Abstract
Ground-based estimates of aboveground wet (fresh) biomass (AWB) are an important input for crop growth models. In this study, we developed empirical equations of AWB for rice, maize, cotton, and alfalfa, by combining several in situ non-spectral and spectral predictors. The non-spectral predictors included: crop height (H), fraction of absorbed photosynthetically active radiation (FAPAR), leaf area index (LAI), and fraction of vegetation cover (FVC). The spectral predictors included 196 hyperspectral narrowbands (HNBs) from 350 to 2500 nm. The models for rice, maize, cotton, and alfalfa included H and HNBs in the near infrared (NIR); H, FAPAR, and HNBs in the NIR; H and HNBs in the visible and NIR; and FVC and HNBs in the visible; respectively. In each case, the non-spectral predictors were the most important, while the HNBs explained additional and statistically significant predictors, but with lower variance. The final models selected for validation yielded an R2 of 0.84, 0.59, 0.91, and 0.86 for rice, maize, cotton, and alfalfa, which when compared to models using HNBs alone from a previous study using the same spectral data, explained an additional 12%, 29%, 14%, and 6% in AWB variance. These integrated models will be used in an up-coming study to extrapolate AWB over 60 × 60 m transects to evaluate spaceborne multispectral broad bands and hyperspectral narrowbands.
Highlights
The quantification of the terrestrial carbon balance is important for understanding anthropogenic climate change and costing ecosystem services, but remains a challenge, due to uncertainties in simulations of carbon sources and sinks over space and time [1]
This study combined the explanatory power of ground-based spectral and non-spectral predictors to demonstrate a non-destructive alternative for areal aboveground wet (fresh) biomass (AWB) estimation in order to facilitate the calibration/validation of remote sensing biomass models with field data
It is recommended that field campaigns take measurements of H and hyperspectral narrowbands (HNBs) collected from the visible and near infrared (NIR) (400–1000 nm) for effective AWB estimation on a per crop basis, while β and HNBs collected from the Short-Wave Infrared 1 (SWIR1) (1000–1700 nm) are useful if the research budget permits
Summary
The quantification of the terrestrial carbon balance is important for understanding anthropogenic climate change and costing ecosystem services, but remains a challenge, due to uncertainties in simulations of carbon sources and sinks over space and time [1]. Agriculture accounts for approximately 25% of the global greenhouse gas budget [2]. The two main sources of agriculture-related emissions—nitrous oxide and methane—come mostly from fertilizer/manure use and production, livestock ruminants and manure management, bush fires, and rice cultivation, while carbon dioxide (CO2) emissions are second to emissions from fossil fuel consumption and are primarily due to land use/cover change [3]. Given its importance in the global greenhouse gas budget and climate forcing, methods for estimating CO2 emissions from agriculture are important for designing appropriate mitigation strategies [4]. Physiological-based crop growth models are used to monitor and forecast crop condition and production to inform farm-level management and local, national, and global policy-making [6]
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