The Experts below are selected from a list of 16053 Experts worldwide ranked by ideXlab platform
Yunjun Yao - One of the best experts on this subject based on the ideXlab platform.
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ERTFM: An Effective Model to Fuse Chinese GF-1 and MODIS Reflectance Data for Terrestrial Latent Heat Flux Estimation
'MDPI AG', 2021Co-Authors: Lilin Zhang, Yunjun Yao, Ke Shang, Xiangyi Bei, Junming Yang, Xiaozheng Guo, Zijing XieAbstract:Coarse spatial resolution sensors play a major role in capturing temporal variation, as satellite images that capture fine spatial scales have a relatively long revisit cycle. The trade-off between the revisit cycle and spatial resolution hinders the access of terrestrial Latent Heat Flux (LE) data with both fine spatial and temporal resolution. In this paper, we firstly investigated the capability of an Extremely Randomized Trees Fusion Model (ERTFM) to reconstruct high spatiotemporal resolution reflectance data from a fusion of the Chinese GaoFen-1 (GF-1) and the Moderate Resolution Imaging Spectroradiometer (MODIS) products. Then, based on the merged reflectance data, we used a Modified-Satellite Priestley–Taylor (MS–PT) algorithm to generate LE products at high spatial and temporal resolutions. Our results illustrated that the ERTFM-based reflectance estimates showed close similarity with observed GF-1 images and the predicted NDVI agreed well with observed NDVI at two corresponding dates (r = 0.76 and 0.86, respectively). In comparison with other four fusion methods, including the widely used spatial and temporal adaptive reflectance fusion model (STARFM) and the enhanced STARFM, ERTFM had the best performance in terms of predicting reflectance (SSIM = 0.91; r = 0.77). Further analysis revealed that LE estimates using ERTFM-based data presented more detailed spatiotemporal characteristics and provided close agreement with site-level LE observations, with an R2 of 0.81 and an RMSE of 19.18 W/m2. Our findings suggest that the ERTFM can be used to improve LE estimation with high frequency and high spatial resolution, meaning that it has great potential to support agricultural monitoring and irrigation management
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evaluation of a satellite derived model parameterized by three soil moisture constraints to estimate terrestrial Latent Heat Flux in the heihe river basin of northwest china
Science of The Total Environment, 2019Co-Authors: Yunjun Yao, Jiquan Chen, Kun Jia, Shaomin Liu, Yuhu Zhang, Qiang Liu, Xiaotong Zhang, Joshua B FisherAbstract:Abstract Satellite-derived terrestrial Latent Heat Flux (LE) models are useful tools to understand regional surface energy and water cycle processes for terrestrial ecosystems in the Heihe River basin (HRB) of Northwest China. This study developed a satellite-derived hybrid LE model parameterized by three soil moisture (SM) constraints: SM, relative humidity (RH), and diurnal air temperature range (DT); and assessed model performance and sensitivity. We used MODerate Resolution Imaging Spectroradiometer (MODIS) and eddy covariance (EC) data from 12 EC Flux tower sites across the HRB. The hybrid model was trained using observed LE over 2012/2013–2014, and validated using observed LE for 2015 and leave-one-out cross-validation. The results show that the three SM constraints schemes exhibited some modeling differences at the Flux tower site scale. LE estimation using SM achieved the highest correlation (R2 = 0.87, p
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an empirical orthogonal function based algorithm for estimating terrestrial Latent Heat Flux from eddy covariance meteorological and satellite observations
PLOS ONE, 2016Co-Authors: Fei Feng, Yunjun Yao, Shunlin Liang, Jiquan Chen, Xiang Zhao, Kun Jia, Krisztina Pinter, Harry J MccaugheyAbstract:Accurate estimation of Latent Heat Flux (LE) based on remote sensing data is critical in characterizing terrestrial ecosystems and modeling land surface processes. Many LE products were released during the past few decades, but their quality might not meet the requirements in terms of data consistency and estimation accuracy. Merging multiple algorithms could be an effective way to improve the quality of existing LE products. In this paper, we present a data integration method based on modified empirical orthogonal function (EOF) analysis to integrate the Moderate Resolution Imaging Spectroradiometer (MODIS) LE product (MOD16) and the Priestley-Taylor LE algorithm of Jet Propulsion Laboratory (PT-JPL) estimate. Twenty-two eddy covariance (EC) sites with LE observation were chosen to evaluate our algorithm, showing that the proposed EOF fusion method was capable of integrating the two satellite data sets with improved consistency and reduced uncertainties. Further efforts were needed to evaluate and improve the proposed algorithm at larger spatial scales and time periods, and over different land cover types.
