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Monitoring surface water area variations of reservoirs using daily MODIS images by exploring sub-pixel information
ISPRS Journal of Photogrammetry and Remote Sensing ( IF 12.7 ) Pub Date : 2020-08-20 , DOI: 10.1016/j.isprsjprs.2020.08.008
Feng Ling , Xinyan Li , Giles M. Foody , Doreen Boyd , Yong Ge , Xiaodong Li , Yun Du

Information on the temporal variation of surface water area of reservoirs is fundamental for water resource management and is often monitored by satellite remote sensing. Moderate Resolution Imaging Spectroradiometer (MODIS) imagery is an attractive data source for the routine monitoring of reservoirs, however, the accuracy is often limited due to the negative impacts associated with its coarse spatial resolution and the effects of cloud contamination. Methods have been proposed to solve these two problems independently but it remains challenging to address both problems simultaneously. To overcome this, this paper proposes a new approach that aims to monitor reservoir surface water area variations accurately and timely from daily MODIS images by exploring sub-pixel scale information. The proposed approach used estimates of reservoir water areas obtained from cloud-free and relatively fine spatial resolution Landsat images and water fraction images by spectral unmixing of coarse MODIS imagery as reference data. For each MODIS pixel, these reference reservoir water areas and their corresponding pixel water fractions were used to construct a linear regression equation, which in turn may be applied to predict the time series of reservoir water areas from daily MODIS water fraction images. The proposed approach was assessed with 21 reservoirs, where the correlation coefficients between reservoir water areas predicted by the common pixel-based analysis method and altimetry water levels were all less than 0.5. With the proposed sub-pixel analysis method, the resultant correlation coefficients were much improved, with eleven values larger than 0.5 including six values larger than 0.8 and the highest value of 0.94. The results show that the proposed sub-pixel analysis method is superior to the pixel based analysis method. The proposed method makes it possible to directly estimate the whole reservoir water area from, potentially, an individual cloud-free MODIS pixel, and is a promising way to improve the accuracy in the usability of MODIS images for the monitoring of reservoir surface water area variations.



中文翻译:

通过探索亚像素信息,使用每日MODIS图像监测水库地表水面积变化

有关水库地表水面积随时间变化的信息是水资源管理的基础,通常由卫星遥感监测。中分辨率成像光谱仪(MODIS)图像是用于常规监测储层的有吸引力的数据源,但是,由于其粗略的空间分辨率和云污染的影响,其准确性常常受到限制。已经提出了独立解决这两个问题的方法,但是同时解决这两个问题仍然具有挑战性。为了克服这个问题,本文提出了一种新方法,旨在通过探索亚像素尺度信息,从MODIS每日图像中准确,及时地监测储层地表水面积变化。所提出的方法使用了从无云和相对精细的空间分辨率Landsat图像和水分量图像中获得的水库水域面积估计值,这些数据是通过将粗糙的MODIS图像进行光谱混合而获得的,作为参考数据。对于每个MODIS像素,这些参考水库水域面积及其对应的像素水分数被用于构建线性回归方程,该方程反过来又可以用于根据每日MODIS水分数图像预测水库水域的时间序列。该方法在21个水库中进行了评估,其中基于通用像素分析方法预测的水库水域与高程水位之间的相关系数均小于0.5。通过提出的亚像素分析方法,相关系数得到了很大的改善,11个大于0.5的值包括6个大于0.8的值和0.94的最高值。结果表明,所提出的子像素分析方法优于基于像素的分析方法。所提出的方法使得有可能直接从单个的无云MODIS像素直接估算整个水库水面积,并且是一种有前途的方法,可以提高MODIS图像用于监测水库地表水面积变化的准确性。 。

更新日期:2020-08-20
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