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ARS Home » Plains Area » Fort Collins, Colorado » Center for Agricultural Resources Research » Water Management and Systems Research » Research » Publications at this Location » Publication #412266
Research Project: Improving Crop Performance and Precision Irrigation Management in Semi-Arid Regions through Data-Driven Research, AI, and Integrated Models

Location: Water Management and Systems Research

Title: A novel remote sensing-based modeling approach for maize light extinction coefficient determination

Author
item COSTA-FILHO, EDSON - Colorado State University
item CHAVEZ, JOSE - Colorado State University
item Zhang, Huihui

Submitted to: Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/4/2024
Publication Date: 3/13/2024
Citation: Costa-Filho, E., Chavez, J.L., Zhang, H. 2024. A novel remote sensing-based modeling approach for maize light extinction coefficient determination. Remote Sensing. 16(6). Article 1012. https://doi.org/10.3390/rs16061012.
DOI: https://doi.org/10.3390/rs16061012

Interpretive Summary: This study created a new way to understand how maize plants interact with light by using data from different remote sensing platforms. A model was developed and tested on farms in Colorado. The model worked well with data from various sensors from ground to satellite platforms, showing a 44% improvement over older models. The model's key metric is the Normalized Difference Vegetation Index (NDVI), making it useful for predicting maize behavior in different environments. Further research is needed to explore specific sensor differences and broaden the model's use in various conditions.

Technical Abstract: This study focused on developing a novel semi-empirical model for maize's light extinction coefficient (kp) by integrating multiple vegetation remotely sensed features from several different remote sensing platforms. The proposed kp model's performance was independently evaluated using Campbell’s (1986) original and simplified kp 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 kp model. Data collection involved multiple remote sensing platforms, including Landsat-8, Sentinel-2, Planet CubeSat, Multispectral Handheld Radiometer, and Unmanned Aerial Systems (UAS). Ground measurements of the Leaf Area Index (LAI) and Fractional Canopy Cover (fc) were included. The study evaluated the novel kp 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 kp. Results indicated that the novel kp model showed strong statistical regression fitting results with a coefficient of determination or R2 of 0.95. Individual remote sensing sensor analysis confirmed consistent regression calibration results among Landsat-8, Sentinel-2, Planet CubeSat, MSR, and UAS. A comparison with Campbell’s (1986) kp models reveals a 44% improvement in accuracy in estimating kp values for maize. A global sensitivity analysis identified the Normalized Difference Vegetation Index (NDVI)'s critical role as an input variable to predict kp across sensors, emphasizing the model's robustness and potential practical environmental applications. Further research should address sensor-specific variations and expand the kp model's applicability to a diverse set of environmental and microclimate conditions.