Scaling Flux Tower Observations of Sensible Heat Flux Using Weighted Area-to-Area Regression Kriging

Sensible heat flux (H) plays an important role in characterizations of land surface water and heat balance. There are various types of H measurement methods that depend on observation scale, from local-area-scale eddy covariance (EC) to regional-scale large aperture scintillometer (LAS) and remote sensing (RS) products. However, methods of converting one H scale to another to validate RS products are still open for question. A previous area-to-area regression kriging-based scaling method performed well in converting EC-scale H to LAS-scale H. However, the method does not consider the path-weighting function in the EC- to LAS-scale kriging with the regression residue, which inevitably brought about a bias estimation. In this study, a weighted area-to-area regression kriging (WATA RK) model is proposed to convert EC-scale H to LAS-scale H. It involves path-weighting functions of EC and LAS source areas in both regression and area kriging stages. Results show that WATA RK outperforms traditional methods in most cases, improving estimation accuracy. The method is considered to provide an efficient validation of RS H flux products.

[1]  D. Baldocchi,et al.  Seasonal variation of carbon dioxide exchange rates above and below a boreal jack pine forest , 1997 .

[2]  G. Laneve,et al.  A simple algorithm to estimate sensible heat flux from remotely sensed MODIS data , 2012 .

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

[4]  Gautam Bisht,et al.  Estimation and comparison of evapotranspiration from MODIS and AVHRR sensors for clear sky days over the Southern Great Plains , 2006 .

[5]  P. Goovaerts.  Geostatistical analysis of health data with different levels of spatial aggregation. , 2012, Spatial and spatio-temporal epidemiology.

[6]  J. Andre,et al.  Regional estimates of heat and evaporation fluxes over non-homogeneous terrain. Examples from the HAPEX-MOBILHY programme , 1990 .

[7]  Area averaging of land surface-atmosphere fluxes in NOPEX: Challenges, results and perspectives , 2002 .

[8]  Ji Zhou,et al.  Intercomparison of methods for estimating land surface temperature from a Landsat-5 TM image in an arid region with low water vapour in the atmosphere , 2012 .

[9]  Jan Kleissl,et al.  Intercomparison of Sensible Heat Flux from Large Aperture Scintillometer and Eddy Covariance Methods: Field Experiment over a Homogeneous Semi-arid Region , 2010 .

[10]  Lalith Chandrapala,et al.  Evapotranspiration fluxes over mixed vegetation areas measured from large aperture scintillometer , 2003 .

[11]  F. Beyrich,et al.  Developments in Scintillometry , 2009 .

[12]  L. Aragão,et al.  Assessment of the MODIS global evapotranspiration algorithm using eddy covariance measurements and hydrological modelling in the Rio Grande basin , 2013 .

[13]  Edzer J. Pebesma,et al.  Bayesian area-to-point kriging using expert knowledge as informative priors , 2014, Int. J. Appl. Earth Obs. Geoinformation.

[14]  A. Chehbouni,et al.  On the application of scintillometry over heterogeneous grids , 2007 .

[15]  P. Kyriakidis.  A Geostatistical Framework for Area-to-Point Spatial Interpolation , 2004 .

[16]  C. Gotway,et al.  Combining Incompatible Spatial Data , 2002 .

[17]  K. Jon Ranson,et al.  The Boreal Ecosystem-Atmosphere Study (BOREAS) : an overview and early results from the 1994 field year , 1995 .

[18]  Wim G.M. Bastiaanssen,et al.  Surface energy balance and actual evapotranspiration of the transboundary Indus Basin estimated from satellite measurements and the ETLook model , 2012 .

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

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

[21]  Anthony Morse,et al.  A Landsat-based energy balance and evapotranspiration model in Western US water rights regulation and planning , 2005 .

[22]  Lisheng Song,et al.  Characterizing the Footprint of Eddy Covariance System and Large Aperture Scintillometer Measurements to Validate Satellite-Based Surface Fluxes , 2015, IEEE Geoscience and Remote Sensing Letters.

[23]  D. Long,et al.  Comparison of three dual‐source remote sensing evapotranspiration models during the MUSOEXE‐12 campaign: Revisit of model physics , 2015 .

[24]  G. J. Fochesatto,et al.  A New Sensitivity Analysis and Solution Method for Scintillometer Measurements of Area-Averaged Turbulent Fluxes , 2013, Boundary-Layer Meteorology.

[25]  Qianyi Zhao,et al.  Upscaling Sensible Heat Fluxes With Area-to-Area Regression Kriging , 2015, IEEE Geoscience and Remote Sensing Letters.

[26]  Dennis D. Baldocchi,et al.  Seasonal and interannual variability of energy fluxes over a broadleaved temperate deciduous forest in North America , 2000 .

[27]  Qing Xiao,et al.  Heihe Watershed Allied Telemetry Experimental Research (HiWATER): Scientific Objectives and Experimental Design , 2013 .

[28]  Tony Persson,et al.  Scale aggregation - comparison of flux estimates from NOPEX , 1999 .

[29]  H. Mengelkamp,et al.  Evaporation over a Heterogeneous Land Surface: EVA_GRIPS and the LITFASS-2003 Experiment—An Overview , 2006 .

[30]  Lars-Christer Lundin,et al.  Energy, water and carbon exchange in a boreal forest landscape - NOPEX experiences , 1999 .

[31]  G. Matheron.  The intrinsic random functions and their applications , 1973, Advances in Applied Probability.

[32]  P. Sellers,et al.  The First ISLSCP Field Experiment (FIFE) , 1988 .