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.
Highlights
Biomass is the organic matter produced by plants and animals with accumulated energy directly or indirectly from photosynthesis
We briefly review the biomass mapping methods based on remote sensing data for two major grain-crops, oil and starch crops, within the category of food-based crops
It is found that the average above-ground biomass (AGB) among crop-type is clear while the one-standard deviation of AGB for each crop type is relatively small; we suggest that the small the one-standard deviation of AGB for each crop type is relatively small; we suggest that the small deviation is due to the small study area with the similar climate condition
Summary
Biomass is the organic matter produced by plants and animals with accumulated energy directly or indirectly from photosynthesis. Agricultural biomass is characterized by two major types, food-based crops and non-food-based crops, albeit with no clear boundaries between these categories. We briefly review the biomass mapping methods based on remote sensing data for two major grain-crops, oil and starch crops, within the category of food-based crops. The methodologies for biomass mapping of herbaceous grain crops are applicable to energy crops. Ahamed et al [1] extensively reviewed remote sensing methods for biomass mapping for feedstock production with a focus on using various vegetation indices (VIs), biophysical and biochemical variables, such as leaf area index (LAI, or L) and chlorophyll content. Chao et al [2] categorized the methods for biomass mapping of energy crops into five types: VI-based simple statistical analysis, Synthetic Aperture
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