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Remote Sensing Dissolved Organic Matter in Freshwater Aquaculture Ponds by the Integration of UAV and Satellite Multispectral Images

文献类型： 外文期刊

作者： Chen, Guangxin 1 ; Wang, Yancang 2 ; Gu, Xiaohe 3 ; Chen, Tianen 1 ;

作者机构： 1.Nongxin Nanjing Smart Agr Res Inst, Nanjing 211800, Jiangsu, Peoples R China

2.North China Inst Aerosp Engn, Coll Remote Sensing Informat Engn, Langfang 065000, Hebei, Peoples R China

3.Beijing Acad Agr & Forestry Sci, Res Ctr Informat Technol, Beijing 100089, Peoples R China

关键词： Aquaculture; Autonomous aerial vehicles; Water quality; Remote sensing; Monitoring; Satellites; Satellite images; Accuracy; Estimation; Reflectivity; Dissolved organic matter; uncrewed aerial vehicle (UAV); multi-source remote sensing; freshwater aquaculture; machine learning

期刊名称：IEEE ACCESS （ 影响因子：3.6； 五年影响因子：3.9 ）

ISSN： 2169-3536

年卷期： 2025 年 13 卷

页码：

收录情况： SCI

摘要： Dissolved organic matter (DOM) is a pivotal indicator for assessing aquatic health and ecological functions. Monitoring DOM in aquaculture ponds using satellite requires validation through field measured samples. However, due to the inherent spatial variability of DOM in aquaculture ponds, individual samples are insufficient to represent the entire pond. Consequently, directly applying field measurements to satellite remote sensing can compromise the accuracy of estimation models. A spatial mapping approach was proposed in the study, which integrated UAV multispectral data with Sentinel-2 images to address scale mismatches between satellite images and ground-based measurements. Then a self-optimizing model was used to estimate and map DOM concentration at county scale. Firstly, high-resolution spatial distribution of DOM in some aquaculture ponds were obtained through field samples and UAV multispectral images. Secondly, a spatial mapping relationship was established between the UAV-derived DOM distribution and the corresponding satellite image pixels, thereby providing high-quality samples for large-scale monitoring of DOM in aquaculture. Results showed that: 1) Among the four models constructed using UAV data, the simulated annealing-optimized random forest (SA-RF) achieved the highest performance, with the R-2 of 0.84, RMSE of 2.66mg/L, and MAE of 2.21mg/L. 2) The spatial mapping method improved the accuracy of DOM concentration estimation based on satellite images. Specifically, the accuracy of SA-RF model increased by 10% compared with the model constructed directly using satellites and ground measurements, achieving an R-2 of 0.78. This study demonstrates that the spatial mapping method provides a novel method for UAV-satellite collaborative inversion of DOM concentration in aquaculture ponds.

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