Monitoring daily evapotranspiration in Northeast Asia using MODIS and a regional Land Data Assimilation System

[1] We applied an approach for daily estimation and monitoring of evapotranspiration (ET) over the Northeast Asia monsoon region using satellite remote sensing observations from the Moderate Resolution Imaging Spectroradiometer (MODIS). Frequent cloud cover results in a substantial loss of remote sensing information, limiting the capability of continuous ET monitoring for the monsoon region. Accordingly, we applied and evaluated a stand-alone MODIS ET algorithm for representative regional ecosystem types and an alternative algorithm to facilitate continuous regional ET estimates using surface meteorological inputs from the Korea Land Data Assimilation System (KLDAS) in addition to MODIS land products. The resulting ET calculations showed generally favorable agreement (root-mean-square error  < 1.3 mm d−1) with respect to in situ measurements from eight regional flux tower sites. The estimated mean annual ET for 3 years (2006 to 2008) was approximately 362.0 ± 161.5 mm yr−1 over the Northeast Asia domain. In general, the MODIS and KLDAS-based ET (MODIS-KLDAS ET) results showed favorable performance when compared to tower observations, though the results were overestimated for a forest site by approximately 39.5% and underestimated for a cropland site in South Korea by 0.8%. The MODIS-KLDAS ET data were generally underestimated relative to the MODIS (MOD16) operational global terrestrial ET product for various biome types, excluding cropland; however, MODIS-KLDAS ET showed better agreement than MOD16 ET for forest and cropland sites in South Korea. Our results indicate that MODIS ET estimates are feasible but are limited by satellite optical-infrared remote sensing constraints over cloudy regions, whereas alternative ET estimates using continuous meteorological inputs from operational regional climate systems (e.g., KLDAS) provide accurate ET results and continuous monitoring capability under all-sky conditions.

[1]  Jinkyu Hong,et al.  HydroKorea and CarboKorea: cross-scale studies of ecohydrology and biogeochemistry in a heterogeneous and complex forest catchment of Korea , 2006, Ecological Research.

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

[3]  J. D. Tarpley,et al.  The multi‐institution North American Land Data Assimilation System (NLDAS): Utilizing multiple GCIP products and partners in a continental distributed hydrological modeling system , 2004 .

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

[5]  J. D. Tarpley,et al.  Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model , 2003 .

[6]  K. Georgakakos,et al.  Estimation of potential evapotranspiration in the mountainous Panama Canal watershed , 2007 .

[7]  Shaoqiang Wang,et al.  Impact of meteorological anomalies in the 2003 summer on Gross Primary Productivity in East Asia , 2009 .

[8]  G. Gayno,et al.  Implementation of Noah land-surface model advances in the NCEP operational mesoscale Eta model , 2003 .

[9]  S. Goetz,et al.  Satellite based analysis of northern ET trends and associated changes in the regional water balance from 1983 to 2005 , 2008 .

[10]  Damian Barrett,et al.  Multi-sensor model-data fusion for estimation of hydrologic and energy flux parameters , 2008 .

[11]  Evaluation of Evapotranspiration Estimation using Korea Land Data Assimilation System , 2010 .

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

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

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

[15]  W. Eugster,et al.  Year‐round measurements of net ecosystem CO2 flux over a montane larch forest in Mongolia , 2005 .

[16]  H. Jones.  Plants and Microclimate: Other environmental factors: wind, altitude, climate change and atmospheric pollutants , 2013 .

[17]  Sun Xiaomin,et al.  Partitioning of evapotranspiration and its controls in four grassland ecosystems: Application of a two-source model , 2009 .

[18]  Shafiqul Islam,et al.  Estimation of evaporative fraction and evapotranspiration from MODIS products using a complementary based model , 2008 .

[19]  Xiaomin Sun,et al.  Environmental controls over carbon exchange of three forest ecosystems in eastern China , 2008 .

[20]  Maosheng Zhao,et al.  Drought-Induced Reduction in Global Terrestrial Net Primary Production from 2000 Through 2009 , 2010, Science.

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

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

[23]  Eric F. Wood,et al.  Quantifying uncertainty in a remote sensing-based estimate of evapotranspiration over continental USA , 2010 .

[24]  Terri S. Hogue,et al.  Evaluation of a MODIS-Based Potential Evapotranspiration Product at the Point Scale , 2008 .

[25]  Changsheng Li,et al.  Mapping paddy rice agriculture in southern China using multi-temporal MODIS images , 2005 .

[26]  Pamela L. Nagler,et al.  Integrating Remote Sensing and Ground Methods to Estimate Evapotranspiration , 2007 .

