## 1 Introduction  
Crop yield, being the outcome of the crop production process, is a complex phenomenon. It is influenced by almost all biotic and abiotic factors, including genetics, climate, soil quality, pest and disease management, water availability, nutrient availability, farming practices and technological advancements [1]. Interaction among these factors affects crop yield, leading to the size of the overall harvest at the end of the growing season [2]. Thus, analysis of possible factors by farmers, agronomists and researchers is needed to make effective decisions for the maximization of crop production and risk reduction. Crop yield is a crucial phenomenon for predicting and planning food production, assessing the economic viability of agricultural systems, evaluating the impact of climate change on agriculture and developing strategies to meet the growing global food demand [3]. Similar to the crop yield phenomenon, crop yield modeling (CYM) is also a challenging task. Crop yield modeling is multifaceted, as crop yield processes and approaches are time-bound and nonlinear in nature [4]. The large and interconnected range of factors that are affected by external aspects with non-arbitrary relationships make crop yield modeling complex [5]. Despite the complexity of crop yield modeling, global food production requiresarea assessment, crop protection and crop yield prediction (CYP) to mitigate hunger for ever-rising populations [6]. Moreover, reliable crop yield modeling is also mandatory, as import and export evaluations of agriculture commodities by policymakers depends on the size of the crop during the season [7]. Among different techniques, remote sensing-based agriculture monitoring, including yield prediction, is an advanced and sustainable approach due to its repeatable, non-destructive and comprehensive information-gathering (wall-to-wall coverage) methods [8]. Crop growth models (CGMs) are also effective tools for yield prediction due to their simulation of growth and physiological development. These models, however, lack vegetation reflectance and spatial characteristics, which are a key basis of remote sensing satellite data [9]. The limitation of CGM lacking vegetation reflectance characteristics [9] could be managed through the use of a radiative transfer model (RTM). An RTM can simulate the spectral and bidirectional reflectance of a crop canopy [10], but not its growth or physiological development dynamics. An RTM is a mathematical framework used to simulate and understand the interaction of electromagnetic radiation with the atmosphere, land surface, vegetation cover and other environmental objects. It provides a quantitative description of how radiation propagates through various media, such as the atmosphere, vegetation canopy or water bodies. These models incorporate physical principles, such as the laws of radiative transfer and the optical properties of different materials. A variety of empirical, remote sensing, growth process, physical and state-of-the-art techniques like machine and deep learning have been investigated. However, as all have their pros and cons, the target of having a single method for crop yield prediction has not been fully achieved. It is, therefore, an integrated approach of new technological advancements to cater to the complexity of the crop yield phenomenon that seems to be promising to optimize crop yield modeling. Different simulation models with machine learning techniques also provide an opportunity to produce reliable and precise predictions of crop yield [11; 12]. This integrated approach of remote sensing data, crop growth and radiative transfer models can allow these techniques to cater to the limitations of each other to produce the best possible estimations. Studies have shown that the integrated modeling framework of crop growth and radiative transfer models results in reliable and comprehensive outputs relevant to temporal changes in response to the spectral reflectance and crop growth behavior underlying physiological developments [13; 14; 15].