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1 Vol9, No 3, Autumn 207 Iranian Remote Sensing & * /9/0 /5/07 ESTRAFM /85 0/9 0/9 0/90 3/7 6/5 6/2 5/3 0/049 0/036 0/030 0/025 RMSE=0/056 R 2= 0/87 Rahimpour@modaresacir *

2 - Hong et al, Merlin et al, 200; Bindhu et 3 Roy et al, 2008; al, 203 Walker et al, 202 Luo et al, 2008 Woodcock and Strahler, 987; Chen et al, 999; Hilker et al, Zhu et al, 200 up-scaling or aggregation 2 down-scaling or disaggregation 3 image fusion 72

3 30 STARFM Gao et al, 2006 Roy et al, 2008; Hilker et al, 2009; Zhu et al, 200 STARFM HIS 2 PCS 3 Yocky, 996 Polh and Van Aiazzi et al, 2002 Carper, 990 Shettigara, 992 Genderen, 998; Zhang, BRDF Genderen and 6 Pohl, 994; Roy et al, 2008; Hilker et al, 2009; Walker et al, 202; Walker et al, STAARCH 2009 STARFM STARFM Terra Roy et al 7 bidirectional reflectance distribution function BRDF 8 spatial temporal adaptive algorithm for mapping reflectance change STAARCH 9 Hilker et al 0 Zhu et al enhanced Starfm intensity hue saturation HIS 2 principal component substitution PCS 3 wavelet decomposition 4 Gao et al 5 spatial and temporal adaptive reflectance fusion model STARFM 73

4 -2-2 ESTRFM STRFM 35'5" 52 02'28" 32 0/006 0/029 Fu et al, 203; Jarihani et al, Tasumi et al, ESTRFM 2 sensor view angles 74

5 37 row path 32 Aqua Terra

6 Gao et al, 2006; Gao and Long, 2008; Roy et al, 2008; Zhu et al, 200 8/5 IDL DN 236 Tasumi et al,

7 -3-2 b a tp t0 3,,, t0 2 tp 2 Zhu et al, 200 STARFM =,,, +,,, 3 =,,, ,,, =,,, +,,, 4,,, tp t0 Fx, y, t0, B, Cx, y, t0, 4 tp t0 B, Cx, y, tp, B tp Gao et al,,,, =,,, +, B C x,y F b a t k Adams et al,

8 K h K tn tm 8 9 = = 0 0 = + Chen et al, = + k tn tm Cn Cm x, y tn M tm i i i f Fin Fim vx, y b a t 0 = tn tm 6 t p t p,,, =,,, +, t n i tm = + tn tm 7,,,,,, h i t=t n -t m 4 4 t n t m 6 7 t n = t m k 8 = 7 t 9 78

9 [ ] K = {,,,,,,,,,,,,,,,,, = {,,,,,,,,,,,,,,,,, Ri i Ci Fi i t n t m w 2006 E C i F i DC i - R DF i x w/ 2, y w/ 2 4 /, /,, t p 2 = /, /,, +,,,,,, d i i i x i,y i i W i N = + + / w/2 4 w D expected value 79

10 t n t m = 5 t p W i W i = / / / D 0 D W i 6 30 W i 2 t p t m Fm x w/ t m Fn x w/ 2, y w/ 2, t p, B t p t n 2, y w/ 2, t p, B /, /,, = /, /,, + /, /,, 7 t n T n T m Julian day number

11 /88 0/87 0/89 0/78 0/026 0/03 0/038 0/ /92 0/024 0/029 0/034 0/ ESTRAFM

12 4 3/9 7/ 6/7 5/7 3/5 6/0 5/6 4/9 RMSE R

13 5 ESTRAFM Zhu et al, ESTRAFM /056 0/78 0/86 5 0/ Jarihani et al 83

14 STARFM

15 206 7 ESTRAFM Aqua Terra -3-3 swath 85

16 Aqua Terra 5 3 Aqua Terra Terra Gao et al, Aqua 43 2 Zhu et al,

17

18 88-4 ESTRAFM 206 0/90 0/9 0/9 0/85 5/3 6/2 6/5 3/ ESTRAFM 0/87 0/056 ESTRAFM Jarihani et al,

