###### Abstract  
The use of remote sensing technology to monitor farmland is currently the mainstream method for crop research. However, in cloudy and misty regions, the use of optical remote sensing image is limited. Synthetic aperture radar (SAR) technology has many advantages, including high resolution, multi-mode, and multi-polarization. Moreover, it can penetrate clouds and mists, can be used for all-weather and all-time Earth observation, and is sensitive to the shape of ground objects. Therefore, it is widely used in agricultural monitoring. In this study, the polarization backscattering coefficient on time-series SAR images during the rice-growing period was analyzed. The rice identification results and accuracy of InSAR technology were compared with those of three schemes (single-time-phase SAR, multi-time-phase SAR, and combination of multi-time-phase SAR and InSAR). Results show that VV and VH polarization coherence coefficients can well distinguish artificial buildings. In particular, VV polarization coherence coefficients can well distinguish rice from water and vegetation in August and September, whereas VH polarization coherence coefficients can well distinguish rice from water and vegetation in August and October. The rice identification accuracy of single-time series Sentinel-1 SAR image (78%) is lower than that of multi-time series SAR image combined with InSAR technology (81%). In this study, Guanghan City, a cloudy region, was used as the study site, and a good verification result was obtained. multi-time series SAR; InSAR; cloudy; rice identification; remote sensing +
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other crops. Therefore, the use of SAR imagery in monitoring rice in cloudy regions is of great significance. At present, two primary methods are used to map rice-planting areas using SAR imagery. One is to identify by using the threshold of SAR data before and after rice planting. The other is to classify by using multi-time-phase SAR data. Previous studies identified rice-planting areas by only using single-time-phase radar images [4; 5]. In addition, most of them are in the C and L bands. The main data types include ERS-1/2, JERS-1, ENVISAT ASAR, and RADARSAT-1. The polarization mode used is VV or HH. In 1989, [PERSON] [6] first put forward the feasibility of rice identification by using SAR imagery. By analyzing the backscattering characteristics of ground objects in the X-band radar image, they found that the change in the backscattering coefficient of rice with time was much greater than that of other crops. Therefore, the use of SAR imagery in rice identification was proposed. [PERSON] [7] classified crops in Dongting Lake experimental area using Gaofen-3 SAR imagery. The overall classification accuracy of rice and other crops was 85%, and the Kappa coefficient was greater than 0.8. The single-time-phase SAR imagery has single polarization mode and only one time phase. In addition, the early SAR imagery has low spatial resolution and poor image quality, which greatly restricts the effect of crop classification and identification by SAR, especially in regions with tattered farmland and complex planting structure. Multi-time-phase SAR imagery, which makes use of a number of time-phase SAR images, can fully reflect the change of rice in the growth cycle and has more advantages than single-time-phase radar [8; 9; 10]. On the basis of the comparative analysis of the difference in backscattering strength between rice in the growing period and other ground objects, the most different time phases are selected to distinguish rice from other ground objects. [PERSON] [11] used the time-series Sentinel-1 SAR imagery to extract rice-planting areas with an accuracy of >80%. [PERSON] [12] mapped rice-planting areas in the Mekong Delta on the basis of time-series dual-polarization (VV/VH) Sentinel-1A interferometric wide (IW) images. The accuracy reached 87.2%. Several studies demonstrated that time-series SAR can efficiently extract rice-planting areas and achieve good results.