| 引用本文: | 石希,夏军强,孙健.基于热红外遥感影像的河流水温反演方法比较——以长江上游流域为例.湖泊科学,2022,34(1):307-319. DOI:10.18307/2022.0125 |
| Shi Xi,Xia Junqiang,Sun Jian.Comparison of methods to derive river water temperature using thermal infrared imagery: A case study of the upper Yangtze River catchment. J. Lake Sci.2022,34(1):307-319. DOI:10.18307/2022.0125 |
|
| 摘要: |
| 水温是影响河流生态环境的重要因素.传统水温观测手段受限于测站的地理位置，数据分布稀疏.近20年来随着热红外卫星传感器在空间分辨率上的革新，热红外遥感影像逐步被应用于反演河流水温，但目前相关研究聚焦于利用单颗卫星数据并使用单一水温反演方法而忽略与其他卫星以及处理方法做对比.本文以长江上游流域为例，分别评估了水文反演流程中3个步骤的差异对水温精度的影响：遥感数据（MODIS和Landsat）、大气校正方法（辐射传递模型法与JM&S单通道算法）和大气校正参数（Atmcorr和GEOS-5 FP-IT）.研究结果表明：（ⅰ）MODIS数据因为空间分辨率较低并不能满足反演长江上游流域水温的需求；（ⅱ）由于长江上游流域大气水汽含量较高，JM&S单通道算法反演水温误差过大；（ⅲ）当流域拥有实测水温资料时，最佳反演流程是使用辐射传递模型法与Atmcorr大气校正参数反演Landsat水温并进一步使用逻辑回归模型校正数据，最终所得反演水温RMSE为1.0~1.1℃；（ⅳ）对于无测站流域，较适合的流程为使用辐射传递模型法和GEOS-5 FP-IT大气校正参数并设定大气校正参数阈值，最终反演水温RMSE为1.2℃.同时研究还表明大气透过率以及大气上行辐射强度是影响反演水温精度的主要因素.因此本文建议在对不同流域进行水温反演时应尽可能对上述两种参数进行敏感性分析. |
| 关键词: 河流水温 反演方法 遥感影像 Landsat MODIS 长江上游 |
| DOI：10.18307/2022.0125 |
| 分类号: |
| 基金项目:国家自然科学基金项目（51725902，U2040215）资助. |
|
| Comparison of methods to derive river water temperature using thermal infrared imagery: A case study of the upper Yangtze River catchment |
|
Shi Xi1, Xia Junqiang1, Sun Jian2
|
|
1.State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, P. R. China;2.State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing 100084, P. R. China
|
| Abstract: |
| Water temperature is an essential factor that affects the ecology and environment of the river. Conventional river water temperature (RWT) measuring methods are limited by the geographic location of the hydrological station; thus, RWT data is under a sparse spatial resolution. With the improvement of the spatial resolution of thermal infrared sensors mounted on the satellites within the past twenty years, thermal infrared imagery has gradually been used to retrieve RWT. Currently, most research focuses on using sole sensor and specified processing methods without comparing with other sensors and retrieval approaches. Taking the upper Yangtze River catchment as a case study, this paper evaluates the impact of the following three steps within the entire process on the accuracy of RWT:thermal infrared data (MODIS and Landsat); atmospheric correction methods (the radiation transfer model and JM&S single-channel algorithm); atmospheric correction parameters (Atmcorr and GEOS-5 FP-IT). The results indicated that (ⅰ) MODIS data cannot meet the requirements of the study area due to its low spatial resolution; (ⅱ) As the atmospheric water vapor content is relatively high in the study catchment, the JM&S single-channel algorithm performs poorly on the retrieval; (ⅲ) When there is measured RWT within the study catchment, the proper retrieval process will be using the RTM combined with Atmcorr atmospheric correction parameter and further corrected by using the logistic regression while the final RMSE is about 1.0-1.1℃; (ⅳ) For the basin without measured RWT, the more suitable way is to use the GEOS-5 FP-IT instead and set an atmospheric correction parameter threshold. Its RMSE is approximately 1.2℃. Meanwhile, the results also present that atmospheric transmittance and upward radiation are the key parameters affecting the accuracy of RWT. Therefore, we highly recommended that a similar sensitivity analysis should be applied to other catchments for further studies. |
| Key words: River water temperature retrieval method remote sensing images Landsat MODIS upper Yangtze River |
