Radar remote sensing technology has become an important method for stable and long-time rice monitoring for its capability to operate in all weather conditions. In this letter, ENVISAT advanced synthetic aperture radar (ASAR) alternative-polarization VV/HH data were used for rice monitoring in the Xinghua rice experiment site in the middle of Jiangsu Province. First, a threshold classification method was developed for mapping rice growth area according to the different characteristic of backscatter coefficients between paddy rice and other land surface objects. Then, relational models were built for retrieving rice growth parameters from ASAR images based on correlation analysis between backscatter coefficients and field measurements. Meanwhile, an optical multispectral image was used as ancillary data for rice parameters retrieval. As expected, the retrieved rice growth parameters were consistent with those of field measurements.

ENVISAT is the first satellite that provides alternative polarization (AP) SAR data to end users. In order to validate the rice mapping capability of ASAR AP data, multi-temporal ASAR VV-VH alternative polarization single look complex (APS) data products were acquired for the test site in Xinghua district of Jiangsu province. The three scenes of APS image were acquired in June 22 July 8 and Oct. 15, 2003. The Institute of agriculture modernization of Jiangsu academy of agriculture science was carrying an operational rice mapping project using Landsat TM data and other sources of ground truth data. The rice mapping results for the year 2003 was used as ground truth for this study. Two kinds of preprocessing methods were validated: backscattering coefficient based method (BSCBM) and the polarimetric SAR data processing method (PSDPM). Different combinations of the output images from the two methods were used as inputs to a Maximum Likelihood Classifier (MLC). It has been observed that the performance of PSDPM is better than that of BSCBM; Multi-temporal VV co-polarization SAR data has higher capability for land cover classification than multi-temporal VH cross-polarization SAR data; Integrating alpha and entropy images with all the 6 intensity images can achieve highest rice classification accuracy, but a litter bit lower total accuracy. Applying PSDPM to APS data and combining all the information from it as inputs to a certain classifier was suggested for operational rice mapping using multi-temporal ASAR APS data.

This paper summarises the objectives of the project, the works undertaken, the results obtained, and outlines the next steps of the project. The objective of the project is to develop methodology to use ENVISAT data for rice mapping and retrieving information characterising rice fields (biomass, photosynthetical activities, water management status) relevant to the modelling of rice growth. The overall goal is the estimation of rice production at local and regional scale and the estimation of the Carbon fluxes (CO2, CH4) at regional scale. In the first phase of the study, remote sensing methodology is developed at selected test areas for rice mapping and retrieving of rice parameters. The activities include ground data collection and analysis of remote sensing data. Meantime, preliminary works on large scale crop modelling in China have been undertaken. The results obtained using ENVISAT data in 2004 and 2005 at the test areas in Jiangsu province indicate that it is possible 1) to map rice fields at a single date using two polarisations of ASAR APP, 2) to retrieve rice biomass using the polarisation ratio, 3) to map the main rice varieties, 4) to achieve regional rice mapping using multidate ASAR WideSwath data, and 5) to detect intermittent drainage. These new findings, still to be validated and confirmed, show great potential for statistics of rice growth areas, and in providing the essential information for the modelling of rice growth. ENVISAT ASAR data are expected in 2006, for a demonstration of the methodology, at least at a regional scale, and for an integration of remote sensing in crop modelling.

This paper presents a study of multi-temporal and dual-polaiization (HH, VV) ASAR data, which aims to assess the use of ASAR data for rice field mapping and monitoring. Temporal variation of backscattering coefficients of 4 categories of rice fields were derived from 2004 ASAR images of the region of Jiangsu. Ground data were collected to calculate wet biomass and LAI of rice, NOAA images were utilized to derive NDVI, and the correlation between the backscattering coefficient and those rice parameters was analyzed. Based on the backscatter behaviors of rice, a method for rice fields mapping using image ratio techniques has been developed, with accuracy higher than 80%. Four classes of rice according to varieties (hybrid or Japonica) and crop calendar have been mapped, in addition to the other main land use classes in the region. The results appear promising for the use of ASAR data on rice monitoring and rice mapping.

The aim of this paper is to assess the use of ENVISAT ASAR alternating polarisation data for rice field mapping. The assessment is carried out using HH and W data acquired in 2005 over the test area in Hongze county, Jiangsu province in China, where ground data have been collected for validation. As previously expected, polarisation ratio HH/VV during the second half of the rice season proves to be a very efficient rice classifier. A simple single-date mapping algorithm has been derived and the result has been compared with in-situ local mapping. The first results appear promising and algorithm refinements are planned in the near future.