###### Abstract  
We evaluate the potential of using a process-based ecosystem model (BEPS) for crop biomass mapping at 20 m resolution over the research site in Manitoba, western Canada driven by spatially explicit leaf area index (LAI) retrieved from Sentinel-2 spectral reflectance throughout the entire growing season. We find that overall, the BEPS-simulated crop gross primary production (GPP), net primary production (NPP), and LAI time-series can explain 82%, 83%, and 85%, respectively, of the variation in the above-ground biomass (AGB) for six selected annual crops, while an application of individual crop LAI explains only 50% of the variation in AGB. The linear relationships between the AGB and these three indicators (GPP, NPP and LAI time-series) are rather high for the six crops, while the slopes of the regression models vary for individual crop type, indicating the need for calibration of key photosynthetic parameters and carbon allocation coefficients. This study demonstrates that accumulated GPP and NPP derived from an ecosystem model, driven by Sentinel-2 LAI data and abiotic data, can be effectively used for crop AGB mapping; the temporal information from LAI is also effective in AGB mapping for some crop types. Sentinel-2; crop; biomass; mapping; remote sensing; Manitoba; Canada Article  
13 January 2021  
18 February 2021  
1 Department of Geography and Planning, University of Toronto, Toronto, ON M5S 3C3, Canada; [EMAIL_ADDRESS] (R.W. ); [EMAIL_ADDRESS] (J.L. ); [EMAIL_ADDRESS] (J.M.C.) 2 Laboratory of Environmental Model and Data Optima, Laurel, MD 20707, USA; [EMAIL_ADDRESS]  
3 Canada Centre for Mapping and Earth Observation, Natural Resources Canada, Ottawa, ON K1A 0E4, Canada School of Geographical Sciences, Fujian Normal University, Fuzhou 350007, China  
5 Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C6 AAFC, Canada; [EMAIL_ADDRESS] ([PERSON]); [EMAIL_ADDRESS] (J.L. ); [EMAIL_ADDRESS] (H.M.)  
3 Canada Centre for Mapping and Earth Observation, Natural Resources Canada, Ottawa, ON K1A 0E4, Canada School of Geographical Sciences, Fujian Normal University, Fuzhou 350007, China  
5 Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C6 AAFC, Canada; [EMAIL_ADDRESS] ([PERSON]); [EMAIL_ADDRESS] (J.L. ); [EMAIL_ADDRESS] (H.M.)  
3 Canada Centre for Mapping and Earth Observation, Natural Resources Canada, Ottawa, ON K1A 0E4, Canada School of Geographical Sciences, Fujian Normal University, Fuzhou 350007, China  
5 Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C6 AAFC, Canada; [EMAIL_ADDRESS] (J.S. ); [EMAIL_ADDRESS] (J.L. ); [EMAIL_ADDRESS] (H.M.)  
3 Canada Centre for Mapping and Earth Observation, Natural Resources Canada, Ottawa, ON K1A 0E4, Canada School of Geographical Sciences, Fujian Normal University, Fuzhou 350007, China  
5 Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C6 AAFC, Canada; [EMAIL_ADDRESS] (J.S. ); [EMAIL_ADDRESS] (J.L. ); [EMAIL_ADDRESS] (H.M.)  
6 Science and Technology Branch, Agriculture and Agri-Food Canada, Winnipeg, MB R3C 3G7, Canada; [EMAIL_ADDRESS]  
6 Senticel-2, 2000-0001-9380-0001-9380-0001-9380-0001-9380-0019-38001-9380-0019-38001-93801-938001 remote sensing data as inputs, the crop height-based method, and crop growth models with remote sensing inputs. Here, we simplify these methods into two categories: plant trait-based methods and photosynthesis-based methods. Plant trait-based methods relate crop biophysical parameters such as LAI, height and/or canopy chlorophyll content, at a critical time (e.g., maximum values in the growing season), to plant biomass. Essentially, various VIs [3; 4; 5; 6] are often used to approximate the maximum photosynthetic capacity of a crop canopy in the growing season in order to estimate the end-of-season biomass. The approach does not consider the effect of abiotic factors, such as soil properties and meteorological variables, on biomass accumulation.