Evapotranspiration Data Product from NESDIS GET-D System Upgraded for GOES-16 ABI Observations

Evapotranspiration (ET) is a major component of the global and regional water cycle. An operational Geostationary Operational Environmental Satellite (GOES) ET and Drought (GET-D) product system has been developed by the National Environmental Satellite, Data and Information Service (NESDIS) in the National Oceanic and Atmospheric Administration (NOAA) for numerical weather prediction model validation, data assimilation, and drought monitoring. GET-D system was generating ET and Evaporative Stress Index (ESI) maps at 8 km spatial resolution using thermal observations of the Imagers on GOES-13 and GOES-15 before the primary operational GOES satellites transitioned to GOES-16 and GOES-17 with the Advanced Baseline Imagers (ABI). In this study, the GET-D product system is upgraded to ingest the thermal observations of ABI with the best spatial resolution of 2 km. The core of the GET-D system is the Atmosphere-Land Exchange Inversion (ALEXI) model, which exploits the mid-morning rise in the land surface temperature to deduce the land surface fluxes including ET. Satellite-based land surface temperature and solar insolation retrievals from ABI and meteorological forcing from NOAA NCEP Climate Forecast System (CFS) are the major inputs to the GET-D system. Ancillary data required in GET-D include land cover map, leaf area index, albedo and cloud mask. This paper presents preliminary results of ET from the upgraded GET-D system after a brief introduction of the ALEXI model and the architecture of GET-D system. Comparisons with in situ ET measurements showed that the accuracy of the GOES-16 ABI based ET is similar to the results from the legacy GET-D ET based on GOES-13/15 Imager data. The agreement with the in situ measurements is satisfactory with a correlation of 0.914 averaged from three Mead sites. Further evaluation of the ABI-based ET product, upgrade efforts of the GET-D system for ESI products, and conclusions for the ABI-based GET-D products are discussed.

[1]  Li Fang,et al.  An inter-comparison of soil moisture data products from satellite remote sensing and a land surface model , 2016, Int. J. Appl. Earth Obs. Geoinformation.

[2]  David J. Stensrud,et al.  Observed Effects of Landscape Variability on Convective Clouds , 1990 .

[3]  K. McNaughton,et al.  A mixed-layer model for regional evaporation , 1986 .

[4]  J. Townshend,et al.  Global land cover classi(cid:142) cation at 1 km spatial resolution using a classi(cid:142) cation tree approach , 2004 .

[5]  Martha C. Anderson,et al.  Evaluation of Drought Indices Based on Thermal Remote Sensing of Evapotranspiration over the Continental United States , 2011 .

[6]  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 .

[7]  Ayse Irmak,et al.  Satellite‐based ET estimation in agriculture using SEBAL and METRIC , 2011 .

[8]  William P. Kustas,et al.  Effects of Vegetation Clumping on Two–Source Model Estimates of Surface Energy Fluxes from an Agricultural Landscape during SMACEX , 2005 .

[9]  R. Allan Freeze,et al.  A Comparison of Rainfall-Runoff Modeling Techniques on Small Upland Catchments , 1985 .

[10]  John M. Norman,et al.  Estimating Fluxes on Continental Scales Using Remotely Sensed Data in an Atmospheric–Land Exchange Model , 1999 .

[11]  M. Moran,et al.  Thermal infrared measurement as an indicator of plant ecosystem health , 2003 .

[12]  William P. Kustas,et al.  A two‐source approach for estimating turbulent fluxes using multiple angle thermal infrared observations , 1997 .

[13]  Martha C. Anderson,et al.  Examining the Relationship between Drought Development and Rapid Changes in the Evaporative Stress Index , 2014 .

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

[15]  T. Tadesse,et al.  The Vegetation Drought Response Index (VegDRI): A New Integrated Approach for Monitoring Drought Stress in Vegetation , 2008 .

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

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

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

[19]  William P. Kustas,et al.  Use of remote sensing for evapotranspiration monitoring over land surfaces , 1996 .

[20]  Martha C. Anderson,et al.  Retrieval of an Available Water-Based Soil Moisture Proxy from Thermal Infrared Remote Sensing. Part I: Methodology and Validation , 2009 .

[21]  B. Séguin,et al.  Review on estimation of evapotranspiration from remote sensing data: From empirical to numerical modeling approaches , 2005 .

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

[23]  Wade T. Crow,et al.  An intercomparison of available soil moisture estimates from thermal infrared and passive microwave remote sensing and land surface modeling , 2011 .

[24]  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 .

[25]  Martha C. Anderson,et al.  Examining Rapid Onset Drought Development Using the Thermal Infrared–Based Evaporative Stress Index , 2013 .

[26]  Martha C. Anderson,et al.  An Intercomparison of Drought Indicators Based on Thermal Remote Sensing and NLDAS-2 Simulations with U.S. Drought Monitor Classifications , 2013 .

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

[28]  S. Manabe,et al.  Cloud Feedback Processes in a General Circulation Model , 1988 .

[29]  Andrew E. Suyker,et al.  Coupling of carbon dioxide and water vapor exchanges of irrigated and rainfed maize–soybean cropping systems and water productivity , 2010 .

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

[31]  David A. Randall,et al.  Implementing the Simple Biosphere Model (SiB) in a general circulation model: Methodologies and results , 1989 .

[32]  Martha C. Anderson,et al.  A climatological study of evapotranspiration and moisture stress across the continental United States based on thermal remote sensing: 2. Surface moisture climatology , 2007 .

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

[34]  J. Norman,et al.  Terminology in thermal infrared remote sensing of natural surfaces , 1995 .

[35]  W. Kustas,et al.  A verification of the 'triangle' method for obtaining surface soil water content and energy fluxes from remote measurements of the Normalized Difference Vegetation Index (NDVI) and surface e , 1997 .