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Figure 3.3 Storage variations of Lake Powell estimated using radar altimetry and Landsat data from 1984 to 2017.

      (Source: Busker, T., A. de Roo, E. Gelati, C. Schwatke, M. Adamovic, B. Bisselink, J.‐F. Pekel, and A. Cottam (2018), A global lake and reservoir volume analysis using a surface water dataset and satellite altimetry. Hydrol. Earth Syst. Sci., 23, 669–690, 2019. Licensed Under CCBY 4.0.)

      Satellite imageries at the visible (VIS), near infrared (NIR), and shortwave infrared (SWIR) bands have been collected continuously for several decades. For instance, since the launch of Earth Resources Technology Satellite 1 (which was later renamed Landsat 1) in 1972, the well known Landsat satellite series have set the record (in terms of both quantity and quality) for acquiring satellite data about Earth. These high‐resolution imageries are both consistent and continuous, and are invaluable for detecting land cover land use change, understanding climate change impacts, as well as for tracking water in lakes and reservoirs. Since 1978, a series of AVHRR instruments has been carried aboard the National Oceanic and Atmospheric Administration’s (NOAA) family of polar orbiting platforms (POES). Compared to Landsat, which has a spatial resolution of 15–60 m, and a temporal resolution of 16 days, the 1.1 km pixel size of AVHRR is much coarser. The AVHRR has daily temporal coverage, however, which enables more cloud free images to be collected. Since the year 2000, MODIS sensors on board Terra (launched in 2000) and Aqua (launched in 2002) have been collecting VIS/NIR/SWIR images twice a day at 250–500 m resolution (which is a major improvement over AVHRR). In general, a low‐resolution sensor such as AVHRR can be used for estimating the area variations of a lake with an area (at capacity) of larger than 200 km², while a high‐resolution sensor such as Landsat can monitor a lake smaller than 1 km². Over the very large lakes, the area estimated from sensors at different resolutions would be similar in most cases. When the reservoir has long shorelines surrounded by seasonal vegetation, however, the area estimated by AVHRR or MODIS will tend to have an exacerbated seasonality, as the effective reflectance of the mixed pixels along the shoreline will be affected by the changing vegetation reflectance (Gao et al., 2012). It is worth noting that these are only a few examples of the many similar sensors that have existed over the years (e.g., Medium Resolution Imaging Spectrometer, Visible Infrared Imaging Radiometer Suite, etc.).

      Based on the various properties of VIS, NIR, and SWIR, many approaches have been developed to extract surface water data from satellite imagery. In Gao (2015), the algorithms were summarized into three categories: threshold‐based, image‐classification‐based, and multiple‐step hybrid approaches. These algorithms typically use single or multiple variables as their classification criteria, ranging from the reflectance of a particular water sensitive band (e.g., SWIR) to indices from multiband reflectance data. A detailed comparison of commonly used Normalized Difference Water Indices (NDWIs) is available from Ji et al. (2009). While thresholding approaches were commonly used to extract water area in earlier studies (Bryant, 1999; Harris & Mason, 1989; Mason et al., 1992), supervised and unsupervised classification techniques are now more commonly used, as they produce more reliable and consistent results in general (Lu et al., 2011; Maulik & Saha, 2010; Seeber et al., 2010). Multistep hybrid approaches usually estimate optimal area values through methods such as combining thresholding and supervised classification (Wang et al., 2014), and then conducting postclassification image enhancement (Zhang et al., 2014).

With the evolutional development of the Google Earth Engine, a new cloud‐computing platform for hosting global scale Earth observation data, the mapping of global surface water is no longer plagued by the need to download and process large volumes of imagery data. Pekel et al. (2016) generated a first of its kind global surface water data set from Landsat (monthly at a 30 m resolution) from 1984 to 2015. Although this data set has a very high classification accuracy, the reservoir surface area values are often underestimated due to contaminations from clouds, cloud shadows, and terrain shadows. Zhao & Gao (2018) developed a novel algorithm that automatically corrected these contaminated classifications in the Pekel et al. (2016) data set. This led to a time series of area values for 6817 global reservoirs (with an integrated capacity of 6099 km³). As shown in Figure 3.4, these area estimations agree well with observed elevation/storage values.

Here we introduce a new hydrological drought index, the Reservoir Area Fraction Index (RAFI), using remotely sensed water area information calculated with equation 3.2:

(3.2)

where A _RS is the remotely sensed reservoir area, and A _{95%_max} is 95% of the maximum water area from 1984 to 2015. The use of a 95 percentile maximum remotely sensed area will allow us to exclude the possibility that the flooded area is larger than the area at capacity. Figure

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