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Open AccessArticle

Crop Biomass Mapping Based on Ecosystem Modeling at Regional Scale Using High Resolution Sentinel-2 Data

1
Department of Geography and Planning, University of Toronto, Toronto, ON M5S 3G3, Canada
2
Laboratory of Environmental Model and Data Optima, Laurel, MD 20707, USA
3
Canada Centre for Mapping and Earth Observation, Natural Resources Canada, Ottawa, ON K1A 0E4, Canada
4
School of Geographical Sciences, Fujian Normal University, Fuzhou 350007, China
5
Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C6AAFC, Canada
6
Science and Technology Branch, Agriculture and Agri-Food Canada, Winnipeg, MB R3C 3G7, Canada
*
Author to whom correspondence should be addressed.
Academic Editor: Emilio Gil
Remote Sens. 2021, 13(4), 806; https://doi.org/10.3390/rs13040806
Received: 13 January 2021 / Revised: 11 February 2021 / Accepted: 18 February 2021 / Published: 22 February 2021
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. View Full-Text
Keywords: Sentinel-2; crop; biomass; mapping; remote sensing; Manitoba; Canada Sentinel-2; crop; biomass; mapping; remote sensing; Manitoba; Canada
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MDPI and ACS Style

He, L.; Wang, R.; Mostovoy, G.; Liu, J.; Chen, J.M.; Shang, J.; Liu, J.; McNairn, H.; Powers, J. Crop Biomass Mapping Based on Ecosystem Modeling at Regional Scale Using High Resolution Sentinel-2 Data. Remote Sens. 2021, 13, 806. https://doi.org/10.3390/rs13040806

AMA Style

He L, Wang R, Mostovoy G, Liu J, Chen JM, Shang J, Liu J, McNairn H, Powers J. Crop Biomass Mapping Based on Ecosystem Modeling at Regional Scale Using High Resolution Sentinel-2 Data. Remote Sensing. 2021; 13(4):806. https://doi.org/10.3390/rs13040806

Chicago/Turabian Style

He, Liming; Wang, Rong; Mostovoy, Georgy; Liu, Jane; Chen, Jing M.; Shang, Jiali; Liu, Jiangui; McNairn, Heather; Powers, Jarrett. 2021. "Crop Biomass Mapping Based on Ecosystem Modeling at Regional Scale Using High Resolution Sentinel-2 Data" Remote Sens. 13, no. 4: 806. https://doi.org/10.3390/rs13040806

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