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 production at 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 EO Data Products For Asia-RICE Information and Product Types Area estimate Cropland mask Rice grown area

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

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

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

8. Xiao et al, 2006 Flooding 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 Monitoring at global scale: MODIS & SPOT VGT

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

10.  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. 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.  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. Objectives of Technical Demonstration sites

15. 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) South Vietnam demonstration site: An Giang province

16. VIETNAM Mekong Delta In 1000 tons 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

17. 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 2013

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

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

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

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

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

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

27. Polarisation ratio 4 Sept ember 2013

28. Polarisation ratio 20 Sept ember 2013

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

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

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

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

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

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

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

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

39. 1.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 Requirements for rice gowth model

40. 24-31 Jul 1-7 Aug 8-14 Aug 15-21 Aug 22-28 Aug 29 Aug - 4 Sep 5-14 Sep Estimated sowing date Estimated sowing date from CSK SAR data

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

42. 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)