Abstract
Dynamics of surface water is of great significance to understand the impacts of global changes and human activities on water resources. Remote sensing provides many advantages in monitoring surface water; however, in large scale, the efficiency of traditional remote sensing methods is extremely low because these methods consume a high amount of manpower, storage, and computing resources. In this paper, we propose a new method for quickly determining what the annual maximal and minimal surface water extent is. The maximal and minimal water extent in the year of 1990, 2000, 2010 and 2017 in the Middle Yangtze River Basin in China were calculated on the Google Earth Engine platform. This approach takes full advantage of the data and computing advantages of the Google Earth Engine’s cloud platform, processed 2343 scenes of Landsat images. Firstly, based on the estimated value of cloud cover for each pixel, the high cloud covered pixels were removed to eliminate the cloud interference and improve the calculation efficiency. Secondly, the annual greenest and wettest images were mosaiced based on vegetation index and surface water index, then the minimum and maximum surface water extents were obtained by the Random Forest Classification. Results showed that (1) the yearly minimal surface water extents were 14,751.23 km2, 14,403.48 km2, 13,601.48 km2, and 15,697.42 km2, in the year of 1990, 2000, 2010, and 2017, respectively. (2) The yearly maximal surface water extents were 18,174.76 km2, 20,671.83 km2, 19,097.73 km2, and 18,235.95 km2, in the year of 1990, 2000, 2010, and 2017, respectively. (3) The accuracies of surface water classification ranged from 86% to 93%. Additionally, the causes of these changes were analyzed. The accuracy evaluation and comparison with other research results show that this method is reliable, novel, and fast in terms of calculating the maximal and minimal surface water extent. In addition, the proposed method can easily be implemented in other regions worldwide.
Highlights
Water resources are critical in promoting sustainable development, as they support human communities, maintain the functions of ecosystems, and ensure economic growth [1]
A method of extracting yearly minimal and maximal surface water based on the Google Earth Engine (GEE) was proposed, which utilized the algorithm of estimating cloud cover for pixels, images mosaicing based on feature index and random forest classification
The areal extent of the surface water in the Middle Yangtze River Basin (MYRB) showed that yearly minimal surface water had changed to 14,403.48 km2 in 2000 from 14,751.23 km2 in 1990, and changed to 13,601.48 km2 in 2010 and changed to 15,697.42 km2 in 2017
Summary
Water resources are critical in promoting sustainable development, as they support human communities, maintain the functions of ecosystems, and ensure economic growth [1]. Surface water is a key indicator of water resources. As a land cover type, it plays an important role in climate regulation, biogeochemical cycling, and surface energy balance, among many others [2]. Many countries, especially developing countries, have experienced rapid urbanization [3]. Changes in surface water caused by human activities strongly affect surface temperature, soil moisture, biological diversity, ecosystem functioning, and even human wellbeing [4,5,6]. Monitoring the dynamics of surface water is of great importance for natural environmental health and sustainable economic development [7]
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