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EO data for Rice monitoring in Asia

EO data for Rice monitoring in Asia. Thuy Le Toan CESBIO, Toulouse, France & The Asia-RICE team. G20 GEOGLAM Goal:.

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EO data for Rice monitoring in Asia

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  1. EO data for Rice monitoring in Asia Thuy Le Toan CESBIO, Toulouse, France & The Asia-RICE team

  2. G20 GEOGLAM Goal: To strengthen the international community’s capacity to produce and disseminate relevant, timely and accurate forecasts of agricultural productionat national, regional and global scales through reinforced use of EO

  3. Information/ Products For Asia-RICE Information and Product Types Area estimate

  4. Information/ Products For Asia-RICE Information and Product Types EO Data Products • Cropland mask • Rice grown area Area estimate

  5. Information/ Products For Asia-RICE Information and Product Types EO Data Products Production estimate - Crop outlook / Early warning/ Damage - Yield forecast • Cropland mask • Rice grown area Area estimate • Agricultural practices • Crop condition indicators • Biophysical variables • Environmental variables (soil moisture) • Weather

  6. Earth Observation data for rice monitoring 2013-2014 Rice grown area estimates and mapping

  7. Monitoring at global scale: MODIS & SPOT VGT Wheat SPOT Vegetation Rice Rice Rice

  8. Monitoring at global scale: MODIS & SPOT VGT Need to use LSWI (Land Surface Water Index or Normalised Difference Water Index) to discriminate rice from other vegetation before using NDVI to monitor rice activity LSWI=SWIR-NIR/ SWIR+NIR NDVI=NIR-R/NIR+R Flooding Xiao et al, 2006

  9. Can we use MODIS for rice grown area estimate ? Rice grown areas at national scale using MODIS. Comparison with National Statistics (Xiao et al., 2006)

  10. Can we use MODIS for rice grown area estimate ? • Various results obtained. Better at global and multi-year average than at local-provincial scales. Sources of error are among others: • - resolution of MODIS vs small field size and non uniform rice crop calendar • - confusion with other crop (specially id direct sowing) • - cloud contamination.. • Major advantages: data widey available and methods • accessible by users

  11. Can we use SAR data for rice grown area estimate? • Relevance of SAR data to monitor land surfaces in • frequently cloud covered regions • Studies show the relevance of C, L, X band data to map • rice grown area • Major shortcomings: • lack of systematic, widely available (and free of charge) • data for operational use • lack of simple and available methods accessible by users

  12. SAR data for rice monitoring 2013-2014

  13. Can we use SAR data for rice grown area estimate? • Relevance of SAR data to monitor land surfaces in • frequently cloud covered regions • Studies show the relevance of C, L, X band data to map • rice grown area • Major shortcomings: • lack of systematic, widely available (and free of charge) • data for operational use • lack of simple and available methods accessible by users

  14. Objectives of Technical Demonstration sites • Phase 1A of Asia-RiCE will consist of four technical demonstration sites which will focus on developing provincial-level rice crop area estimations. • Phase 1B, and/or Phase 2, other technical demonstrators will be added, and/or the scope may be increased to produce whole country estimates.

  15. South Vietnam demonstration site: An Giang province VAST: Lam Dao Nguyen, Hoang Phi Phung CESBIO: Thuy Le Toan, Alexandre Bouvet Objective phase 1: • To develop area estimation using all available data in 2013-2014 (SAR and optical) • To compare the results and to define the data type than can be used for the country estimates (for SAR: resolution, mode, frequency, polarisation, acquisition timing.., but also long term availability and cost)

  16. Choice of the An Giang province: Increase in the third season rice (Autumn-Winter) made possible by construction of dykes to protect the fields from seasonal floods VIETNAM Mekong Delta In 1000 tons

