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Automated estimation of daily surface water fraction from MODIS and Landsat images using Gaussian process regression

机译：使用高斯过程回归自动估计MODIS和Landsat图像的日常表面水分分数

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摘要

Satellite remote sensing has been widely used to monitor surface water, but its application in observing rapid inundation changes remains challenging. Observations relying on only one sensor could hardly achieve both high temporal and high spatial resolutions. High spatial resolution images are not frequent enough to capture the fast-changing inundation, while high temporal resolution images do not provide sufficient detail on spatial variations. To address this resolution compromise for rapid response, here we propose a new automated method to estimate daily sub-pixel water cover, the surface water fraction, from Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat images. First, water indices are derived from MODIS and Landsat images, and the Landsat derived water indices are used to generate a binary water map. We then aggregate the Landsat water map to the coarser MODIS resolution, making a surface water fraction map. Second, the relationship between the MODIS derived water indices and the Landsat derived surface water fraction is fitted using a Gaussian Process Regression (GPR) model. Lastly, the fitted GPR model is applied to new MODIS imagery to estimate surface water fraction. The experiment results showed that the proposed method could provide water fraction with Root Mean Squared Errors of less than 7%. By mapping fractional water cover automatically and regularly, the proposed method could facilitate daily emergency responses and long-term analyses.
机译：卫星遥感已被广泛用于监测地表水，但其在观察到快速淹没变化方面的应用仍然具有挑战性。依赖于一个传感器的观察几乎无法实现高时和高空间分辨率。高空间分辨率图像不足以捕获快速变化的淹没，而高时的分辨率图像在空间变化上不提供足够的细节。为了解决这种决议，为了快速响应，我们提出了一种新的自动化方法来估计每日亚像素水覆盖，表面水分馏分，从中等分辨率成像光谱辐射计（MODIS）和Landsat图像。首先，水指数来自MODIS和LANDSAT图像，并且Landsat衍生的水指数用于产生二进制水图。然后，我们将Landsat水映射汇集到较粗糙的MODIS分辨率，制作表面水分分数图。其次，使用高斯工艺回归（GPR）模型拟合MODIS衍生水指数与LANDSAT导出的地表水分之间的关​​系。最后，拟合的GPR模型应用于新的MODIS图像以估计表面水分。实验结果表明，该方法可以提供小于7％的根部平均平均误差的水分。通过自动和定期绘制分数水盖，所提出的方法可以促进日常应急响应和长期分析。

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  • 来源
    《International journal of remote sensing》 |2021年第12期|4261-4283|共23页
  • 作者

    Liang Jiayong; Liu Desheng;

  • 作者单位

    Ohio State Univ Dept Geog 1036 Derby Hall 154 North Oval Mall Columbus OH 43210 USA;

    Ohio State Univ Dept Geog 1036 Derby Hall 154 North Oval Mall Columbus OH 43210 USA;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
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