Accurate Monitoring of Submerged Aquatic Vegetation in a Macrophytic Lake Using Time-Series Sentinel-2 Images

Submerged aquatic vegetation (SAV) is one of the most important biological groups in shallow lakes ecosystems, and it plays a vital role in stabilizing the structure and function of water ecosystems. The study area of this research is Baiyangdian, which is a typical macrophytic lake with complex land cover types. This research aims to solve the low accuracy problem of the remote sensing extraction of SAV, which is mainly caused by water level fluctuations, differences in life-history characteristics, and mixed-pixel phenomena. Here, we developed a phenology-pixel method to determine the spatial distribution of SAV and the start and end dates of its growing season by using all Sentinel-2 images collected over a year on the Google Earth Engine platform. The experimental results show the following: (1) The phenology-pixel algorithm can effectively identify the maximum spatial distribution and growth period of submerged aquatic vegetation in Baiyangdian Lake throughout the year. The unique normalized difference vegetation index (NDVI) peak characteristics of Potamogeton crispus from March to May were used to effectively distinguish it from the low Phragmites australis population. Textural features based on the modified normalized difference water index (MNDWI) index effectively removed the mixed-pixel phenomenon of macrophytic lakes (such as dikes and sparse reeds). (2) A complete five-day interval NDVI time-series dataset was obtained, which removes potential noise on the temporal scale and fills in noisy observations by the harmonic analysis of time series (HANTS) method. We determined the two phenological periods of typical SAV by analyzing the intrayear variation characteristics of NDVI and MNDWI. (3) Using field-survey data for accuracy verification, the overall accuracy of our method was determined to be 94.8%, and the user's accuracy and producer's accuracy were 93.3% and 87.3%, respectively. Determining the temporal and spatial distribution of different SAV populations provides important technical support for actively promoting the maintenance and reconstruction of lake and reservoir ecosystems.

Automated summary

This study used a phenology-pixel method to extract the spatial distribution and growing season of submerged aquatic vegetation in Baiyangdian Lake. The experimental results showed that the method could effectively identify different vegetation types and obtain a complete time-series dataset. The accuracy of the method was validated using field-survey data.