Accuracy comparison of remotely sensed evapotranspiration products and their associated water stress footprints under different land cover types in Korean peninsula

Abstract Robust spatial information of evapotranspiration from multiple land cover types is deemed critical for several applications in agriculture and water balance studies. Energy balance models, used in association with satellite observations, are beneficial to map spatial variability of evapotranspiration which is mainly governed by different vegetation practices and local environmental conditions. This study utilize the Surface Energy Balance System model to estimate actual evapotranspiration and water scarcity footprints under complex landscape of Korean peninsula using Moderate-Resolution Imaging Spectroradiometer satellite data for a complete hydrological year of 2012. The modeled evapotranspiration was compared with flux tower measurements obtained from a subhumid cropland and temperate forest sites for the accuracy assessment. This accuracy comparison at daily scale had good agreement yielding reasonable coefficient of determination (0.72, 0.51), bias (0.41 mm day−1, 1.01 mm day−1) and root mean squared error (0.92 mm day−1, 1.53 mm day−1) at two observation (cropland, forest) sites, respectively. Furthermore, the monthly aggregated evapotranspiration from Surface Energy Balance System showed promising results than those of obtained from Moderate-Resolution Imaging Spectroradiometer based readymade global evapotranspiration product, i.e., MOD16, when both products were compared with unclosed and closed flux tower measurements. However, the variations in monthly evapotranspiration obtained from both products were significantly controlled by several climate factors and vegetation characteristics. Water stress mapping at regional and monthly scale also revealed strong contrast between the products of two approaches. Highest mean water stress (0.74) was observed for land use areas associated with evergreen forest and under sparsely vegetation condition by using estimated evapotranspiration from Surface Energy Balance System while an extreme mean water stress value of 0.56 by using end product of MOD16 evapotranspiration was raised from cropland regions. Overall, this study revealed the performance and suitability of two distinctive remote sensing approaches for characterizing the footprints of water fluxes in the Korean peninsula and provides a baseline for the policy makers to setup the sustainable use of existing water resources in this and other similar regions.

[1]  Jeffrey P. Walker,et al.  THE GLOBAL LAND DATA ASSIMILATION SYSTEM , 2004 .

[2]  William P. Kustas,et al.  Upscaling of evapotranspiration fluxes from instantaneous to daytime scales for thermal remote sensing applications , 2013 .

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

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

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

[6]  Minha Choi,et al.  Evaluation of remotely sensed actual evapotranspiration products from COMS and MODIS at two different flux tower sites in Korea , 2015 .

[7]  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.

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

[9]  A. Bondeau,et al.  Towards global empirical upscaling of FLUXNET eddy covariance observations: validation of a model tree ensemble approach using a biosphere model , 2009 .

[10]  Juan Antonio Rodríguez Díaz,et al.  Linking water footprint accounting with irrigation management in high value crops , 2015 .

[11]  Olivier Merlin,et al.  Intercomparison of four remote-sensing-based energy balance methods to retrieve surface evapotranspiration and water stress of irrigated fields in semi-arid climate , 2013 .

[12]  Xuelong Chen,et al.  Development of a 10-year (2001–2010) 0.1° data set of land-surface energy balance for mainland China , 2014 .

[13]  Wim G.M. Bastiaanssen,et al.  Satellite surveillance of evaporative depletion across the Indus Basin , 2002 .

[14]  Baharin Bin Ahmad,et al.  SENSITIVITY ANALYSIS OF METRIC–BASED EVAPOTRANSPIRATION ALGORITHM , 2013 .

[15]  Chenghu Zhou,et al.  A Review of Current Methodologies for Regional Evapotranspiration Estimation from Remotely Sensed Data , 2009, Sensors.

[16]  Minha Choi,et al.  Surface energy fluxes in the Northeast Asia ecosystem: SEBS and METRIC models using Landsat satellite images , 2015 .

[17]  Deg-Hyo Bae,et al.  Potential changes in Korean water resources estimated by high-resolution climate simulation , 2008 .

[18]  M. Mccabe,et al.  Estimating Land Surface Evaporation: A Review of Methods Using Remotely Sensed Surface Temperature Data , 2008 .

[19]  William P. Kustas,et al.  An intercomparison of three remote sensing-based surface energy balance algorithms over a corn and soybean production region (Iowa, U.S.) during SMACEX , 2009 .

[20]  Minha Choi,et al.  A SWAT modeling approach to assess the impact of climate change on consumptive water use in Lower Chenab Canal area of Indus basin , 2016 .

[21]  Z. Su,et al.  Multisensor Global Retrievals of Evapotranspiration for Climate Studies Using the Surface Energy Budget System , 2010 .

[22]  Sami F. Masri,et al.  The energy-water agriculture nexus: the past, present and future of holistic resource management via remote sensing technologies , 2016 .

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

[24]  Jeanine Engelbrecht,et al.  Particular uncertainties encountered in using a pre-packaged SEBS model to derive evapotranspiration in a heterogeneous study area in South Africa , 2011 .

[25]  Minha Choi,et al.  Spatio‐temporal distribution of actual evapotranspiration in the Indus Basin Irrigation System , 2015 .

[26]  Minha Choi Parameterizing daytime downward longwave radiation in two Korean regional flux monitoring network sites , 2013 .

[27]  Tae-Woong Kim,et al.  Evapotranspiration estimation using the Landsat-5 Thematic Mapper image over the Gyungan watershed in Korea , 2011 .

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

[29]  Eleftherios Iakovou,et al.  The emerging role of water footprint in supply chain management: A critical literature synthesis and a hierarchical decision-making framework , 2016 .

[30]  K. S. Copeland,et al.  Deriving Hourly Evapotranspiration Rates with SEBS: A Lysimetric Evaluation , 2013 .

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

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

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

[34]  James L. Wright,et al.  Satellite-Based Energy Balance for Mapping Evapotranspiration with Internalized Calibration (METRIC)—Applications , 2007 .

[35]  R. Allen,et al.  Operational Remote Sensing of ET and Challenges , 2012 .

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

[37]  Mingbin Huang,et al.  Comparison of three remote sensing based models to estimate evapotranspiration in an oasis-desert region , 2016 .

[38]  Matthew F. McCabe,et al.  Spatial and temporal patterns of land surface fluxes from remotely sensed surface temperatures within an uncertainty modelling framework , 2005 .

[39]  Wilfried Brutsaert,et al.  Daytime evaporation and the self-preservation of the evaporative fraction and the Bowen ratio , 1996 .

[40]  Minha Choi,et al.  Seasonal trends of satellite-based evapotranspiration algorithms over a complex ecosystem in East Asia , 2013 .

[41]  Richard G. Allen,et al.  Satellite-Based Energy Balance for Mapping Evapotranspiration with Internalized Calibration (METRIC)—Model , 2007 .

[42]  Minha Choi,et al.  Dual-model approaches for evapotranspiration analyses over homo- and heterogeneous land surface conditions , 2014 .

[43]  Hugh Turral,et al.  Diagnosing irrigation performance and water productivity through satellite remote sensing and secondary data in a large irrigation system of Pakistan , 2009 .