Lakes can tip the scales from healthy to potential environmental hazard quickly when they become eutrophic. In this state, an abundance of nutrients accelerates algae growth, which then crowd the water's surface and block light from reaching organisms below. Without light, they can't make oxygen and life in the water begins to die off. Luckily, researchers can monitor inland lakes for eutrophication with remote sensing technologies; however, those technologies could be adjusted to make more accurate assessments, according to researchers based in China.
The team published their evaluation of the technologies, as well as recommended paths for improvement, on Sept 3 in the Journal of Remote Sensing.
Current technologies comprise remote sensing instruments that capture features of the planet's surface, called spatial resolution, and can capture the same features multiple times, referred to as temporal resolution. The more detailed the imaging is, and the more frequently it is repeated, the higher the resolutions. But there are compromises between the resolutions - the higher the spatial resolution, the lower the temporal resolution tends to be, and vice versa.
"The tradeoffs between the spatial and temporal resolutions for the remote sensing instruments limit their capacity to monitor the eutrophic states of inland lakes," said co-corresponding author Linwei Yu, associate professor at China University of Geosciences. "Spatiotemporal fusion (STF) provides a cost-effective way to generate remote sensing data with both high spatial and temporal resolutions by blending multi-sensor information, and it has been widely used for the fine-scale monitoring of Earth surface dynamics."
However, the researchers said, the issue is that the processing and modeling errors of the fused monitoring may influence the quality of the images - particularly when capturing reflective surfaces, like lakes, that have a relatively low signal-to-noise ratio (SNR). This ratio refers to the difference in relevant information and other details.
"This study preliminarily presents a comprehensive evaluation to understand the potential and limitations of applying STF techniques for monitoring chlorophyll-a (Chla) concentration in inland eutrophic lakes," said co-corresponding author Huanfeng Shen, professor at Wuhan University, explaining that Chla is an indicator of the state of eutrophication. "The findings will help to provide guidelines to design STF framework for monitoring aquatic environment of inland waters with remote sensing data."
The researchers found that STF methods effectively capture the highly dynamic status of eutrophic inland lakes, but that those assessing the imaging should pay "special attention" to sources of error.
"Among the influential factors, the atmospheric correction and geometric errors have large impacts on the fusion results," Yu said. "We recommend a working pipeline so that the fusion images can be integrated with real observations to produce temporally dense Chla datasets."
The working pipeline, detailed in their study, provides a comprehensive understanding of the potential and uncertainties involved in using STF methods for aquatic applications, according to the researchers.
"With this understanding, it is feasible to estimate temporally dense Chla concentration in inland eutrophic lakes by blending multi-sensor observations," Shen said. "In future studies, the goal is to integrate data from sensors of different resolutions and generate Chla datasets with both high spatial and temporal resolutions of the lakes over a large scale."
Other co-authors include Lei Zhang and Rui Peng, School of Geography and Information Engineering, China University of Geosciences; Chao Zheng, School of Resources and Environmental Science, Wuhan University; and Hongtao Duan, Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, and Northwest University's College of Urban and Environmental Sciences.
The National Natural Science Foundation of China supported this work.
