Opportunities and challenges of crop biomass mapping based on ecosystem modeling at regional scale using high resolution Sentinel-2 data
Abstract
Accurate mapping of crop biomass at field scale and beyond it is crucial for reliable crop yield estimation. Regional, continental and global scale mapping of biomass depends on the availability of information on abiotic and biotic factors that control the productivity of agricultural ecosystems. The recent Sentinel-2 mission, with its five-day revisit and nearly global coverage, provides an important source of high resolution (up to 10 m) remote sensing data for accurate and timely monitoring of crop conditions at key growth stages. In this study, we evaluate the potential of using a process-based ecosystem model for crop biomass mapping at 20-m resolution over the research site in western Canada (Manitoba Province). The model is driven by spatially explicit leaf area index (LAI) retrieved from Sentinel-2 measurements, and abiotic variables from soil and meteorological data available throughout the entire growing season. We find that overall, the simulated crop gross primary production (GPP) can explain 82% of the variation in the above-ground biomass (AGB) for six selected annual crops, while an application of crop LAI explains only 32% of the variation in AGB. The linear relationship between the GPP and AGB are rather high for the six crops having R2 values ranging from 0.65 to 0.96. The slopes of the regression models for four major crops are close to 1, but for two other crops these slopes are ~0.7 and ~0.4, indicating the need for calibration of key photosynthetic parameters and carbon allocation coefficients. This study demonstrates that accumulated GPP derived from an ecosystem model, driven by Sentinel-2 LAI data and abiotic data, can be effectively used for crop AGB mapping.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2019
- Bibcode:
- 2019AGUFM.B31M2486H
- Keywords:
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- 0402 Agricultural systems;
- BIOGEOSCIENCES;
- 0434 Data sets;
- BIOGEOSCIENCES;
- 0480 Remote sensing;
- BIOGEOSCIENCES;
- 1842 Irrigation;
- HYDROLOGY