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a satellite based hybrid algorithm to determine the priestley taylor parameter for global terrestrial Latent Heat Flux estimation across multiple biomes
Remote Sensing of Environment, 2015Co-Authors: Yunjun Yao, Shunlin Liang, Jiquan Chen, Kun Jia, Jie Cheng, Joshua B Fisher, Kaicun Wang, Bo Jiang, Thomas GrunwaldAbstract:Abstract Accurate estimation of the terrestrial Latent Heat Flux (LE) for each plant functional type (PFT) at high spatial and temporal scales remains a major challenge. We developed a satellite-based hybrid algorithm to determine the Priestley–Taylor (PT) parameter for estimating global terrestrial LE across multiple biomes. The hybrid algorithm combines a simple empirical equation with physically based ecophysiological constraints to obtain the sum of the weighted ecophysiological constraints (f(e)) from satellite-based normalized difference vegetation index (NDVI) and ground-measured air temperature (Ta), relative humidity (RH), vapor pressure deficit (VPD) and LE for 2000 to 2009 provided by 240 globally distributed FluxNET eddy covariance (ECOR) tower sites. Cross-validation analysis indicated that the optimization at a PFT level performed well with a RMSE of less than 0.15 and a R2 between 0.61 and 0.88 for estimated monthly f(e). Cross-validation analysis also revealed good performance of the hybrid-based PT method in estimating seasonal variability with a RMSE of the monthly LE varying from 4.3 W/m2 (for 6 deciduous needleleaf forest sites) to 18.1 W/m2 (for 34 crop sites) and with a R2 of more than 0.67. The algorithm's performance was also good for predicting among-site and inter-annual variability with a R2 of more than 0.78 and 0.70, respectively. We implemented the global terrestrial LE estimation from 2003 to 2005 for a spatial resolution of 0.05°by recalibrating the coefficients of the hybrid algorithm using Modern Era Retrospective Analysis for Research and Applications (MERRA) meteorological data, Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI product and ground-measured LE. This simple but accurate hybrid algorithm provides an alternative method for mapping global terrestrial LE, with a performance generally improved as compared to other satellite algorithms that are not calibrated with tower. The calibrated f(e) differs for different PFTs, and all driving forces of the algorithm can be acquired from satellite and meteorological observations.
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bayesian multimodel estimation of global terrestrial Latent Heat Flux from eddy covariance meteorological and satellite observations
Journal of Geophysical Research, 2014Co-Authors: Yunjun Yao, Shunlin Liang, Jiquan Chen, Jie Cheng, Joshua B Fisher, Yang Hong, Nannan Zhang, Shaohua ZhaoAbstract: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.
Shunlin Liang - One of the best experts on this subject based on the ideXlab platform.
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an empirical orthogonal function based algorithm for estimating terrestrial Latent Heat Flux from eddy covariance meteorological and satellite observations
PLOS ONE, 2016Co-Authors: Fei Feng, Yunjun Yao, Shunlin Liang, Jiquan Chen, Xiang Zhao, Kun Jia, Krisztina Pinter, Harry J MccaugheyAbstract:Accurate estimation of Latent Heat Flux (LE) based on remote sensing data is critical in characterizing terrestrial ecosystems and modeling land surface processes. Many LE products were released during the past few decades, but their quality might not meet the requirements in terms of data consistency and estimation accuracy. Merging multiple algorithms could be an effective way to improve the quality of existing LE products. In this paper, we present a data integration method based on modified empirical orthogonal function (EOF) analysis to integrate the Moderate Resolution Imaging Spectroradiometer (MODIS) LE product (MOD16) and the Priestley-Taylor LE algorithm of Jet Propulsion Laboratory (PT-JPL) estimate. Twenty-two eddy covariance (EC) sites with LE observation were chosen to evaluate our algorithm, showing that the proposed EOF fusion method was capable of integrating the two satellite data sets with improved consistency and reduced uncertainties. Further efforts were needed to evaluate and improve the proposed algorithm at larger spatial scales and time periods, and over different land cover types.
