A class-based approach to characterizing and mapping the uncertainty of the MODIS ocean chlorophyll product

Moore, Timothy S.; Campbell, J. W.; Dowell, Mark

doi:10.1016/j.rse.2009.07.016

Cited by 299 publications

(230 citation statements)

References 12 publications

Supporting

Mentioning

196

Contrasting

Unclassified

Order By: Relevance

“…Moore et al, 2009Moore et al, , 2014; OC-CCI uncertainty products, http:// www.esa-oceancolour-cci.org/?q=webfm_send/321). In the model, all properties are known precisely everywhere allowing details of how the optical constituents and their interactions impacts reflectance and the derived products.…”

Section: Discussion and Summarymentioning

confidence: 99%

“…Recognizing that waters can have distinct optical properties (Moore et al, 2009;Szeto et al, 2011), there have been several projects to produce regionally distinct algorithms (e.g. Szeto et al, 2011;Johnson et al, 2013; latest release (V3) of the OC-CCI project, https://www.oceancolour.org).…”

Section: Approach 3: Region-specific Coefficients (Ra)mentioning

confidence: 99%

“…Our goal is to illustrate the use of a numerical laboratory that (a) helps users of ocean colour, particularly modellers, gain further understanding of the products they use and (b) helps the ocean colour community to explore other ocean colour products, their biases and uncertainties, as well as to aid in future algorithm development. 50 % (Moore et al, 2009). Uncertainties arise from clouds, patchiness, atmospheric corrections and measurement errors, as well as the algorithm used to deduce Chl a.…”

mentioning

confidence: 99%

“…The relationship is typically considered robust in open ocean conditions, where the optical effects of phytoplankton co-vary with other optical constituents including coloured dissolved organic matter (CDOM) and detritus (so called Case-I conditions; Smith and Baker, 1977;Morel, 1988;O'Reilly et al, 2000). Though even in these waters the error estimate is about 35 % (Moore et al, 2009). Uncertainties that arise from this type of algorithm can be attributed to the potential divergence in the relative role of the optically important water constituents (e.g.…”

mentioning

confidence: 99%

“…The observation that satellite-derived products have large errors and specific regional biases is relatively well understood in the ocean colour scientific community (Hu et al, 2000;Moore et al, 2009;Blondeau-Parissier et al, 2014;Szeto et al, 2011). However, there remain many aspects of errors, biases and uncertainties that are poorly quantified, particularly in regions where there are little or no in situ data to compare to the satellite-derived products.…”

mentioning

confidence: 99%

See 4 more Smart Citations

Modelling ocean-colour-derived chlorophyll <i>a</i>

2018

View full text Add to dashboard Cite

Abstract. This article provides a proof of concept for using a biogeochemical/ecosystem/optical model with a radiative transfer component as a laboratory to explore aspects of ocean colour. We focus here on the satellite ocean colour chlorophyll a (Chl a) product provided by the often-used blue/green reflectance ratio algorithm. The model produces output that can be compared directly to the real-world ocean colour remotely sensed reflectance. This model output can then be used to produce an ocean colour satellite-like Chl a product using an algorithm linking the blue versus green reflectance similar to that used for the real world. Given that the model includes complete knowledge of the (model) water constituents, optics and reflectance, we can explore uncertainties and their causes in this proxy for Chl a (called "derived Chl a" in this paper). We compare the derived Chl a to the "actual" model Chl a field. In the model we find that the mean absolute bias due to the algorithm is 22 % between derived and actual Chl a. The real-world algorithm is found using concurrent in situ measurement of Chl a and radiometry. We ask whether increased in situ measurements to train the algorithm would improve the algorithm, and find a mixed result. There is a global overall improvement, but at the expense of some regions, especially in lower latitudes where the biases increase. Not surprisingly, we find that regionspecific algorithms provide a significant improvement, at least in the annual mean. However, in the model, we find that no matter how the algorithm coefficients are found there can be a temporal mismatch between the derived Chl a and the actual Chl a. These mismatches stem from temporal decoupling between Chl a and other optically important water constituents (such as coloured dissolved organic matter and detrital matter). The degree of decoupling differs regionally and over time. For example, in many highly seasonal regions, the timing of initiation and peak of the spring bloom in the derived Chl a lags the actual Chl a by days and sometimes weeks. These results indicate that care should also be taken when studying phenology through satellite-derived products of Chl a. This study also reemphasizes that ocean-colourderived Chl a is not the same as the real in situ Chl a. In fact the model derived Chl a compares better to real-world satellite-derived Chl a than the model actual Chl a. Modellers should keep this is mind when evaluating model output with ocean colour Chl a and in particular when assimilating this product. Our goal is to illustrate the use of a numerical laboratory that (a) helps users of ocean colour, particularly modellers, gain further understanding of the products they use and (b) helps the ocean colour community to explore other ocean colour products, their biases and uncertainties, as well as to aid in future algorithm development.

