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引用本文:

DOI:

10.11834/jrs.20232460

收稿日期:

2022-09-04

修改日期:

2023-02-05

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千岛湖水体表层温度遥感估算方法对比研究
张琳敏1, 梅格致1, 刘明亮2, 李渊1, 施坤3, 朱梦圆3, 李慧赟3, 郭宇龙4, 王嘉诚1
1.浙江工商大学 旅游与城乡规划学院;2.杭州市生态环境科学研究院;3.中国科学院南京地理与湖泊研究所 湖泊与环境国家重点实验室;4.河南农业大学 资源与环境学院
摘要:

湖泊水体表层温度是气候和环境变化的重要指示因子,遥感技术是水体表层温度监测的重要手段。温度反演算法在不同湖库水体的适用性各异,针对清洁型深水湖库的水体表层温度反演算法适用性仍有待进一步研究。本研究以千岛湖为研究区,利用Landsat 8卫星数据,对比了基于辐射传输方程的算法(RTE)、单窗算法(MWA)、普适性单通道算法(GSCA)、实用单通道算法(PSCA)、劈窗算法(SWA_D和SWA_G)和Landsat 8 Collection 2 Level-2(C2L2)温度产品的精度,探究了各算法中相关参数的适用性和敏感性,刻画了千岛湖2013-2021年水体表层温度时空分布特征。研究结果表明:(1)针对Landsat 8数据的第10和11波段,千岛湖最适宜的水体比辐射率分别为0.9926和0.9877;(2)整体上,劈窗算法的精度优于单通道算法,Landsat温度产品的估算精度适中。其中,劈窗算法SWA_G精度最优,平均相对误差(MAPE)为7.61%,均方根误差(RMSE)为2.0 ℃;(3)千岛湖水体表层温度具有显著的时空分异特征。季节上,千岛湖水体表层温度冬季最低(14.2 ± 0.6 ℃),夏季最高(31.0 ± 0.5 ℃)。空间上,西北库区(23.0 ± 0.3 ℃)和西南库区(22.8 ± 0.2 ℃)水体表层温度最高,东北库区(22.2 ± 0.3 ℃)水体表层温度最低。本研究验证了不同温度反演算法在清洁型深水湖库的适用性,为清洁型深水湖库水体表层温度反演提供了经验借鉴。

Comparison of lake surface water temperature retrieval algorithms: A case study of Lake Qiandaohu
Abstract:

[Objective] Lake surface water temperature is an important indicator of water quality, lake physical environment and climate change. Monitoring lake surface water temperature and understanding its spatiotemporal variations are critical for local government to protect lake ecosystem. Remote sensing is an effective method to monitor lake surface water temperature, and many algorithms were developed and applied to retrieve lake surface water temperature. However, the suitability of these algorithms is varied in different lakes. Especially, the suitability of these algorithms in deep, oligotrophic-to-mesotrophic lakes still needs to be discussed. Thus, taking Lake Qiandaohu, China as the study area, we are trying to validate the performance of various land surface temperature retrieval algorithms, analyze the sensitivity of the parameters in each algorithm and mapping the spatiotemporal distribution of lake surface water temperature. [Method] In this study, six land surface temperature retrieval algorithms (Radiative Transfer Equation algorithm, Mono-window algorithm, Generalized Single-Channel algorithm, Practical Single-Channel algorithm and two Split-Window algorithms) were selected to retrieve lake surface water temperature using Landsat 8 data in Lake Qiandaohu. The performance of these algorithms and the Landsat 8 Collection 2 Level-2 (C2L2) temperature product were validated with in-situ buoy data. By applying the best performed algorithm to 37 cloud-free Landsat 8 data collected from 2013 to 2021, the spatial and temporal distribution of lake surface water temperature in Lake Qiandaohu were mapped. Furthermore, the sensitivity of the relevant parameters (water surface emissivity, effective mean atmospheric temperature, atmospheric water vapor content, up-welling radiance, down-welling radiance and atmospheric transmittance) in each algorithm were explored. [Result] The results showed that: (1) for the band 10 and band 11 of Landsat 8 data, the most suitable water surface emissivity in Lake Qiandaohu is 0.9926 and 0.9877, respectively; (2) the accuracy of the Split-Window algorithm is better than that of the Single-Channel algorithm, and the estimation accuracy of Landsat temperature product is moderate. The Split-Window Algorithm, SWA_G, shows the best performance, with the mean absolute percentage error (MAPE) of 7.61% and the root mean square error (RMSE) of 2.0 ℃. The MPAE and RMSE value of the Landsat 8 C2L2 temperature product are 9.33% and 2.08 ℃, respectively; (3) lake surface water temperature in Lake Qiandaohu has significant spatial and temporal variation. Seasonally, the lake surface water temperature of Lake Qiandaohu has the lowest value (14.2 ± 0.6 ℃) and highest value (31.0 ± 0.5 ℃) in winter and summer, respectively. Spatially, the lake surface water temperature was higher in the northwest segment (23.0 ± 0.3 ℃) and southwest segment (22.8 ± 0.2 ℃) and lower in the northeast segment (22.2 ± 0.3 ℃); (4) The Radiative Transfer Equation algorithm is sensitive to the up-welling radiance and atmospheric transmittance. The Mono-window algorithm shows less sensitivity to the effective mean atmospheric temperature and atmospheric transmittance. [Conclusion] In conclusion, in Lake Qiandaohu, the Split-Window algorithm has the best performance and the least dependency with atmospheric parameters. On the other hand, the Single-Channel algorithm is suit for retrieval long-term lake surface water temperature utilizing Landsat series data. Our study validated the performance of various land surface temperature retrieval algorithms in deep, oligotrophic-to-mesotrophic lake and provided a reference for the remotely estimating lake surface water temperature in other similar lakes.

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