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Area estimation in the MARS project. A summary history
J. Gallego,– MARS AGRI4CAST
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Early history
• Area frame sampling for crop area estimation• USDA (since the 30’s)• France: TER-UTI (since the 60’s): clustered points• Italy: AGRIT. In the early 80’s • A lot of developing countries implemented USDA method with
USDA support• Accuracy can be improved with a geographical
covariate (classified satellite images). • Regression estimator (sampling units are the so-called
segments)• Calibration estimator (points)• Small area estimators
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Satellite images
• Some projects try to estimate areas using only remote sending
• Usual covariates are classified medium resolution classified images. • Resolution 10-60 m, • Swath 60-400 km. • 1-5 images per year
• But anything can be a covariate. Main conditions: • More or less exhaustive knowledge (there is always some
missing data)• Same quality in the sample and outside the sample• Good correlation with the target variable (crop area)
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Area estimation in MARS. 1987-1998
Two main activities• Regional crop Inventories
• Field survey on an area frame• Further improvement with satellite images
(regression estimator)
• Rapid estimates of crop area chages at EU level• 60 sites of 40x40 km in the EU• Estimates mainly based on satellite images. No
ground data of the current year.
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MARS Regional Crop inventories
• Purpose: testing and adapting USDA method• Conclusions:
• Stratification gives a moderate efficiency, but is cheap and good for several years: cost-efficient
• Area frame is a valid alternative to list frame if: Lists (census) are not updatedImages can be obtained at low costFor estimates other than area: Farmers need to be identified when the field
has been located.
• Combining field survey with classified images was technically feasible
But the efficiency was much lower than in the USMore complex landscapes
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MARS Regional Crop inventories: conclusions (2)
• The value added by remote sensing is proportional to the effort made in the ground survey. • Example: relative efficiency=2, sample size= 1000
segments, the value added by images is equivalent to 1000 segments
• sample size= 100 segments, the value added by images is equivalent to 100 segments
• The cost-efficiency threshold of remote sensing could not be reached at that time.
• Square segments are as efficient as segments with physical boundaries and much cheaper
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Rapid crop area change estimates
• Sample of 60 sites of 40x40 km
• “pure” remote sensing
• Estimates of inter-annual change
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MARS “Rapid Estimates” (Action 4/Activity B):
Average RMS errors of the area changes
• In many cases the estimates were better in April (nearly no images) than in October, after most images analysis.
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Rapid crop area change estimates (3)
• Some a posteriori validation: • Correlation of the area change per site (images vs. field
survey)• R2<0.1 for major crops • Better for France (image analysis team was French)• 40-50% of the pixels could change class tuning the
classification in a different way.
• The estimates had little to do with the images• Rather based on external information (press, local
experts…)
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Rapid crop aera change estimates (4)
• An expert is somebody who has made all possible mistakes in a specific field
• Niels Bohr• The MARS team became much more expert with
the “Rapid crop area change estimates”• The big mistake: not realizing early enough that
“pure remote sensing” estimates have a large margin of subjectivity.