  17. Dates of satellite data acquisitions in Autumn-Winter 2013 crop over An Giang: Cosmo-Skymed: 10 dates 19 August, 4 September, 20 September, 6 October, 14 October, 22 October, 30 October, 7 November, 15 November, 23 November Radarsat-2: 4 dates 30 August, 23 September, 17 October, 10 November TerraSAR-X: 3 dates 25 September, 17 October, 28 October 17 2013

  18. For the diversity of SAR data, method development needs to be based on knowledge of scattering physics Attenuated ground scattering Stem-ground interaction Scattering on leaves, ears

  19. c b The relative contributions of volume, surface and volume-surface(interaction) scattering depend on rice growth stage, radar frequency, incidence angle and polarisation Example at X-band Rice backscatter model Le Toan et al, 1989

  20. Examples of measurements at X-band 25° of incidence 55° of incidence Inoue et al., 2004

  21. CosmoSkymed data 20/09/2013 R: HH G:HH/VV B: VV

  22. Cosmo-Skymed HH 19 August 2013

  23. Cosmo-Skymed HH 4 September 2013

  24. Cosmo-Skymed HH 20September 2013 Use of backscatter temporal variation to distinguish rice

  25. COSMO-SKYMED data 19 August 2013 Polarization HH Polarization VV Use of polarization (HH and VV) to distinguish rice from other land use types

  26. Polarisation ratio 19 August 2013 HH/VV Developed rice plants

  27. Polarisation ratio 4 Sept ember 2013

  28. Polarisation ratio 20 Sept ember 2013

  29. Details of rice fields structure VV 19 August, 4 Sept, 20 Sept

  30. Rice varieties - 50404 (circle) - OM4218 (square) - Jasmine (+) - 7347 (losange) unknown (x) Temporal variation of the backscatter 20 09 2013 04 09 2013 06 10 2013 14 10 2013 19 08 2013

  31. Temporal variation of the backscatter 04 09 2013 20 09 2013 06 10 2013 14 10 2013 19 08 2013

  32. Temporal variation of the backscatter 20 09 2013 14 10 2013 06 10 2013 04 09 2013 HH/VV Ratio 19 08 2013

  33. Temporal variation of the backscatter 04 09 2013 19 08 2013 20 09 2013 06 10 2013 14 10 2013

  34. Different experiments, same scattering physics Angle : 50°-55° 30 cm tillering 10 cm 30 cm 2-3 leaves stem extension 70cm 70 cm An Giang, Aug-Oct 2013 CosmoSkymed, X band SAR Camargue, 1988 X band airborne SAR

  35. Use of robust indicators for rice mapping Châu Phú Chợ Mới Châu Thành TP Long Xuyên Thoại Sơn Autumn-Winter rice map as of October 14 2013

  36. AW 2007 crop from ASAR APP AW 2010 crop from ASAR APP AW 2013 crop from CSK PP (14/10/2013) 36

  37. Requirements for rice gowth model • Development rates: require weather and phenological • observations: sowing date, emergence time, tillering, heading, • flowering, maturity • 2. Output of the model to be adjusted with measurements: • -LAI, Biomass of stems, leaves, panicles • At least at 6 sampling times (provided if possible by EO) • - Transplanting • - Maximum tillering • - Panicle initiation • - Flowering • - Grain filling • - Maturity

  38. Estimated sowing date from CSK SAR data Estimated sowing date

  39. Assessment of sowing date estimate Sowing date +/- 3 days Date from August to Sept 2013 RMSE=4,1 days

  40. SUMMARY • For Rice monitoring in Asia, various EO data sources exist • Works are to be done to combine different data sources • for rice grown area esimates (low resolution optical, • narrow/large swath SAR data, sampling strategies..) • For Rice yield estimates, research effort is still needed • There is a need to assess the methods not only at a single • site, but across Asia • There is an action to be undertaken by Asia-RICE • /GEOGLAM for future data acquisition for Rice • (e.g.towards Sentinel-1 and ALOS-2)

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