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a satellite based hybrid algorithm to determine the priestley taylor parameter for global terrestrial Latent Heat Flux estimation across multiple biomes
Remote Sensing of Environment, 2015Co-Authors: Yunjun Yao, Shunlin Liang, Jiquan Chen, Kun Jia, Jie Cheng, Joshua B Fisher, Kaicun Wang, Bo Jiang, Thomas GrunwaldAbstract:Abstract Accurate estimation of the terrestrial Latent Heat Flux (LE) for each plant functional type (PFT) at high spatial and temporal scales remains a major challenge. We developed a satellite-based hybrid algorithm to determine the Priestley–Taylor (PT) parameter for estimating global terrestrial LE across multiple biomes. The hybrid algorithm combines a simple empirical equation with physically based ecophysiological constraints to obtain the sum of the weighted ecophysiological constraints (f(e)) from satellite-based normalized difference vegetation index (NDVI) and ground-measured air temperature (Ta), relative humidity (RH), vapor pressure deficit (VPD) and LE for 2000 to 2009 provided by 240 globally distributed FluxNET eddy covariance (ECOR) tower sites. Cross-validation analysis indicated that the optimization at a PFT level performed well with a RMSE of less than 0.15 and a R2 between 0.61 and 0.88 for estimated monthly f(e). Cross-validation analysis also revealed good performance of the hybrid-based PT method in estimating seasonal variability with a RMSE of the monthly LE varying from 4.3 W/m2 (for 6 deciduous needleleaf forest sites) to 18.1 W/m2 (for 34 crop sites) and with a R2 of more than 0.67. The algorithm's performance was also good for predicting among-site and inter-annual variability with a R2 of more than 0.78 and 0.70, respectively. We implemented the global terrestrial LE estimation from 2003 to 2005 for a spatial resolution of 0.05°by recalibrating the coefficients of the hybrid algorithm using Modern Era Retrospective Analysis for Research and Applications (MERRA) meteorological data, Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI product and ground-measured LE. This simple but accurate hybrid algorithm provides an alternative method for mapping global terrestrial LE, with a performance generally improved as compared to other satellite algorithms that are not calibrated with tower. The calibrated f(e) differs for different PFTs, and all driving forces of the algorithm can be acquired from satellite and meteorological observations.
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bayesian multimodel estimation of global terrestrial Latent Heat Flux from eddy covariance meteorological and satellite observations
Journal of Geophysical Research, 2014Co-Authors: Yunjun Yao, Shunlin Liang, Jiquan Chen, Jie Cheng, Joshua B Fisher, Yang Hong, Nannan Zhang, Shaohua ZhaoAbstract: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.
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modis driven estimation of terrestrial Latent Heat Flux in china based on a modified priestley taylor algorithm
Agricultural and Forest Meteorology, 2013Co-Authors: Yunjun Yao, Shunlin Liang, Xiang Zhao, Kun Jia, Jie Cheng, Shaomin Liu, Joshua B Fisher, Xudong Zhang, Qiming QinAbstract:a b s t r a c t Because of China's large size, satellite observations are necessary for estimation of the land surface Latent Heat Flux (LE). We describe here a satellite-driven Priestley-Taylor (PT)-based algorithm constrained by the Normalized Difference Vegetation Index (NDVI) and Apparent Thermal Inertia (ATI) derived from temperature change over time. We compare to the satellite-driven PT-based approach, PT-JPL, and vali- date both models using data collected from 16 eddy covariance Flux towers in China. Like PT-JPL, our proposed algorithm avoids the computational complexities of aerodynamic resistance parameters. We run the algorithms with monthly Moderate Resolution Imaging Spectroradiometer (MODIS) products (0.05 ◦ resolution), including albedo, Land Surface Temperature (LST), surface emissivity, and NDVI; and, Insolation from the Japan Aerospace Exploration Agency (JAXA). We find good agreement between our estimates of monthly LE and field-measured LE, with respective Root Mean Square Error (RMSE) and bias differences of 12.5 W m −2
Joshua B Fisher - One of the best experts on this subject based on the ideXlab platform.
