## 1 Introduction  
Rice is one of the most important cereal grains and serves as a staple food resource for more than half of the world's population. For this reason, rice consumption increases dramatically together with population raising, and the demand for rice is predicted to be strong [1; 2]. International Food Policy Research Institute analyses show that the demand for rice is increasing by about 1.8% per year. It will be difficult to increase rice production to satisfy this demand because land and water resources become more scarce with increasing competition from fast-growing non-farming sectors [3]. Especially, rice crop is a heat and water-loving crop [4], then, sometimes the rice badly suffers from cold stress during the seedling stage when it is grown in winter environment [5]. Therefore, monitoring the global productivity of rice is one of the adequate solutions to meet the demand for rice. Rice parameters (height, dry biomass, leaf area index, salinity, etc.) are primarily monitored through two approaches, including field measurements by farmers and model retrievals from remote sensing data. While the filed measurement is time-consuming, remote sensing is able to perform rice parameters measurement on a large scale without directly contacting the crop [6]. Spatial remote sensing provides the opportunity to have information on a regional scale with high spatial and temporal resolution. Data acquired from satellites provides a great tool for tracking temporal changes in soil and crop conditions, mapping their characteristics over large areas. There are two kinds of remote sensing techniques: one uses optical sensors and the other uses synthetic aperture radar data (SAR) sensors. In addition, the fusion of optical and SAR data can be exploited as well [7; 8]. Optical sensors (e.g., UAV, multispectral, and hyperspectral data) are suited for monitoring agricultural areas [9; 10; 11]. For example, the joint analysis of time series of vegetation and water indices derived from these sensors, such as Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Normalized Difference Water Index (NDWI), can be used to estimate rice parameters [9; 12]. However, a large cloud coverage can limit the use of optical sensors [13]. Unlike passive optical sensors, SAR systems are capable of producing high-quality images of the earth even in cloud cover conditions. Most rice fields are always found in cloudy cover conditions, and SAR sensors can work in these conditions, so it is more effective for monitoring rice fields than optical sensors [14]. Furthermore, for agriculture, SAR data can provide information on activities such as plowing, field preparation, planting, and the state of growth of the crops from germination to maturity stages. Recent studies have shown the high potentialities of using such data for crop monitoring [15; 16]. Thus, the application of SAR data in agriculture is becoming more popular. In previous studies, many investigations have been carried out on monitoring of rice growing. Various of them are based on rice parameters estimation [17; 18; 19], the others are focused on the used water within the rice fields [20], and some of them are on soil phenology and organic matters [21]. Besides several SAR data which were used to map and monitor rice such as RADARSAT-1/2 [22], ENVISAT ASAR [23], ERS-1/2 [24], Sentinel-1 data is one of them. The Sentinel-1 mission is based on a constellation of two satellites (A and B). Sentinel-1 as a C-band SAR imaging satellite constellation ensures the continuity of ERS and ENVISAT missions [25; 26]. Sentinel-1 data are systematically acquired in terrain observation with progressive scan (TOPS) mode with a 6-day revisit period (We note that outside the European zones, it drops to 12 days.). Dense time series of open access Sentinel-1 data at high spatial resolution (20 m) offers new opportunities for monitoring agriculture [27].