellite-based Priestley-Taylor LE algorithm, and the semi-empirical Penman LE algorithm. We validated the BMA method using data for 2000–2009 and by comparison with a simple model averaging (SA) method and five process-based algorithms. Validation data were collected for 240 globally distributed eddy covariance tower sites provided by FLUXNET projects. The validation results demonstrate that the five process-based algorithms used have variable uncertainty and the BMA method enhances the daily LE estimates, with smaller root mean square errors (RMSEs) than the SA method and the individual algorithms driven by tower-specific meteorology and Modern Era Retrospective Analysis for Research and Applications (MERRA) meteorological data provided by the NASA Global Modeling and Assimilation Office (GMAO), respectively. The average RMSE for the BMA method driven by daily tower-specific meteorology decreased by more than 5 W/m2 for crop and grass sites, and by more than 6 W/m2 for forest, shrub, and savanna sites. The average coefficients of determination (R2) increased by approximately 0.05 for most sites. To test the BMA method for regional mapping, we applied it for MODIS data and GMAO-MERRA meteorology to map annual global terrestrial LE averaged over 2001–2004 for spatial resolution of 0.05°. The BMA method provides a basis for generating a long-term global terrestrial LE product for characterizing global energy, hydrological, and carbon cycles