###### Abstract
The capacity of these indices to track biomass changes is reduced when crops begin to senesce as these VIs are not sensitive to non-green biomass. Unmanned aerial vehicle (UAV) [7; 8; 9] or Lidar-derived crop height [10] relates to crop volume or biomass during vegetative stages but this approach is unable to track increases in biomass at reproductive stages when crop height is relatively stable. SAR-based methods relate the crop volume-scattering with biomass [11], but they are affected by uncertainties related to soil moisture and canopy water content contributions to scattering, especially before harvest [12]. Therefore, plant trait-based methods can be more effective in tracking crop biomass accumulation during vegetative growth stages. The use of multi-temporal data can mitigate the above-mentioned limitations, as the envelope of LAI time series contains information on crop growing season, e.g., the amount of accumulated heat units [13; 14]. In contrast, photosynthesis-based methods track the accumulation of biomass beyond vegetative accumulation and into reproductive stages [15]. These methods take into consideration both biotic and abiotic impacts on crop growth and therefore, are less prone to the saturation problems than plant trait-based methods. Both light use efficiency (LUE) models [16; 17; 18; 19] and mechanistic enzymatic ecosystem models (such as DSSAT [20] and EPIC [21]) belong to this category. A crop growth model can be based on either LUE models or enzymatic ecosystem process models, but with additional modules and parameters describing the crop phenological developments and carbon allocations [22]. Photosynthetic models are especially useful for tracking the accumulation of above-ground biomass (AGB) of annual crops as the AGB is accumulated from crop emergence. The use of photosynthetic models for crop biomass estimation also has a sound mechanism: [PERSON] [23] noted that the carbon (C) content of biomass is almost always between 45 and 50% per unit weight; therefore, the photosynthesis-based method relates C assimilation to the total amount of crop biomass accumulation. Crop growth models rely on numerous inputs of plant traits which are often unavailable for large-scale applications. Assimilation of satellite spectral information [24] or satellite-derived plant biophysical parameters [25; 26] into crop growth models can help improve the accuracy of biomass mapping [27]. In addition to methodological advancements, research on crop biomass retrieval using optical satellite images has evolved from low to high spatial resolutions, e.g., from 1 km AVHRR [27], 250 m MODIS [28], 30 m Landsat [29], 10 m Sentinel-2 [30], 6.5 m Rapid-Eye [31] and sub-meter UAV [7]. Particularly, the Sentinel-2 A and B missions operated by the European Space Agency's Copernicus Programme acquires data in 13 optical bands at resolutions from 60 to 10 m, and with a 5-day revisit. This twin satellites constellation offers nearly global coverage and the data are free and open. As such, Sentinel-2 is an important source of data for operational agricultural monitoring, as well as biomass and yield mapping [32]. Although VI-based methods are used widely for biomass mapping [33; 30; 34; 35], integration of multi-temporal Sentinel-2 and abiotic information into photosynthetic models can outperform these VI methods for crop biomass mapping [32; 36]. The objective of this study is to demonstrate capabilities and evaluate the performance of an ecosystem model to simulate annual crop gross primary production (GPP) and NPP for the purpose of crop AGB estimation at a regional scale using high-resolution Sentinel-2 data. To achieve this objective, Sentinel-2 reflectance data are used to derive time-series LAI as an input to the ecosystem model. These LAI maps, along with other abiotic variables, are integrated to simulate crop GPP and NPP (Section 2). The correlations between simulatedGPP, NPP and measured AGB for several crops are examined in Section 3; the correlation between AGB and individual LAI or LAI time-series is also examined.