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Validation of MODIS Snow Mapping Algorithm

Validation of MODIS Snow Mapping Algorithm. Jiancheng Shi Institute for Computational Earth System Science University of California, Santa Barbara. Introduction.  Importance of snow cover  Weather and Climatology  Hydrology  Hazard

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Validation of MODIS Snow Mapping Algorithm

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  1. Validation of MODIS Snow Mapping Algorithm Jiancheng Shi Institute for Computational Earth System Science University of California, Santa Barbara

  2. Introduction •  Importance of snow cover •  Weather and Climatology •  Hydrology •  Hazard • MODIS product: global coverage of snow covered area resolution with 500 m • Objective: Validation of MODIS snow mapping algorithm under different environmental condition • Validation concept: • Accuracy of total snow cover at ten km scale for weather and climatic applications • Accuracy at pixel scale for hydrological applications

  3. Validation Technique •  Validation with Airborne Data • AVIRIS => MODIS, TM and ASTER • Airborne Validation • High resolution photo => Ground truth • Ground truth to Validate MODIS • Test Available Algorithm for ASTER & TM •  TM (Hall et al & Rosenthal and Dozier, 1996) •  ASTER (three15 m bands) • Development of unmixing technique for ASTER and TM

  4. Validationof MODIS Snow Product Using Airborne Data Photo at 1-4m Snow map at 20m Snow map at 500m Classify Resample Co-registration function Validation Spectral Algorithm Spatial Estimated SCA MODIS AVIRIS

  5. Validation with Simulated MODIS Data67 AVIRIS scenes - April to July - Sierra and S. Cascades Overall 25.1 Max =37.9 Max: 49.5 SCA from MODIS RMSE =21.9 RMSE =14.6 SCA from Photo SCA from Photo Pixel Based RMSE from Each Scene, Unit in % Total Snow Covered Area at Scene Scale, Unit in km2

  6. Effect of Snow Spatial Distribution NDSI Relative Error (%) in total SCA Snow fraction in % distribution at 500m Pixel resolution in m

  7. Validation Summary from Airborne simulated MODIS Data • Alpine Region Validation – One of two most difficult environmets • Current Results from Airborne Data •  Accurate for input of climatic study •  Need improvement for hydrological applications •  Weakness •  effect of parched snow cover •  atmosphere may cause some level of uncertainties

  8. “Ground Truth” Assessment for Real MODIS Data Validation Test three algorithms for using ASTER and TM MODIS Rosenthal & Dozier ASTER 3-15m bands Max=24.3 Max=26.6 Max=15.1 SCA in km2 RMSE=8.9 RMSE=8.0 RMSE=5.7 Pixel based in % RMSE=15.6 & Max=30.4 RMSE=14.2 & Max=28.4 RMSE=12.6 & Max=22.5

  9. Linear Unmixing in Snow Mapping • Basic Principle • In remote Sensing • Techniques in selection of spectral endmembers • Supervised - single endmember for each target • Unsupervised - multiple endmembers (convex hull) • Unsupervised - model simulated + spectral library

  10. Effects of Terrain on Linear Unmixing •  Terrain Effects: Tc - terrain correction factor/pixel • when Tc is constant • when Tc differs Common technique:

  11. Example of Terrain Correction Factor • Statistical Properties: • Mean: 1.05 • Standard Deviation: 0.24 • Possibility of Error from scene selected endmembers • less error if similar surface gradient • larger error if they are in opposite facing

  12. Effect of Illumination Angle on Linear Unmixing R(60°) R(60°)/R(20°) r=0.1mm thick r=0.5mm thick r=0.5mm thin Wave length in µm Effects:

  13. Newly Developed Unmixing Techniqueto derive “ground truth” for ASTER & ETM+ • Characteristics of our new technique • Un-supervised • Multi-endmember unmixing • Automatic selection of scene based spectral endmembers with consideration of terrain effects • Using atmospheric and terrain corrected data • Using only atmospheric corrected data

  14. An Example of Using Simulated ASTER • Relative error for total area — 0.7% • Snow-free » snow 1.8% • Snow » snow-free 1.3% • Snow fraction —RMSE 5.4% • Computing 28 min Color coding % of snow 0 10 20 30 40 50 60 70 80 90 100 ASTER Photo

  15. Project Summary • 1) summary onData collection •  67 AVIRIS scenes in West U.S for April – July • high resolution (1 – 4 m) ground truth from the photos • simulated MODIS, TM, ETM+ and ASTER image data •  2 ETM+ scenes (12/2/00 and 12/18/00) at Mammoth Mt. •  will collect ASTER and ETM+ scenes •  will be available on MERCURY and NSIDC data systems • 2) Summary on technical issues •  focus on how to derive “ground truth” of snow cover • 3) Publications •  Several conference papers •  Manuscript – Effects of Terrain on Estimating Sub-pixel Snow Cover in Linear Unmixing

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