Toward Long-Term Aquatic Science Products from Heritage Landsat Missions
|Author(s)||Pahlevan Nima1, 2, Balasubramanian Sundarabalan V.1, 3, Sarkar Sudipta1, 2, Franz Bryan A.1|
|Affiliation(s)||1 : NASA, Goddard Space Flight Ctr, 8800 Greenbelt Rd, Greenbelt, MD 20771 USA.
2 : Sci Syst & Applicat Inc, 10210 Greenbelt Rd,Suite 600, Lanham, MD 20706 USA.
3 : Univ Maryland, Dept Geog Sci, College Pk, MD 20740 USA.
|Source||Remote Sensing (2072-4292) (Mdpi), 2018-09 , Vol. 10 , N. 9https://w , P. 1337 (23p.)|
|WOS© Times Cited||21|
|Note||Special Issue Remote Sensing of Water Quality|
|Keyword(s)||Landsat, coastal/inland waters, atmospheric correction, vicarious calibration, validation, water quality, time-series applications|
This paper aims at generating a long-term consistent record of Landsat-derived remote sensing reflectance (R-rs) products, which are central for producing downstream aquatic science products (e.g., concentrations of total suspended solids). The products are derived from Landsat-5 and Landsat-7 observations leading to Landsat-8 era to enable retrospective analyses of inland and nearshore coastal waters. In doing so, the data processing was built into the SeaWiFS Data Analysis System (SeaDAS) followed by vicariously calibrating Landsat-7 and -5 data using reference in situ measurements and near-concurrent ocean color products, respectively. The derived R rs products are then validated using (a) matchups using the Aerosol Robotic Network (AERONET) data measured by in situ radiometers, i.e., AERONET-OC, and (b) ocean color products at select sites in North America. Following the vicarious calibration adjustments, it is found that the overall biases in R-rs products are significantly reduced. The root-mean-square errors (RMSE), however, indicate noticeable uncertainties due to random and systematic noise. Long-term (since 1984) seasonal R-rs composites over 12 coastal and inland systems are further evaluated to explore the utility of Landsat archive processed via SeaDAS. With all the qualitative and quantitative assessments, it is concluded that with careful algorithm developments, it is possible to discern natural variability in historic water quality conditions using heritage Landsat missions. This requires the changes in R-rs exceed maximum expected uncertainties, i.e., 0.0015 [1/sr], estimated from mean RMSEs associated with the matchups and intercomparison analyses. It is also anticipated that Landsat-5 products will be less susceptible to uncertainties in turbid waters with R-rs(660) > 0.004 [1/sr], which is equivalent of similar to 1.2% reflectance. Overall, end-users may utilize heritage R-rs products with "fitness-for-purpose" concept in mind, i.e., products could be valuable for one application but may not be viable for another. Further research should be dedicated to enhancing atmospheric correction to account for non-negligible near-infrared reflectance in CDOM-rich and extremely turbid waters.