CROP-CIS User utility assessment of Geoland2 BioPar products Comparison of G2 BioPar vs. JRC-MARSOP SPOT- VGT NDVI & fAPAR products M. Meroni, C. Atzberger,

CROP-CISUser utility assessment of Geoland2 BioPar products

Comparison of G2 BioPar vs. JRC-MARSOP

SPOT- VGT NDVI & fAPAR products

M. Meroni, C. Atzberger, O. Leo. JRC-MARS

2G2 Interim Review Meeting, JRC Ispra15/12/2011

Index

Objective of the analysis

Methods (spatial and temporal analysis)

Data and study areas

Main results of the comparison

Ongoing activities on BP full archive (JRC + IGIK)

G2 Interim Review Meeting, JRC Ispra 3

Objective

15/12/2011

To provide a first assessment of new BioPar products by comparison with the “well known” JRC-MARSOP using a comprehensive statistical protocol

The analysis can: describe existing differences between the two datasets identify and point out inconsistencies in a specific product provide a basis for more in-depth analysis at specific locations / times

The analysis can’t: say which product is best! (validation is required for this purpose)


Methods

15/12/2011

Analysis of spatial and temporal agreement separately Spatial comparison

Compare layer by layer Summarize this comparison by a metric

(e.g. R2) Plot the metric across time (possibly

stratified by land use classes)

The result of the spatial comparison is a time series

Data cube 1

Data cube 2

xy

z

data1 data1 data1

data2 data2 data2

Land cover

class a class b class c


Methods

15/12/2011

Compare pixel by pixel Summarize this comparison by a metric

(e.g. R2) Plot the metric across space (possibly

deriving some summary statistics of such maps)

The result of the temporal comparison is a map

Temporal comparison

Data cube 1

Z1 Z2

Z1

Z2

Data cube 2

Land cover


Cloud screening

Atmo correct.

BRDF norm.

Compositing window

fAPAR algorithm

NDVI fAPAR (days)

BIOPAR y y y BRDF norm. BRDF norm. 30 Geoland2

MARSOP-FS y y n constr. max NDVI constr. max NDVI 10 LIGHT-CYCLOPES

MARSOP-A4C y y y max fAPAR max fAPAR 10 JRC-fAPAR

Compositing rule

Data: SPOT-VGT 10-day

15/12/2011

Geoland2 BioPar data

vs. JRC-MARS data

MARSOP-FS for the global window (FOODSEC action)

MARSOP-A4C for the extended European window (AGRI4CAST action), original and filtered (mod-SWETS)

G2 Interim Review Meeting, JRC Ispra 715/12/2011

Biopar compositing window

dekadal products: composites updated every 10 days; 30 days compositing* window is asymmetric around the “most representative”

day (16 day before and 13 after it, equally weighted);

Considering the required processing time, the overall delay for data delivery is 16 days (MARSOP delay = 8 days)

An issue for MARS NRT applications

* Note that for BP the term “compositing” is not fully appropriate because the value assigned to the dekad is derived from the inversion of the linear reflectance model of Roujean et al. (1992) applied to normalize the bidirectional effects during the synthesis period of 30 days.

G2 Interim Review Meeting, JRC Ispra 815/12/2011

Data: SPOT-VGT 10-day

Time domain: 2 years of BP GEOV1 demo products

available (2003 and 2004)

Spatial domain:Three 10° x 10° BioPar tiles (1120 x 1120 pixels) selected in different agro-climatic regions monitored by JRC-MARS:

France (temperate - Mediterranean); Brazil (tropical); Niger (arid).


RESULTS – Cloud screening

15/12/2011

Fraction of valid observations (examples using fAPAR)

MARSOP BIOPAR

NIGER(semi-arid)

BRAZIL(tropical-humid)

Similar in arid areas, MARSOP>BIOPAR in humid areas



15/12/2011

Temporal profile of fraction of valid observations

MARSOP BIOPAR

NIGER(semi-arid)


NIGER(semi-arid)


MARSOP>>BIOPAR in cloudy/rainy periods

Cropland, Niger

Cropland, Brazil



15/12/2011

BP has consistently lower fraction of valid observations; Difference is large for Brazil (severe cloud cover) and small for Niger (low cloudiness); Unrealistic drops in MARSOP temporal profiles.

