Weather-Constrained Acquisition of UAV Remote Sensing Data for Environmental Monitoring and Precision Agriculture in Southwestern Ohio

Unmanned aerial vehicle (UAV)-based remote sensing supports environmental monitoring and precision agriculture, but weather affects both flight safety and the acquisition of consistent, high-quality observations for multi-temporal analysis. This study presents a reproducible framework for quantifying weather-related constraints using five years (2021–2025) of hourly meteorological observations from Greene and Clark Counties, Ohio, a representative agricultural region dominated by corn and soybean production.

Hourly NOAA observations were assessed against mission-specific thresholds for temperature, wind speed, precipitation occurrence, and post-application rain-free intervals. The analysis examined multispectral mapping platforms (DJI Mavic 3 Multispectral and Phantom 4 Multispectral), whose operational requirements affect the reliability of environmental imagery. Spray UAVs were included to illustrate how weather-constrained remote sensing informs downstream precision agriculture workflows. Deployability was summarized by daytime acquisition window, meteorological season, and the primary growing period (May–July). Suitable conditions varied considerably by season. Mapping missions were most feasible in summer and least feasible in winter, while wind and precipitation constraints further limited spray operations. During May–July, mapping missions were feasible for approximately 77% of daytime hours. Baseline spray operations were feasible for approximately 70%, decreasing to 63% and 55% with 2-hour and 6-hour rain-free requirements, respectively.

The framework offers a transferable approach to planning environmentally consistent UAV image acquisition and improving the temporal reliability of observations used in agricultural and environmental monitoring. It can support mission scheduling, long-term environmental data collection, and integration with satellite-based remote sensing programs.