## 1 Introduction  
Timely and accurate regional crop growth monitoring and yield forecasting play a significant role in guiding agricultural production, ensuring national food security and maintaining sustainable agriculture development [1; 2; 3; 4]. In the past few decades, conventional agro-meteorological models and empirical statistical regression models based on field-measured yields and related remotely-sensed spectral vegetation indices [5] were used to monitor crop growth and yields, with the disadvantages of high-cost, laboriousness, inefficiency and only being applicable for local regions [6; 7; 8]. Process-orientedcrop growth models can offer powerful tools to simulate the crop growth and obtain the crop yield under various environmental and management conditions with the advantages of cost, timeliness, accuracy and suitability at the field scale [6; 9; 10; 11; 12; 13; 14], while they were also dynamically accounting for several limiting factors (e.g., soil, weather, water, nitrogen and field management data) at the regional scale [3; 15]. With the development of remote sensing technology, extensive remotely-sensed data provide a synopsis of timely and up-to-date crop growing conditions over large areas throughout the crop's growing season and can be employed to provide many potential data for crop model simulations [16]. Therefore, data assimilation approaches that integrate crop growth models with remote sensing data have been proposed recently and recognized as important approaches for monitoring crop growth conditions and improving the accuracy of yield estimations at the regional scale [6; 7; 9; 17; 18; 19; 20; 21; 22; 23; 24; 25]. Commonly, there are two main kinds of strategies for data assimilation to reduce the discrepancy between observations and simulations by adjusting the initial model conditions, parameters (e.g., canopy and growth parameters) [7; 17; 21] or model state variables [3; 10; 26]: (1) recalibration/re-initialization methods and (2) state-updating methods [27]. The recalibration methods can be carried out using optimization algorithms (e.g., Price [19; 28], Powell [29; 30], simulated annealing (SA) [31], the shuffled complex evolution method developed at the University of Arizona (SCE-UA) [32], etc. [33; 34]) to re-initialize or re-parameterize a crop growth model to minimize the merit function between modeled parameters and remotely-sensed parameters. However, the disadvantages of these methods are that they could not consider uncertainties in observations, and the model itself, usually failing to capture and maintain the crop system dynamics, and the iterative process may require excessive computing time [4; 7; 34; 35]. Thus, the state-updating methods carried out using assimilation algorithms (e.g., an ensemble Kalman filter (EnKF) [3; 10; 36; 37], particle filters (PF) [22; 38; 39], traditional variational methods [40; 41] or ensemble-based four-dimensional variation [9; 42; 43], etc.) were introduced into crop growth simulations to alleviate the shortcomings of the recalibration/re-initialization methods and can combine many types of observations at random times, and the state variables can also be continually updated and simulated accurately [4]. Especially EnKF, as one of the typical state-updating methods, has received much attention because of its simple conceptual formulation and relative ease of implementation [3; 10; 21; 36; 41]. However, the disadvantages of this method are that it may result in an obvious deficiency when solving complicated estimation issues in a nonlinear and non-Gaussian dynamic system, and it also has low computational efficiency since it is designed to incorporate sequential information only [9]. To solve these problems associated with the EnKF, PF have received increasing attention thanks to their improved performance. Similar to EnKF, PF is also a Monte Carlo technique that uses particles to estimate the underlying probability density function (PDF) of model states/parameters [9].