@article{854017a7208f40739bb7d757c055aa09,
title = "A general methodology for the quantification of crop canopy nitrogen across diverse species using airborne imaging spectroscopy",
abstract = "Accurate monitoring of crop nitrogen (N) across spatial and temporal scales is a fundamental goal for meeting precision agriculture requirements and promoting sustainable agriculture. The planning and implementation of several spaceborne imaging spectroscopy missions in recent years holds great promise for such large scale and intricate monitoring. Several N retrieval models have been developed for specific crop species, but a generalized model across diverse species is lacking. By leveraging imaging spectroscopy data collected by the Global Airborne Observatory (GAO), and leaf samples collected from commercial and research farms, we used partial least squares regression to calibrate and validate the retrieval of mass-based crop canopy N concentrations across diverse species and several major agricultural regions in the contiguous United States. The performance statistics indicated high precision and accuracy of the model results, suggesting that the development of a generalized N retrieval model is possible (R2: 0.78; RMSE: 0.49% N). Maps derived from GAO data provided quantitative crop N information at fine spatial resolution (i.e., 0.6 m), capturing both inter- and intra-species variations across agricultural locations. The algorithm was also successfully tested on simulated moderate resolution (i.e., 30 m) imagery, corresponding to data to be collected by forthcoming spaceborne imaging spectroscopy missions. Imaging spectroscopy offers an effective approach to quantify crop N concentration that could be incorporated to promote sustainable agriculture and improve global food security.",
keywords = "Agriculture, Crop nitrogen, Global airborne observatory, Hyperspectral, Imaging spectroscopy, Partial least squares regression",
author = "Jie Dai and Elahe Jamalinia and Vaughn, {Nicholas R.} and Martin, {Roberta E.} and Marcel K{\"o}nig and Hondula, {Kelly L.} and Justin Calhoun and Joseph Heckler and Asner, {Gregory P.}",
note = "Funding Information: This work was supported by funding from Carbon Mapper, Inc. We are grateful to T. Ingalls at Arizona State University, R. Reesor and R. Hyslope at Rouge River Farms, Florida, B. Hornbuckle and A. VanLoocke at Iowa State University, C. Tran and B. Miller at NEON, J. Chlapecka and B. Wilson at Fisher Delta Research Center, University of Missouri, H. Zakeri at California State University, Chico, S. Steinmaus at California Polytechnic State University, San Luis Obispo, A. Fox at California State Polytechnic University, Pomona, as well as other contributors to our field data collection. We thank three anonymous reviewers and the handling editors for comments that greatly improved the manuscript. The Global Airborne Observatory (GAO) is managed by the Center for Global Discovery and Conservation Science at Arizona State University and made possible by support from private foundations, visionary individuals, and Arizona State University . Funding Information: This work was supported by funding from Carbon Mapper, Inc. We are grateful to T. Ingalls at Arizona State University, R. Reesor and R. Hyslope at Rouge River Farms, Florida, B. Hornbuckle and A. VanLoocke at Iowa State University, C. Tran and B. Miller at NEON, J. Chlapecka and B. Wilson at Fisher Delta Research Center, University of Missouri, H. Zakeri at California State University, Chico, S. Steinmaus at California Polytechnic State University, San Luis Obispo, A. Fox at California State Polytechnic University, Pomona, as well as other contributors to our field data collection. We thank three anonymous reviewers and the handling editors for comments that greatly improved the manuscript. The Global Airborne Observatory (GAO) is managed by the Center for Global Discovery and Conservation Science at Arizona State University and made possible by support from private foundations, visionary individuals, and Arizona State University. Publisher Copyright: {\textcopyright} 2023 Elsevier Inc.",
year = "2023",
month = dec,
day = "1",
doi = "10.1016/j.rse.2023.113836",
language = "English (US)",
volume = "298",
journal = "Remote Sensing of Environment",
issn = "0034-4257",
publisher = "Elsevier Inc.",
}