## 1 Introduction
### Background
[50; 51] find that developing separate regression models for different growth stages provides the best results, while [29] finds that including cultivar, planting pattern, and growth stage in the model could improve the performance of the retrievals. While the secondary factors in [29; 50; 51] do not correspond to the secondary factors in physical radiative transfer models such as PROSAIL, their indirect connection to the leaf and canopy parameters used by PROSAIL [33; 34; 35; 36] allows them to reduce the uncertainty caused by the secondary effects. Nevertheless, the work on including secondary effects is quite limited and hampered by lack of available data [28] to span the large spatiotemporal variability in these secondary factors, calling for new approaches to address this issue. In order to address the uncertainty caused by secondary factors, it is necessary to obtain data that covers the extent of their spatiotemporal variability. Crop growth models provide one possible avenue to obtain information on the secondary factor leaf and soil properties. The use of crop growth models to obtain information about the secondary factors has been best explored in coupling studies [52; 53; 54; 55], where remote sensing data is assimilated into a combined model consisting of a crop growth model, a canopy radiative transfer model, and formalisms linking the outputs of the growth model with the inputs of the radiative transfer model. These studies [52; 53; 54; 55] have been successful in coupling several variables from the crop growth models, such as LAI, leaf structure parameter, water content, dry matter content, total chlorophyll content, and relative soil dryness. The variables coupled in addition to LAI are secondary factors that affect LAI retrieval [32] and the coupling can be understood to provide constraints on these secondary factors from the biological mechanics of growth and its interaction with the weather/soil environment. In addition, if available, any genetic (cultivar choice) or management information inputted into the crop model can provide additional constraints on the secondary factors [56]. Unfortunately, it is difficult to use crop growth models to gain information about these secondary parameters at a regional scale as information about \(G\times M\) parameters is limited at this scale [57]. As a result, regional crop growth model simulations are generally validated only against crop yields and phenological dates [6; 20; 58; 59; 60] and consequently may have significant uncertainty in their prediction of in-season state variables (many of which are secondary factors in LAI retrieval) [61]. In contrast, field-scale crop growth model simulations have been validated in much more detail with respect to in-season state variables. For example, several studies [62; 63; 64; 2; 65] evaluate their performance in predicting LAI, canopy cover, biomass, soil moisture, soil nitrogen, plant nitrogen, evapotranspiration, and phenology as well as yield. The crop model's stronger performance at field-scale in predicting both the yield and individual within-season process can be attributed to the availability of significantly more accurate agromanagement information, and to a lesser extent to more accurate soil and weather data, at this scale [66]. Thus, incorporating field-scale crop growth modeling of secondary parameters in training and testing agricultural satellite retrieval algorithms [67] can potentially provide for significant advances in addressing the uncertainty caused by site and growth stage specific secondary factors.