Abstract Scarcity of water temperature data in rivers may limit a diversity of studies considering this property, which regulates many physical, chemical, and biological processes. We present a robust method to generate a consistent, continuous daily river water temperature (RWT) data series for medium and large rivers using the combined techniques of remote sensing and water temperature modelling. In order to validate our approach, we divided this study into two parts: (i) we evaluated methods to derive RWT from Landsat 7 ETM+ and Landsat 8 TIRS imagery; and (ii) we evaluated the calibration and validation of river temperature models, using these data, to generate the continuous RWT data series. A 1.2 km section of the White River located near Hazleton, IN, USA, was selected to assess this method mainly due to river width and data availability. We tested three methods to retrieve RWT from Landsat 7 and four from Landsat 8, and we also applied a simple thermal sharpening technique. For Landsat 7, the methods showed bias and RMSE of 0.01–0.46 °C and 1.32–1.84 °C, while for Landsat 8, the methods showed bias and RMSE of 0.08–1.27 °C and 1.74–2.17 °C, and in both cases, the best results were found applying the radiative transfer equation with NASA's Atmospheric Correction Parameter Calculator. For the second part of the validation process, we compared a stochastic model and a hybrid model, air2stream, using as input two datasets: the RWT data derived from Landsat 7 only, and a combined dataset of both Landsat 7 and 8 derived RWT. The air2stream model outperformed the stochastic model when calibrated with Landsat 7 data only, with RMSE of 1.83 °C, but both models showed similar results when calibrated with the combined Landsat data, when air2stream showed RMSE of 1.58 °C. Due to its physical basis, better calibration procedure, and higher consistency, air2stream was considered the best model for deriving the continuous RWT data series. When compared to the measured daily mean RWT data, there was no observed tendency in under or overestimating the RWT in low or high temperature conditions by the modelled series. While further tests are needed in order to evaluate if our approach can be applied to analyse past behaviour and present trends, and the impacts of climate change on the temperature of rivers, the consistent results indicate that this approach has the potential to be applied in rivers with no measured temperature data, for example, in the spatial modelling of longitudinal profiles of rivers and the modelling of tributary river temperatures.

中文翻译：

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结合遥感和水温模型推导一致、连续的每日河流温度数据系列

摘要 河流中水温数据的稀缺可能会限制考虑到这一特性的研究的多样性，该特性调节许多物理、化学和生物过程。我们提出了一种强大的方法，可以使用遥感和水温建模的组合技术为中型和大型河流生成一致、连续的每日河流水温 (RWT) 数据系列。为了验证我们的方法，我们将这项研究分为两部分：(i) 我们评估了从 Landsat 7 ETM+ 和 Landsat 8 TIRS 图像推导出 RWT 的方法；(ii) 我们使用这些数据评估了河流温度模型的校准和验证，以生成连续的 RWT 数据系列。选择位于美国印第安纳州 Hazleton 附近的 White River 的 1.2 公里部分来评估该方法，主要是因为河流宽度和数据可用性。我们测试了三种从 Landsat 7 检索 RWT 的方法和从 Landsat 8 检索 RWT 的四种方法，我们还应用了一种简单的热锐化技术。对于 Landsat 7，该方法显示的偏差和 RMSE 为 0.01–0.46 °C 和 1.32–1.84 °C，而对于 Landsat 8，该方法显示的偏差和 RMSE 为 0.08–1.27 °C 和 1.74–2.17 °C，并且在两者中在某些情况下，最好的结果是使用 NASA 的大气校正参数计算器应用辐射传输方程。对于验证过程的第二部分，我们比较了随机模型和混合模型 air2stream，使用两个数据集作为输入：仅从 Landsat 7 派生的 RWT 数据，以及 Landsat 7 和 8 派生的 RWT 的组合数据集。仅使用 Landsat 7 数据校准时，气流模型优于随机模型，RMSE 为 1.83 °C，但是当使用组合 Landsat 数据校准时，两个模型都显示出相似的结果，当 air2stream 显示 RMSE 为 1.58 °C 时。由于其物理基础、更好的校准程序和更高的一致性，air2stream 被认为是导出连续 RWT 数据系列的最佳模型。与测量的每日平均 RWT 数据相比，没有观察到模型系列在低温或高温条件下低估或高估 RWT 的趋势。虽然需要进一步的测试来评估我们的方法是否可以应用于分析过去的行为和当前的趋势，以及气候变化对河流温度的影响，但一致的结果表明这种方法有可能应用于河流没有测量的温度数据，例如，