## 1 Introduction  
Real-time monitoring and precise prediction of large-scale, long-term crop growth processes constitute a pivotal element in ensuring food security and optimizing agricultural production strategies [1]. While traditional empirical statistical models, regression models, and Markov models have provided some degree of support for agricultural production, their limitations in addressing extreme environmental stressors, evaluating crop variety adaptability, and predicting growth cycles have increasingly come to light [2, 3]. In response, researchers are actively exploring the integration of Crop Growth Models (CGMs) with Remote Sensing (RS) technology to develop a monitoring and forecasting platform that closely approximates real-world conditions while offering fine-grained spatial information on crop growth [4]. This fusion strategy, integrating RS data with ground-based biomass predictions, enables cross-scale accurate estimation of critical parameters such as crop yield, pest, and disease thresholds [5; 6]. This empowers agricultural decision-makers to swiftly respond to various risks and implement precision management measures, thereby enhancing both economic and ecological benefits of agricultural production [7]. Furthermore, delving deeper into the practical applications of such assimilation strategies in precision agriculture not only contributes to the transformation and upgrading of agricultural paradigms but also holds profound practical significance for refining agricultural production systems and fortifying the defense lines of food security [8]. Since the 1970s, RS technology has achieved remarkable advancements across numerous fields, particularly demonstrating immense potential in agricultural monitoring, crop yield estimation, and precision agriculture applications. This is largely due to its unique advantages such as synchronicity, timeliness, continuous coverage, and extensive observation ranges [9; 10]. With further technological development, a series of quantitative inversion products closely related to crop physiological and biochemical parameters have emerged, providing rich and valuable data resources for agricultural early warning, monitoring, and yield assessment [11; 12; 13; 14; 15]. For example, in agriculture, horticulture, plant science, and other fields, the use of remote sensing data for plant growth modeling can significantly improve the accuracy and efficiency of the model [16; 17; 18; 19]. Moreover, studies have shown that RS technology also exhibits broad application value in revealing environmental change dynamics, agricultural forecasting, and monitoring [20; 21; 22; 23; 24]. However, it still faces significant limitations in deeply uncovering crop growth mechanisms and intricately characterizing the complex interactions between crops and their environment [14]. Externally, this manifests as issues with poor temporal and spatial continuity, limited model fitting accuracy, and insufficient generality [4; 25]. Additionally, the substantial costs and time required to process vast amounts of RS data represent a major challenge hindering its widespread application in the agricultural sector. Compared to RS technology, CGMs operate within a rigorous mathematical framework, enabling them to meticulously track the dynamic physiological and ecological processes of crops [26]. Studies demonstrate that CGMs, through their deep understanding of the intertwined effects of intrinsic crop growth patterns and environmental factors, have become effective tools for simulating crop growth dynamics and multi-dimensional yield prediction [27]. Different from models that simply rely on meteorological statistics, CGMs comprehensively consider atmospheric conditions, soil characteristics, crop genetic traits, and field management practices, which can more accurately simulate the actual growth trajectory of crops and effectively solve the shortcomings of complex system modeling [28].