###### Abstract
Compared with single-time phase, multi-time phase [13; 14; 15; 16] can better reflect the polarization backscattering strength of rice in the growing period and its changes so as to better separate other ground objects. The coherent processing is performed by using two SAR imagery in the same region taken at similar times but different viewing geometries. The phase difference information (interference phase) between the corresponding pixels is obtained, and the phase information is used to acquire the displacement along the line of sight of the radar in the target region. Thus, interference SAR (InSAR) [17; 18] is established. InSAR technology is very sensitive to the spatial distribution, height, shape, and direction of surface scatterers. Therefore, interferometric radar has more advantages than single polarimetric radar in the extraction of vertical structure information of surface vegetation. The one relevant for most satellite systems is the repeat-pass interferometry configuration. This mode utilizes the fact that the repeat cycle of the satellite is not perfect, resulting in an across-track shift of orbits of typically a few hundred meters. And this means that the repeat-pass data are acquired at different times compared with the single-pass modes. This provides the possibility to study phenomena related to surface changes and feature sensitivity such as land deformation, land cover changes, and crop identification occurring between the satellite acquisitions. InSAR technology combined with time-series SAR can improve the application accuracy of SAR imagery to a certain extent. The coherent images obtained by [PERSON] [19] in invasive plant species, which were based on L-band PALSAR (HH/VV) data combined with InSAR technology, played an important role in mapping invasive plant species in the sub-coronal layer of tropical rain forests. Thus, the invasive and fast-growing plants were successfully identified. [PERSON] [20] improved the accuracy of land-cover research by using InSAR covariance matrix of HH polarimetric TerraSAR-X. The overall accuracy reached 82.46%, and the classification accuracy by InSAR was improved by about 9%. Atpresent, InSAR technology has been widely used in the monitoring, identification, and classification of forests, snow, wetlands, soil, and cities, as well as in digital elevation model (DEM) production generation, deformation measurement, earthquake observation and measurement, and 3D reconstruction [21; 22; 23]. In cloudy regions, the use of optical remote sensing image in the monitoring and identification of rice and other crops is greatly restricted. However, the traditional method is mainly used in single- and multi-time-phase radar images; that is, the difference in the backscattering strength among ground objects is based to distinguish rice from other ground objects while overcoming the problems of the SAR image itself. Polarimetric InSAR technology is used for rice identification. It is very sensitive to the spatial distribution, height, shape, and direction of surface vegetation scatterers. Therefore, it can more efficiently extract the rice-planting areas compared with other technologies. Although it is not a precedent to monitor rice based on InSAR technology, some studies only calculated and adopted the theoretical coherence diagram, namely, the coherence diagram of the two most different time phases. Based on the time-series polarimetric interference radar, combined with the change of backscattering strength of rice and coherence coefficients, this study created pairs across time, performed rice identification using a time-series Sentinel-1 and a time-series Sentinel-1 scheme incorporating the polarization coherence coefficient, and used high-resolution Sentinel-2 optical images as the result validation. The rice-planting areas were extracted by using the object-oriented classification method, which provides a reference for the monitoring and identification of rice and other crops in cloudy regions.