Cloud screening algorithm applied by BIOPAR is more conservative and realistic (.. larger compositing window for BIOPAR..)

When MARSOP shows unrealistic drops, BIOPAR is missing or not/less affected

Both NDVI and fAPAR, MARSOP-FS and -A4C:

Temporal profile of fAPAR (pixel of forest, Brazil)


RESULTS – Overall agreement (space and time pooled together)

15/12/2011

~70% FAPAR < 0.5

~30% FAPAR < 0.5

Example for fAPAR

FS and A4C data: fAPARMARS < fAPARBIOPAR

Largest differences observed for France (A4C) statistically significant differences between

distributions were found for Brazil (MARSOP-FS) and France (MARSOP- A4C)

ECDF, example for all land cover classes (pooled together)

NIGER BRAZIL FRANCE

% of pixels showing statistically different data distribution

BP Vs. MARSOP-FS BP Vs. MARSOP-A4C


RESULTS – Overall agreement (space and time pooled together)

15/12/2011

Correlation (example for fAPAR)

Regional differences in OLS coefficients: Niger: very small offset and slope greater than 1; similar profile minima, larger

BIOPAR maxima; Brazil: positive offset and slope close to 1. BIOPAR is consistently higher than

MARS; France: large offset and slope smaller than 1. Highest differences between the two

datasets are found for low fAPAR (wintertime values).

NIGER BRAZIL FRANCE

Density scatter plot and linear regression (BIOPAR = intercept + slope * MARS)

A) B) C)



RESULTS – Spatial comparison

15/12/2011

Temporal evolution of spatial agreement

(fAPAR of forest land cover)

Mean profiles

Large systematic component of the difference

Seasonality in spatial Agreement Coefficient (Ji & Gallo, 2006)

rainy season winter timerainy season


Spatial AC varying over time.

What’s the source of this variability/scatter?



15/12/2011

Factors contributing to the scatter:

Different cloud screening effectiveness (example on fAPAR)

BIOPAR

FS



15/12/2011


Presence of brightness contrast in MARS-FS (example for NDVI, Woodland, Niger) due to BRDF

Dekad 22

MARS

G2

MARSOP-FS

BIOPAR




15/12/2011



Presence of brightness contrast in MARS-FS (example for NDVI, Woodland, Niger) due to BRDF

«Unexpected» wintertime BIOPAR signal (France)


RESULTS – Temporal comparison

15/12/2011


Temporal agreement

Regions of very low agreement in Niger and Brazil can be explained

Spatial distribution of Agreement Coefficient (example for fAPAR)

NIGER BRAZIL FRANCE

AC

Arid area with very low fAPAR variability

Areas with high cloud cover

Low agreement over large areas

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 10 20 30 40 50 60 70 80

fAPA

R

Time (decades)

MARSOP-CTIV

G2


RESULTS – Temporal comparison

15/12/2011

Starting from the assumption that fAPAR varies smoothly over vegetated land we investigated the temporal smoothness of the two datasets.

mean absolute value of the first derivative of fAPAR over time

40% of FS absolute dekadal variation > 0.05 FAPAR units

such frequency is implausible in the given geographical setting. BP appears more realistic.

Mean |fAPAR′|

Forest Cropland

MARSOP-FS BioPar

Temporal smoothness, example of Brazil (MARSOP-FS)


Conclusions on 2 years of demo data

15/12/2011

BP “Compositing” window may be problematic for MARS NRT application Significant differences between BIOPAR and MARSOP (both spatial and

temporal variability) The differences in cloud screening effectiveness and compositing method

make BioPar products more realistic than MARSOP-FS Same holds true for MARSPO-A4C. However, positive BP anomalies in

wintertime deserve further investigation

Scientific paper submitted to INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION


Ongoing activities [JRC]

15/12/2011

Intercomparison of the products extended to the full archive (1999-2011):

