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Lake level variation is an important hydrological indicator of water balance, biodiversity and climate change in drainage basins. This paper illustrates the use of moderate-resolution imaging spectroadiometer (MODIS) data to characterize complex water level variation in Poyang Lake, the largest freshwater lake in China. MODIS data were used in conjunction with in situ topographic data, otherwise known as the land-water contact method, to investigate the potential of this hybrid water level spatiotemporal variability measurement technique. An error analysis was conducted to assess the derived water level relative to gauge data. Validation results demonstrated that the land-water contact method can satisfactorily capture spatial patterns and seasonal variations in water level fluctuations. The correlation coefficient ranged from 0.684 to 0.835, the root-mean-square-error from 0.79 m-1.09 m, and the mean absolute bias error from 0.65 m to 0.86 m for five main gauge stations surrounding the lake. Additionally, seasonal and interannual variations in the lake's water level were revealed in the MODIS-based results. These results indicate that the land-water contact method has the potential to be applied in mapping water level changes in Poyang Lake. This study not only provides a foundation for basic hydrological and ecological studies, but is also valuable for the conservation and management of water resources over gauge-sparse regions in Poyang Lake. [ABSTRACT FROM AUTHOR]

Lakes have been identified as an important indicator of climate change and a finer lake area can better reflect the changes. In this paper, we propose an effective unsupervised deep gradient network (UDGN) to generate a higher resolution lake area from remote sensing images. By exploiting the power of deep learning, UDGN models the internal recurrence of information inside the single image and its corresponding gradient map to generate images with higher spatial resolution. The gradient map is derived from the input image to provide important geographical information. Since the training samples are only extracted from the input image, UDGN can adapt to different settings per image. Based on the superior adaptability of the UDGN model, two strategies are proposed for super-resolution (SR) mapping of lakes from multispectral remote sensing images. Finally, Landsat 8 and MODIS (moderate-resolution imaging spectroradiometer) images from two study areas on the Tibetan Plateau in China were used to evaluate the performance of UDGN. Compared with four unsupervised SR methods, UDGN obtained the best SR results as well as lake extraction results in terms of both quantitative and visual aspects. The experiments prove that our approach provides a promising way to break through the limitations of median-low resolution remote sensing images in lake change monitoring, and ultimately support finer lake applications. [ABSTRACT FROM AUTHOR]