Publication | Open Access
Dynamic Monitoring of the Largest Freshwater Lake in China Using a New Water Index Derived from High Spatiotemporal Resolution Sentinel-1A Data
69
Citations
41
References
2017
Year
Earth ObservationEnvironmental MonitoringEngineeringWater QuantityDynamic MonitoringLargest Freshwater LakePoyang LakeSynthetic Aperture RadarGeographyWater QualityEarth Observation DataSentinel-1a Water IndexHydrologyLand Cover MapClimatologyWater ResourcesWater MonitoringSurface-water HydrologyRemote SensingOptical Remote SensingRemote Sensing SensorSurface Water
Poyang Lake, China’s largest freshwater lake, is ecologically and economically important, yet its water surface area cannot be dynamically monitored with optical remote sensing due to cloud cover. This study proposes a novel Sentinel‑1A‑based method to overcome this limitation and monitor lake area changes. We constructed a Sentinel‑1A water index (SWI) via a linear model and stepwise multiple regression using co‑dated Sentinel‑1A and Landsat‑8 imagery, then extracted lake surface areas from 24 May 2015 to 14 Nov 2016 every 12 days using a threshold on the SWI time series. The SWI threshold classification achieved ≈99 % accuracy, revealing lake areas fluctuating between 1,726.73 km² and 3,729.19 km², with maximum 875.57 km² (35 %) and average 197.58 km² (8.2 %) changes over 12 days, especially pronounced in the mid‑western region, thus providing baseline data for high‑frequency ecological monitoring and wetland management.
Poyang Lake is the largest freshwater lake in China and is well known for its ecological function and economic importance. However, due to the influence of clouds, it is difficult to dynamically monitor the changes in water surface areas using optical remote sensing. To address this problem, we propose a novel method to monitor these changes using Sentinel-1A data. First, the Sentinel-1A water index (SWI) was built using a linear model and a stepwise multiple regression analysis method with Sentinel-1A and Landsat-8 imagery acquired on the same day. Second, water surface areas of Poyang Lake from 24 May 2015 to 14 November 2016 were extracted by the threshold method utilizing time-series SWI data with an interval of 12 days. The results showed that the SWI threshold classification method could be applied to different regions during different periods with high quantity accuracy (approximately 99%). The water surface areas ranged between 1726.73 km2 and 3729.19 km2 during the study periods, indicating an extreme variability in the short term. The maximum and average values of the changed areas were 875.57 km2 (with a change rate of 35%) and 197.58 km2 (with a change rate of 8.2%), respectively, after 12 days. The changes in the mid-western region of Poyang Lake were more dramatic. These results provide baseline data for high-frequency monitoring of the ecological environment and wetland management in Poyang Lake.
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