| Summary: | In applying quantitative remote sensing in water quality monitoring for small inland rivers, the time-frequency of monitoring dramatically impacts the accuracy of time-spatial changes estimates of the water quality parameters. Due to the limitation of satellite sensor design and the influence of atmospheric conditions, the number of spatiotemporal dynamic monitoring images of water quality parameters is insufficient. Meanwhile, MODIS and other high temporal resolution images’ spatial resolution is too low to effectively extract small inland river boundaries. To solve the problem, many researchers used Spatio-temporal fusion models in multisource data remote sensing monitoring of ground features. The wildly used Spatio-temporal fusion models, such as FSDAF (flexible spatial-temporal data fusion), have poor performance in heterogeneous changes of ground objects. We proposed a spatiotemporal fusion algorithm SR-FSDAF (Super-resolution based flexible spatiotemporal data fusion) to solve the problem. Based on the FSDAF, it added ESPCN to reconstruct the spatial change prediction image, so as to obtain better prediction results for heterogeneous changes. Both qualitative and quantitative evaluation results showed that our fusion algorithm obtained better results. We compared the band sensitivity of the images before and after fusion to find out that the sensitive band combination of NH<sub>3</sub>-N has not changed, which proved that the fusion method can be used to improve the time-frequency of NH<sub>3</sub>-N inversion. After the fusion, we compared the accuracy of linear regression and random forest inversion models and selected the random forest model with better accuracy to predict the NH<sub>3</sub>-N concentration. The inversion accuracy of NH<sub>3</sub>-N was as follows: the R<sup>2</sup> was 0.75, the MAPE was 23.7% and the RMSE was 0.15. The overall concentration change trend of NH<sub>3</sub>-N in the study area was high-water period < water-stable period < low water period. NH<sub>3</sub>-N pollution was serious in some reaches.
|
|---|