Statistical approach (similar to that described so far)

Operational approach (simulating actual MARS operations): Analysis of vegetation anomalies Bulletin production: differences in data quality (with BP taking more

observations into account) against delivery time (with MARSOP data being in principle “more recent”)

Within-season crop yield predictions in Tunisia: evaluate possible performance improvements using BioPar data instead of MARSOP


Anomaly analysis, preliminary results

15/12/2011

Overall correlation (example for France, MARSOP-A4C)

fAPAR from HIST archive (1998-2010) Focus on anomalies as z-scores, i.e. normalization of each dekad as distance from

mean expressed in SD units

where x is the fAPAR profile of a given dekad

Low correlation of z-scores



15/12/2011

Example of time profiles

MARSOP and BP: roughly parallel development, but important scatter



15/12/2011

Trend analysis (France)

For each dekad, the z-score values of all pixels are averaged (one line for each dekad).

BP shows a clear positive trend with time, not visible in MARSOP. Is this greening really happening?

MARSOP BP

0

2 z

-2 z

0

2 z

-2 z



15/12/2011

Example of application: detection of known droughts

Monthly averages of fAPAR over France 2003 (heat waves between May and August)

Good agreement between datasets, both see the anomaly, spatial pattern more plausible for BP

MARSOP BPApril May

June July

August Sept

April May

June July

August Sept

0

2

-2

Z-score

Wheat yield forecasting in Europe.

Comparison of performances using G2 BioPar and MARSOP time series, preliminary results.

Katarzyna Dabrowska-Zielinska

IGIK, Institute of Geodesy and Cartography, Warsaw (Poland)

User utility assessment of Geoland2 BioPar products

Objective

Test the performance of MARSOP and BioPar for wheat yield monitoring/forecasting in Europe

Data

RS: dekadal SPOT-VGT NDVI and fAPAR from HIST archive (1999-2009)

Yield: Regional Agricultural Statistics Database of EUROSTAT

Calibration of a Partial Least Square model tuned at NUTS2 level

The explanatory variables are all the RS dekadal observations extracted from the growing season period as defined on the basis of an agro-climatic classification.

Agro-climatic zones in Europe (Iglesias, A. et al., 2009)

Two modes of operation of the model:Monitoring mode: (yield estimation after EOS) all dekadal RS indices of growing season are available

Forecasting mode: (yield estimation within season) unknown dekadal indices are set to their long term average values

Methods

SOS & EOS

Results in monitoring mode

Best performances are marked in yellow

Yield estimates over 1999-2009, comparison BioPar, MARSOP and “null model” (mean yield):

Cross-validation (Jackknifing) prediction errors (RMSE, MPE, MAPE) for agro-climatic zones

• The model doesn’t outperform the “null model” in all regions

• Small performance differences using either MARSOP or BioPar

Results in monitoring mode

• The largest errors in absolute terms are observed in Southwest of Europe and in the most northern region of Finland;

• Again small performance differences using either MARSOP or BioPar.

Spatial distribution of the error (MPEs)

Results in forecasting mode

• No substantial differences between MARSOP and BioPar

• Forecasted yield performs better than simple average in few regions only (red bars shorter than the blue ones)

Example of forecast for year 2009 using data 1999-2008. Model performances (DecMAPE, mean absolute forecast error) and compared to the “null model” (the mean yield).

SUMMARY

• No statistical differences in predicting wheat yield using either MARSOP or BioPar data.

• The differences in crop yield prediction are minimal and in favour of BioPar [MARSOP] in monitoring [forecasting] mode;

• Overall, poor performances of the model, especially when used in “forecasting mode”. This behaviour could be explained by the short RS time series available (11 years) and the huge gaps in EUROSTAT yield data;

• Current activities: investigation of different methods for region grouping (period of forecast); collection of more ground truth EUROSTAT data

Documents

CROP-CIS User utility assessment of Geoland2 BioPar products Comparison of G2 BioPar vs. JRC-MARSOP SPOT- VGT NDVI & fAPAR products M. Meroni, C. Atzberger,