Global Land Service

Operated through a consortium, led by

Angular normalisation of PROBA-V 300m

NDVI

Jonathan León-Tavares Else Swinnen Bruno Smets Carolien Toté

VITO

Jean-Louis Roujean ACSATIS

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Free and Open Access

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NDVI 300m [Version-1]

• Based on Proba-V 300m data (cloud screened, atmospherically corrected).

• S10 Maximum Value composites (MVC).

20160701

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NDVI 300m [Version-1]

• Based on Proba-V 300m data (cloud screened, atmospherically corrected).

• S10 Maximum Value composites (MVC).

20160621

Swath patterns -- directional effects

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Continuity and Sustainability

Sensors for global ecosystems monitoring

SPOT-VEGETATION 1 SPOT-VEGETATION 2

METOP-A

SENTINEL-3A SENTINEL-3B

SENTINEL-3C

SENTINEL-1A SENTINEL-1B

SENTINEL-1C

METOP-B METOP-C

METOP-2nd Gen

PROBA-V

SENTINEL-1D

SENTINEL-3D

98 99 00 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28

SENTINEL-2A SENTINEL-2B

SENTINEL-2C SENTINEL-2D

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BRDF Processor: Angular Normalised Toc (ANT)

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Copernicus NDVI 300m [Version 2]

• Inputs: Proba-V 300m L2A TOA data.

• Angular normalised (using semi-empirical BRDF model from Roujean+1992).

• Compositing strategy aimed to conserve NRT.

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Compositing strategy

Following user requirements (i.e. dekadal frequency), only highlighted compositing are further considered.

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Different compositing strategies

MSG/SEVIRI

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Using the Discrete Correlation Function (DCF)

The DCF does not require interpolation of data and was conceived (Edelson & Krolik 1988) to probe correlation in two time series that are unevenly sampled and for which uncertainties are known.

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SEVIRI-NDVI300m lag

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Selecting compositing strategy

Temporal weighting functions t t t

Compositing strategy selection based on lowest normalised lag between NDVI angular normalised time series from MSG/SEVIRI and Proba-V .

Sample of 47 European sites with good quality MSG/SEVIRI time series.

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NDVI300m NRT

The mean lag between normalised NDVI 300m Version-2 time series (produced with B1 configurations using the ANT processor) and MSG/SEVIRI time series is 4 days .

t t t

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NDVI300m NRT

Small lags for NDVI V2

DCF(MSG/SEVIRI, PBV300m NDVI V1)

DCF(MSG/SEVIRI, PBV300m NDVI V2)

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Noise reduction in NDVI300m time series

We estimate the noise in NDVI time series as in Breon + 2015

𝜎𝑁𝐷𝑉𝐼−𝑉1 = 3.3

𝝈𝑵𝑫𝑽𝑰−𝑽𝟐 = 𝟏. 𝟕

(lat,lon)=(39.028,-2.074)

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Comparison with MODIS

Good consistency with MCD43A4 for Europe. Global test validation is ongoing. Differences to bear in mind are: • Difference in spectral bands • Semi empirical BRDF model used

in angular normalisation is not the same (Modis –Ross thick Li-Sparse, NDVI300m V2 - Roujean)

For evaluation, we consider the Nadir BRDF-Adjusted Reflectance 16-Day L3 Global 500m MODIS MCD43A4 product Version-5.

ND

VI

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Comparison with MODIS

Good consistency with MCD43A4 for Europe. Global test validation is ongoing. Differences to bear in mind are: • Difference in spectral bands • Semi empirical BRDF model used

in angular normalisation is not the same (Modis –Ross thick Li-Sparse, NDVI300m V2 - Roujean)

For evaluation, we consider the Nadir BRDF-Adjusted Reflectance 16-Day L3 Global 500m MODIS MCD43A4 product Version-5.

ND

VI

ND

VI

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Summary

• Ongoing development of a BRDF corrected NDVI product at 300m within

the framework of Copernicus Global Land Service. NDVI 300m Version-2 aimed to maintain NRT as possible.

• Based on a time series cross-correlation analysis (using the DCF method) with angular normalised NDVI time series from MSG/SEVIRI, we have selected a compositing strategy.

• BRDF corrected NDVI time series obtained with the selected compositing strategy (10-days, temporal weighting function: ramp-step) show a good agreement with MSG/SEVIRI angular normalised NDVI time series – with an average lag of 4 days.

• Good agreement with MODIS MDC43A4 (V5) for Europe. Evaluation at global scale is ongoing.