crop-cis user utility assessment of geoland2 biopar products comparison of g2 biopar vs. jrc-marsop...
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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)
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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
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Methods
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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
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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)
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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.
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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).
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RESULTS – Cloud screening
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Fraction of valid observations (examples using fAPAR)
MARSOP BIOPAR
NIGER(semi-arid)
BRAZIL(tropical-humid)
Similar in arid areas, MARSOP>BIOPAR in humid areas
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RESULTS – Cloud screening
15/12/2011
Temporal profile of fraction of valid observations
MARSOP BIOPAR
NIGER(semi-arid)
BRAZIL(tropical-humid)
NIGER(semi-arid)
BRAZIL(tropical-humid)
MARSOP>>BIOPAR in cloudy/rainy periods
Cropland, Niger
Cropland, Brazil
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RESULTS – Cloud screening
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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)
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RESULTS – Overall agreement (space and time pooled together)
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~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
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RESULTS – Overall agreement (space and time pooled together)
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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)
BP Vs. MARSOP-FS BP Vs. MARSOP-A4C
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RESULTS – Spatial comparison
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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
BP Vs. MARSOP-FS BP Vs. MARSOP-A4C
Spatial AC varying over time.
What’s the source of this variability/scatter?
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RESULTS – Spatial comparison
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Factors contributing to the scatter:
Different cloud screening effectiveness (example on fAPAR)
BIOPAR
FS
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RESULTS – Spatial comparison
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Different cloud screening effectiveness (example on fAPAR)
Presence of brightness contrast in MARS-FS (example for NDVI, Woodland, Niger) due to BRDF
Dekad 22
MARS
G2
MARSOP-FS
BIOPAR
Factors contributing to the scatter:
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RESULTS – Spatial comparison
15/12/2011
Factors contributing to the scatter:
Different cloud screening effectiveness (example on fAPAR)
Presence of brightness contrast in MARS-FS (example for NDVI, Woodland, Niger) due to BRDF
«Unexpected» wintertime BIOPAR signal (France)
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RESULTS – Temporal comparison
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BP Vs. MARSOP-FS BP Vs. MARSOP-A4C
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
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RESULTS – Temporal comparison
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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)
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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
G2 Interim Review Meeting, JRC Ispra 21
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
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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
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Anomaly analysis, preliminary results
15/12/2011
Example of time profiles
MARSOP and BP: roughly parallel development, but important scatter
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Anomaly analysis, preliminary results
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
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Anomaly analysis, preliminary results
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