Unattended processing of shipborne hyperspectral reflectance measurements

AbstractHyperspectral remote-sensing reflectance (Rrs) from above-surface (ir)radiance measurements is derived using a new, automated method that is suitable for use on moving platforms. The sensors are mounted on a rotating platform that compensates for changing solar and ship azimuth angles, optimizing the sensor azimuth for minimal contribution of sky radiance to measured water-leaving radiance. This sea-surface reflectance (ρs) lies in the order of 2.5–8% of sky radiance, and is determined through spectral optimization, minimizing the propagation of atmospheric absorption features to Rrs. Up to 15 of these gas absorption features are frequently recognized in (ir)radiance spectra under clear and overcast skies. Rrs was satisfactorily reproduced for a wide range of simulated Case 2 waters and clear sky conditions. A set of 13,784 in situ measurements collected with optimized viewing angles on the high-absorption, low-scattering Baltic Sea was collected in April and July 2010–2011. The processing procedure yielded a 22% retrieval rate of ρs for the field data. The shape of the subsurface irradiance reflectance measurements (R(0−)) measured at anchor stations was well reproduced in above-surface Rrs in those cases where the algorithm converged on a solution for ρs, except under unstable or weak illumination conditions. Clear-sky conditions resulted in the best correspondence of Rrs and R(0−) and gave the highest (>50%) retrieval rates of ρs. Two indices, derived from the available sensor data, are given to describe illumination conditions, and are shown to predict the ability of the algorithm to retrieve Rrs

Secchi depth calculations in BALTSEM

Secchi depth measurements have been carried out for over 100 years in the Baltic Sea and the changes in Secchi depth give indications of the development of phytoplankton biomass in response to eutrophication. In the implementation of the ecosystem approach to Baltic Sea management, indicators based on Secchi depth are unique in that targets representing a good environmental status can be obtained from actual observations, whereas most other indicators lack observational evidence of a reference state representing conditions before substantial eutrophication. In the on‐going revision of the HELCOM Baltic Sea Action Plan, new targets on e.g., Secchi depth have been developed (HELCOM2012). The following step is to use modeling to find nutrient inputs to the Baltic Sea, so called Maximum Allowable Inputs, that result in ecosystem changes so that eventually the good environmental status indicated by the targets is reached. This modeling effort is carried out using the coupled physical‐biogeochemical model BALTSEM developed at BNI. The BALTSEM model resolves the Baltic Sea horizontally with 13 sub‐basins, but each of these with high vertical resolution. The biogeochemical model includes inorganic and bioavailable organic nitrogen, phosphorus and silica, three phytoplankton groups, zooplankton and oxygen. Benthic nutrient regeneration and retention are modeled in addition. This report describes a statistical post‐processing algorithm to calculate Secchi depth from BALTSEM results to provide additional accuracy and confidence of Secchi depth estimates compared to the simplistic intrinsic transparency calculations within the BALTSEM model. The additional quality in the Secchi depth calculation results isof major importance for the results of the calculation of the Maximum Allowable Inputs

Secchi depth calculations in BALTSEM

Secchi depth measurements have been carried out for over 100 years in the Baltic Sea and the changes in Secchi depth give indications of the development of phytoplankton biomass in response to eutrophication. In the implementation of the ecosystem approach to Baltic Sea management, indicators based on Secchi depth are unique in that targets representing a good environmental status can be obtained from actual observations, whereas most other indicators lack observational evidence of a reference state representing conditions before substantial eutrophication. In the on‐going revision of the HELCOM Baltic Sea Action Plan, new targets on e.g., Secchi depth have been developed (HELCOM2012). The following step is to use modeling to find nutrient inputs to the Baltic Sea, so called Maximum Allowable Inputs, that result in ecosystem changes so that eventually the good environmental status indicated by the targets is reached. This modeling effort is carried out using the coupled physical‐biogeochemical model BALTSEM developed at BNI. The BALTSEM model resolves the Baltic Sea horizontally with 13 sub‐basins, but each of these with high vertical resolution. The biogeochemical model includes inorganic and bioavailable organic nitrogen, phosphorus and silica, three phytoplankton groups, zooplankton and oxygen. Benthic nutrient regeneration and retention are modeled in addition. This report describes a statistical post‐processing algorithm to calculate Secchi depth from BALTSEM results to provide additional accuracy and confidence of Secchi depth estimates compared to the simplistic intrinsic transparency calculations within the BALTSEM model. The additional quality in the Secchi depth calculation results isof major importance for the results of the calculation of the Maximum Allowable Inputs

Measuring marine plastic debris from space: Initial assessment of observation requirements

Sustained observations are required to determine the marine plastic debris mass balance and to support effective policy for planning remedial action. However, observations currently remain scarce at the global scale. A satellite remote sensing system could make a substantial contribution to tackling this problem. Here, we make initial steps towards the potential design of such a remote sensing system by: (1) identifying the properties of marine plastic debris amenable to remote sensing methods and (2) highlighting the oceanic processes relevant to scientific questions about marine plastic debris. Remote sensing approaches are reviewed and matched to the optical properties of marine plastic debris and the relevant spatio-temporal scales of observation to identify challenges and opportunities in the field. Finally, steps needed to develop marine plastic debris detection by remote sensing platforms are proposed in terms of fundamental science as well as linkages to ongoing planning for satellite systems with similar observation requirements