An Empirical Orthogonal Function-Based Algorithm for Estimating Terrestrial Latent Heat Flux from Eddy Covariance, Meteorological and Satellite Observations

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.

[1]  S. Running,et al.  Regional evaporation estimates from flux tower and MODIS satellite data , 2007 .

[2]  Seung Oh Lee,et al.  Validation of MODIS 16 global terrestrial evapotranspiration products in various climates and land cover types in Asia , 2012 .

[3]  Irving M. Klotz,et al.  Symposia of the Society for Experimental Biology , 1952, The Yale Journal of Biology and Medicine.

[4]  Bo Hu,et al.  A Weak-Constraint-Based Data Assimilation Scheme for Estimating Surface Turbulent Fluxes , 2007, IEEE Geoscience and Remote Sensing Letters.

[5]  Shaomin Liu,et al.  Validation of remotely sensed evapotranspiration over the Hai River Basin, China , 2012 .

[6]  Shunlin Liang,et al.  An improved method for estimating global evapotranspiration based on satellite determination of surface net radiation, vegetation index, temperature, and soil moisture , 2008, IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium.

[7]  Carlos Jimenez,et al.  On Uncertainty in Global Terrestrial Evapotranspiration Estimates from Choice of Input Forcing Datasets , 2015 .

[8]  Xuhui Lee,et al.  Correcting surface solar radiation of two data assimilation systems against FLUXNET observations in North America , 2013 .

[9]  Maosheng Zhao,et al.  Development of a global evapotranspiration algorithm based on MODIS and global meteorology data , 2007 .

[10]  P. Dirmeyer,et al.  Trends in Land–Atmosphere Interactions from CMIP5 Simulations , 2013 .

[11]  Fabio Castelli,et al.  Mapping of Land-Atmosphere Heat Fluxes and Surface Parameters with Remote Sensing Data , 2003 .

[12]  D. Baldocchi,et al.  Global estimates of the land–atmosphere water flux based on monthly AVHRR and ISLSCP-II data, validated at 16 FLUXNET sites , 2008 .

[13]  J. Fisher,et al.  On the net surface water exchange rate estimated from remote-sensing observation and reanalysis , 2014 .

[14]  S. Seneviratne,et al.  Evaluation of global observations‐based evapotranspiration datasets and IPCC AR4 simulations , 2011 .

[15]  Alvaro Montenegro,et al.  The net carbon drawdown of small scale afforestation from satellite observations , 2009 .

[16]  M. Mccabe,et al.  Multi-site evaluation of terrestrial evaporation models using FLUXNET data , 2014 .

[17]  Joshua B. Fisher,et al.  ET come home: potential evapotranspiration in geographical ecology , 2011 .

[18]  K. Davis,et al.  Global estimates of evapotranspiration and gross primary production based on MODIS and global meteorology data , 2010 .

[19]  Dara Entekhabi,et al.  Estimation of surface turbulent heat fluxes via variational assimilation of sequences of land surface temperatures from Geostationary Operational Environmental Satellites , 2014 .

[20]  C. Priestley,et al.  On the Assessment of Surface Heat Flux and Evaporation Using Large-Scale Parameters , 1972 .

[21]  Martha C. Anderson,et al.  Mapping daily evapotranspiration at field scales over rainfed and irrigated agricultural areas using remote sensing data fusion , 2014 .

[22]  W. Oechel,et al.  Energy balance closure at FLUXNET sites , 2002 .

[23]  J. Monteith.  Evaporation and environment. , 1965, Symposia of the Society for Experimental Biology.

[24]  Nannan Zhang,et al.  Validation and Application of the Modified Satellite-Based Priestley-Taylor Algorithm for Mapping Terrestrial Evapotranspiration , 2014, Remote. Sens..

[25]  S. Sorooshian,et al.  Multi-model ensemble hydrologic prediction using Bayesian model averaging , 2007 .

[26]  Renaud Mathieu,et al.  Validation of Global Evapotranspiration Product (MOD16) using Flux Tower Data in the African Savanna, South Africa , 2014, Remote. Sens..

[27]  S. Running,et al.  A continuous satellite‐derived global record of land surface evapotranspiration from 1983 to 2006 , 2010 .

[28]  J. Beckers,et al.  EOF Calculations and Data Filling from Incomplete Oceanographic Datasets , 2003 .