show abstract

Section: Discussion and Summarymentioning

confidence: 99%

Section: Approach 3: Region-specific Coefficients (Ra)mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 3 more Smart Citations

Modelling ocean-colour-derived chlorophyll <i>a</i>

2018

View full text Add to dashboard Cite

show abstract

Global monthly sea surface nitrate fields estimated from remotely sensed sea surface temperature, chlorophyll, and modeled mixed layer depth

Arteaga

Pahlow

Oschlies

2015

Geophysical Research Letters

View full text Add to dashboard Cite

Information about oceanic nitrate is crucial for making inferences about marine biological production and the efficiency of the biological carbon pump. While there are no optical properties that allow direct estimation of inorganic nitrogen, its correlation with other biogeochemical variables may permit its inference from satellite data. Here we report a new method for estimating monthly mean surface nitrate concentrations employing local multiple linear regressions on a global 1° by 1° resolution grid, using satellite‐derived sea surface temperature, chlorophyll, and modeled mixed layer depth. Our method is able to reproduce the interannual variability of independent in situ nitrate observations at the Bermuda Atlantic Time Series, the Hawaii Ocean Time series, the California coast, and the southern New Zealand region. Our new method is shown to be more accurate than previous algorithms and thus can provide improved information on temporal and spatial nutrient variations beyond the climatological mean at regional and global scales.

show abstract

An assessment of phytoplankton primary productivity in the Arctic Ocean from satellite ocean color/in situ chlorophyll‐a based models

et al. 2015

View full text Add to dashboard Cite

We investigated 32 net primary productivity (NPP) models by assessing skills to reproduce integrated NPP in the Arctic Ocean. The models were provided with two sources each of surface chlorophyll‐ a concentration (chlorophyll), photosynthetically available radiation (PAR), sea surface temperature (SST), and mixed‐layer depth (MLD). The models were most sensitive to uncertainties in surface chlorophyll, generally performing better with in situ chlorophyll than with satellite‐derived values. They were much less sensitive to uncertainties in PAR, SST, and MLD, possibly due to relatively narrow ranges of input data and/or relatively little difference between input data sources. Regardless of type or complexity, most of the models were not able to fully reproduce the variability of in situ NPP, whereas some of them exhibited almost no bias (i.e., reproduced the mean of in situ NPP). The models performed relatively well in low‐productivity seasons as well as in sea ice‐covered/deep‐water regions. Depth‐resolved models correlated more with in situ NPP than other model types, but had a greater tendency to overestimate mean NPP whereas absorption‐based models exhibited the lowest bias associated with weaker correlation. The models performed better when a subsurface chlorophyll‐ a maximum (SCM) was absent. As a group, the models overestimated mean NPP, however this was partly offset by some models underestimating NPP when a SCM was present. Our study suggests that NPP models need to be carefully tuned for the Arctic Ocean because most of the models performing relatively well were those that used Arctic‐relevant parameters.

show abstract

A class-based approach to characterizing and mapping the uncertainty of the MODIS ocean chlorophyll product

Cited by 299 publications

References 12 publications

Modelling ocean-colour-derived chlorophyll <i>a</i>

Modelling ocean-colour-derived chlorophyll <i>a</i>

Global monthly sea surface nitrate fields estimated from remotely sensed sea surface temperature, chlorophyll, and modeled mixed layer depth

An assessment of phytoplankton primary productivity in the Arctic Ocean from satellite ocean color/in situ chlorophyll‐a based models

Contact Info

Product

Resources

About

A class-based approach to characterizing and mapping the uncertainty of the MODIS ocean chlorophyll product

Cited by 299 publications

References 12 publications

Modelling ocean-colour-derived chlorophyll &lt;i&gt;a&lt;/i&gt;

Modelling ocean-colour-derived chlorophyll &lt;i&gt;a&lt;/i&gt;

Global monthly sea surface nitrate fields estimated from remotely sensed sea surface temperature, chlorophyll, and modeled mixed layer depth

An assessment of phytoplankton primary productivity in the Arctic Ocean from satellite ocean color/in situ chlorophyll‐a based models

Contact Info

Product

Resources

About

Modelling ocean-colour-derived chlorophyll <i>a</i>

Modelling ocean-colour-derived chlorophyll <i>a</i>