| Summary: | This study focused on developing a novel semi-empirical model for maize’s light extinction coefficient (k<sub>p</sub>) by integrating multiple remotely sensed vegetation features from several different remote sensing platforms. The proposed k<sub>p</sub> model’s performance was independently evaluated using Campbell’s (1986) original and simplified k<sub>p</sub> approaches. The Limited Irrigation Research Farm (LIRF) in Greeley, Colorado, and the Irrigation Innovation Consortium (IIC) in Fort Collins, Colorado, USA, served as experimental sites for developing and evaluating the novel maize k<sub>p</sub> model. Data collection involved multiple remote sensing platforms, including Landsat-8, Sentinel-2, Planet CubeSat, a Multispectral Handheld Radiometer, and an unmanned aerial system (UAS). Ground measurements of leaf area index (LAI) and fractional vegetation canopy cover (f<sub>c</sub>) were included. The study evaluated the novel k<sub>p</sub> model through a comprehensive analysis using statistical error metrics and Sobol global sensitivity indices to assess the performance and sensitivity of the models developed for predicting maize k<sub>p</sub>. Results indicated that the novel k<sub>p</sub> model showed strong statistical regression fitting results with a coefficient of determination or R<sup>2</sup> of 0.95. Individual remote sensor analysis confirmed consistent regression calibration results among Landsat-8, Sentinel-2, Planet CubeSat, the MSR, and UAS. A comparison with Campbell’s (1986) k<sub>p</sub> models reveals a 44% improvement in accuracy. A global sensitivity analysis identified the role of the normalized difference vegetation index (NDVI) as a critical input variable to predict k<sub>p</sub> across sensors, emphasizing the model’s robustness and potential practical environmental applications. Further research should address sensor-specific variations and expand the k<sub>p</sub> model’s applicability to a diverse set of environmental and microclimate conditions.
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