Comparison of Sun-Induced Chlorophyll Fluorescence and Hyperspectral Reflectance for Monitoring Southern Corn Rust

Southern corn rust (SCR) is one of the critical diseases affecting corn yield and quality. Early monitoring of SCR infection in corn holds significant importance for disease prevention and control. This study investigated the accuracy and adaptability of early SCR monitoring by collecting canopy spectral data of corn plants with SCR disease indices (DI<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SCR</inf>) using a field spectroradiometer. Based on the Fraunhofer dark line principle, we employed the 3FLD (three-band Fraunhofer Line Discrimination) method to extract solar-induced chlorophyll fluorescence (SIF) under different DI<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SCR</inf> stress levels. Comparative analysis was conducted on the sensitivity of SIF and reflectance data for DI<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SCR</inf> monitoring. Remote sensing monitoring models for DI<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SCR</inf> were developed using both reflectance data and SIF data, with model accuracy verified through independent samples. The results indicate that: (1) Under mild SCR stress <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\mathbf{DI}_{\text{SCR}}\leqslant 30\%)$</tex>, SIF exhibits higher sensitivity to DI<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SCR</inf> variations compared to reflectance data, and the SIF-based DI<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SCR</inf> monitoring model demonstrates statistical significance at a highly significant level; (2) Under moderate SCR stress (31%< <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{DI}_{\text{SCR}}\leqslant 50\%)$</tex>, both SIF and reflectance data enable remote sensing monitoring of DI<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SCR</inf>, but the model accuracy of SIF-based DI<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SCR</inf> monitoring proves superior to reflectance-based approaches; (3)Under severe SCR stress (DI<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SCR</inf> >50<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup>), both reflectance data and SIF data establish DI<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SCR</inf> monitoring models that reach highly significant levels, demonstrating comparable effectiveness in DI<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SCR</inf> monitoring through both data modalities. The research findings provide new methodologies for remote sensing monitoring of SCR, offering reference basis for utilizing satellite-based fluorescence data products in early monitoring of SCR.