A practical approach to retrieving the finer areas of algal bloom in inland lakes from coarse spatial resolution satellite data. Issue 14 (18th July 2017)

• Record Type: Journal Article
• Authors: Lyu, Heng; Wang, Yannan; Jin, Qi; Li, Xiaojun; Cao, Kai; Wang, Qiao; Li, Yunmei
• Is Part Of: International journal of remote sensing. Volume 38:Issue 14(2017)
• Page Start: 4069
• Page End: 4085
• Publication Date: 2017-07-18
• Subjects: Remote sensing -- Periodicals; Télédétection -- Périodiques; 621.3678
• DOI: 10.1080/01431161.2017.1312621
• Languages: English
• ISSNs: 0143-1161

Abstract

ABSTRACT: Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) images have been used to monitor algal blooms in the open ocean, coastal waters, and inland lakes. However, it is difficult to obtain an accurate definition of algal bloom areas in inland lakes due to the spatial resolution of the generated images. This study developed a practical approach that uses a linear spectral mixing model with a moving window (LSMM), to obtain a finer algal bloom area. The approach analyses the differences in areas of algal bloom retrieved from MODIS images with 250 and 500 m spatial resolutions from 2012 to 2015 and synchronous VIIRS images with 750 m spatial resolution. Forty-two data sets with 126 satellite images were selected. The results showed that the average relative area difference (RAD) of algal bloom in the MODIS 500 m image was approximately 21.31% compared with the MODIS 250 m image and approximately 33.77% compared with the VIIRS image. A 5 × 5 window size was selected for the MODIS 500 m and VIIRS images. The results demonstrated that the approach can be successfully applied to MODIS 500 m and VIIRS images because the RAD significantly improved. The average RAD decreased to 9.39% in the MODIS 500 m image and to 12.84% in the VIIRS image. The relationship between the landscape of the algal bloom patch and the RAD showed that the performance of the LSMM method improved as the patch density (PD) increased from 0 to 2. When the perimeter-area ratio (PARA) is greater than 2 and the mean patch size (MPS) is less than approximately 5 km 2, the LSMM method significantly improved the RAD. An independent validation demonstrated that the LSMM method developed for MODIS and VIIRS images can be successfully applied to other coarser-resolution spatial imageries such as Geostationary Ocean Color Imager (GOCI) images. The LSMM method is more effective than the other methods for determining the fragmented landscape of algal blooms.