Surface water bodies, such as rivers, lakes and reservoirs are irreplaceable water resources for human life and ecosystems. Changes in surface water may result in disasters, such as flood or drought issues. Measuring and monitoring surface water using remote sensing technology is therefore an essential topic in many research areas, including flood-related studies and water resource management. A variety of remote sensors have been applied in detecting and monitoring surface water since the 1970s, such as Landsat Multispectral Scanner (MSS) and Thematic Mapper (TM)/Enhanced Thematic Mapper Plus (ETM+) [1
], NOAA Advanced Very High Resolution Radiometer (AVHRR) [7
], and Moderate Resolution Imaging Spectroradiometer (MODIS) [9
Recent advances in satellite remote sensing promise more choices for data sources for detecting surface water area. For example, Operational Land Imager (OLI) onboard Landsat 8 with a spatial resolution of 30 m is considered a continuation of the Landsat series. Its value in surface water detection has been tested [12
]. The Suomi National Polar-orbiting Partnership (Suomi NPP) is a new generation of satellites intended to replace the Earth Observing System satellites [13
]. Its Visible Infrared Imaging Radiometer Suite (VIIRS) provides a range of visible and infrared bands with spatial resolutions ranging from 375 m to 750 m. It is considered as an upgrade and replacement of the AVHRR and MODIS as a wide-swath multispectral sensor [14
]. Its potential in surface water monitoring has been preliminarily proven [15
Among the aforementioned numerous remote sensors that have been applied for surface water detection, there is generally a trade-off between the spatial and temporal resolutions [16
]. Medium- to high-resolution images, such as Landsat, are typically available fortnightly or less often, they cannot capture quick water dynamic events. Relative lower spatial resolution sensors like MODIS and NPP-VIIRS scan the earth’s surface once or several times a day, but their coarse resolution hampers the accurate mapping of surface water body. Besides, there is also usually a trade-off between the spatial and spectral resolutions of remote sensors. For example, Landsat provides multispectral bands that are generally at a spatial resolution of 30 m. In the meantime, its panchromatic band (Pan band), whose band width is broader but spatial resolution is as high as 15 m.
Image blending, or image fusion, is a remote sensing technique that aims to exploit more information by integrating data from different sources [19
]. In general, it can be divided into two categories [20
]. The first category, spatial-spectral fusion, is known as Pan-sharpening, which blends a lower resolution multispectral image with a higher resolution Pan image [21
]. The second category, spatial-temporal fusion, aims to blend high spatial resolution data with high temporal resolution data to achieve both high spatial and high temporal resolutions. It has been proven to be an effective solution for spatial and temporal trade-off issue. Wu et al. [23
] presented a spatio-temporal integrated temperature fusion model to blend multi-sources of remotely sensed data to achieve both higher spatial and temporal resolutions of land surface temperature observation. Gao et al. [24
] proposed a Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) to generate daily synthetic Landsat-like image by blending Landsat and MODIS images. This model was later modified by Zhu et al. [25
] as Enhanced STARFM (ESTARFM), which improves the prediction in complex heterogeneous regions by involving additional input images and taking into account spatial heterogeneity within mixed pixels. Both STARFM and ESTARFM have been widely used due to their ease of implementation and reasonable algorithm complexity [26
Jarihani et al. [33
] tried to blend Landsat and MODIS images to generate multispectral indices and conducted a comprehensive comparison of “Index-then-Blend” (IB) and “Blend-then-Index” (BI) approaches. The BI approach first blends all necessary reflectance bands and then calculates indices from the blended result, while the IB approach first calculates the index images from the input data and then blends them to synthesize higher resolution index images. They found that the IB approach consistently outperformed the BI approach for all of the nine tested indices, including the modified Normalized Difference Water Index (mNDWI), which is a popular index for delineating water from land [34
]. However, their findings were based on the difference and deviation of blended indices comparing to the referencing indices calculated from actual Landsat images. They did not examine the performance of these indices in extracting surface water. Therefore, their results cannot intuitively reveal which approach is better for surface water mapping.
This study therefore aims to propose a method that combines Pan-sharpening and ESTARFM to blend Landsat and NPP-VIIRS data for mapping surface water at 30 m resolution. The objectives include (1) generating 30 m resolution mNDWI images by blending Landsat and NPP-VIIRS through IB and BI approaches; (2) comparing the resultant mNDWI images of both approaches; and (3) evaluating the performance of blending results by matching them with the actual referencing Landsat image.
Monitoring the dynamics of surface water generally requires both high spatial and high temporal resolutions, especially in the case of flood inundation monitoring. Unfortunately, for most of the remote sensors, there exists a trade-off between their spatial and temporal resolutions, which makes it difficult to monitor surface water intensively with high accuracy.
This study blended newly available Suomi NPP-VIIRS data with Landsat data for the purpose of acquiring both high spatial and high temporal resolutions to improve surface water monitoring. We employed the widely accepted water index mNDWI and tested two approaches, namely index-then-blend (IB) and blend-then-index (BI). It has been found that the Suomi NPP-VIIRS can replace MODIS imagery to be blended with Landsat data for achieving the daily monitoring of surface water at 30 m resolution. Both approaches can derive reasonable water detection results. They have a general agreement with the actual referencing Landsat image. It seems that the employed fusion model, namely the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), can not only be used for reflectance fusion, but also performs well in blending water index. It has also been noticed that the IB approach generated a water map that was slightly better than the BI approach. The BI method generally underestimates the water distribution, especially when the water area expands drastically at the prediction time. Moreover, it requires multiple bands to be blended in order to calculate the index later, which thus consumes more computation time when conducting image blending. The IB approach calculates the index first and thus only needs to blend a single index image. It can not only save the computation time, but also derives better water mapping results. This has important reference values for other blending work in making decision on whether the IB or BI approach should be chosen. This study has also exemplified the application of blending approaches in improving detection results, not only in surface water monitoring, but also in other related fields, such as vegetation cover monitoring.