A Submonthly Surface Water Classification Framework via Gap-Fill Imputation and Random Forest Classifiers of Landsat Imagery

Charles Labuzzetta,Yuyu Zhou,Zhengyuan Zhu,Xinyue Chang

doi:10.3390/rs13091742

Charles Labuzzetta, Yuyu Zhou + Show 2 more

Open Access

https://doi.org/10.3390/rs13091742

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Journal: Remote Sensing	Publication Date: Apr 30, 2021
Citations: 6	License type: CC BY 4.0

Affiliation: Iowa State University

Abstract

Global surface water classification layers, such as the European Joint Research Centre’s (JRC) Monthly Water History dataset, provide a starting point for accurate and large scale analyses of trends in waterbody extents. On the local scale, there is an opportunity to increase the accuracy and temporal frequency of these surface water maps by using locally trained classifiers and gap-filling missing values via imputation in all available satellite images. We developed the Surface Water IMputation (SWIM) classification framework using R and the Google Earth Engine computing platform to improve water classification compared to the JRC study. The novel contributions of the SWIM classification framework include (1) a cluster-based algorithm to improve classification sensitivity to a variety of surface water conditions and produce approximately unbiased estimation of surface water area, (2) a method to gap-fill every available Landsat image for a region of interest to generate submonthly classifications at the highest possible temporal frequency, (3) an outlier detection method for identifying images that contain classification errors due to failures in cloud masking. Validation and several case studies demonstrate the SWIM classification framework outperforms the JRC dataset in spatiotemporal analyses of small waterbody dynamics with previously unattainable sensitivity and temporal frequency. Most importantly, this study shows that reliable surface water classifications can be obtained for all pixels in every available Landsat image, even those containing cloud cover, after performing gap-fill imputation. By using this technique, the SWIM framework supports monitoring water extent on a submonthly basis, which is especially applicable to assessing the impact of short-term flood and drought events. Additionally, our results contribute to addressing the challenges of training machine learning classifiers with biased ground truth data and identifying images that contain regions of anomalous classification errors.

Highlights

Remote sensors gather observations of Earth’s surface and atmosphere in a wide range of spectral, temporal, and spatial resolutions [1]
While developing the Surface Water IMputation (SWIM) classification framework it became evident that higher sensitivity to a variety of surface water conditions would improve waterbody extent estimation compared to the Joint Research Centre (JRC) study
To validate the accuracy of the SWIM classification framework, we studied a sample of waterbodies from the National Hydrography Dataset (NHD) [37]

Summary

Introduction

Remote sensors gather observations of Earth’s surface and atmosphere in a wide range of spectral, temporal, and spatial resolutions [1]. Data collected by these sensors are often classified at the pixel level to produce land cover maps of the Earth’s surface [2,3,4]. The accuracy and scope of analysis of these maps are significantly affected by factors such as spatial and temporal resolution, presence of cloud cover, and cost of acquisition of the remote sensing imagery [5]. Recent advances in computation and open access to Landsat sensor data have led to the generation of high-resolution global land cover maps such as the European Joint Research Centre’s (JRC) Monthly Water History v1.0 dataset, which 4.0/).

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