Unsupervised Sub-Pixel Water Body Mapping with Sentinel-3 OLCI Image

Xia Wang,Yaolin Liu,Shuna Xu,Feng Ling,Huaiying Yao

doi:10.3390/rs11030327

Abstract

Mapping land surface water bodies from satellite images is superior to conventional in situ measurements. With the mission of long-term and high-frequency water quality monitoring, the launch of the Ocean and Land Colour Instrument (OLCI) onboard Sentinel-3A and Sentinel-3B provides the best possible approach for near real-time land surface water body mapping. Sentinel-3 OLCI contains 21 bands ranging from visible to near-infrared, but the spatial resolution is limited to 300 m, which may include lots of mixed pixels around the boundaries. Sub-pixel mapping (SPM) provides a good solution for the mixed pixel problem in water body mapping. In this paper, an unsupervised sub-pixel water body mapping (USWBM) method was proposed particularly for the Sentinel-3 OLCI image, and it aims to produce a finer spatial resolution (e.g., 30 m) water body map from the multispectral image. Instead of using the fraction maps of water/non-water or multispectral images combined with endmembers of water/non-water classes as input, USWBM directly uses the spectral water index images of the Normalized Difference Water Index (NDWI) extracted from the Sentinel-3 OLCI image as input and produces a water body map at the target finer spatial resolution. Without the collection of endmembers, USWBM accomplished the unsupervised process by developing a multi-scale spatial dependence based on an unsupervised sub-pixel Fuzzy C-means (FCM) clustering algorithm. In both validations in the Tibet Plate lake and Poyang lake, USWBM produced more accurate water body maps than the other pixel and sub-pixel based water body mapping methods. The proposed USWBM, therefore, has great potential to support near real-time sub-pixel water body mapping with the Sentinel-3 OLCI image.

Highlights

For the observations of natural water on Earth’s surface, satellite sensors, including the Sea-viewing Wide Field-of-view Sensor (SeaWiFS), the Medium Resolution Imaging Spectrometer (MERIS) and Moderate resolution Imaging Spectroradiometer (MODIS), have been applied widely to provide near real-time dynamics of water surfaces [1,2], due to their very fine temporal resolution (e.g., 1–3 days)
With the Sentinel Toolboxes of Sentinel Application Platform (SNAP), the particular data pre-processing of atmospheric correction, coordinate transformation, and cloud detection was done for the two used Sentinel-3 Ocean and Land Colour Instrument (OLCI) images
Difficult to guarantee that Normalized Difference Water Index (NDWI) images based on one certain NIR band would always achieve the best performance in water body mapping of Sentinel-3 OLCI image

Summary

Introduction

For the observations of natural water on Earth’s surface, satellite sensors, including the Sea-viewing Wide Field-of-view Sensor (SeaWiFS), the Medium Resolution Imaging Spectrometer (MERIS) and Moderate resolution Imaging Spectroradiometer (MODIS), have been applied widely to provide near real-time dynamics of water surfaces [1,2], due to their very fine temporal resolution (e.g., 1–3 days). With respect to the above applications, most of them were done using the Sentinel-3 OLCI image for quantitative inversion, as it can generate more continuous spectral curves than many other satellite sensors, such as MODIS, Landsat Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), Operational Land Imager (OLI). It is of great interest for Sentinel-3 OLCI to map water bodies because it has many spectral bands that are sensitive to the water surface and are able to provide near real-time water body monitoring

Objectives

Methods

Results

Conclusion