Validation of MODIS Snow Mapping Algorithm

Jiancheng Shi Institute for Computational

Earth System Science

University of California, Santa Barbara

Introduction 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 TechniqueValidation 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

Validation of MODIS Snow Product Using Airborne Data

Photo at 1-4m Snow map at 20m Snow map at 500m

Co-registration function Validation

AVIRIS MODIS Estimated SCA

Spectral

Spatial

Algorithm

Classify Resample

Validation with Simulated MODIS Data 67 AVIRIS scenes - April to July - Sierra and S. Cascades

Validation with Simulated MODIS Data 67 AVIRIS scenes - April to July - Sierra and S. Cascades

Total Snow Covered Area at Scene Scale, Unit in km2

SCA from Photo

SCA from Photo

SC

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rom

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DIS

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 DistributionEffect 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

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

“Ground Truth” Assessment for Real MODIS Data Validation

Test three algorithms for using ASTER and TM

MODIS Rosenthal & Dozier ASTER 3-15m bands

SC

A in

km

2P

ixel

bas

ed in

%

Max=24.3

RMSE=15.6 & Max=30.4

RMSE=5.7

Max=15.1Max=26.6

RMSE=8.9 RMSE=8.0

RMSE=14.2 & Max=28.4 RMSE=12.6 & Max=22.5

Linear Unmixing in Snow Mapping 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

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Effects of Terrain on Linear Unmixing Effects of Terrain on Linear Unmixing

Terrain Effects: Tc - terrain correction factor/pixel

• when Tc is constant

• when Tc differs

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Example of Terrain Correction Factor 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

Effect of Illumination Angle on Linear Unmixing

Wave length in µm

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Newly Developed Unmixing Techniqueto derive “ground truth” for ASTER & ETM+

Newly Developed Unmixing Techniqueto 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 ASTERAn Example of Using Simulated ASTER

0 10 20 30 40 50 60 70 80 90 100

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 SummaryProject 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