LiDAR-based indices and machine learning efforts to model biophysical estimations of corn (Zea mays L.)

K. Colton Flynn; Gurjinder Baath; Bala Ram Sapkota; Douglas R. Smith

doi:https://doi.org/10.5194/egusphere-egu25-2602

[Back] [Session ESSI4.11]

EGU25-2602, updated on 14 Mar 2025

https://doi.org/10.5194/egusphere-egu25-2602

EGU General Assembly 2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

LiDAR-based indices and machine learning efforts to model biophysical estimations of corn (Zea mays L.)

K. Colton Flynn

¹, Gurjinder Baath², Bala Ram Sapkota

², and Douglas R. Smith¹

K. Colton Flynn et al.

¹United States Department of Agriculture, Agricultural Research Service, United States of America (colton.flynn@usda.gov)
²AgriLife, Texas A&M University, Temple, United States of America (gurjinder.baath@ag.tamu.edu)

How to cite: Flynn, K. C., Baath, G., Sapkota, B. R., and Smith, D. R.: LiDAR-based indices and machine learning efforts to model biophysical estimations of corn (Zea mays L.), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2602, https://doi.org/10.5194/egusphere-egu25-2602, 2025.

This abstract has been withdrawn on 25 Jul 2025.