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Towards an Operational Methodology to Map Crop Photosynthetic Capacity from Airborne Remote Sensing

Project Team

  • Dr. Kaiyu Guan, Assistant Professor, Dept. of Natural Resources and Environmental Sciences
  • Dr. Elizabeth Ainsworth, Adjunct Professor, Dept. of Plant Biology
  • Dr. Sheng Wang, Postdoctoral Research Associate, Institute for Sustainability, Energy, and Environment

Abstract

Photosynthesis is an essential process that determines crop productivity and yield. The maximum carboxylation rate of Rubisco (Vcmax) is a key parameter describing photosynthetic potential and is widely used in the modeling field, ecosystem and global net primary productivity. Vcmax is highly sensitive to environmental factors (e.g., leaf temperature and nitrogen). Accurate information on Vcmax is important for crop monitoring. Traditionally, Vcmax can be estimated by leaf gas-exchange measurements. However, these approaches cannot operationally offer spatially continuous nondestructive monitoring of Vcmax at the regional scale. Remote sensing (RS) is a cost-effective tool to collect spectral data to detect crop physiological status. Studies have demonstrated the potential of various RS techniques to retrieve Vcmax at the leaf (0.01-0.1 m) and satellite (>1 km) scale. The accuracy of these approaches at the canopy level (~1 m) and their performance at crop sites remains uncertain. Thus, this study proposes to evaluate the accuracies of RS approaches to retrieve Vcmax at Illinois crop sites (corn and soybean) with airborne RS and identify the sensitivity of Vcmax to the leaf temperature and nitrogen components (leaf total nitrogen, chlorophyll nitrogen and Rubisco nitrogen). To achieve this objective, an airborne system including hyperspectral optical, solar-induced fluorescence (SIF), and thermal infrared cameras will be deployed to collect high spatial, temporal and spectral resolution imagery during the growing season of 2019. We select Illinois Energy Farm, SoyFACE and T-FACE as the pilot study sites, which can provide diverse experimental plots with different levels of nitrogen and temperature treatments. Machine learning approaches and a state-of-the-art vegetation radiative transfer model will be implemented to retrieve Vcmax.

This study on mapping and interpreting the spatial and temporal variability of Vcmax from airborne RS can identify the most effective approach to retrieve Vcmax at the regional scale and open a new door to understanding the mechanisms to control Vcmax. The airborne methodology can be applied in routine applications to estimate high spatial resolution Vcmax for precision agriculture, and thus significantly improve the detection of crop stress and the prediction of crop yield. The airborne technique developed at the Illinois pilot sites has the potential to be operationally applied to crop fields in the U.S. Corn Belt and beyond. Furthermore, this approach can be scaled up to satellites to derive the large-scale crop photosynthesis and yield information and has great benefits for regional crop management. The proposal directly addresses the goals for the Illinois Center for Digital Agriculture and carries great promises to advance farmers’ ability to monitor and detect crop stresses.