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Point on crop area estimation in G2

Point on crop area estimation in G2. Ispra 14/05/2012. H. Kerdiles, O. Leo, J. Gallego, S. Spyratos (JRC MARS Unit) Q. Dong, R. Van Hoolst (VITO), AIFER & CAAS (China) S. Skakun , O. Kravchenko (NASU-NSAU, Ukraine). Why looking at crop areas?.

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Point on crop area estimation in G2

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  1. Point on crop area estimation in G2 Ispra 14/05/2012 H. Kerdiles, O. Leo, J. Gallego, S. Spyratos (JRC MARS Unit) Q. Dong, R. Van Hoolst (VITO), AIFER & CAAS (China) S. Skakun, O. Kravchenko (NASU-NSAU, Ukraine)

  2. Why looking at crop areas? • Crop production (t) is the product of (harvested) area (ha) x yield (t/ha). • In most countries, in particular in Europe and in countries where production is for direct consumption, the area sown in a given crop is more stable over the years than the yield • Consequence: for production forecast purposes over Europe, effort has been put mostly on crop monitoring & yield prediction • Question: Is yield the main determinant of production in all countries? • Purpose: analyze statistical series of yield and area for major crops to determine the impact of interannual yield and area variations on production variations

  3. Methodology • Assuming that Yield (Y) and sown Area (A) are independent variables (i.e. the farmers do not know whether their yield will be good or bad at the time of sowing), then the variance of the production (P = Y.A) can be derived from Var(Y) and Var(A) as follows: = • The contribution of yield variations and of area variations can be assessed with: • and resp.

  4. Yield determinant of production • Example of North China Plain district • Maize yield variance contributes 88% of maize production variance (and area 12%) • Source: official statistics 1994 - 2009

  5. Area determinant of production • Example of North China Plain district • Wheat area variance contributes 94% of wheat production variance (and yield 6%) • Source: official statistics 1994 - 2009

  6. Preliminary analysis: wheat • North China Plain – Wheat (official stats 1994 – 2009) • the variations in production are mainly due to variations in yield in the north of the plain and in area in the south Area contribution to production variance Yield contribution to production variance Number of years of data available for the calculation of the variance

  7. Preliminary analysis: maize • North China Plain – Maize (official stats 1994 – 2009) • For most districts, the variations in production are due to variations in area Yield contribution to production variance Area contribution to production variance Number of years of data available for the calculation of the variance

  8. Importance of monitoring crop areas • Need additional analysis: • Look at average area % of wheat and maize at district level and at the variations in production • Repeat the analysis at province level. • Message: crop area is an important component of production • -> Interest of the EC in monitoring crop areas of the main producers using RS

  9. What did we do in G2 for crop area estimation • Two threads: • 1. Use of HR & MR imagery to complement AFS • Ukraine 2010: 3 oblasts, 5 types of images with GSD from 5m to 250m (RE, IRS LISS 3, AWiFS, TM & MODIS) • N. China Plain (NCP) 2011: 1 county, Spot5 (10m)+TM • Constraint: ground survey • 2. Use of LR & MR data in combination with HR classification to derive crop area fractions (subpixel classification): • -> not as accurate as HR data but high frequency of acquisition (advantage for cloudy areas); less need for ground data • Question: can we detect trends at regional level? • Use of MODIS 250 m in Ukraine (MERIS 300m maybe) • Use of VGT 1km (and MODIS) in the NCP • JRC goal: draw conclusions from a user point of view

  10. Conclusions for AFS + RS • HR data classifications are useful to correct the ground sample estimation (no sampling bias due to wall to wall coverage) and reduce the variance of the direct expansion estimator for the mean % of crop C • Relative efficiency (Var of direct expansion estimator / Var regression estimator function of R2 between survey and classification % on the segments) around 1.5 in Ukraine, 2.5 in the NCP county (i.e. RS  to adding 50% or 150% segments to base sample) • Cost efficiency: function of ground survey cost vs image & processing cost • -> Free data (TM and MODIS) cost efficient in Ukraine with basic survey of 30 segments / oblast • -> large swath HR imagery promising (Sentinel 2)

  11. Subpixel classification of MR/LR data ZH K KH • NN classification method established • NDVI profile = f(% of crops) • Needs to be validated • in space: how does the classifier work outside the calibration area? • Spatial extrapolation to be tested in China and Ukraine • Ukraine: JRC to check if classification accuracy can be higher in KH

  12. Subpixel classification of LR data • NN classification method needs to be validated • In time: does the classifier work for another year than the calibration year (i.e. of HR classification)? • Hyp: variation in the LR (NDVI) response between year X and Y is mainly due to variation in crop area and not to different weather (e.g. drought) • Temporal extrapolation to be tested in China (HR classifications from 2005, 2006, 2007, 2009), maybe Ukraine with official stats

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