## 1 Introduction
Consequently, promising results of crop yield estimation at the regional scale are possible through remote sensing reflectance assimilation into CGM. Moreover, there is a rising need to investigate reliable retrieval techniques to achieve quantification and retrieve spatiotemporal information about crop traits. Accurate and reliable quantification of biophysical and chemical parameters is essential for precision agriculture [16], crop trait monitoring [17] and phenotyping [18]. These reliable and accurate quantifications are also vital for improvements in yield prediction [17; 19]. Similarly, RTMs are often coupled with remote sensing data, which allows for estimation of important crop variables at larger scales. Remote sensing platforms provide spatially explicit information on crop canopy properties, such as leaf area index (LAI) [20], vegetation indices [16; 21] and photosynthetic capacity (biomass) [22]. Integrating remote sensing data with an RTM and CGM enables the estimation of crop yields over large areas, facilitating regional or global assessments of agricultural productivity. Researchers have worked on novel approaches and algorithms for sustainable management practices in agriculture systems through the estimation of crop physiological processes from Earth Observation (EO) data [23]. They worked on yield prediction and retrieval of biophysical variables of crops for food security through different crop management strategies. Results showed RTM inversion to retrieve LAI, followed by its assimilation into a crop growth model (SAFY), produced more accurate assessments of crop yields compared to open loop model runs. This assimilation approach had an RMSE of 1.14 tons per ha as compared to 4.42 tons per ha for the open loop model [23]. Several articles have been published regarding the use of RTMs in agriculture since 2015. Research studies are even available on RTM applications in aquatic vegetation [24]. However, aquatic vegetation research in this field is limited and faces problems of the complexity of scattering media, ground measurements, model validation and performance evaluation [25]. Researchers are still exploring possibilities for future aquatic vegetation research [24; 25]. However, there are hardly any systematic or comprehensive reviews published in any journal that can provide insight into RTM applications for crop yield and crop traits estimation. A few review articles [26; 27; 28; 29; 30] relevant to the retrieval of crop traits using RTMs are available. Among these, only the review article [26] outlined a conceptual framework that integrates hybrid retrieval models using parametric and nonparametric techniques to obtain biophysical and biochemical variables (crop traits). The review article, however, lacked information about the accuracy and reliability analysis of these methods to retrieve crop traits. Other review articles contained limited information about radiative transfer models for the retrieval of crop traits. The review article [27] focused on vegetation cover fraction through ground and aerial methods, describing the role of RTMs in retrieval accuracy improvement without any knowledge about other traits or methods. Other review articles [28; 29] described the RTM role for crop stress detection, focusing on spectral synergies from multi-sensors [28] and crop nitrogen monitoring in addition to other available remote sensing methods [29]. The review article [30] covered remote sensing applications for agriculture with limited description of RTM utility in the retrieval of primary and agronomic variables. Therefore, we can state that this study is the first systematic literature review (SLR) about the publications relevant to RTM use for crop yield prediction and crop traits retrieval. This review also highlights the outcomes of crop growth model and radiative transfer model integration towards improving crop yield prediction and crop traits retrieval. This current study will be a valuable resource for researchers interested in understanding RTM research and its integration with CGMs.