Regional Daily ET Estimates Based on the Gap-Filling Method of Surface Conductance

Remote sensing allows regional evapotranspiration (ET) values to be obtained. Surface conductance is a key variable in estimating ET and controls surface flux interactions between the underlying surface and atmosphere. Limited by the influence of clouds, ET can only be estimated on cloud-free days. In this study, a gap-filling method is proposed to acquire daily surface conductance, which was coupled into a Penman-Monteith (P-M) equation, to estimate the regional daily ET over the Hai River Basin. The gap-filling method is coupled with the canopy conductance, surface conductance and a simple time extension method, which provides more mechanisms and is more comprehensive. Field observations, including eddy covariance (EC) fluxes and meteorological elements from automatic weather station (AWS), were collected from two sites for calibration and validation. One site is located in Guantao County, which is cropped in a circular pattern with winter wheat and summer maize. The other site is located in Miyun County, which has orchard and summer maize crops. The P-M equation was inverted to the computed surface conductance at the field scale, and latent heat fluxes from EC were processed and converted to daily ET. The results show that the surface conductance model used in the gap-filling method performs well compared with the inverted surface conductance, which suggests that the model used here is reasonable. In addition, the relationship between the results estimated from the gap-filling method and EC measurements is more pronounced than that between the other method and the EC measurements. The R 2 values improve from 0.68 to 0.75 at the Guantao site and from 0.79 to 0.88 at the Miyun site. The improvement mainly occurs during the growing crop season, according to the temporal variations in the results.

[1]  J. Norman,et al.  Remote sensing of surface energy fluxes at 101‐m pixel resolutions , 2003 .

[2]  Alfred Stein,et al.  Validation of ETWatch using field measurements at diverse landscapes: A case study in Hai Basin of China , 2012 .

[3]  Wim G.M. Bastiaanssen,et al.  Remote sensing for irrigated agriculture: examples from research and possible applications , 2000 .

[4]  Bingfang Wu,et al.  An Improved Approach for Estimating Daily Net Radiation over the Heihe River Basin , 2017, Sensors.

[5]  W. Bastiaanssen,et al.  A remote sensing surface energy balance algorithm for land (SEBAL). , 1998 .

[6]  Robert E. Dickinson,et al.  Modeling Evapotranspiration for Three‐Dimensional Global Climate Models , 2013 .

[7]  Wilfried Brutsaert,et al.  Use of pan evaporation to estimate terrestrial evaporation trends: The case of the Tibetan Plateau , 2013 .

[8]  M. Field,et al.  The meteorological office rainfall and evaporation calculation system -- MORECS , 1983 .

[9]  Françoise Gellens-Meulenberghs,et al.  Evapotranspiration modelling at large scale using near-real time MSG SEVIRI derived data , 2010 .

[10]  L. S. Pereira,et al.  Crop evapotranspiration : guidelines for computing crop water requirements , 1998 .

[11]  Yu Zhang,et al.  An Improvement of Roughness Height Parameterization of the Surface Energy Balance System (SEBS) over the Tibetan Plateau , 2013 .

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

[13]  Zhao-Liang Li,et al.  How sensitive is SEBAL to changes in input variables, domain size and satellite sensor? , 2011 .

[14]  Bingfang Wu,et al.  A method for sensible heat flux model parameterization based on radiometric surface temperature and environmental factors without involving the parameter KB-1 , 2016, Int. J. Appl. Earth Obs. Geoinformation.

[15]  Amélie Rajaud,et al.  A simple surface conductance model to estimate regional evaporation using MODIS leaf area index and the Penman‐Monteith equation , 2008 .

[16]  Matthew F. McCabe,et al.  Modeling Evapotranspiration during SMACEX: Comparing Two Approaches for Local- and Regional-Scale Prediction , 2005 .

[17]  Bingfang Wu,et al.  An Improved Method for Deriving Daily Evapotranspiration Estimates From Satellite Estimates on Cloud-Free Days , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[18]  Wilfried Brutsaert,et al.  Daily evaporation over a region from lower boundary layer profiles measured with radiosondes , 1991 .

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

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

[21]  Martha C. Anderson,et al.  Mapping daily evapotranspiration at Landsat spatial scales during the BEAREX’08 field campaign , 2012 .

[22]  Martha C. Anderson,et al.  Comparison of prognostic and diagnostic surface flux modeling approaches over the Nile River basin , 2014 .

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

[24]  Martha C. Anderson,et al.  A climatological study of evapotranspiration and moisture stress across the continental United States based on thermal remote sensing: 1. Model formulation , 2007 .

[25]  Bingfang Wu,et al.  ETWatch for monitoring regional evapotranspiration with remote sensing , 2008 .

