1. Classification Method Validation for Rice Mapping Using ENVISAT APS Data Erxue CHEN(1), Zengyuan LI(1), BingxiangTan(1) ， Wei He(1), Bingbai LI(2) (1)Institute of Forest Resources Information Techniques, Chinese Academy of Forestry, 100091, Beijing, PRC. China (2) Institute of Agriculture Modernization, Jiangsu Academy of Agriculture Sciences, 210014, Nanjing, PRC. China Chenerx@forestry.ac.cn, zengyuan.li@forestry.ac.cn, pangy@forestry.ac.cn, tan@forestry.ac.cn

2. Outlines  Introduction;  Test site and EO data;  ASAR APS data pre-processing;  Classification evaluation methods;  Results and analysis;  Conclusions.

3.  Introduction Regular monitoring of the crop is necessary in the major rice producing countries for political, economical and food security reasons; We validated two kinds of APS preprocessing schemes and compared their classification performance.

4.  Test site and EO data Xinghua district of Jiangsu Province, PRC. China Test site location on China map

5. Ground true data Ground true map generated from Landsat TM image interpretation and aided by field work Landsat TM image. R: b 5; G: b4; B: b3 ENVISAT ASAR APS data used

6.  ASAR APS data pre-processing Backscattering coefficient based method (BSCBM ); Polarimetric SAR data processing method (PSDPM ).

7. BSCBM T1T2T3 Radiometric calibration BSC1BSC2BSC3 complex Intensity Spatial speckle filtering Geocoded ellipsoid correction Multi-look (6az*2rg) SAR to TM image co-registration BSC1BSC2BSC3 db Lee speckle filter Range-doppler Model MLC INPUTS

8. PSDPM VV+VH Scattering vector Scattering matrix VV+VH Covariance matrix (multi-looked) Lee Polarimetric Filter Multi-looked and filtered [C] H-Alpha decomposition Entropy or HAlpha Intensity-VHIntensity-VV MLC INPUTS POLSARPro©ESA used for all these processing routes.

9.  Classification evaluation methods 1.BSCBM 6BSC-(3VV+3VH): All 6 BSC images from three dates; 3BSC-VV: the 3 VV polarization BSC images from three dates; 3BSC-VH: the 3 VH polarization BSC images from three dates. 2.PSDPM 6I-(3VV+3VH): All the 6 intensity images from three dates; 3I-VV: the 3 VV polarization intensity images from three dates; 3I-VH: the 3 VH polarization intensity images from three dates; 12IAE-(6I+3A+3E): 6 intensity and 3 alpha and 3 entropy images. 6AE-(3A+3E): All the 3 alpha and entropy images. Dataset inputs to the MLC

10. Multi-temporal VV BSC imageMulti-temporal VV intensity Image Multi-temporal VH BSC imageMulti-temporal VH Intensity image 1.BSCBM 2.PSDPM

11. Polarimetric alpha image Polarimetric entropy image 2.PSDPM Training and test samples collected from ground true data

12.  Results and Analysis  classification performance of PSDPM is better than BSCBM PSDPM compared with BSCBM

13. The classification accuracy of VV is better than VH. VV compared with VH  Results and Analysis

14. Intensity combined with E+Alpha leads to lower classification accuracy ， but highest rice acc.; E+Alpha only achieved the worst performance The contribution of Entropy and Alpha

15.  Conclusions (1)For rice mapping using ENVISAT ASAR APS level-1 data, it has been observed that polarimetric data processing method is better than backscattering coefficient based processing method; (2)Multi-temporal VV co-polarization SAR data has higher capability for land cover classification in agricultural region than multi-temporal VH cross- polarization SAR data in C band; (3) Applying PSDPM to APS data and combining all the information from it as inputs to a certain classifier was suggested for operational rice mapping.

16.  ACKNOWLEDGES Thanks for European Space Agency to provide all the ENVISAT ASAR datasets used in this study. Many thanks towards Dr. Mike Wooding of RSAC of UK. for helps to order the ASAR APS data from ESRIN.

17. Thanks!