## 1 Introduction  
There is an increasing need for continuous monitoring of crop growth conditions and its response to environment and management activities for the development and assessment of best management practices (BMP). The Food and Agriculture Organization (FAO) of the United Nations has set out the 2030 Agenda of transforming the world through food and agriculture [1]. With an increasing emphasis on sustainable agriculture, the metrics of land management activities throughout the life cycle of crop production become even more important, especially in the context of climate change. In-season information on the onset of key stages of crop production, from soil preparation, seeding to harvest, as well as crop residue management, is of great value to a wide range of user communities, including producers, agri-food, and renewable energy industry, commodity trade board, and crop modelers. Information on land management activities is also key to evaluating environmental footprint resulted from crop production [2; 3; 4]. Earth observation (EO) data have long been used for land cover and land use mapping. In particular, free-access optical satellite data, such as Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat data, have been extensively used for agricultural applications at local, regional and global scales. The most frequent applications are using EO data for crop type mapping using various image classification methods. Because crops are affected by seasonal growth patterns, continuous observation is needed to track the growing conditions, particularly during critical phenological stages. The short revisit time of the MODIS sensor allows for crop emergence detection and seeding date estimation [5; 6; 7], however the spatial resolution of the data is relatively coarse for field-level applications. Given the many benefits of the optical sensors, there are still three inherent shortcomings, e.g., data acquisition is susceptible to interference by weather conditions (e.g., cloud cover), data is insensitive to crop structure and soil disturbances, and signal saturates when vegetation biomass is high. These shortcomings limit the operational applications of optical EO in agriculture, because fields are frequently managed and crops vary continuously throughout the growing season [8; 9; 10]. In contrast, radar has the capability of acquiring data day-and-night and under all-weather conditions due to the use of an active radiation source in the microwave frequency range. Since radar is sensitive to target's structural and dielectric properties [11; 12; 13], it can be very useful in capturing field management activities impacting surface structures and also for detecting crop phenological stages [14; 15; 16]. Many successful applications in using radar, especially synthetic aperture radar (SAR), have been reported in the literature. For instance, Canada has been using RADARSAT-2 SAR data for its national annual crop inventory mapping since 2013 [17; 18]. Other than crop type classification, SAR has also been extensively used to monitor the growth conditions, track the phenology cycles, and retrieve biomass [14; 19; 20] of crops. While this information is valuable, other land management information related to the life cycle of crop production is also urgently needed by a wide range of user groups [9; 12; 13]. For instance, information on seeding date is needed by crop modelers and commodity groups and crop insurance companies, information on plant flowering is needed by beekeepers, and information on harvest status is needed by transportation department to arrange shipping logistics. In addition to using SAR backscattering coefficients obtained on a few isolated dates, incoherent change detection techniques using time-series SAR data have been increasingly explored for monitoring land surface dynamics [21; 22; 23].