Dear list

 

WANG et al. 2009 "Satellite remote sensing applications for surface soil
moisture monitoring" derived a Landsat soil moisture index: 

 

SMI = (Tsmax - Ts) / (Tsmax - Tsmin)

 

where Tsmax, Tsmin are the maximum and minimum surface temperatures for a
given NDVI value, and Ts is the surface temperature at a given pixel for a
given NDVI. Values near 1 (low surface temp & low greenness) have the
highest estimated soil moisture, while values near 0 (high surface temp &
greenness) have the lowest.

 

Could anyone please advise me how I can create an image matrix that will
extract the min and max surface temperatures for each unique NDVI value. I
then need to apply a linear regression to the max temp vs NDVI & min temp vs
NDVI scatter plots, and use the corresponding slopes and intercepts to
calculate Tsmax and Tsmin

 

Tsmax = (a1 x NDVI) + b1

TSmin = (a2 x NDVI) + b2

 

Hoping this is not such a difficult task for someone mathematically inclined

 

Thanks

Allen

 


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