Design and Implementation of a LoRa-Based Multi-Sensor Meteorological Observation System for UAVs

The atmospheric boundary layer, as the critical region for material and energy exchange between the Earth's surface and the free atmosphere, plays a vital role in enhancing weather forecast accuracy, analyzing pollutant dispersion mechanisms, and deepening climate change research through the detection of its fine structure. In recent years, the rapid advancement of unmanned aerial vehicle (UAV) platform technology and low-power wide-area network (LPWAN) communication technology has opened new technical avenues for meteorological monitoring through vertical profiling observations using UAV-mounted sensors. Addressing the scientific challenge of scarce high-resolution vertical observation data in the atmospheric boundary layer, this study designed and implemented a multi-sensor fusion-based UAV meteorological detection system. Centered around the STM32F103C8T6 microcontroller as the core processing unit, the system adopts a modular design philosophy. It integrates high-precision temperature and humidity sensors (SHT30), barometric pressure sensors (BMP280), and laser-based particulate matter sensors (SDS011) to form a payload unit capable of synchronous multi-parameter acquisition. An innovative application layer protocol and energy-saving scheduling strategy, tailored to the characteristics of LoRa narrowband communication, were designed to address the reliability challenges of large-data transmission in low-power wide-area networks. Vertical profile and time-series observation experiments conducted in the Nanjing region clearly revealed boundary layer characteristics such as inversion layer structure and vertical pollutant gradients. Test results demonstrate the system's low-cost and high-flexibility advantages, providing an effective solution for teaching, research, and refined meteorological detection.