Remote sensing techniques to monitor nitrogen-driven carbon dynamics in field corn
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Date
2009-08-20
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Citation of Original Publication
Lawrence A. Corp, Elizabeth M. Middleton, Petya K. Entcheva Campbell, K. Fred Huemmrich, Yen-Ben Cheng, and Craig S. T. Daughtry "Remote sensing techniques to monitor nitrogen-driven carbon dynamics in field corn", Proc. SPIE 7454, Remote Sensing and Modeling of Ecosystems for Sustainability VI, 745403 (20 August 2009); https://doi.org/10.1117/12.825508
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This work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.
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Abstract
Patterns of change in vegetation growth and condition are one of the primary indicators 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 R, 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, 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 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 three important 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.