Literature DB >> 27004998

The new Landsat 8 potential for remote sensing of colored dissolved organic matter (CDOM).

E Terrence Slonecker1, Daniel K Jones2, Brian A Pellerin2.   

Abstract

Due to a combination of factors, such as a new coastal/aerosol band and improved radiometric sensitivity of the Operational Land Imager aboard Landsat 8, the atmospherically-corrected Surface Reflectance product for Landsat data, and the growing availability of corrected fDOM data from U.S. Geological Survey gaging stations, moderate-resolution remote sensing of fDOM may now be achievable. This paper explores the background of previous efforts and shows preliminary examples of the remote sensing and data relationships between corrected fDOM and Landsat 8 reflectance values. Although preliminary results before and after Hurricane Sandy are encouraging, more research is needed to explore the full potential of Landsat 8 to continuously map fDOM in a number of water profiles. Published by Elsevier Ltd.

Entities:  

Keywords:  Absorbance; Advanced land imager; Atmospheric correction; Colored dissolved organic matter (CDOM); Dissolved organic matter (DOM); EO-1; Fluorescence; Landsat 8; Operational land imager; Reflectance; Remote sensing; fluorescent fraction of CDOM (fDOM)

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Year:  2016        PMID: 27004998     DOI: 10.1016/j.marpolbul.2016.02.076

Source DB:  PubMed          Journal:  Mar Pollut Bull        ISSN: 0025-326X            Impact factor:   5.553


  3 in total

1.  Environmental effects of ozone depletion and its interactions with climate change: Progress report, 2016.

Authors: 
Journal:  Photochem Photobiol Sci       Date:  2017-02-15       Impact factor: 3.982

2.  Field-Validated Detection of Aureoumbra lagunensis Brown Tide Blooms in the Indian River Lagoon, Florida, Using Sentinel-3A OLCI and Ground-Based Hyperspectral Spectroradiometers.

Authors:  Taylor J Judice; Edith A Widder; Warren H Falls; Dulcinea M Avouris; Dominic J Cristiano; Joseph D Ortiz
Journal:  Geohealth       Date:  2020-06-20

3.  Predictive performance of regression models to estimate Chlorophyll-a concentration based on Landsat imagery.

Authors:  Miguel Ángel Matus-Hernández; Norma Yolanda Hernández-Saavedra; Raúl Octavio Martínez-Rincón
Journal:  PLoS One       Date:  2018-10-12       Impact factor: 3.240

  3 in total

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