Chlorophyll-a (Chl-a) concentration estimation by remote sensing is an important means for monitoring offshore water quality and eutrophication. In-situ hyperspectral data can achieve accurate analyses of Chl-a, but it is not suitable for regional inversion. Satellite remote sensing provides the possibility for regional inversion, but the precision is lower limited to atmospheric correction result. Therefore, this work uses machine learning to fuse in-situ hyperspectral data and Sentinel-2 multispectral instrument images to combine their complementary advantages, so as to improve the precision of regional Chl-a concentration inversion. First, the in-situ spectra were resampled based on the satellite spectral response function to obtain equivalent reflectance. Second, the spectral feature bands of Chl-a were determined by correlation analysis. Then three machine learning models, support vector regression, random forest, and back propagation neural network, were used to establish mapping relationships of feature bands between equivalent reflectance and satellite image reflectance so as to correct the satellite feature bands. Finally, Chl-a inversion models were constructed based on the satellite feature bands before and after correction. The results demonstrate that the corrected inversion model shows an increase in R2 by 0.25 and a decrease in mean relative error by 7.6%. This fusion method effectively improves the accuracy of large-scale Chl-a concentration estimation.
In recent years, the runoff and sand transportation of the Yellow River is declining, the corrosion of shoreline in the Yellow River Delta becomes more and more serious, and this brings up much hidden trouble for the inshore biological environment and engineering facilities. In this article, according to RS and GIS, we analyzed the muti-temporal RS image between 1987 and 2003, abstracted the spatial-temporal information of the shoreline using unsupervised classified
method. Built the quantificational-relative model between the runoff and sand transportation and the corrosive area of shoreline based on data from Lijin Hydrology Station and statistical method, and did elementary forecast on the evaluative trend of the shoreline.
Based on Remote Sensing and Geographical Technique, we have analyzed the situation and changes of the Land Cover/Use in Yellow River Delta Wetland and evaluated the changed information by means of sight analyzing. Then the change drives are concluded and some suggestions are provided for the reasonable use and protection of Yellow River Delta Wetland.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.