A simple method for estimating macroalgae area under clouds on MODIS imagery

Abstract

The presence of clouds interferes with optical remote sensing monitoring of macroalgae blooms. To solve this problem, we propose a simple method for estimating macroalgae area under clouds (Area_cloud_GT) on MODIS imagery using the principle behind the lowpass filter. The method is based on a rectangle with clouds and eight identical adjacent rectangles surrounding it that contain macroalgae. The cloud rectangle is a central ‘pixel’ (Cloud) and the eight adjacent rectangles are ‘pixels’ GT1–GT8. The core operation is to calculate the central ‘pixel’ value, i.e., the macroalgae coverage rate in the Cloud rectangle. The macroalgae area detected by semi-simultaneous fine resolution images in the same region was taken as the ‘real’ value. A comparison of the estimation results and the ‘real’ value shown that the mean relative difference between them (MRD) was 30.09% when the time interval of the images within 10 minutes. When the time interval was over 3 hours, the MRD was more than 60%. The MRD increased significantly with increasing time interval because of the constant movement of the macroalgae and the limitations of the remote sensing image. The results indicate that this simple method is effective to a certain extent. These results can provide a reference for the quantitative analysis of green tide.

All-Weather Monitoring of Ulva prolifera in the Yellow Sea Based on Sentinel-1, Sentinel-3, and NPP Satellite Data

“…In VH polarization mode, changes in 𝜎 were not obvious for low-and middle-aggregation areas but only increased by more than three times for high-aggregation areas. The threshold segmentation classification algorithm is widely employed as a practical method for water extraction in the field of remote sensing [41]. Its principle involves segmenting radar images by identifying the backscattering coefficient thresholds of different ground objects, and the accuracy of the final results greatly depends on the selection of these thresholds.…”

“…The advantage of high threshold accuracy becomes evident when monitoring targets with clear ranges. Due to the uneven distribution of backscattering coefficients in seawater across different regions, U. prolifera was extracted using a multi-region mask and a dynamic threshold extraction The threshold segmentation classification algorithm is widely employed as a practical method for water extraction in the field of remote sensing [41]. Its principle involves segmenting radar images by identifying the backscattering coefficient thresholds of different ground objects, and the accuracy of the final results greatly depends on the selection of these thresholds.…”

Capturing Secchi disk depth by using Sentinel-2 MSI imagery in Jiaozhou Bay, China from 2017 to 2021

“…Besides, due to small amount of data obtained and poor spatial-temporal continuity of data, it is unable to realize real-time synchronous monitoring of large areas of water (Liu et al, 2020;Yin et al, 2021a). Compared with the Secchi disk method, remote sensing technology has the advantages of low measurement cost, large amount of data, wide monitoring range, high collection efficiency, and periodic dynamic coating cover (An et al, 2022). It can realize the monitoring of the areas difficult for the experimental personnel to reach, making up for the defects of the Secchi disk method (Yin et al, 2021b;Zhou et al, 2021).…”