衛星データにより中国の湖水量の全体的増加を解明(Surface Water and Ocean Topography Satellite Reveals Overall Increase in China’s Lake Volumes)

2026-03-23 中国科学院(CAS)

中国科学院航空航天信息研究所(AIRCAS)などの研究チームは、SWOT衛星を用いて中国全域の湖沼体積変動を高精度で把握できることを示した。水位と面積の同時観測に地形データを組み合わせ、小規模湖も含めた1,596湖を解析した結果、月平均0.7754Gtの増加傾向を確認し、その約85%は自然湖が占めた。従来困難だった小湖や遠隔地の観測精度も大幅に改善され、誤差は多くが10%以内に収まった。地域ごとの季節変動も明確となり、本手法は水資源管理や気候変動評価に有用とされる。

衛星データにより中国の湖水量の全体的増加を解明(Surface Water and Ocean Topography Satellite Reveals Overall Increase in China’s Lake Volumes)
Lake extent derived from SWOT L2 HR LakeSP data compared to Sentinel-2 optical images in case lakes. (Image by AIRCAS)

<関連情報>

SWOT衛星による湖水量モニタリングの性能検証:中国の湖沼を事例とした研究 Exploring the Performance of SWOT Satellite to Monitor Lake Volumes: A Case Study of Chinese Lakes

Ruofan Jing, Jingjuan Liao, Shanmu Ma, Xiangyu Liu, and Yanhong Wu
Journal of Remote Sensing  Published:29 Jan 2026
DOI:https://doi.org/10.34133/remotesensing.1026

Abstract

Changes in lake volumes are very important for basin water balance, water resource management, and disaster prevention. Remote sensing technologies, including satellite altimetry, optical, and SAR (synthetic aperture radar) remote sensing, can monitor lake volume changes in real time. However, current methods face challenges such as inconsistent time resolution, satellite transit times, and difficulty monitoring small lakes. The Surface Water and Ocean Topography (SWOT) satellite, launched in December 2022, carries the Ka-band interferometer KaRIn, which is capable of wide-swath altimetry and water detection, providing synchronous lake levels and lake areas, and is expected to monitor about 65% of changes in the total lake volume. It is therefore necessary to explore the potential of officially released SWOT data in practical applications. In this study, we estimate the lake volumes in China based on the latest SWOT Lake Product during 2023–2024, assesses the application potential of SWOT data, and combines multi-source bathymetry data to analyze the variations of lake volumes. The results showed that SWOT data greatly improved the ability to monitor small lakes, and the estimated water volume changes had a high accuracy, with errors of within 10% for the validation reservoirs, reaching a minimum of 3.92%. We also suggest that SWOT-observed lake areas tend to be overestimated compared to existing high-accuracy datasets. Within the study area, lake volume exhibited an upward trend and obvious seasonal variations were observed in some regions. Approximately 85% of the changes originate from natural lakes, and large lakes are the main source of the increase. With future updates to SWOT data versions and advancements in data processing methods, the accuracy and coverage of lake volume monitoring are expected to improve further.

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