I'd like to know if the approach I used to derive LAI from NDVI is correct.

STEP 1: I've got 46 field point values of LAI (leaf area index, namely the

cover of plant leafs)

STEP 2: I derived the NDVI index from a multispectral image.

STEP 3: For every field plot I calculated the mean NDVI of 3x3 neighbour

cells

STEP 4: I made a regression between mean_NDVI and LAI.

STEP 5: r^2 was low (0.34), r being 0.70, but t, measured as

r/sqr((1-r^2)/(n-2)) was over the minimum t 2.7, being my t 5.75

STEP 6: Since the correlation was highly significant p<0.01 I applied the

equation of the regression line y= 4.9053x + 0.2406 where y was LAI and x

was NDVI to the NDVI map, obtaining the LAI map

STEP 7: I made a control on the accuracy of the model by measuring the mbe

(mean bias error) calculated as the mean of single errors for every plot

(46 measures): mean of P-O

where P was the estimated LAI value and O the observed, by obtaining a mbe

of 0.03249587

Questions:

1. Could I apply the equation as in step 6?

2. Could I control my model by using the same input observed values as in

step 7?

Thanks

Duccio

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