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evaluation of a satellite derived model parameterized by three soil moisture constraints to estimate terrestrial Latent Heat Flux in the heihe river basin of northwest china
Science of The Total Environment, 2019Co-Authors: Yunjun Yao, Jiquan Chen, Kun Jia, Shaomin Liu, Yuhu Zhang, Qiang Liu, Xiaotong Zhang, Joshua B FisherAbstract:Abstract Satellite-derived terrestrial Latent Heat Flux (LE) models are useful tools to understand regional surface energy and water cycle processes for terrestrial ecosystems in the Heihe River basin (HRB) of Northwest China. This study developed a satellite-derived hybrid LE model parameterized by three soil moisture (SM) constraints: SM, relative humidity (RH), and diurnal air temperature range (DT); and assessed model performance and sensitivity. We used MODerate Resolution Imaging Spectroradiometer (MODIS) and eddy covariance (EC) data from 12 EC Flux tower sites across the HRB. The hybrid model was trained using observed LE over 2012/2013–2014, and validated using observed LE for 2015 and leave-one-out cross-validation. The results show that the three SM constraints schemes exhibited some modeling differences at the Flux tower site scale. LE estimation using SM achieved the highest correlation (R2 = 0.87, p
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a satellite based hybrid algorithm to determine the priestley taylor parameter for global terrestrial Latent Heat Flux estimation across multiple biomes
Remote Sensing of Environment, 2015Co-Authors: Yunjun Yao, Shunlin Liang, Jiquan Chen, Kun Jia, Jie Cheng, Joshua B Fisher, Kaicun Wang, Bo Jiang, Thomas GrunwaldAbstract:Abstract Accurate estimation of the terrestrial Latent Heat Flux (LE) for each plant functional type (PFT) at high spatial and temporal scales remains a major challenge. We developed a satellite-based hybrid algorithm to determine the Priestley–Taylor (PT) parameter for estimating global terrestrial LE across multiple biomes. The hybrid algorithm combines a simple empirical equation with physically based ecophysiological constraints to obtain the sum of the weighted ecophysiological constraints (f(e)) from satellite-based normalized difference vegetation index (NDVI) and ground-measured air temperature (Ta), relative humidity (RH), vapor pressure deficit (VPD) and LE for 2000 to 2009 provided by 240 globally distributed FluxNET eddy covariance (ECOR) tower sites. Cross-validation analysis indicated that the optimization at a PFT level performed well with a RMSE of less than 0.15 and a R2 between 0.61 and 0.88 for estimated monthly f(e). Cross-validation analysis also revealed good performance of the hybrid-based PT method in estimating seasonal variability with a RMSE of the monthly LE varying from 4.3 W/m2 (for 6 deciduous needleleaf forest sites) to 18.1 W/m2 (for 34 crop sites) and with a R2 of more than 0.67. The algorithm's performance was also good for predicting among-site and inter-annual variability with a R2 of more than 0.78 and 0.70, respectively. We implemented the global terrestrial LE estimation from 2003 to 2005 for a spatial resolution of 0.05°by recalibrating the coefficients of the hybrid algorithm using Modern Era Retrospective Analysis for Research and Applications (MERRA) meteorological data, Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI product and ground-measured LE. This simple but accurate hybrid algorithm provides an alternative method for mapping global terrestrial LE, with a performance generally improved as compared to other satellite algorithms that are not calibrated with tower. The calibrated f(e) differs for different PFTs, and all driving forces of the algorithm can be acquired from satellite and meteorological observations.
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bayesian multimodel estimation of global terrestrial Latent Heat Flux from eddy covariance meteorological and satellite observations
Journal of Geophysical Research, 2014Co-Authors: Yunjun Yao, Shunlin Liang, Jiquan Chen, Jie Cheng, Joshua B Fisher, Yang Hong, Nannan Zhang, Shaohua ZhaoAbstract: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.