[29]  Thomas M. Smith,et al.  Reconstruction of Historical Sea Surface Temperatures Using Empirical Orthogonal Functions , 1996 .

[30]  Lucien Wald,et al.  Some terms of reference in data fusion , 1999, IEEE Trans. Geosci. Remote. Sens..

[31]  R. Dickinson,et al.  Evidence for decadal variation in global terrestrial evapotranspiration between 1982 and 2002: 1. Model development , 2010 .

[32]  R. Pinker,et al.  An Empirical Orthogonal Function Iteration Approach for Obtaining Homogeneous Radiative Fluxes from Satellite Observations , 2007 .

[33]  S. Liang,et al.  Bayesian multimodel estimation of global terrestrial latent heat flux from eddy covariance, meteorological, and satellite observations , 2014 .

[34]  J. Townshend,et al.  A long-term Global LAnd Surface Satellite (GLASS) data-set for environmental studies , 2013 .

[35]  J. Beckers,et al.  Reconstruction of incomplete oceanographic data sets using empirical orthogonal functions: application to the Adriatic Sea surface temperature , 2005 .

[36]  Jiemin Wang,et al.  Intercomparison of surface energy flux measurement systems used during the HiWATER‐MUSOEXE , 2013 .

[37]  C. Tucker,et al.  Climate-Driven Increases in Global Terrestrial Net Primary Production from 1982 to 1999 , 2003, Science.

[38]  T. Jacksona,et al.  Effects of remote sensing pixel resolution on modeled energy flux variability of croplands in Iowa , 2004 .

[39]  Shunlin Liang,et al.  Evidence for decadal variation in global terrestrial evapotranspiration between 1982 and 2002: 2. Results , 2010 .

[40]  Xubin Zeng,et al.  Evaluation of multireanalysis products with in situ observations over the Tibetan Plateau , 2012 .

[41]  R. Dickinson,et al.  A review of global terrestrial evapotranspiration: Observation, modeling, climatology, and climatic variability , 2011 .

[42]  Shunlin Liang,et al.  Integrating MODIS and CYCLOPES Leaf Area Index Products Using Empirical Orthogonal Functions , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[43]  B. Scanlon,et al.  Uncertainty in evapotranspiration from land surface modeling, remote sensing, and GRACE satellites , 2014 .

[44]  Matthew F. McCabe,et al.  Effects of spatial aggregation on the multi-scale estimation of evapotranspiration , 2013 .

[45]  Markus Reichstein,et al.  Benchmark products for land evapotranspiration: LandFlux-EVAL multi-data set synthesis , 2013 .

[46]  Jeng-Ming Chen,et al.  Interpretation of Extended Empirical Orthogonal Function (EEOF) Analysis , 1993 .

[47]  S. Liang,et al.  Estimating turbulent fluxes through assimilation of geostationary operational environmental satellites data using ensemble Kalman filter , 2011 .

[48]  Zhuguo Ma,et al.  Comparison of satellite-based evapotranspiration models over terrestrial ecosystems in China , 2014 .

[49]  Shaomin Liu,et al.  A comparison of eddy-covariance and large aperture scintillometer measurements with respect to the energy balance closure problem , 2011 .

[50]  S. Seneviratne,et al.  Global intercomparison of 12 land surface heat flux estimates , 2011 .

[51]  Xiquan Dong,et al.  Evaluation and Intercomparison of Cloud Fraction and Radiative Fluxes in Recent Reanalyses over the Arctic Using BSRN Surface Observations , 2012 .

[52]  J. Norman,et al.  Correcting eddy-covariance flux underestimates over a grassland , 2000 .

[53]  Eric F. Wood,et al.  Global estimates of evapotranspiration for climate studies using multi-sensor remote sensing data: Evaluation of three process-based approaches , 2011 .

[54]  Ian T. Jolliffe,et al.  Empirical orthogonal functions and related techniques in atmospheric science: A review , 2007 .

[55]  S. Liang,et al.  MODIS-driven estimation of terrestrial latent heat flux in China based on a modified Priestley-Taylor algorithm , 2013 .

[56]  Maosheng Zhao,et al.  Improvements of the MODIS terrestrial gross and net primary production global data set , 2005 .

[57]  Ming Xu,et al.  Evapotranspiration models compared on a Sierra Nevada forest ecosystem , 2005, Environ. Model. Softw..

[58]  Maosheng Zhao,et al.  Improvements to a MODIS global terrestrial evapotranspiration algorithm , 2011 .