Hu, Chuanmin: University of South Florida (Project Lead)
Project Funding:
2014 - 2016
NRA: 2013 NASA: The Science of Terra and Aqua
Funded by NASA
Abstract:
Problem statement: Two problems exist in the current global ocean color data products: 1) data coverage (quantity), and 2) data accuracy (quality). These problems not only create data gaps and potential spatial/temporal aliasing, but also lead to large inconsistencies between sensors. For example, global monthly mean chlorophyll-a from the default algorithms (ChlOCx) can differ by 5-10% between SeaWiFS and MODIS/Aqua, and this difference can reach 10-20% or more on regional scales. Such differences can overwhelm realistic changes of the ocean (often < 10%) in response to climate variability.
A novel algorithm concept, namely the Ocean Color Index (OCI) algorithm, has shown great potentials in achieving multi-sensor consistency and recovering low quality (i.e., flagged) ChlOCx data to increase coverage. This is because that the OCI algorithm is nearly immune to the spectrally related errors in the input remote-sensing reflectance (Rrs) data for clear waters (Chl <= 0.25 mg m-3, representing ~80% of the global ocean).
Objectives: For over 30 years, the ocean color community has adopted the band-ratio concept in deriving several EOS data products such as Chl and Kd(490) (diffuse attenuation coefficient at 490 nm, m-1). The novel OCI algorithm concept of using band- difference has been proven superior to band-ratio algorithms in several aspects of algorithm design for clear waters. The project is designed to fully realize the potentials of this new algorithm concept to improve both data quantity and data quality and to establish a seamless multi-sensor climate data record (CDR) of ocean chlorophyll-a concentrations. Specifically, the project has the following objectives:
1) Recover >50% of the low-quality ChlOCx data to valid observations to improve spatial and temporal coverage for most ocean areas. These data are currently discarded in the global data composites;
2) Refine the OCI algorithm to minimize the impact of colored dissolved organic matter (CDOM) on Chl retrievals, with cross-sensor differences reduced from >10% to <5% and cross-basin biases minimized;
3) Extend the same algorithm concept to higher-Chl waters and to another key EOS data product, nKd(490) (normalized Kd(490));;
4) Revisit some of the profound findings using the new products, for example decadal ocean Chl changes in ocean gyres;
5) Make recommendations to future sensor design and data processing.
Approach: The project relies entirely on retrospective analyses of both satellite data (through NASA's Ocean Biology Processing Group) and in situ data (NASA's SeaBASS archive). Specifically,
1) Quality control flags will be examined against ChlOCI data quality to determine which flags can be relaxed (and to what degree) in order to increase data quantity without compromising quality. Validation will be performed using both in situ data and spatial/temporal consistency checks, with uncertainty estimated;
2) Algorithm coefficients will be fine-tuned using ratios of Rrs(412)/ Rrs(443) and SeaBASS data partitioned to major ocean basins in order to minimize the influence of CDOM on Chl retrieval accuracy and to minimize cross-basin biases;
3) The new algorithm concept is applicable to ~80% of the ocean (clear waters). To extend to higher-Chl waters, Rrs spectral slope will be incorporated. Likely, a similar approach will be developed for another key EOS product, nKd(490);
4) Time series will be analyzed to re-examine some of the important decadal-scale questions, for example ocean response to ENSO, ocean gyre expansion, etc.
5) Based on the results above, recommendations on future sensor design (e.g., signal-to-noise) and data processing will be made.
The project will focus on MODIS (Terra and Aqua) and several other sensors (SeaWiFS, MERIS, VIIRS). The outcomes are expected to have an immediate impact on global and regional-scale studies of ocean chlorophyll-a changes and primary productivity changes in response to climate variability.
Publications:
Hu, C., Feng, L., Lee, Z., Franz, B. A., Bailey, S. W., Werdell, P. J., Proctor, C. W. 2019. Improving Satellite Global Chlorophyll
a
Data Products Through Algorithm Refinement and Data Recovery. Journal of Geophysical Research: Oceans. 124(3), 1524-1543. DOI: 10.1029/2019JC014941
Le, C., Zhou, X., Hu, C., Lee, Z., Li, L., Stramski, D. 2018. A Color-Index-Based Empirical Algorithm for Determining Particulate Organic Carbon Concentration in the Ocean From Satellite Observations. Journal of Geophysical Research: Oceans. 123(10), 7407-7419. DOI: 10.1029/2018JC014014
More details may be found in the following project profile(s):
