4R Nutrient Stewardship Certification Program – Western Lake Erie Basin
Western Great Basin Reflectance Analysis and Model Performance ATMS 792 – Remote Sensing Western...
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![Page 1: Western Great Basin Reflectance Analysis and Model Performance ATMS 792 – Remote Sensing Western Great Basin Reflectance Analysis and Model Performance.](https://reader035.fdocuments.us/reader035/viewer/2022062222/5697c0111a28abf838ccb5dc/html5/thumbnails/1.jpg)
Western Great Basin Reflectance Analysis and Model Performance
ATMS 792 – Remote Sensing
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Outline• Data used / Domain of study / Hypothesis• Model algorithm• How does this model perform for our region?• 2-D spatial plots • Scatter plots
• Magnitude of error dependent on region• Overall climo (kind of…) stats• Conclusion
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Data / Methods• Reduce noise and parse out mostly clear
days for June-July 2010/2011• Month w/ least amount of erroneous
surface reflectance values• Minimal monsoonal influence
• Hand picked 24 days total to work with• 13 days in 2010• 11 days in 2011
• NOTE: Snow caps during summer add higher values = increased variance
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Theoretical Model EquationsRemer (2005)
• Needs clear skies……… Good luck• Needs clean air…….. Good luck again• Works well in vegetated regions. Really?• How about arid regions? (East of Reno)
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Raw images and their respective reflectance
21 June 2011 (Upper-level Cirrus)
660nm
2130nm
470nm
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Raw images and their respective reflectance
5 July 2010 (Perfectly clear)
660nm
470nm
2130nm
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Raw images and their respective reflectance
8 July 2011 (Perfectly clear)
660nm
470nm
2130nm
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Spatial Anomalies (Target minus Predicted)
5 July 2010470nm 660nm
• Model under predicts reflectance at both wavelengths• Green-vegetated areas with no snow closely agree w/ model
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Spatial Anomalies (Target minus Predicted)
8 July 2011470nm 660nm
• Model under predicts reflectance at both wavelengths• Green-vegetated areas with no snow closely agree w/ model
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• Divide data into two domains• Green/Lush/Forest• Dry/Arid/Desert
• ~ 9500 data points in each box
• How does the Remer (2005) equation perform in both regions?
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So how does the model perform in the two different land regimes?
5 July 2010 – Dry Regime
470nm Scatterplot 660nm Scatterplot
• 470nm consistently out performs 660nm • Average Error and RMSE always greater at 660nm
Y=.25x
Y=. 5x
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So how does the model perform in the two different land regimes?
5 July 2010 – Forest/Lush Regime
470nm Scatterplot 660nm Scatterplot
• Weird “line” of data points may be due Lakes in domain (1:1 ratio)• Only ~1-1.5% error
Y=.25x Y=. 5x
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So how does the model perform in the two different land regimes?
8 July 2011 – Dry Regime
470nm Scatterplot 660nm Scatterplot
• 470nm again out performs 660nm statistically
Y=.25x
Y=. 5x
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So how does the model perform in the two different land regimes?
8 July 2011 – Forest/Lush Regime
470nm Scatterplot 660nm Scatterplot
• Huge snow season before this summer• More widespread snow pack increases variance• Still only 5% error
Y=.25x
Y=. 5x
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Statistics“Climatology” over all 24 days
660 – 660th Forest/Lush Desert/Arid
470 – 470th Forest/Lush Desert/Arid
Average Error .0530 .0550 .0469 .0338
Root Mean
Square Error
.0938 .0648 .0852 .0457
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Conclusion• Observed reflectance > model reflectance in dry/desert regions of W.
Great Basin• Observed reflectance is higher in mountains/forest/lush areas• But… Data is skewed higher due to snow caps• Would be almost 1:1 if snow caps didn’t exist.
• Difficult to measure performance of observed and model due to seasonal variance (i.e. snow caps, monsoonal cloud tops, etc.)
• Model best used in “greener” regions and not highly reflective desert surfaces
• Best results after “drier” wet seasons.• Filtering/smoothing process could have been used but this muddles
raw data.
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Questions?