气候变化领域集成服务门户(CCPortal): Derivation of consistent, continuous daily river temperature data series by combining remote sensing and water temperature models

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DOI	10.1016/j.rse.2020.111721
	Derivation of consistent, continuous daily river temperature data series by combining remote sensing and water temperature models
	Tavares M.H.; Cunha A.H.F.; Motta-Marques D.; Ruhoff A.L.; Fragoso C.R.; Jr.; Munar A.M.; Bonnet M.-P.
发表日期	2020
ISSN	00344257
卷号	241
英文摘要	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. © 2020 Elsevier Inc.
英文关键词	Atmospheric correction; Landsat; Temperature modelling; Temperature-based validation; Thermal infrared; Thermal response; Water surface temperature
语种	英语
scopus关键词	Atmospheric chemistry; Calibration; Climate change; NASA; Remote sensing; Rivers; Stochastic models; Stochastic systems; Atmospheric corrections; LANDSAT; Temperature modelling; Thermal infrared; Thermal response; Water surface temperature; Atmospheric temperature; calibration; correction; infrared imagery; Landsat; model validation; performance assessment; physicochemical property; radiative transfer; remote sensing; river water; satellite data; satellite imagery; water temperature; United States; White River [United States]
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179385
作者单位	Instituto de Pesquisas Hidráulicas, Universidade Federal do Rio Grande do Sul, Porto Alegre, 91501-970, Brazil; Centro de Tecnologia, Universidade Federal de Alagoas, Maceió, 57072–970, Brazil; Escuela de Ciencias Agrícolas, Pecuarias y del Medio Ambiente, Universidad Nacional Abierta y a Distancia, Bogotá, 111411, Colombia; UMR 228 Espace-DEV, Institut de Recherche pour le Développement, Marseille, 13001, France
推荐引用方式 GB/T 7714	Tavares M.H.,Cunha A.H.F.,Motta-Marques D.,et al. Derivation of consistent, continuous daily river temperature data series by combining remote sensing and water temperature models[J],2020,241.
APA	Tavares M.H..,Cunha A.H.F..,Motta-Marques D..,Ruhoff A.L..,Fragoso C.R..,...&Bonnet M.-P..(2020).Derivation of consistent, continuous daily river temperature data series by combining remote sensing and water temperature models.Remote Sensing of Environment,241.
MLA	Tavares M.H.,et al."Derivation of consistent, continuous daily river temperature data series by combining remote sensing and water temperature models".Remote Sensing of Environment 241(2020).