面向季节性水体的湖盆地形多源遥感协同定量估算研究
Combining multisource satellite data to estimate lake basin bathymetry for seasonal water bodies
- 2023年 页码：1-16
网络出版日期： 2023-05-15
DOI： 10.11834/jrs.20232588
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摘要
季节性水体是地表水的重要组成单元，在调蓄区域洪水、维持生物多样性等方面具有重要作用。如何获取高精度水深信息，是有效支撑季节性水体水储量和碳通量估算的关键，对于深入理解区域水文过程及物质能量平衡等问题具有重要意义。针对季节性水体下垫面条件的复杂性及传统测深技术的局限性，本文基于GEE云平台，联合激光雷达和光学传感器数据，提出了一种面向季节性水体的湖盆地形定量估算方法，并以鄱阳湖典型碟形子湖为研究对象，开展了该方法的精度评价及适用性分析。研究结果表明，以ICESat-2/ATLAS剖面光子高程为基础，协同Sentinel-2获取的淹没频率信息，实现季节性水体湖盆地形“由点及面”的定量估算方法切实可行，估算值和实测值相关性较好，决定系数（R
2
）优于0.7，均方根误差（RMSE）控制在1m内；随着湖泊面积的增大，地形估算的精度总体呈上升趋势，同时受淹没频率范围及光子轨迹分布的影响，不同区域及下垫面条件上的子湖，其估算精度也存在着差异性。本文提出的方法可实现面向季节性水体大范围、低成本、长时序的水下地形定量估算，有望为全球范围内季节性水体水深数据的获取提供新思路。
Abstract
Seasonal water bodies are an important component of global surface water and play an indispensable role in regional flood storage and local biodiversity maintenance. Obtaining high-precision bathymetric information is the key to effectively supporting the estimation of water storage and carbon flux in seasonal water bodies
which is helpful to understand the regional hydrological processes and material-energy balance
and other issues. On one hand
so far only a few existing studies have focused on seasonal water bodies because of their complex subsurface conditions
particularly small water bodies
which make it even harder to evaluate bathymetry. On the other hand
using traditional bathymetric techniques may encounter great difficulties
where a single sensor is unable to balance cost
efficiency
and accuracy. To this end
this paper proposes a quantitative estimation method of underwater topography for seasonal water bodies
combined with active LiDAR and passive optical sensor data. The LiDAR data is obtained from ICESat-2/ATLAS global geolocated photon data (ATL03) product
which provides high-precision photons vertical profile information of the lake basin. Meanwhile
optical sensor data can be derived from Sentinel-2 MSI datasets based on the Google Earth Engine (GEE) cloud platform
where massive commonly-used datasets can be accessed
and then the regional inundation frequency (IF) distribution will be generated. Each Photon along ICESat-2 ground tracks is time tagged and geolocated
so it is possible to obtain each photon’s height (Hgt) and IF value by geographical intersecting. In the same lake basin
every point’s height and inundation frequency are correlated in theory
so we can build an “Hgt-IF” model to fit this correlation. Then apply this model so that regional IF distribution can be translated into lake-floor elevation over the lake basin. As a typical seasonal lake composed of some dished lakes
Poyang Lake is taken as the research object in this paper and systematical evaluation is performed to evaluate the estimation accuracy and applicability of the method. The results show that the quantitative estimation method is feasible based on the photon elevation of ICESat-2/ATLAS profiles and the inundation frequency information obtained from Sentinel-2 MSI to achieve the “point-to-surface” topography of seasonal water bodies. As for most dished lakes selected
the R square values between the predicted and measured yield are greater than 0.7 and the root mean square errors (RMSEs) are controlled within 1.0 meter. However
we also notice that the simulation accuracy of dished lakes in different areas is also different due to the combined effects of various factors such as lake area
subsurface conditions
inundation frequency range
and photon track distribution. On summary
the method proposed in this paper can realize the quantitative estimation of underwater topography for seasonal water bodies in general terms. Combining active and passive remote sensing data can make up for the shortcomings of a single sensor
especially when it comes to a large scale
low cost
and long time series situation. This method is also expected to provide ideas and directions for the development of bathymetric retrieving models for seasonal water bodies at the global scale.
关键词
季节性水体湖盆地形卫星测深ICESat-2Sentinel-2淹没频率
Keywords
seasonal water bodieslake bathymetrysatellite-based bathymetryICESat-2Sentinel-2inundation frequency
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