Rescaling NDVI from the VEGETATION instrument into apparent fraction cover for dryland studies

1. Rescaling NDVI from the VEGETATION instrument into apparent fraction cover for dryland studies E. Bartholomé*, P. Bogaert†, M. Cherlet°, P. Defourny†, P. Mathoux†, P. Vogt* * Joint Research Centre Ispra †Université Catholique de Louvain-la-Neuve °FAO - Rome

2. The problem • In arid regions soil is a major component of the NDVI signal • Time series keep atmospheric contamination and angular effects • A biophysical measurement is more explicit than an index

3. Bare soil Yearly NDVI average over bare soils shows a high degree of variability

4. If NDVI is never < 0.2, NDVI bare soil is set to 0.1, i. e. close to most frequent • average bare soil NDVI value Solution: an original per-pixel adjustment procedure Iterative procedure to eliminate vegetated pixels in temporal profile • Values above a threshold defined according to a gaussian probability function are eliminated • The iterative process stops when difference between previous and current averages <1%

5. Bare soil NDVI offset correction

6. Adjustment to the max cover • NDVImax = 0.85≈ Covermax • Defined from the time series over the whole window

7. Max cover in 2000 0% 50% 100%

8. Meaning of the new product NDVI – fractional cover relationship is well documented in the literature For VEGETATION data this translates into: For pixels where bare soil value cannot be measured

9. assumptions LAI>3  cover ≈ fPAR Both models and statistical fitting indicate that the relationship is not linear, But linear assumption is usually considered as sufficient for low res. applications

10. Identification of vegetated pixels • Based on the analysis of NDVI variation on bare soils, a pixel is declared vegetated if NDVI >NDVIbare soil + 2% cover • To further reduce the risk of contamination, pixels are retained as vegetated if they were identified as vegetated on the previous product

11. Time-series smoothing • 5th degree Polynomial applied on periods of 21 decades • Iterative process (7 cycles) to replace low NDVI values by polynomial values • 5-decade overlap between periods • At the end the highest NDVI value is retained (polynomial or actual value)

12. Jebel Haruj es Sawda Tibesti Known limitations sparse vegetation or bare soil • High NDVI variability over some dark (volcanic) surfaces (above the normal threshold)

13. ? Known limitations – dense vegetation Residual atmospheric effects

14. conclusions • The fractional cover makes the data interpretation easy for land cover mapping in arid regions • Procedure easily implementable for inter-instrument data comparison • Soil effects are well removed, marginal improvement to be implemented • Needs further improvement for regions with high aerosol load.