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  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

Validation Technique  Validation with Airborne Data AVIRIS => MODIS, TM and ASTER Airborne Validation High resolution photo => Ground truth  Ground truth to Validate MODIS 1. Test Available Algorithm for ASTER & TM  TM (Hall et al & Rosenthal and Dozier, 1996)  ASTER (three15 m bands) 2. Development of unmixing technique for ASTER and TM

Validation of MODIS Snow Product Using Airborne Data Photo at 1-4mSnow map at 20mSnow map at 500m Co-registration functionValidation AVIRIS MODIS Estimated SCA Spectral Spatial Algorithm ClassifyResample

Validation with Simulated MODIS Data 67 AVIRIS scenes - April to July - Sierra and S. Cascades Total Snow Covered Area at Scene Scale, Unit in km 2 SCA from Photo SCA from MODIS RMSE =21.9 Max =37.9 RMSE =14.6 Pixel Based RMSE from Each Scene, Unit in % Overall 25.1 Max: 49.5

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

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

“Ground Truth” Assessment for Real MODIS Data Validation Test three algorithms for using ASTER and TM MODISRosenthal & DozierASTER 3-15m bands SCA in km 2 Pixel based in % Max=24.3 RMSE=15.6 & Max=30.4 RMSE=5.7 Max=15.1Max=26.6 RMSE=8.9RMSE=8.0 RMSE=14.2 & Max=28.4RMSE=12.6 & Max=22.5

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

Effects of Terrain on Linear Unmixing  Terrain Effects: T c - terrain correction factor/pixel when T c is constant when T c differs Common technique:

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

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

Newly Developed Unmixing Technique to derive “ground truth” for ASTER & ETM+ Characteristics of our new technique 1. Un-supervised 2. Multi-endmember unmixing 3. Automatic selection of scene based spectral endmembers with consideration of terrain effects Using atmospheric and terrain corrected data Using only atmospheric corrected data

An Example of Using Simulated ASTER Color coding % of snow 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 Photo ASTER

Project Summary 1) summary on Data 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