[27]  Ralph Dubayah,et al.  Estimation of surface net radiation in the boreal forest and northern prairie from shortwave flux measurements , 1997 .

[28]  Ryuichi Hirata,et al.  Mapping evapotranspiration using MODIS and MM5 Four-Dimensional Data Assimilation , 2010 .

[29]  Xiaomin Sun,et al.  Water-use efficiency of forest ecosystems in eastern China and its relations to climatic variables. , 2008, The New phytologist.

[30]  H. Kwon.  Estimation of Net Radiation in Three Different Plant Functional Types in Korea , 2009 .

[31]  Charles J Vörösmarty,et al.  Intercomparison of Methods for Calculating Potential Evaporation in Regional and Global Water Balance Models , 1996 .

[32]  Keunchang Jang,et al.  Evaluation of Shortwave Irradiance and Evapotranspiration Derived from Moderate Resolution Imaging Spectroradiometer (MODIS) , 2009 .

[33]  Minoru Gamo,et al.  Spatial distribution of carbon balance in forest ecosystems across East Asia , 2008 .

[34]  W. J. Shuttleworth,et al.  Toward a South America Land Data Assimilation System: Aspects of land surface model spin‐up using the Simplified Simple Biosphere , 2006 .

[35]  Xiaoliang Lu,et al.  Evaluating evapotranspiration and water-use efficiency of terrestrial ecosystems in the conterminous United States using MODIS and AmeriFlux data , 2010 .

[36]  J. S. Kimball,et al.  Improving continuity of MODIS terrestrial photosynthesis products using an interpolation scheme for cloudy pixels , 2005 .

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

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

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

[40]  E. Boegh,et al.  Combining weather prediction and remote sensing data for the calculation of evapotranspiration rates: application to Denmark , 2004 .

[41]  M. Mccabe,et al.  Closing the terrestrial water budget from satellite remote sensing , 2009 .

[42]  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 .

[43]  Rachel T. Pinker,et al.  Shortwave radiative fluxes from MODIS: Model development and implementation , 2009 .

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

[45]  Y. Ryu,et al.  Evaluation of land surface radiation balance derived from moderate resolution imaging spectroradiometer (MODIS) over complex terrain and heterogeneous landscape on clear sky days , 2008 .

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

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

[48]  Gautam Bisht,et al.  Estimation of net radiation from the MODIS data under all sky conditions: Southern Great Plains case study , 2010 .

[49]  Gautam Bisht,et al.  Estimation of the net radiation using MODIS (Moderate Resolution Imaging Spectroradiometer) data for clear sky days , 2005 .

[50]  Chun-Ho Cho,et al.  Surface Exchange of Energy and Carbon Dioxide between the Atmosphere and a Farmland in Haenam, Korea , 2003 .

[51]  D. Lettenmaier,et al.  Satellite‐based near‐real‐time estimation of irrigated crop water consumption , 2009 .

[52]  Minoru Gamo,et al.  Temporal and spatial variations in the seasonal patterns of CO2 flux in boreal, temperate, and tropical forests in East Asia , 2008 .

[53]  Lucas Alados-Arboledas,et al.  Relationship between net radiation and solar radiation for semi-arid shrub-land , 2003 .

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

[55]  Jiquan Chen,et al.  Energy balance and partition in Inner Mongolia steppe ecosystems with different land use types , 2009 .

[56]  S. Kanae,et al.  Global Hydrological Cycles and World Water Resources , 2006, Science.

[57]  E. Noordman,et al.  SEBAL model with remotely sensed data to improve water-resources management under actual field conditions , 2005 .

[58]  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.

[59]  A-Xing Zhu,et al.  Prediction of Continental-Scale Evapotranspiration by Combining MODIS and AmeriFlux Data Through Support Vector Machine , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[60]  Ramakrishna R. Nemani,et al.  An operational remote sensing algorithm of land surface evaporation , 2003 .

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

[62]  John S. Kimball,et al.  Satellite assessment of land surface evapotranspiration for the pan‐Arctic domain , 2009 .

[63]  Seungtaek Jeong,et al.  Evaluation of MODIS-derived Evapotranspiration at the Flux Tower Sites in East Asia , 2009 .

[64]  최민하,et al.  실제 증발산 산정에 관한 The Surface Energy Balance System (SEBS) 모형 알고리즘 연구 , 2012 .

[65]  K. Trenberth,et al.  Estimates of the Global Water Budget and Its Annual Cycle Using Observational and Model Data , 2007 .

[66]  Shunlin Liang,et al.  Global atmospheric downward longwave radiation over land surface under all‐sky conditions from 1973 to 2008 , 2009 .