1. 1 National Institute for Agro-Environmental Sciences, Tsukuba, Japan 2 Aomori-ITC, Kuroishi, Japan Estimating Biophysical Variables in Rice Canopies by High-Resolution X- and C-SAR Signatures Yoshio Inoue 1 , Eiji Sakaiya 2 , Naoki Ishitsuka 1

2. Yield Rice yield in Japan 2009 2008 2007 2006 2005 2004 2003 2002 2001 2000 1999 1998 1997 1996 1995 1994 1993

3. 1. Multi-frequency Ka --- 35.25 GHz Ku --- 15.95 GHz X --- 9.60 GHz C --- 5.75 GH L --- 1.26 GHz 2. Full-Polarization HH, HV, VH, VV 3. Multi-angular Incident angle: 20 º ～ 60 º  25 º , 35 º , 45 º , 55 º Azimuth angle: -60 º ～ +60 º  -28º, -14º, 0º, 14º, 28º Inoue et al. (RSE 2002) Paddy field

4. Objective To investigate the capability of high-resolution satellite-SAR imagery in C- and X-bands for assessment of rice growth. ➢ Operational field scale ➢ Detailed plant data

5. CASI-Hyper Tsugaru site 5 km C-band @ 1 m HH 25º VH 21 º [Radarsat-2 spotlight] Year: 2009,2010 X-band @ 1 m VV 54 º VV 54 º [Skymed spotlight] Year: 2009, 2010 Sky conditions for SAR

6. Inoue et al. (RSE 2002) Ka Ku X C L

7. Inoue et al. (RSE 2002) Ka Ku X C L

8. Datasets Ground-based measurements

9. 1. Canopy height 2. Hill density 3. Stem density 4. Leaf Area Index 5. fAPAR 6. Chlorophyll index 7. Leaf FW, DW, W% 8. Stem FW, DW, W% 9. Head FW, DW, W% 10. Total FW, DW, W% 11. Leaf number/m 2 12. Leaf length 13. Leaf width 14. Leaf thickness 15. Leaf density/m 3 16. Head layer depth 17. Leaf layer depth 18. Stem layer depth 19. Stem diameter Major plant variables 1. Panicle initiation stage 2. Maturity stage

13. S=0.69dB/1LAI S=13.2dB/1kg S=1.2dB/1kg S=2.8dB/1fAPAR S=3.8dB/1m S=-0.03dB/1% Relations of C-VH σ 0 with major plant variables fAPAR C-VH σ 0 (dB) C-VH σ 0 (dB) Leaf-W % Plant height (m) LAI Leaf-DW (g m-2) Total-DW (g m-2)

17. Increase with grain growth Seasonal change of X-VV σ 0 at 55º over a rice canopy Inoue et al. (RSE 2002) 15 dB 15 dB Jump with transplanting

20. r = 0.5 Summary of correlations between X-VV σ 0 and plant variables

23. 5. The X-band signature was found to be best correlated with head biomass indicating a good capability for direct assessment of rice grain yield at regional scales. Conclusions (3 /3) 6. Systematic approaches would be useful to improve the accuracy; modeling/ optical sensors.