多源数据协同的金沙江上游河段水面积遥感监测方法研究

Research on remote sensing monitoring method of water surface area in upper reaches of Jinsha River using multi-source data synergy

摘要

【目的】多源数据协同遥感监测是提升河流水体监测频次的重要途径,但不同空间分辨率卫星监测河流水体的精度不同,多源卫星协同监测河流水体时序变化存在不确定性。定量评价不同空间分辨率卫星监测河段水面积精度差异,构建多源卫星监测河段水面积修正模型,对实现高精度、长时序、高频次河流水体多源卫星协同监测具有重要意义。【方法】以金沙江上游汊道数指数分别为1(顺直)、1~2(分汊)、2~6(辫状)的奔子栏、石鼓、正科乡3个不同形态河段为研究区,利用无人机MS600航拍影像、Landsat系列卫星、Sentinel-1/2卫星、GF-1/2/6卫星、HJ-2A/2B卫星等作为主要数据源,通过建立3个河段时空匹配的高空间分辨率-中高空间分辨率卫星遥感监测河段水面积样本对,验证不同河段多源中高空间分辨率卫星水面积监测精度并构建修正模型,定量评价不同卫星监测精度与模型修正效果,探讨多源卫星监测河段水体精度影响因素、修正模型适用性以及多源卫星协同监测优势。【结果】结果显示,正科乡、石鼓、奔子栏河段多源卫星监测水面积RMSE分别介于0.180 9~0.706 8 km²、0.040 8~0.096 3 km²、0.015 8~0.041 4 km²,经过模型修正,Landsat-8/9 OLI、Sentinel-2 MSI、Sentinel-1 SAR和16 m国产卫星在3个河段平均MAPE分别从14.16%、7.26%、20.06%、9.37%降低至10.30%、5.33%、7.59%、7.32%,多源卫星协同监测水面积与相对真值差值的标准差(Standard Deviation)由0.221 8 km²、0.037 4 km²、0.019 1 km²降低至0.159 1 km²、0.023 4 km²、0.012 8 km²;有效监测频次由平均12次/年提升至260次/年;同时分析了河流水体遥感监测精度与卫星空间分辨率、卫星传感器类型与河道形态等因素的关系。【结论】结果表明,不同空间分辨率卫星监测不同形态河段水体精度存在明显差异,卫星空间分辨率、卫星传感器类型和河段形态复杂程度是影响河段水面积监测精度的重要因素;多源卫星遥感监测水面积修正模型可以提升不同空间分辨率卫星监测河段水面积的精度和一致性;多源卫星协同监测可以显著提升河段水体监测频次。研究成果在长时序河流水体面积重建、水位流量监测与水文模型校准等方面具有推广应用潜力。

Abstract

[Objective] Multi-source data synergistic remote sensing is a crucial pathway for enhancing the monitoring frequency of river water bodies. However, the accuracy of river water body monitoring varies among satellites with different spatial resolutions,leading to uncertainties in the time-series analysis of river water bodies derived from multi-source satellite synergy. A quantitative evaluation of the accuracy differences in water surface area monitoring for river sections by satellites with different spatial resolutions, and the development of a correction model for multi-source satellite-based water surface area monitoring, hold significant importance for achieving high-precision, long-term, and high-frequency satellite synergistic monitoring of river water bodies. [Methods] Three river sections of the upper reaches of the Jinsha River with different channel patterns—Benzilan(straight, channel count index = 1), Shigu(anabranched, channel count index = 1~2), and Zhengkexiang(braided, channel count index = 2 ~ 6)—were selected as the study areas. Data acquired from the MS600 unmanned aerial vehicle( UAV),Landsat series satellites, Sentinel-1/2 satellites, GF-1/2/6 satellites, and HJ-2A/2B satellites were utilized as the main data sources. Spatiotemporally matched sample pairs of river water surface area were established by pairing high-spatial-resolution data with medium-to-high-spatial-resolution satellite data for the three river sections. The accuracy of water surface area monitoring using multi-source medium-to-high-resolution satellites for different river sections was validated, and correction models were constructed. Additionally, the monitoring accuracy of different satellites along with the model' s correction effectiveness were quantitatively evaluated. The factors influencing the accuracy of multi-source satellite monitoring for river sections, the applicability of correction models, and the advantages of multi-source collaborative monitoring were explored. [Results] The result showed that the RMSE of multi-source satellite-monitored water surface area for the Zhengkexiang, Shigu, and Benzilan river sections ranged from 0. 180 9 km² to 0. 706 8 km², 0. 040 8 km² to 0. 096 3 km², and 0. 015 8 km² to 0. 041 4 km²,respectively. After model correction, the average MAPE of Landsat-8/9 OLI, Sentinel-2 MSI, Sentinel-1 SAR, and the 16-meter domestic satellites across the three river sections reduced from 14. 16%, 7. 26%, 20. 06%, and 9. 37% to 10. 30%, 5. 33%,7. 59%, and 7. 32%, respectively. The standard deviation of the differences between the water surface area monitored by multisource satellite synergy and the reference values decreased from 0. 221 8 km², 0. 037 4 km², and 0. 019 1 km² to 0. 159 1 km²,0. 023 4 km², and 0. 012 8 km². The effective monitoring frequency increased from an average of 12 times per year to 260 times per year. Furthermore, the relationships between river water body monitoring accuracy and factors such as satellite spatial resolution, satellite sensor type, and channel morphology were analyzed. [Conclusion] The result demonstrate significant differences in the accuracy of water body monitoring for different morphologies of river sections using satellites of different spatial resolutions. Satellite spatial resolution, satellite sensor type, and the complexity of river section morphology are identified as important factors influencing the accuracy of river water surface area monitoring. The correction model for multi-source satellite remote sensing of water surface area can improve both the accuracy and consistency of water surface area monitoring for river sections from satellites with different spatial resolutions. Multi-source satellite synergistic monitoring can significantly enhance the monitoring frequency of river section water bodies. The research findings have potential for broader application in aspects such as the long-term reconstruction of river water surface area, water level and discharge monitoring, and hydrological model calibration.

关键词

多源数据协同 / 河流水体遥感监测 / 金沙江上游 / 精度验证 / 修正模型 / 适用性 / 协同优势 / 影响因素

Key words

multi-source data synergy / remote sensing monitoring of river water bodies / upper reaches of Jinsha River / accuracy verification / correction model / applicability / synergistic advantage / influencing factors

基金资助

国家自然科学基金项目(52309040)

国家自然科学基金项目(52279031)

国家重点研发计划项目(2023YFF1304202)

中国长江电力股份有限公司资助(Z432402001)