[26]  Li Jia,et al.  Monitoring of Evapotranspiration in a Semi-Arid Inland River Basin by Combining Microwave and Optical Remote Sensing Observations , 2015, Remote. Sens..

[27]  Matthew F. McCabe,et al.  Evaluation of Remotely Sensed Evapotranspiration Over the CEOP EOP-1 Reference Sites , 2007 .

[28]  Yan Nana,et al.  ETWatch:calibration methods , 2011 .

[29]  Martha C. Anderson,et al.  A Two-Source Time-Integrated Model for Estimating Surface Fluxes Using Thermal Infrared Remote Sensing , 1997 .

[30]  B. Choudhury,et al.  A BIOPHYSICAL PROCESS-BASED ESTIMATE OF GLOBAL LAND SURFACE EVAPORATION USING SATELLITE AND ANCILLARY DATA , 2000 .

[31]  Bingfang Wu,et al.  A Method for Deriving the Boundary Layer Mixing Height from MODIS Atmospheric Profile Data , 2015 .

[32]  Thomas Pütz,et al.  Estimating Precipitation and Actual Evapotranspiration from Precision Lysimeter Measurements , 2013 .

[33]  S. Liang Narrowband to broadband conversions of land surface albedo I Algorithms , 2001 .

[34]  P. Jarvis The Interpretation of the Variations in Leaf Water Potential and Stomatal Conductance Found in Canopies in the Field , 1976 .

[35]  Le Jiang,et al.  A satellite-based Daily Actual Evapotranspiration estimation algorithm over South Florida , 2009 .

[36]  Martha C. Anderson,et al.  Monitoring daily evapotranspiration over two California vineyards using Landsat 8 in a multi-sensor data fusion approach , 2016 .

[37]  Wu Bingfang,et al.  ETWatch: models and methods , 2011 .

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

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

[40]  Mingzhao Yu,et al.  A Method to Estimate Sunshine Duration Using Cloud Classification Data from a Geostationary Meteorological Satellite (FY-2D) over the Heihe River Basin , 2016, Sensors.

[41]  Jerald A. Brotzge,et al.  Examination of the Surface Energy Budget: A Comparison of Eddy Correlation and Bowen Ratio Measurement Systems , 2003 .

[42]  A. Holtslag,et al.  A remote sensing surface energy balance algorithm for land (SEBAL)-1. Formulation , 1998 .

[43]  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 .

[44]  Jin Chen,et al.  A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter , 2004 .

[45]  Mingguo Ma,et al.  revised surface resistance parameterisation for estimating latent heat flux from emotely sensed data , 2012 .

[46]  Wolfgang Durner,et al.  Simultaneous Estimation of Soil Hydraulic and Root Distribution Parameters from Lysimeter Data by Inverse Modeling , 2013 .

[47]  J. Dudhia,et al.  Coupling an Advanced Land Surface–Hydrology Model with the Penn State–NCAR MM5 Modeling System. Part I: Model Implementation and Sensitivity , 2001 .

[48]  J. Kleissl,et al.  Large Aperture Scintillometer Intercomparison Study , 2008 .

[49]  S. Planton,et al.  A Simple Parameterization of Land Surface Processes for Meteorological Models , 1989 .

[50]  Di Xu,et al.  Evapotranspiraton estimation based on scaling up from leaf stomatal conductance to canopy conductance , 2011 .

[51]  A. Dalcher,et al.  A Simple Biosphere Model (SIB) for Use within General Circulation Models , 1986 .

[52]  Z. Su The Surface Energy Balance System (SEBS) for estimation of turbulent heat fluxes , 2002 .

[53]  H. L. Penman Natural evaporation from open water, bare soil and grass , 1948, Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences.

[54]  Chaoyang Wu,et al.  Predicting Forest Evapotranspiration by Coupling Carbon and Water Cycling Based on a Critical Stomatal Conductance Model , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[55]  Martha C. Anderson,et al.  A Multiscale Remote Sensing Model for Disaggregating Regional Fluxes to Micrometeorological Scales , 2004 .

[56]  Martha C. Anderson,et al.  Use of Landsat thermal imagery in monitoring evapotranspiration and managing water resources , 2012 .

[57]  J. Norman,et al.  Source approach for estimating soil and vegetation energy fluxes in observations of directional radiometric surface temperature , 1995 .

[58]  W. Oechel,et al.  FLUXNET: A New Tool to Study the Temporal and Spatial Variability of Ecosystem-Scale Carbon Dioxide, Water Vapor, and Energy Flux Densities , 2001 .

[59]  A. Huete,et al.  Evaluation of optical remote sensing to estimate actual evapotranspiration and canopy conductance , 2013 .