## 4 Conclusions  
This research explores the potential of integrating high spatio-temporal leaf area index (LAI) estimates derived from Sentinel-2 spectral reflectance data with an ecosystem model for crop above-ground biomass (AGB) mapping at the regional scale. The accuracy of this approach is evaluated using crop AGB field data collected during the SMAPVEX16-MB experiment conducted in western Canada, in 2016. Considering abiotic factors derived from meteorological data and soils data and LAI from Sentinel-2, we find that the model-simulated crop gross primary production (GPP) at a 20 m resolution explains 82% of the variation in the annual crop AGB for six annual crop types. The results outperform the biomass mapping using LAI alone. The correlation between the model-simulated GPP and ground measured AGB for each of the six crop types is also strong, suggesting that our approach is reliable for AGB mapping at a high spatial resolution. Similarly, crop NPP and accumulative LAI explain 83% and 85% of the variation in the crop AGB, suggesting the importance of using temporal information from remote sensing.  
The challenges for crop AGB mapping at high resolution are also discussed. These include difficulty in acquiring temporally consistent LAI time series due to cloud contamination, the lack of soil nutrition and water management information, the need for model calibration at the regional and national scales, and the high cost of computation. A practical approach for crop biomass mapping should include a compromise among data accessibility, model complexity, and computational resource availability.  
The following are available online at [[https://www.mdpi.com/2072-429/2/13/4/806/s1](https://www.mdpi.com/2072-429/2/13/4/806/s1)]([https://www.mdpi.com/2072-429/2/13/4/806/s1](https://www.mdpi.com/2072-429/2/13/4/806/s1)), Figure S1: Soil texture at the SMAPVEX16 field campaign. Figure S2: (a) Successful smoothing of LAI time-series derived from Sentinel-2; (b) Unsuccessful LAI smoothing due to missing of LAI data in the growing season (August) in 2016. Figure S3: BEPS-simulated crop GPP vs. above-ground biomass for all crop types in the study area including these fields with wrong LAIs as shown in the red circle. Figure S4: The correlation between crop above ground biomass (AGB) and Sentinel-2 derived LAI for all crops in the study area. Figure S5: The correlation between crop above ground biomass (AGB) and Sentinel-2 derived LAI for each of the crop types in the study area. Figure S6: The correlation between crop leaf dry biomass and Sentinel-2 derived LAI for all crop types in the study area. Figure S7: The correlation between crop leaf biomass and Sentinel-2 derived green LAI for each crop types in the study area. Table S1: Statistics of above-ground biomass for each crop type in the study area.  
Conceptualization and methodology, L.H., R.W. and J.M.C.; software, J.M.C., L.H. and J.L. ([PERSON]); data curation, L.H., R.W. and G.M.; field data collection, H.M., J.P., J.S., J.L. ([PERSON]); writing--original draft preparation, L.H.; writing--review and editing, all authors. All authors have read and agreed to the published version of the manuscript.  
This study was partially supported by the Canadian Space Agency grants 17 SUSOARTO and 14 SUSMAPTO.  
The Sentinel-2 data are available from Open Access Hub ([[https://schub.copernicus.eu/dhus/#/home](https://schub.copernicus.eu/dhus/#/home)]([https://schub.copernicus.eu/dhus/#/home](https://schub.copernicus.eu/dhus/#/home)) (accessed on 30 July 2019); the crop biomass data are available from [[https://nsidc.org/data/SV16M_V/versions/1](https://nsidc.org/data/SV16M_V/versions/1)]([https://nsidc.org/data/SV16M_V/versions/1](https://nsidc.org/data/SV16M_V/versions/1)) (accessed on 30 July 2019).  
The authors declare no conflict of interest.