• Rural Digital Europe

We present a data set on remote sensing reflectance (RRS) at 1nm resolution from 350 to 800nm obtained from measurements in the coastal and open ocean areas of the South China Sea and Sulu Sea from 18 to 27 November 2011. For the measurements we used radiometric hyperspectral (3.3 nm sampling, 10 nm FWHM) underwater profile measurements down to the 0.1 % light level using RAMSES (TriOS GmbH, Germany) sensors which measured depth resolved the upwelling radiance and downwelling irradiance, both corrected by incident sunlight fluctuations with a second RAMSES sensor measuring the above water downwelling irradiance. The later sensor data were also used to finally calculate RRS. We followed the protocol by Mueller et al. (2003) further modified by Matsuoka et al. (2007) and Stramski et al. (2008), as described for our instrument set-up in Taylor et al. (2011). Our method is further described and assessed for its uncertainty in Tilstone et al. (2020). The campaign is described in detail in Cheah et al. (2013) and was also optical constituents hyperspectral absorption data (Bracher et al. 2021a, b) and phytoplankton pigments (Bracher 2014) were measured. We are indebted to Maria Altenburg-Soppa, Sonja Wiegmann and Joseph Palermo for their assistance in the sampling on RV Sonne and acknowledge the help of the chief scientist Birgit Quack, the crew and captain of the RV Sonne during SHIVA-Sonne in performing our measurements.

We present a data set on remote sensing reflectance (RRS) at 1nm resolution from 350 to 800nm obtained from measurements in the North Sea and Sogne Fjord from 30 April to 7 May 2016. For the measurements we used radiometric hyperspectral (3.3 nm sampling, 10 nm FWHM) underwater profile measurements down to the 0.1 % light level using RAMSES (TriOS GmbH, Germany) sensors which measured depth resolved the upwelling radiance and downwelling irradiance, both corrected by incident sunlight fluctuations with a second RAMSES sensor measuring the above water downwelling irradiance. The later sensor data were also used to finally calculate RRS. We followed the protocol by Mueller et al. (2003) further modified by Matsuoka et al. (2007) and Stramski et al. (2008), as described for our instrument set-up in Taylor et al. (2011). Our method is further described and assessed for its uncertainty in Tilstone et al. (2020). The same campaign was sampled for optical constituents hyperspectral absorption data in Bracher et al. (2021a-d) and for phytoplankton pigments in Bracher and Wiegmann (2019).

Since 1960, remote sensing satellite imagery reconnaissance has played a significant role in military operations by providing information concerning enemy missiles, troop deployments and military positioning using photographic images from lighter-than-air balloons to aircraft platforms and finally satellite remote sensing imagery with little attention given to broader war impacts. However, besides the war-related uses of such technology, many academic researchers have taken pains to use such advanced technology for examining war impacts. This paper highlights the applications of this technology for detecting war impacts.