Real-Time Multisource Fusion and Processing of Satellite–UAV Remote Sensing Images and Ground Acoustic Signals for Cotton Water and Fertilizer Management

Abstract

Cotton is one of the most important economic crops in the world, and effective water and fertilizer management is particularly important to optimize cotton yield and water and fertilizer use efficiency. Remote sensing technology and real-time processing of multisource data provide innovative solutions for accurate monitoring of cotton water and fertilizer status: through satellite remote sensing to observe cotton growth from a macroscopic point of view, combined with real-time transmission of ultrasonic signal data of cotton drought stress, water and fertilizer anomalies can be quickly identified; for the normalized vegetation index (NDVI)-similar areas, the simultaneous fusion of drone hyperspectral data and real-time monitoring data from ground meteorological stations is used to analyze changes in canopy structure through texture features. In addition, crop drought stress acoustic signals are dynamically collected based on acoustic monitoring equipment, and water-related acoustic features, such as amplitude–frequency–energy, are extracted in real time. This real-time synergistic processing of heterogeneous data from multiple sources achieves dynamic diagnosis of water and fertilizer status from the canopy to the root zone, and provides decision support for optimal water and fertilizer management at different fertility periods. By setting up different water and fertilizer gradient experiments, we comprehensively analyzed the correlation patterns between yield, water and fertilizer utilization, and multisource parameters. The results show that NDVI and contrast have significant application potential in plant status monitoring, while crop water status monitoring based on ground acoustic signals also shows a broad application prospect.