Search Results (3)

In ocean color remote sensing, most of the radiative energy received by sensors comes from the atmosphere, requiring highly accurate atmospheric correction. Although atmospheric correction models based on ground measurements—especially the Ground-Aerial-Satellite Atmospheric Correction (GASAC) method that integrates multi-scale synchronous data—are theoretically optimal, their application in nearshore areas is limited by the lack of synchronous samples, pixel mismatches, and nonlinear atmospheric effects. This study focuses on Tangdao Bay in Qingdao, Shandong Province, China, and proposes an innovative GASAC method for nearshore waters using synchronized surface spectrometer data and UAV hyperspectral imagery collected during Sentinel-2 satellite overpasses. The method first resolves pixel mismatch issues in UAV data through Pixel-by-Pixel Matching (MPP) and applies the Empirical Line Model (ELM) for high-accuracy ground-aerial atmospheric correction. Then, based on spectrally unified UAV and satellite data, a large amount of high-quality spatial atmospheric reference data is obtained. Finally, a Transformer model optimized by an Exponential-Trigonometric Optimization (ETO) algorithm is used to fit nonlinear atmospheric effects and perform aerial-to-satellite correction, forming a stepwise GASAC framework. The results show that GASAC achieves high accuracy and good generalization in local areas, with predicted remote sensing reflectance reaching $R^2 = 0$.962 and $RMSE$ = 12.54 × 10⁻⁴ sr⁻¹, improving by 5.2% and 23.5%, respectively, over the latest deep learning baseline. In addition, the corrected data achieved R² = 0.866 in a Chl-a retrieval model based on in situ measurements, demonstrating strong application potential. This study offers a precise and generalizable atmospheric correction method for satellite imagery in nearshore water quality monitoring, with important value for coastal aquatic ecological sensing. Full article

Multispectral satellite imagery has been widely applied in water quality monitoring, but limitations in spatial–temporal resolution and acquisition delays often hinder accurate assessments in small water bodies. In this study, a DJI M600PRO UAV equipped with a Sequoia multispectral sensor was used to assess the water quality in Zhangshan Reservoir, a small inland reservoir in Chuzhou, Anhui, China. Two regression approaches—the Window Averaging Method (WAM) and the Matching Pixel-by-Pixel Method (MPP)—were used to link UAV-derived spectral indices with in situ measurements of total nitrogen (TN), total phosphorus (TP), and chemical oxygen demand (COD). Despite a limited sample size (n = 60) and single-day sampling, MPP outperformed WAM, achieving higher predictive accuracy (R² = 0.970 for TN, 0.902 for TP, and 0.695 for COD). The findings demonstrate that UAV-based MPP effectively captures fine-scale spatial heterogeneity and offers a promising solution for monitoring water quality in small and turbid reservoirs, overcoming key limitations of satellite-based remote sensing. However, the study is constrained by the temporal coverage and sample density, and future work should integrate multi-temporal UAV observations and expand the dataset to improve the model robustness and generalizability. Full article

Multispectral, as well as multi-temporal, satellite images, coupled with measurements, in situ, have been widely applied to the water quality monitoring of reservoirs. However, the spatial resolutions of the current multispectral satellite imageries are inadequate for trophic state mapping of small reservoirs which merely cover several hectares. Moreover, the temporal gap between effective satellite imaging and measurements, in situ, is usually a few days or weeks; this time lag hampers the establishment of regression models between band ratios and water quality parameters. In this research, the RGB and NIR sensors carried on an unmanned aerial vehicle (UAV) were applied to the trophic state mapping of Tain-Pu reservoir, which is one of the small reservoirs in Kinmen, Taiwan. Due to the limited sampling points and the uncertainty of water fluidity, the average method and the matching pixel-by-pixel (MPP) method were employed to search for the optimal regression models. The experimental results indicate that the MPP method can lead to better regression models than the average method, and the trophic state maps show that the averages of Chl-a, TP, and SD are 179.7 μg·L⁻¹, 108.4 μg·L⁻¹, and 1.4 m, respectively. Full article