Surface irradiance estimations on watercourses with remote sensing data

Marie-Luise Kautz; Pierre Karrasch; Christine Wessollek

doi:10.1117/12.2325373

9 October 2018 Surface irradiance estimations on watercourses with remote sensing data

Marie-Luise Kautz, Pierre Karrasch, Christine Wessollek

Proceedings Volume 10790, Earth Resources and Environmental Remote Sensing/GIS Applications IX; 107900X (2018) https://doi.org/10.1117/12.2325373
Event: SPIE Remote Sensing, 2018, Berlin, Germany

Abstract

The vegetation in the riparian zone of a watercourse influences the water state with multiple factors, first via direct substance discharge and secondly via shadow casting on the water surface. Shadowing directly regulates the solar radiant energy arriving at the water surface. Solar radiation input to aquatic environments is the most important abiotic factor for aquatic flora and fauna habitat development. Thus, to adequately asses the ecological state of water courses it is necessary to quantify the solar surface irradiance E (W/m²) arriving on the water surface. When estimating the solar surface irradiance the complex coherence between incoming solar radiation, atmospheric influences, and spatial-temporal geometries need to be investigated. This work established a work flow to compute the solar surface irradiance for water bodies using different remote sensing data. The work flow was tested on regional level for a section of the river Freiberger Mulde, Saxony, for the year 2016. Product of the calculations is a map visualising the annual sum of the solar surface irradiance (kWh/m²) arriving on the Freiberger Mulde water surface and the surrounding terrain. Based on these information bio-hydrological issues can be further examinated.

Citation Download Citation

Marie-Luise Kautz, Pierre Karrasch, and Christine Wessollek "Surface irradiance estimations on watercourses with remote sensing data", Proc. SPIE 10790, Earth Resources and Environmental Remote Sensing/GIS Applications IX, 107900X (9 October 2018); https://doi.org/10.1117/12.2325373

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KEYWORDS

Clouds

Solar energy

Solar radiation

Vegetation

Sun

Atmospheric modeling

Remote sensing

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