Next Article in Journal
Forest Canopy Height Estimation Using Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) Technology Based on Full-Polarized ALOS/PALSAR Data
Previous Article in Journal
Accuracy of Vaisala RS41 and RS92 Upper Tropospheric Humidity Compared to Satellite Hyperspectral Infrared Measurements
Article

Dynamic Mapping of Subarctic Surface Water by Fusion of Microwave and Optical Satellite Data Using Conditional Adversarial Networks

1
Geological Survey of Japan (GSJ), National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba 305-8567, Japan
2
Institute for Space-Earth Environmental Research (ISEE), Nagoya University, Nagoya 464-8601, Japan
3
Graduate School of Bioresources, Mie University, 1577 Kurimamachiya-cho, Tsu 514-8507, Japan
4
Graduate School of Bioagricultural Sciences, Nagoya University, Nagoya 464-8601, Japan
*
Author to whom correspondence should be addressed.
Remote Sens. 2021, 13(2), 175; https://doi.org/10.3390/rs13020175
Received: 8 December 2020 / Revised: 4 January 2021 / Accepted: 4 January 2021 / Published: 6 January 2021
(This article belongs to the Special Issue Remote Sensing of Water Cycle Science in the Cryosphere)
Surface water monitoring with fine spatiotemporal resolution in the subarctic is important for understanding the impact of climate change upon hydrological cycles in the region. This study provides dynamic water mapping with daily frequency and a moderate (500 m) resolution over a heterogeneous thermokarst landscape in eastern Siberia. A combination of random forest and conditional generative adversarial networks (pix2pix) machine learning (ML) methods were applied to data fusion between the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Advanced Microwave Scanning Radiometer 2, with the addition of ancillary hydrometeorological information. The results show that our algorithm successfully filled in observational gaps in the MODIS data caused by cloud interference, thereby improving MODIS data availability from 30.3% to almost 100%. The water fraction estimated by our algorithm was consistent with that derived from the reference MODIS data (relative mean bias: −2.43%; relative root mean squared error: 14.7%), and effectively rendered the seasonality and heterogeneous distribution of the Lena River and the thermokarst lakes. Practical knowledge of the application of ML to surface water monitoring also resulted from the preliminary experiments involving the random forest method, including timing of the water-index thresholding and selection of the input features for ML training. View Full-Text
Keywords: data fusion; subarctic thermokarst lakes; AMSR2; MODIS; random forest; conditional GAN data fusion; subarctic thermokarst lakes; AMSR2; MODIS; random forest; conditional GAN
Show Figures

Graphical abstract

MDPI and ACS Style

Mizuochi, H.; Iijima, Y.; Nagano, H.; Kotani, A.; Hiyama, T. Dynamic Mapping of Subarctic Surface Water by Fusion of Microwave and Optical Satellite Data Using Conditional Adversarial Networks. Remote Sens. 2021, 13, 175. https://doi.org/10.3390/rs13020175

AMA Style

Mizuochi H, Iijima Y, Nagano H, Kotani A, Hiyama T. Dynamic Mapping of Subarctic Surface Water by Fusion of Microwave and Optical Satellite Data Using Conditional Adversarial Networks. Remote Sensing. 2021; 13(2):175. https://doi.org/10.3390/rs13020175

Chicago/Turabian Style

Mizuochi, Hiroki, Yoshihiro Iijima, Hirohiko Nagano, Ayumi Kotani, and Tetsuya Hiyama. 2021. "Dynamic Mapping of Subarctic Surface Water by Fusion of Microwave and Optical Satellite Data Using Conditional Adversarial Networks" Remote Sensing 13, no. 2: 175. https://doi.org/10.3390/rs13020175

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop