Water surface temperature profiles for the Rhine River derived from Landsat ETM+ data

Fricke, Katharina; Baschek, Björn

doi:10.1117/12.2028669

Cited by 4 publications

(9 citation statements)

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“…The mean bias after correction for emissivity (−0.85 AE 0.15°C) is similar to that of Landsat ETM+ data of the research area, 6 but the original satellite dataset from several acquisition dates has a much higher variance compared to the aerial data.…”

Section: Resultsmentioning

confidence: 51%

“…The standard deviation of the difference between remote sensing temperatures and in situ measurement increased with the pixel size in this comparison (see Fig. 6), but it is still lower than the standard deviation of the original Landsat ETM+ scenes for the Rhine River (σ ¼ 1.3°C) from a previous study, 6 which are additionally affected by the atmosphere and sensor characteristics. …”

Section: Evaluation Of Original Calibrated and Corrected Aerial Surmentioning

confidence: 84%

“…It reduced the mean temperature difference between the corrected aerial survey and in situ measurements to 0.14 AE 0.33°C, similar to the correction of satellite data for the same area. 6 While the expenditures for in situ measurements are lower for this method, it requires information about the atmospheric situation and more effort for the correction. Also, in this case, it overestimated the in situ bulk temperatures and did not take other influences than the atmosphere into account.…”

Section: Resultsmentioning

confidence: 99%

“…The accuracy of temperatures from Landsat ETM+ and the possibility to identify dischargers along the Rhine River has been evaluated in a previous study. 6 In this paper, the capabilities of satellite data will be evaluated based on artificial satellite datasets derived from the aerial survey (see Sec. 4.3).…”

Section: Satellite Datamentioning

confidence: 99%

“…3,4 TIR data from satellite sensors have also been used for the investigation of inland water bodies, e.g., time series and longitudinal profiles of stream temperatures, [5][6][7] thermal water pollution and river plumes in estuaries and lagoons, 8,9 lake surface temperatures, 10 and groundwater inflow and thermal anomalies. 11 For smaller water bodies, the resolution of satellite sensors may be insufficient and airborne thermal remote sensing is necessary to observe the desired processes.…”

Section: Introductionmentioning

confidence: 99%

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Temperature monitoring along the Rhine River based on airborne thermal infrared remote sensing: estimation ofin situwater temperatures and inflow detection compared to artificial satellite data

Fricke¹,

Baschek²

2015

J. Appl. Remote Sens

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Abstract. Water temperature is an important parameter of water quality. It influences the survival and growth of animal and plant species in river ecosystems. Current in situ measurements do not allow for the spatial coverage that is necessary for continuous monitoring. Hence, the ability of remote sensing temperatures from aerial and artificial satellite datasets to accurately identify and investigate water temperature and inflows is evaluated for a case study of the Upper and Middle Rhine River. Water surface temperatures acquired by an aerial survey and the results of two correction methods were evaluated: the difference to in situ measurements could be reduced to 0.04 AE 0.21°C with a calibration based on in situ measurements and to 0.14 AE 0.33°C based on atmospheric parameters modeled with MODTRAN ® 5.3.2. Inflows and mixing processes of water bodies with differing temperatures could be identified successfully with the change point analysis method and based on the standard deviation of the distributed longitudinal profile even for smaller dischargers. With the artificial satellite datasets, the ability to detect inflows decreases mainly with the noise of the dataset, also leading to a higher number of false positive change points.

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Section: Resultsmentioning

confidence: 51%

Section: Evaluation Of Original Calibrated and Corrected Aerial Surmentioning

confidence: 84%

Section: Resultsmentioning

confidence: 99%

Section: Satellite Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Temperature monitoring along the Rhine River based on airborne thermal infrared remote sensing: estimation ofin situwater temperatures and inflow detection compared to artificial satellite data

Fricke¹,

Baschek²

2015

J. Appl. Remote Sens

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Temperature monitoring along the Rhine River based on airborne thermal infrared remote sensing: qualitative results compared to satellite data and validation with in situ measurements

Fricke

Baschek

2014

SPIE Proceedings

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Water temperature is an important parameter of water quality and influences other physical and chemical parameters. It also directly influences the survival and growth of animal and plant species in river ecosystems. In situ measurements do not allow for a total spatial coverage of water bodies and rivers that is necessary for monitoring and research at the Federal Institute of Hydrology (BfG), Germany. Hence, the ability of different remote sensing products to identify and investigate water inflows and water temperatures in Federal waterways is evaluated within the research project 'Remote sensing of water surface temperature'. The research area for a case study is the Upper and Middle Rhine River from the barrage in Iffezheim to Koblenz.Satellite products (e. g. Landsat and ASTER imagery) can only be used for rivers at least twice as wide as the spatial resolution of the satellite images. They can help to identify different water bodies only at tributaries with larger inflow volume (Main and Mosel) or larger temperature differences between the inflow (e. g. from power plants working with high capacity) and the river water. To identify and investigate also smaller water inflows and temperature differences, thermal data with better ground and thermal resolution is required.An aerial survey of the research area was conducted in late October 2013. Data of the surface was acquired with two camera systems, a digital camera with R, G, B, and Near-IR channels, and a thermal imaging camera measuring the brightness temperature in the 8-12 µm wavelength region (TIR). The resolution of the TIR camera allowed for a ground resolution of 4 m, covering the whole width of the main stream and larger branches. The RGB and NIR data allowed to eliminate land surface temperatures from the analysis and to identify clouds and shadows present during the data acquisition. By degrading the spatial resolution and adding sensor noise, artificial Landsat ETM+ and TIRS datasets were created to evaluate whether the methods applied to the aerial survey data are also applicable for satellite datasets. In situ measurements were obtained from water quality measurement stations and specifically deployed temperature loggers.Two alternative methods to correct for atmospheric influences were evaluated: calibration based on in situ water temperature measurements and atmospheric correction based on atmospheric parameters modelled with MODTRAN R 5. Both methods rely on input data, the former on in situ measurements of the water temperature, the latter on data from climate stations. The results are validated by the dataset of independent in situ measurements. The remaining difference of the corrected aerial survey to the in situ measurements could be reduced to 0.0±0.2 • C for the calibration and 0.1±0.3 • C for the atmospheric correction. The variance of the atmospheric correction proved to be larger than of the in situ calibration method, but still smaller than the variance of atmospherically corrected, real LANDSAT ETM+ data.Inflows with differing wat...

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Monitoring Water Diversity and Water Quality with Remote Sensing and Traits

Lausch,

Bannehr,

Berger

et al. 2024

Remote Sensing

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Changes and disturbances to water diversity and quality are complex and multi-scale in space and time. Although in situ methods provide detailed point information on the condition of water bodies, they are of limited use for making area-based monitoring over time, as aquatic ecosystems are extremely dynamic. Remote sensing (RS) provides methods and data for the cost-effective, comprehensive, continuous and standardised monitoring of characteristics and changes in characteristics of water diversity and water quality from local and regional scales to the scale of entire continents. In order to apply and better understand RS techniques and their derived spectral indicators in monitoring water diversity and quality, this study defines five characteristics of water diversity and quality that can be monitored using RS. These are the diversity of water traits, the diversity of water genesis, the structural diversity of water, the taxonomic diversity of water and the functional diversity of water. It is essential to record the diversity of water traits to derive the other four characteristics of water diversity from RS. Furthermore, traits are the only and most important interface between in situ and RS monitoring approaches. The monitoring of these five characteristics of water diversity and water quality using RS technologies is presented in detail and discussed using numerous examples. Finally, current and future developments are presented to advance monitoring using RS and the trait approach in modelling, prediction and assessment as a basis for successful monitoring and management strategies.

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Water surface temperature profiles for the Rhine River derived from Landsat ETM+ data

Cited by 4 publications

References 0 publications

Temperature monitoring along the Rhine River based on airborne thermal infrared remote sensing: estimation ofin situwater temperatures and inflow detection compared to artificial satellite data

Temperature monitoring along the Rhine River based on airborne thermal infrared remote sensing: estimation ofin situwater temperatures and inflow detection compared to artificial satellite data

Temperature monitoring along the Rhine River based on airborne thermal infrared remote sensing: qualitative results compared to satellite data and validation with in situ measurements

Monitoring Water Diversity and Water Quality with Remote Sensing and Traits

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