Downscaling Surface Water Inundation from Coarse Data to Fine-Scale Resolution: Methodology and Accuracy Assessment

Guiping Wu,Yuanbo Liu

doi:10.3390/rs71215813

Abstract

The availability of water surface inundation with high spatial resolution is of fundamental importance in several applications such as hydrology, meteorology and ecology. Medium spatial resolution sensors, like MODerate-resolution Imaging Spectroradiometer (MODIS), exhibit a significant potential to study inundation dynamics over large areas because of their high temporal resolution. However, the low spatial resolution provided by MODIS is not appropriate to accurately delineate inundation over small scale. Successful downscaling of water inundation from coarse to fine resolution would be crucial for improving our understanding of complex inundation characteristics over the regional scale. Therefore, in this study, we propose an innovative downscaling method based on the normalized difference water index (NDWI) statistical regression algorithm towards generating small-scale resolution inundation maps from MODIS data. The method was then applied to the Poyang Lake of China. To evaluate the performance of the proposed downscaling method, qualitative and quantitative comparisons were conducted between the inundation extent of MODIS (250 m), Landsat (30 m) and downscaled MODIS (30 m). The results indicated that the downscaled MODIS (30 m) inundation showed significant improvement over the original MODIS observations when compared with simultaneous Landsat (30 m) inundation. The edges of the lakes become smoother than the results from original MODIS image and some undetected water bodies were delineated with clearer shapes in the downscaled MODIS (30 m) inundation map. With respect to high-resolution Landsat TM/ETM+ derived inundation, the downscaling procedure has significantly increased the R2 and reduced RMSE and MAE both for the inundation area and for the value of landscape metrics. The main conclusion of this study is that the downscaling algorithm is promising and quite feasible for the inundation mapping over small-scale lakes.

Highlights

Terrestrial surface water, as a fundamental component of the global water cycle, is key to hydrology, ecology and meteorology [1,2,3]
We presented a novel downscaling method based on the normalized difference water index (NDWI) statistical regression algorithm to generate small-scale resolution inundation map from coarse data
The downscaling is a linear calibration of the NDWI index from MODerate-resolution Imaging Spectroradiometer (MODIS) imagery to Landsat imagery, which is based on the assumption that the relationships between fine resolution and coarse resolution are invariable

Summary

Introduction

Terrestrial surface water, as a fundamental component of the global water cycle, is key to hydrology, ecology and meteorology [1,2,3]. The detection of terrestrial water surface relies on in situ gauge measurements and hydrological models. These methods, cannot provide an overall distribution pattern on a regional scale since their low efficiency or sometimes absence in inaccessible regions [8]. The development of remote sensing has presented us with new methods of surface water inundation observation. These include multispectral, synthetic aperture radar (SAR), and passive-microwave observations [9,10,11]. With the development of remote sensing technique, downscaling becomes an attractive option to overcome this limitation [24]

Objectives

Methods

Results

Conclusion