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Area estimation in the MARS project. A summary history

Area estimation in the MARS project. A summary history. J. Gallego,– MARS AGRI4CAST. 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

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Area estimation in the MARS project. A summary history

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  1. Area estimation in the MARS project. A summary history J. Gallego,– MARS AGRI4CAST

  2. 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 geographicalcovariate (classified satellite images). • Regression estimator (sampling units are the so-called segments) • Calibration estimator (points) • Small area estimators

  3. 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)

  4. 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.

  5. 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 updated • Images can be obtained at low cost • For 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 US • More complex landscapes

  6. 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

  7. Rapid crop area change estimates • Sample of 60 sites of 40x40 km • “pure” remote sensing • Estimates of inter-annual change

  8. 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.

  9. 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…)

  10. 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.

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