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modis driven estimation of terrestrial Latent Heat Flux in china based on a modified priestley taylor algorithm
Agricultural and Forest Meteorology, 2013Co-Authors: Yunjun Yao, Shunlin Liang, Xiang Zhao, Kun Jia, Jie Cheng, Shaomin Liu, Joshua B Fisher, Xudong Zhang, Qiming QinAbstract:a b s t r a c t Because of China's large size, satellite observations are necessary for estimation of the land surface Latent Heat Flux (LE). We describe here a satellite-driven Priestley-Taylor (PT)-based algorithm constrained by the Normalized Difference Vegetation Index (NDVI) and Apparent Thermal Inertia (ATI) derived from temperature change over time. We compare to the satellite-driven PT-based approach, PT-JPL, and vali- date both models using data collected from 16 eddy covariance Flux towers in China. Like PT-JPL, our proposed algorithm avoids the computational complexities of aerodynamic resistance parameters. We run the algorithms with monthly Moderate Resolution Imaging Spectroradiometer (MODIS) products (0.05 ◦ resolution), including albedo, Land Surface Temperature (LST), surface emissivity, and NDVI; and, Insolation from the Japan Aerospace Exploration Agency (JAXA). We find good agreement between our estimates of monthly LE and field-measured LE, with respective Root Mean Square Error (RMSE) and bias differences of 12.5 W m −2
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Latent Heat Flux and canopy conductance based on penman monteith priestley taylor equation and bouchet s complementary hypothesis
Journal of Hydrometeorology, 2013Co-Authors: Kaniska Mallick, Joshua B Fisher, Andy Jarvis, Eva Boegh, Dev NiyogiAbstract:AbstractA novel method is presented to analytically resolve the terrestrial Latent Heat Flux (λE) and conductances (boundary layer gB and surface gS) using net radiation (RN), ground Heat Flux (G), air temperature (Ta), and relative humidity (RH). This method consists of set of equations where the two unknown internal state variables (gB and gS) were expressed in terms of the known core variables, combining diffusion equations, the Penman–Monteith equation, the Priestley–Taylor equation, and Bouchet’s complementary hypothesis. Estimated λE is validated with the independent eddy covariance λE observations over Soil Moisture Experiment 2002 (SMEX-02); the Global Energy and Water Cycle Experiment (GEWEX) Continental-Scale International Project (GCIP) selected sites from FluxNET and tropics eddy Flux, representing four climate zones (tropics, subtropics, temperate, and cold); and multiple biomes. The authors find a RMSE of 23.8–54.6 W m−2 for hourly λE over SMEX-02 and GCIP and 23.8–29.0 W m−2 for monthly λE ...
Jiquan Chen - One of the best experts on this subject based on the ideXlab platform.
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evaluation of a satellite derived model parameterized by three soil moisture constraints to estimate terrestrial Latent Heat Flux in the heihe river basin of northwest china
Science of The Total Environment, 2019Co-Authors: Yunjun Yao, Jiquan Chen, Kun Jia, Shaomin Liu, Yuhu Zhang, Qiang Liu, Xiaotong Zhang, Joshua B FisherAbstract:Abstract Satellite-derived terrestrial Latent Heat Flux (LE) models are useful tools to understand regional surface energy and water cycle processes for terrestrial ecosystems in the Heihe River basin (HRB) of Northwest China. This study developed a satellite-derived hybrid LE model parameterized by three soil moisture (SM) constraints: SM, relative humidity (RH), and diurnal air temperature range (DT); and assessed model performance and sensitivity. We used MODerate Resolution Imaging Spectroradiometer (MODIS) and eddy covariance (EC) data from 12 EC Flux tower sites across the HRB. The hybrid model was trained using observed LE over 2012/2013–2014, and validated using observed LE for 2015 and leave-one-out cross-validation. The results show that the three SM constraints schemes exhibited some modeling differences at the Flux tower site scale. LE estimation using SM achieved the highest correlation (R2 = 0.87, p
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an empirical orthogonal function based algorithm for estimating terrestrial Latent Heat Flux from eddy covariance meteorological and satellite observations
PLOS ONE, 2016Co-Authors: Fei Feng, Yunjun Yao, Shunlin Liang, Jiquan Chen, Xiang Zhao, Kun Jia, Krisztina Pinter, Harry J MccaugheyAbstract:Accurate estimation of Latent Heat Flux (LE) based on remote sensing data is critical in characterizing terrestrial ecosystems and modeling land surface processes. Many LE products were released during the past few decades, but their quality might not meet the requirements in terms of data consistency and estimation accuracy. Merging multiple algorithms could be an effective way to improve the quality of existing LE products. In this paper, we present a data integration method based on modified empirical orthogonal function (EOF) analysis to integrate the Moderate Resolution Imaging Spectroradiometer (MODIS) LE product (MOD16) and the Priestley-Taylor LE algorithm of Jet Propulsion Laboratory (PT-JPL) estimate. Twenty-two eddy covariance (EC) sites with LE observation were chosen to evaluate our algorithm, showing that the proposed EOF fusion method was capable of integrating the two satellite data sets with improved consistency and reduced uncertainties. Further efforts were needed to evaluate and improve the proposed algorithm at larger spatial scales and time periods, and over different land cover types.