19 -5 Adams, JB, Smith, M D and Johnson, P E 986, Spectral mixture modehing A new analysis of rock and soil types at the viking lander site Journal of Geophysical Research Solid Earth 98, PP Aiazzi, B, Alparone, L, Baronti, S & Garzelli, A, 2002, Context-Driven Fusion of High Spatial and Spectral Resolution Images Based on Oversampled Multiresolution Analysis, IEEE Transactions on Geoscience and Remote Sensing, 400, PP Bindhu,V, Narasimhan, B & Sudheer, K, 203, Development and Verification of a Non-Linear Disaggregation Method NL- DisTrad to Downscale Land Surface Temperature to the Spatial Scale of Thermal Data to Estimate Evapotranspiration, Remote Sensing of Environment, 35, PP 8 29 Carper, WJ, 990, The Use of Intensity-Hue- Saturation Transformations for Merging SPOT Panchromatic and Multispectral Image Data, Photogramm Eng Remote Sens, 564, PP Chen, B, Ge, Q, Fu, D, Yu, G, Sun, X, Wang, S & Wang, H, 200, A Data-Model Fusion Approach for Upscaling Gross Ecosystem Productivity to the Landscape Scale Based on Remote Sensing and Flux Footprint Modelling, Biogeosciences, 79, PP Chen, J, Liu, J, Cihlar, J & Goulden, M, 999, Daily Canopy Photosynthesis Model through Temporal and Spatial Scaling for Remote Sensing Applications, Ecological Modelling, 242, PP 99 9 Fu, D, Chen, B, Wang, J, Zhu, X & Hilker, T, 203, An Improved Image Fusion Approach Based on Enhanced Spatial and Temporal the Adaptive Reflectance Fusion Model, Remote Sensing, 52, PP Gao, F, Masek, J, Schwaller, M & Hall, F, 2006, On the Blending of the and Surface Reflectance Predicting Daily Surface Reflectance, IEEE Transactions on Geoscience and Remote Sensing, 448, PP Genderen, JV & Pohl, C, 994, Image Fusion Issues, Techniques and Applications, Strasbourg, France, PP 8 26 Geo Y and Long D, 2008, Intercomparison of remote sensing-based models for estimation of evapotranpiration and accavacy assessment based on swat Hydrological Processes, 2225, pp Hilker, T, Wulder, MA, Coops, NC, Linke, J, McDermid, G, Masek, JG, Gao, F & White, JC, 2009, A New Data Fusion Model for High Spatial-and Temporal- Resolution Mapping of Forest Disturbance Based on and, Remote Sensing of Environment, 38, PP Hilker, T, Wulder, MA, Coops, NC, Seitz, N, White, JC, Gao, F, Masek, JG & Stenhouse, G, 2009, Generation of Dense Time Series Synthetic Data through Data Blending with Using a Spatial and Temporal Adaptive Reflectance Fusion Model, Remote Sensing of Environment, 39, Hong, S-h, Hendrickx, JM & Borchers, B, 2009, Up-Scaling of SEBAL Derived Evapotranspiration Maps from 30m to 250m Scale, Journal of Hydrology, 370, PP Jarihani, AA, McVicar, TR, Van Niel, TG, Emelyanova, IV, Callow, JN & Johansen, K, 204, Blending and Data to Generate Multispectral Indices A Comparison of INDEX-then-Blend and Blend-then-Index Approaches, Remote Sensing, 60, PP Luo, Y, Trishchenko, AP & Khlopenkov, KV, 2008, Developing Clear-Sky, Cloud and Cloud Shadow Mask for Producing Clear- Sky Composites at 250-Meter Spatial Resolution for the Seven Land Bands over Canada and North America, Remote Sensing of Environment, 22, PP Merlin, O, Duchemin, B, Hagolle, O, Jacob, F, Coudert, B, Chehbouni, G, Dedieu, G, Garatuza, J & Kerr, Y, 200, Disaggregation of Surface Temperature over an Agricultural Area Using a Time Series of Formosat-2 Images, Remote Sensing of Environment, 4, PP

20 Polh, C & Van Genderen, J, 998, Multisensor Image Fusion in Remote Sensing Concepts, Methods and Applications, International Journal of Remote Sensing, 95, PP Roy, DP, Ju, J, Lewis, P, Schaaf, C, Gao, F, Hansen, M & Lindquist, E, 2008, Multi- Temporal Data Fusion for Relative Radiometric Normalization, Gap Filling, and Prediction of Data, Remote Sensing of Environment, 26, PP Shettigara, V, 992, A Generalized Component Substitution Technique for Spatial Enhancement of Multispectral Images Using a Higher Resolution Data Set, Photogrammetric Engineering and Remote Sensing, 585, PP Tasumi, M, Allen, RG & Trezza, R, 2008, At- Surface Reflectance and Albedo from Satellite for Operational Calculation of Land Surface Energy Balance, Journal of Hydrologic Engineering, 32, PP 5 63 Walker, J, De Beurs, K & Wynne, R, 204, Dryland Vegetation Phenology across an Elevation Gradient in Arizona, USA, Investigated with Fused and Data, Remote Sensing of Environment, 44, PP Walker, J, De Beurs, K, Wynne, R & Gao, F, 202, Evaluation of and Data Fusion Products for Analysis of Dryland Forest Phenology, Remote Sensing of Environment, 7, PP Woodcock, CE & Strahler, AH, 987, The Factor of Scale in Remote Sensing, Remote Sensing of Environment,, PP Yocky, DA, 996, Multiresolution Wavelet Decomposition I me Merger of Thematic Mapper and SPOT Panchromatic Data, Photogrammetric Engineering & Remote Sensing, 629, PP Zhang, Y, 2004, Understanding Image Fusion, Photogramm Eng Remote Sens, 706, PP Zao, S, Yang Y, Qiu, G, Yao, Y, and Li, C, 200, Remote detection of baresoil moisture using a surface -tenperature-based soil evaporation transfer coefficient International Journal of Applied Earth observation and Geoin formation Zhu, X, Chen, J, Gao, F, Chen, X & Masek, JG, 200, An Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model for Complex Heterogeneous Regions, Remote Sensing of Environment, 4, PP