Remote sensing dissolved organic matter in freshwater aquaculture ponds by the integration of UAV and satellite multispectral images

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 R2 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 UAVsatellite collaborative inversion of DOM concentration in aquaculture ponds.