Extraction of Mangrove Community of Kandelia obovata in China Based on Google Earth Engine and Dense Sentinel-1/2 Time Series Data

Although significant progress has been made in the remote sensing extraction of mangroves, research at the species level remains relatively limited. Kandelia obovata is a dominant mangrove species and is frequently used in ecological restoration projects in China. However, owing to the fragmented distribution of K. obovata within mixed mangrove communities and the significant spectral and textural similarities among mangrove species, accurately extracting large-scale K. obovata-based remote sensing data remains a challenging task. In this study, we conducted extensive field surveys and developed a comprehensive sampling database covering K. obovata and other mangrove species across mangrove-distributing areas in China. We identified the optimal bands for extracting K. obovata by utilizing time-series remote sensing data from Sentinel-1 and Sentinel-2, along with the Google Earth Engine (GEE), and proposed a method for extracting K. obovata communities. The main conclusions are as follows: (1) The spectral-temporal variability characteristics of the blue and red-edge bands play a crucial role in the identification of K. obovata communities. The 90th percentile metric of the blue wavelength band ranks first in importance, while the 75th percentile metric of the blue wavelength band ranks second; (2) This method of remote sensing extraction using spectral-temporal variability metrics with time-series optical and radar remote sensing data offers significant advantages in identifying the K. obovata species, achieving a producer's accuracy of up to 94.6%; (3) In 2018, the total area of pure K. obovata communities in China was 4825.97 ha; (4) In the southern provinces of China, Guangdong Province has the largest K. obovata community area, while Macau has the smallest. This research contributes to the understanding of mangrove ecosystems and provides a methodological framework for monitoring K. obovata and other coastal vegetation using advanced remote sensing technologies.