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a satellite based hybrid algorithm to determine the priestley taylor parameter for global terrestrial Latent Heat Flux estimation across multiple biomes
Remote Sensing of Environment, 2015Co-Authors: Yunjun Yao, Shunlin Liang, Jiquan Chen, Kun Jia, Jie Cheng, Joshua B Fisher, Kaicun Wang, Bo Jiang, Thomas GrunwaldAbstract:Abstract Accurate estimation of the terrestrial Latent Heat Flux (LE) for each plant functional type (PFT) at high spatial and temporal scales remains a major challenge. We developed a satellite-based hybrid algorithm to determine the Priestley–Taylor (PT) parameter for estimating global terrestrial LE across multiple biomes. The hybrid algorithm combines a simple empirical equation with physically based ecophysiological constraints to obtain the sum of the weighted ecophysiological constraints (f(e)) from satellite-based normalized difference vegetation index (NDVI) and ground-measured air temperature (Ta), relative humidity (RH), vapor pressure deficit (VPD) and LE for 2000 to 2009 provided by 240 globally distributed FluxNET eddy covariance (ECOR) tower sites. Cross-validation analysis indicated that the optimization at a PFT level performed well with a RMSE of less than 0.15 and a R2 between 0.61 and 0.88 for estimated monthly f(e). Cross-validation analysis also revealed good performance of the hybrid-based PT method in estimating seasonal variability with a RMSE of the monthly LE varying from 4.3 W/m2 (for 6 deciduous needleleaf forest sites) to 18.1 W/m2 (for 34 crop sites) and with a R2 of more than 0.67. The algorithm's performance was also good for predicting among-site and inter-annual variability with a R2 of more than 0.78 and 0.70, respectively. We implemented the global terrestrial LE estimation from 2003 to 2005 for a spatial resolution of 0.05°by recalibrating the coefficients of the hybrid algorithm using Modern Era Retrospective Analysis for Research and Applications (MERRA) meteorological data, Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI product and ground-measured LE. This simple but accurate hybrid algorithm provides an alternative method for mapping global terrestrial LE, with a performance generally improved as compared to other satellite algorithms that are not calibrated with tower. The calibrated f(e) differs for different PFTs, and all driving forces of the algorithm can be acquired from satellite and meteorological observations.
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bayesian multimodel estimation of global terrestrial Latent Heat Flux from eddy covariance meteorological and satellite observations
Journal of Geophysical Research, 2014Co-Authors: Yunjun Yao, Shunlin Liang, Jiquan Chen, Jie Cheng, Joshua B Fisher, Yang Hong, Nannan Zhang, Shaohua ZhaoAbstract: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.
Kun Jia - One of the best experts on this subject based on the ideXlab platform.
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evaluation of a satellite derived model parameterized by three soil moisture constraints to estimate terrestrial Latent Heat Flux in the heihe river basin of northwest china
Science of The Total Environment, 2019Co-Authors: Yunjun Yao, Jiquan Chen, Kun Jia, Shaomin Liu, Yuhu Zhang, Qiang Liu, Xiaotong Zhang, Joshua B FisherAbstract:Abstract Satellite-derived terrestrial Latent Heat Flux (LE) models are useful tools to understand regional surface energy and water cycle processes for terrestrial ecosystems in the Heihe River basin (HRB) of Northwest China. This study developed a satellite-derived hybrid LE model parameterized by three soil moisture (SM) constraints: SM, relative humidity (RH), and diurnal air temperature range (DT); and assessed model performance and sensitivity. We used MODerate Resolution Imaging Spectroradiometer (MODIS) and eddy covariance (EC) data from 12 EC Flux tower sites across the HRB. The hybrid model was trained using observed LE over 2012/2013–2014, and validated using observed LE for 2015 and leave-one-out cross-validation. The results show that the three SM constraints schemes exhibited some modeling differences at the Flux tower site scale. LE estimation using SM achieved the highest correlation (R2 = 0.87, p
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an empirical orthogonal function based algorithm for estimating terrestrial Latent Heat Flux from eddy covariance meteorological and satellite observations
PLOS ONE, 2016Co-Authors: Fei Feng, Yunjun Yao, Shunlin Liang, Jiquan Chen, Xiang Zhao, Kun Jia, Krisztina Pinter, Harry J MccaugheyAbstract:Accurate estimation of Latent Heat Flux (LE) based on remote sensing data is critical in characterizing terrestrial ecosystems and modeling land surface processes. Many LE products were released during the past few decades, but their quality might not meet the requirements in terms of data consistency and estimation accuracy. Merging multiple algorithms could be an effective way to improve the quality of existing LE products. In this paper, we present a data integration method based on modified empirical orthogonal function (EOF) analysis to integrate the Moderate Resolution Imaging Spectroradiometer (MODIS) LE product (MOD16) and the Priestley-Taylor LE algorithm of Jet Propulsion Laboratory (PT-JPL) estimate. Twenty-two eddy covariance (EC) sites with LE observation were chosen to evaluate our algorithm, showing that the proposed EOF fusion method was capable of integrating the two satellite data sets with improved consistency and reduced uncertainties. Further efforts were needed to evaluate and improve the proposed algorithm at larger spatial scales and time periods, and over different land cover types.
