## 3 Results and Discussion
### Challenges in Using Sentinel-2 Data for Crop Biomass Mapping  
Crop identification/classification, crop growth monitoring and crop yield estimation/prediction are among the most important remote sensing applications in agriculture, and they are tightly linked. Crop identification from satellite data provides the basis for annual crop inventory; while crop inventory provides the first order information for yield prediction; a crop classification map is also an important input to the ecosystem model, where the crop-specific parameters can be assigned. Crop growth monitoring from remote sensing involves mapping time-series of crop biophysical parameters (LAI, height, clumping, etc.) and biochemical parameters (e.g., leaf chlorophyll content, Vcmax); a biophysical parameter, AGB, can also be mapped from SAR images at a certain accuracy; the ecosystem model (or some crop growth models) can integrate these remote sensed information, meteorological data, and some available management information to produce reliable GPP, NPP and biomass. Crop biomass, along with crop-specific HI, can then provide more information for yield prediction/estimation than the crop inventory can. In this study, we tested BEPS for biomass mapping using Sentinel-2-derived LAI time-series as main input and with an existing archive of crop inventory map from AAFC. Although crop AGB is robustly estimated for the majority of our study area, there are a few challenges in applying the same methodology to larger regions at continental and global scales. The same challenges may also apply to general crop growth models with explicit modules of crop phenology development and carbon allocations. The first challenge is to acquire consistent LAI time-series that are free of cloud contamination. In our study area, there is a one-month gap in August without cloud-free Sentinel-2 images. Although temporal smoothing can mitigate the impact of these gaps to some extent, missing LAI estimates for cloud-contaminated pixels leads to incorrect AGB estimation. It is also a challenge to produce smoothing of LAI time series for near real-time AGB estimation, given the knowledge needed of crop growth stage. To track crop phenology for reliable estimation of AGB, one solution would be to fuse multi-satellite data at similar spatial resolutions, such as from Landsat 7 and 8 optical data and/or Synthetic Aperture Radar data from Sentinel-1 [76]. Figure 11: Averaged AGB by crop type in 2016 derived for the SMAPVEX16-MB study area. Beans and peas are characterized by lower AGB values. The second challenge is to perform rigorous model calibration for different crop types, especially for carbon allocations of crops for different cultivars and species, as plants develop different acclimation processes to maximize their photosynthetic capacity. For example, the AGB to GPP ratios range from 0.4 to 1.02 for the six target crops. This is especially true for legume crops with root rhizdeposition. Though not tested in our study, tuber crops such as potatoes can account for a significant amount of underground carbon, and their AGB might have been overestimated in our study. The accuracy of using C3 model to simulate AGB of C4 plant (corn in this study) is acceptable but not optimal. The third challenge is to acquire soil nutrition conditions of the field and relate this information to photosynthetic capacity of crops. Although some crop growth models have a mechanism to relate nitrogen pool of crop system to leaf V\({}_{\text{cmax}}\), such nitrogen management information is unavailable at the regional and national scales. Although Sentinel-2 has the red-edge bands for retrieval of leaf chlorophyll content [77; 78; 79; 80], how leaf chlorophyll content correlates with leaf V\({}_{\text{cmax}}\) among species, crop growth stages and radiation conditions are not completely understood. The fourth challenge is to acquire pixel-level irrigation information for crop growth models. Without correct irrigation information as an input, crop GPP and NPP may be underestimated by the models [46].