Skip all navigation and jump to content Jump to site navigation
About MODIS News Data Tools /images2 Science Team Science Team Science Team

   + Home
MODIS Publications Link
MODIS Presentations Link
MODIS Biographies Link
MODIS Science Team Meetings Link



Urquhart, EA; Zaitchik, BF; Hoffman, MJ; Guikema, SD; Geiger, EF (2012). Remotely sensed estimates of surface salinity in the Chesapeake Bay: A statistical approach. REMOTE SENSING OF ENVIRONMENT, 123, 522-531.

In coastal and estuarine environments, near-surface salinity varies significantly in space and time. As absolute salinity and salinity gradients are central to many physical and ecological processes in these environments, reliable and consistent salinity estimates are a priority for marine research and application communities. Satellite remote sensing has a great potential to meet this need, yet sensors and algorithms designed to monitor open ocean salinity are typically ill-suited for high resolution applications to coastlines and estuaries. Here we present results of multiple statistical models that predict daily. gridded surface salinity at 1 km resolution across Chesapeake Bay as a function of level 2 surface reflectance estimates from the NASA Moderate Resolution Imaging Spectroradiometer (MODIS), onboard the Aqua platform. Eight statistical methods were tested and it was found that sea surface salinity can be accurately predicted via remote sensed products with an accuracy that is more than sufficient for many physical and ecological applications. For the best-performing statistical model, mean absolute error was 1.82 relative to mean Chesapeake Bay salinity of 16.5. (C) 2012 Elsevier Inc. All rights reserved.



NASA Home Page Goddard Space Flight Center Home Page