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a satellite based hybrid algorithm to determine the priestley taylor parameter for global terrestrial Latent Heat Flux estimation across multiple biomes
Remote Sensing of Environment, 2015Co-Authors: Yunjun Yao, Shunlin Liang, Jiquan Chen, Kun Jia, Jie Cheng, Joshua B Fisher, Kaicun Wang, Bo Jiang, Thomas GrunwaldAbstract:Abstract Accurate estimation of the terrestrial Latent Heat Flux (LE) for each plant functional type (PFT) at high spatial and temporal scales remains a major challenge. We developed a satellite-based hybrid algorithm to determine the Priestley–Taylor (PT) parameter for estimating global terrestrial LE across multiple biomes. The hybrid algorithm combines a simple empirical equation with physically based ecophysiological constraints to obtain the sum of the weighted ecophysiological constraints (f(e)) from satellite-based normalized difference vegetation index (NDVI) and ground-measured air temperature (Ta), relative humidity (RH), vapor pressure deficit (VPD) and LE for 2000 to 2009 provided by 240 globally distributed FluxNET eddy covariance (ECOR) tower sites. Cross-validation analysis indicated that the optimization at a PFT level performed well with a RMSE of less than 0.15 and a R2 between 0.61 and 0.88 for estimated monthly f(e). Cross-validation analysis also revealed good performance of the hybrid-based PT method in estimating seasonal variability with a RMSE of the monthly LE varying from 4.3 W/m2 (for 6 deciduous needleleaf forest sites) to 18.1 W/m2 (for 34 crop sites) and with a R2 of more than 0.67. The algorithm's performance was also good for predicting among-site and inter-annual variability with a R2 of more than 0.78 and 0.70, respectively. We implemented the global terrestrial LE estimation from 2003 to 2005 for a spatial resolution of 0.05°by recalibrating the coefficients of the hybrid algorithm using Modern Era Retrospective Analysis for Research and Applications (MERRA) meteorological data, Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI product and ground-measured LE. This simple but accurate hybrid algorithm provides an alternative method for mapping global terrestrial LE, with a performance generally improved as compared to other satellite algorithms that are not calibrated with tower. The calibrated f(e) differs for different PFTs, and all driving forces of the algorithm can be acquired from satellite and meteorological observations.
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modis driven estimation of terrestrial Latent Heat Flux in china based on a modified priestley taylor algorithm
Agricultural and Forest Meteorology, 2013Co-Authors: Yunjun Yao, Shunlin Liang, Xiang Zhao, Kun Jia, Jie Cheng, Shaomin Liu, Joshua B Fisher, Xudong Zhang, Qiming QinAbstract:a b s t r a c t Because of China's large size, satellite observations are necessary for estimation of the land surface Latent Heat Flux (LE). We describe here a satellite-driven Priestley-Taylor (PT)-based algorithm constrained by the Normalized Difference Vegetation Index (NDVI) and Apparent Thermal Inertia (ATI) derived from temperature change over time. We compare to the satellite-driven PT-based approach, PT-JPL, and vali- date both models using data collected from 16 eddy covariance Flux towers in China. Like PT-JPL, our proposed algorithm avoids the computational complexities of aerodynamic resistance parameters. We run the algorithms with monthly Moderate Resolution Imaging Spectroradiometer (MODIS) products (0.05 ◦ resolution), including albedo, Land Surface Temperature (LST), surface emissivity, and NDVI; and, Insolation from the Japan Aerospace Exploration Agency (JAXA). We find good agreement between our estimates of monthly LE and field-measured LE, with respective Root Mean Square Error (RMSE) and bias differences of 12.5 W m −2
