Method for Obtaining Water-Leaving Reflectance from Unmanned Aerial Vehicle Hyperspectral Remote Sensing Based on Air–Ground Collaborative Calibration for Water Quality Monitoring

Highlights: What are the main findings? This study proposed an air–ground collaboration + neural network method, which achieved superior water-leaving reflectance inversion (450–900 nm band), with inversion curves closely matching ground measurements obtained using an analytical spectral device (ASD). In addition, the proposed method reduced the average spectral angle matching (SAM) from 0.5433 using existing methods to 0.1070, improving quantitative accuracy of approximately 80%. High-precision water quality inversion models (R² > 0.85 for turbidity, color, TN, and TP) were established and validated in both the demonstration areas (Three Gorges and Poyang Lake), showing strong applicability across diverse water bodies. What is the implication of the main finding? It addresses key limitations of traditional water-leaving reflectance methods, such as satellite dependence, limited ground applicability, and low-accuracy UAV approaches. It introduces a reliable UAV hyperspectral processing solution that enables accurate three-dimensional water monitoring. The constructed water quality parameter inversion models demonstrated high accuracy and verified the feasibility of air–ground integrated UAV monitoring, thereby addressing the research gap in non-linear conversion from hyperspectral to water-leaving reflectance and providing practical support for water quality assessment. Unmanned aerial vehicle (UAV) hyperspectral remote sensing imaging systems have demonstrated significant potential for water quality monitoring. However, accurately obtaining water-leaving reflectance from UAV imagery remains challenging due to complex atmospheric radiation transmission above water bodies. This study proposes a method for water-leaving reflectance inversion based on air–ground collaborative correction. A fully connected neural network model was developed using TensorFlow Keras to establish a non-linear mapping between UAV hyperspectral reflectance and the measured near-water and water-leaving reflectance from ground-based spectral. This approach addresses the limitations of traditional linear correction methods by enabling spatiotemporal synchronization correction of UAV remote sensing images with ground observations, thereby minimizing atmospheric interference and sensor differences on signal transmission. The retrieved water-leaving reflectance closely matched measured data within the 450–900 nm band, with the average spectral angle mapping reduced from 0.5433 to 0.1070 compared to existing techniques. Moreover, the water quality parameter inversion models for turbidity, color, total nitrogen, and total phosphorus achieved high determination coefficients (R² = 0.94, 0.93, 0.88, and 0.85, respectively). The spatial distribution maps of water quality parameters were consistent with in situ measurements. Overall, this UAV hyperspectral remote sensing method, enhanced by air–ground collaborative correction, offers a reliable approach for UAV hyperspectral water quality remote sensing and promotes the advancement of stereoscopic water environment monitoring.