DOWN-SCALING OF SEBAL DERIVED EVAPOTRANSPIRATION MAPS 1 FROM MODIS ( 250 m ) TO LANDSAT ( 30 m ) SCALE 2 3

10 11 The major problem with high spatial resolution satellite images from Landsat 7 is that 12 imagery is not available very often (i.e. every 16 days or longer) and the coverage area is 13 relatively small (swath width 185km), while images of lower spatial resolution from MODIS are 14 available daily and one image covers a relatively large area (swath width 2,330km). This paper 15 considers the feasibility of applying various down-scaling methods to combine MODIS and 16 Landsat imagery in order to obtain both high temporal and high spatial resolution. The Surface 17 Energy Balance Algorithm for Land (SEBAL) was used to derive daily evapotranspiration (ET) 18 distributions from Landsat 7 and MODIS images. Two down-scaling procedures were evaluated: 19 input down-scaling and output down-scaling. In each down-scaling scheme, disaggregated 20 imagery was obtained by two different processes: subtraction and regression. The primary 21 objective of this study was to investigate the effect of the different down-scaling schemes on the 22 spatial distribution of SEBAL derived ET. We found that all of the four proposed down-scaling 23 methodologies can generate reasonable spatial patterns of the disaggregated ET map. The results 24

[1]  Martha C. Anderson,et al.  Estimating subpixel surface temperatures and energy fluxes from the vegetation index-radiometric temperature relationship , 2003 .

[2]  Jan M. H. Hendrickx,et al.  Effect of scaling transfer between evapotranspiration maps derived from LandSat 7 and MODIS images , 2005, SPIE Defense + Commercial Sensing.

[3]  Jan M. H. Hendrickx,et al.  Mapping sensible and latent heat fluxes in arid areas using optical imagery , 2005, SPIE Defense + Commercial Sensing.

[4]  G. Foody,et al.  Sub-pixel land cover composition estimation using a linear mixture model and fuzzy membership functions , 1994 .

[5]  Tony F. Chan,et al.  Variational PDE models and methods for image processing , 1999 .

[6]  John F. Mustard,et al.  REGIONAL PATTERNS OF PLANT COMMUNITY RESPONSE TO CHANGES IN WATER: OWENS VALLEY, CALIFORNIA , 2003 .

[7]  Garrison Sposito,et al.  Upscaling and downscaling methods for environmental research , 2001 .

[8]  G. Schultz.  Remote sensing applications to hydrology: runoff , 1996 .

[9]  Jan M. H. Hendrickx,et al.  Regional mapping of root zone soil moisture using optical satellite imagery , 2005, SPIE Defense + Commercial Sensing.

[10]  Frédéric Baret,et al.  Developments in the 'validation' of satellite sensor products for the study of the land surface , 2000 .

[11]  D. Wolock,et al.  Effects of digital elevation model map scale and data resolution on a topography‐based watershed model , 1994 .

[12]  B. Séguin,et al.  Using midday surface temperature to estimate daily evaporation from satellite thermal IR data , 1983 .

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

[14]  David M. Stoms.  Effects of habitat map generalization in biodiversity assessment , 1992 .

[15]  Lee de Cola,et al.  Simulating and Mapping Spatial Complexity Using Multi-Scale Techniques , 1994, Int. J. Geogr. Inf. Sci..

[16]  J. Bezdek,et al.  FCM: The fuzzy c-means clustering algorithm , 1984 .

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

[18]  N. Lam,et al.  On the Issues of Scale, Resolution, and Fractal Analysis in the Mapping Sciences* , 1992 .

[19]  M. Tasumi.  Progress in operational estimation of regional evapotranspiration using satellite imagery , 2003 .

[20]  Anthony Morse,et al.  Application of SEBAL for Western US Water Rights Regulation and Planning , 2003 .

[21]  John M. Briggs,et al.  The effect of spatial scale on Konza landscape classification using textural analysis , 1989, Landscape Ecology.

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

[23]  J. Stromberg.  Dynamics of Fremont cottonwood (Populus fremontii) and saltcedar (Tamarix chinensis) populations along the San Pedro River, Arizona , 1998 .

[24]  Keeley R. Costigan,et al.  Atmospheric/hydrologic models for the Rio Grande Basin: simulations of precipitation variability , 2000 .

[25]  J. Settle,et al.  Linear mixing and the estimation of ground cover proportions , 1993 .

[26]  Ferran Marqués,et al.  Automatic satellite image georeferencing using a contour-matching approach , 2003, IEEE Trans. Geosci. Remote. Sens..

[27]  H. Kerdiles,et al.  NOAA-AVHRR NDVI decomposition and subpixel classification using linear mixing in the Argentinean Pampa , 1995 .

[28]  P. Atkinson,et al.  Introduction Neural networks in remote sensing , 1997 .

[29]  D. Montgomery,et al.  Digital elevation model grid size, landscape representation, and hydrologic simulations , 1994 .

[30]  Baxter E. Vieux,et al.  DEM aggregation and smoothing effects on surface runoff modeling , 1993 .

[31]  Eberhard Parlow,et al.  Validation of satellite observed thermal emission with in-situ measurements over an urban surface , 2006 .

[32]  I. Kanellopoulos,et al.  Land-cover discrimination in SPOT HRV imagery using an artificial neural network - a 20-class experiment , 1992 .

[33]  Jing Chen,et al.  Spatial scaling of evapotranspiration as affected by heterogeneities in vegetation, topography, and soil texture , 2006 .

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

[35]  V. Caselles,et al.  Mapping land surface emissivity from NDVI: Application to European, African, and South American areas , 1996 .

[36]  W. J. Shuttleworth,et al.  The water use of two dominant vegetation communities in a semiarid riparian ecosystem , 2000 .

[37]  Marco Gianinetto,et al.  GEOREF: a Software for Improving the Use of Remote Sensing Images in Environmental Applications , 2002 .

[38]  J. Hogg.  Quantitative remote sensing of land surfaces , 2004 .

[39]  D. Mark,et al.  Scale-dependent fractal dimensions of topographic surfaces: An empirical investigation, with applications in geomorphology and computer mapping , 1984 .

[40]  J. Lagouarde,et al.  The assessment of regional crop water conditions from meteorological satellite thermal infrared data , 1991 .

[41]  M. S. Moran,et al.  Combining the Penman-Monteith equation with measurements of surface temperature and reflectance to estimate evaporation rates of semiarid grassland , 1996 .

[42]  Ling Bian,et al.  Response of a distributed watershed erosion model to variations in input data aggregation levels , 1993 .