Submitted to: Proceedings of SPIE
Publication Type: Proceedings
Publication Acceptance Date: 10/5/2009
Publication Date: 10/20/2009
Citation: Corp, L.A., Middleton, E.M., Campbell, P.K., Huemmrich, K.F., Cheng, Y.B., Daughtry, C.S. 2009. Remote sensing techniques to monitor nitrogen-driven carbon dynamics in field corn. Proceedings of Society Photo-optical and Instrumental Engineering Proceddings. 2009 CDROM. Interpretive Summary:
Technical Abstract: Vegetation change is the primary indicator of the present and future ecological status of the globe. Nitrogen (N) is involved in photochemical processes and is one of the primary resources regulating plant growth. As a result, biological carbon (C) sequestration is driven by N availability. Large scale monitoring of photosynthetic processes are currently possible only with remote sensing systems that rely heavily on passive reflectance (R) information. Unlike reflectance, fluorescence (F) emitted from chlorophyll is directly related to photochemical reactions and has been extensively used for the elucidation of the photosynthetic pathways. Recent advances in passive fluorescence instrumentation have made the remote acquisition of solar-induced fluorescence possible. The goal of this effort is to evaluate existing reflectance and emerging fluorescence methodologies for determining vegetation parameters related to photosynthetic function and carbon sequestration dynamics in plants. Field corn N treatment levels of 280, 140, 70, and 0 kg N / ha were sampled from an intensive test site for a multi-disciplinary project entitled Optimizing Production Inputs for Economic and Environmental Enhancement (OPE). Aircraft, near-ground, and leaf-level measurements were used to compare and contrast treatment effects within this experiment site assessed with both reflectance and fluorescence approaches. A number of spectral indices including but not limited to the R derivative index D730/D705, the normalized difference of R750 vs. R705, and simple ratio R800/R750 differentiated three of the four N fertilization rates and yielded high correlations to the carbon parameters C:N, light use efficiency, and grain yield. These results advocate the application of hyperspectral sensors for remotely monitoring carbon cycle dynamics in terrestrial ecosystems.