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Open AccessArticle

Continuous Dynamics Monitoring of Multi-Lake Water Extent Using a Spatial and Temporal Adaptive Fusion Method Based on Two Sets of MODIS Products

1
State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
2
Land Satellite Remote Sensing Application Center, Ministry of Natural Resources of China, Beijing 100048, China
3
State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
4
Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
5
Changjiang Institute of Survey, Planning, Design and Research, Wuhan 430010, China
*
Authors to whom correspondence should be addressed.
Sensors 2019, 19(22), 4873; https://doi.org/10.3390/s19224873
Received: 9 September 2019 / Revised: 19 October 2019 / Accepted: 5 November 2019 / Published: 8 November 2019
(This article belongs to the Special Issue Applications of Remote Sensing Data in Water Resources Management)
Due to the widespread presence of noise, such as clouds and cloud shadows, continuous, high spatiotemporal-resolution dynamic monitoring of lake water extents is still limited using remote sensing data. This study aims to take an approach to mapping continuous time series of highly-accurate lake water extents. Four lakes from diverse regions of China were selected as cases. In order to reduce the impact of noise and ensure high spatial and temporal resolution of the final results, two sets of MODIS products (including MOD09A1 and MOD13Q1) are used to extract water bodies. This approach mainly comprises preliminary classification, post processing and data fusion. The preliminary classification used the Random Forest (RF) classifier to efficiently and automatically obtain the initial classification results. Post-processing is implemented to repair the classification results affected by noise as much as possible. The processed results of the two sets of products are fused by using the Homologous Data-Based Spatial and Temporal Adaptive Fusion Method (HDSTAFM), which reduces the effect of noise and also improve the temporal and spatial resolution for the final water results. We determined the accuracy using Landsat-based water results, and the values of overall accuracy (OA), user’s accuracy (UA), producer’s accuracy (PA), and kappa coefficients (KC) are mostly greater than 0.9. Good correlation was achieved for a time series of water area and altimetry data, obtained by multiple satellites, and also for water-level data selected from hydrological stations. View Full-Text
Keywords: lake water extent; continuous dynamics monitoring; data fusion; MODIS; HDSTAFM lake water extent; continuous dynamics monitoring; data fusion; MODIS; HDSTAFM
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Rao, P.; Lou, L.; Jiang, W.; Wang, Y.; Wang, X.; Cao, X. Continuous Dynamics Monitoring of Multi-Lake Water Extent Using a Spatial and Temporal Adaptive Fusion Method Based on Two Sets of MODIS Products. Sensors 2019, 19, 4873.

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