Global maps of Forel–Ule index, hue angle and Secchi disk depth derived from 21 years of monthly ESA Ocean Colour Climate Change Initiative data

Abstract. We document the development and public release of a new dataset (1997–2018), consisting of global maps of the Forel–Ule index, hue angle and Secchi disk depth. Source data come from the European Space Agency (ESA) Ocean Colour (OC) Climate Change Initiative (CCI), which is providing merged multi-sensor data from the mid-resolution sensors in operation at a specific time from 1997 to the present day. Multi-sensor satellite datasets are advantageous tools for ecological studies because they increase the probabilities of cloud-free data over a given region as data from multiple satellites whose overpass times differ by a few hours are combined. Moreover, data-merging from heritage and present satellites can expand the duration of the time series indefinitely, which allows the calculation of significant trends. Additionally, data are remapped consistently and analysis-ready for scientists. Also, the products described in this article have the exclusive advantage of being linkable to in situ historic observations and thus enabling the construction of very long time series. Monthly data are presented at a spatial resolution of ∼4 km at the Equator and are available at PANGAEA (https://doi.org/10.1594/PANGAEA.904266; Pitarch et al., 2019a). Two smaller and easier-to-handle test datasets have been produced from the former: a global dataset at 1∘ spatial resolution and another one for the North Atlantic at 0.25∘ resolution. The computer code for the generation of the Forel–Ule index, hue angle and Secchi disk depth from a given remote-sensing reflectance is also shared at https://doi.org/10.5281/zenodo.4439646 (Pitarch et al., 2021) and can be easily set in loop mode for batch calculations.

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Global maps of Forel-Ule index, hue angle and Secchi disk depth derived from twenty-one years of monthly ESA-OC-CCI data

10.5194/essd-2020-316 ◽

2020 ◽

Author(s):

Jaime Pitarch ◽

Marco Bellacicco ◽

Salvatore Marullo ◽

Hendrik J. van der Woerd

Keyword(s):

Time Series ◽

Spatial Resolution ◽

European Space Agency ◽

Sensor Data ◽

Secchi Disk ◽

Secchi Disk Depth ◽

The North ◽

Space Agency ◽

Free Data ◽

Hue Angle

Abstract. We document the development and public release of a new dataset (1997–2018), consisting of global maps of the Forel-Ule index, hue angle and Secchi disk depth. Source data comes from the European Space Agency (ESA) Ocean Colour (OC) Climate Change Initiative (CCI), which is providing merged multi-sensor data from the mid-resolution sensors in operation at a specific time from 1997 to the present day. Multi-sensor satellite datasets are advantageous tools for ecological studies because they increase the probabilities of cloud-free data over a given region, as data from multiple satellites whose overpass times differ by a few hours are combined. Moreover, data merging from heritage and present satellites can expand the duration of the time series indefinitely, which allows the calculation of significant trends. Additionally, data are remapped consistently and analysis-ready for scientists. Also, the products described in this article have the exclusive advantage of being linkable to in-situ historic observations and thus enabling the construction of very long time series. Monthly data are presented at a spatial resolution of ~4 km at the equator and are available at PANGAEA, https://doi.org/10.1594/PANGAEA.904266 (Pitarch et al., 2019a). Two smaller and easier to handle test datasets have been produced from the former: a global dataset at 1 degree spatial resolution and another one for the North Atlantic at 0.25 degree resolution.

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Review of “Global maps of Forel-Ule index, hue angle, and Secchi disk depth derived from twenty-one years of monthly ESA-OC-CCI data” by J. Pitarch, M. Bellacicco, S. Marullo, and H.J. van der Woerd

10.5194/essd-2020-316-rc1 ◽

2020 ◽

Author(s):

Anonymous

Keyword(s):

Secchi Disk ◽

Secchi Disk Depth ◽

Hue Angle

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Potential and Challenges of Harmonizing 40 Years of AVHRR Data: The TIMELINE Experience

Remote Sensing ◽

10.3390/rs13183618 ◽

2021 ◽

Vol 13 (18) ◽

pp. 3618

Author(s):

Stefan Dech ◽

Stefanie Holzwarth ◽

Sarah Asam ◽

Thorsten Andresen ◽

Martin Bachmann ◽

...

Keyword(s):

Climate Change ◽

Time Series ◽

Observation Time ◽

Sensor Data ◽

High Temporal Resolution ◽

Statistical Parameters ◽

Medium Resolution ◽

Long Time ◽

Earth Observation Satellite

Earth Observation satellite data allows for the monitoring of the surface of our planet at predefined intervals covering large areas. However, there is only one medium resolution sensor family in orbit that enables an observation time span of 40 and more years at a daily repeat interval. This is the AVHRR sensor family. If we want to investigate the long-term impacts of climate change on our environment, we can only do so based on data that remains available for several decades. If we then want to investigate processes with respect to climate change, we need very high temporal resolution enabling the generation of long-term time series and the derivation of related statistical parameters such as mean, variability, anomalies, and trends. The challenges to generating a well calibrated and harmonized 40-year-long time series based on AVHRR sensor data flown on 14 different platforms are enormous. However, only extremely thorough pre-processing and harmonization ensures that trends found in the data are real trends and not sensor-related (or other) artefacts. The generation of European-wide time series as a basis for the derivation of a multitude of parameters is therefore an extremely challenging task, the details of which are presented in this paper.

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Interactive comment on “Global maps of Forel-Ule index, hue angle and Secchi disk depth derived from twenty-one years of monthly ESA-OC-CCI data” by Jaime Pitarch et al.

10.5194/essd-2020-316-rc2 ◽

2020 ◽

Author(s):

Anonymous

Keyword(s):

Secchi Disk ◽

Secchi Disk Depth ◽

Hue Angle

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The Ocean Colour Climate Change Initiative: III. A round-robin comparison on in-water bio-optical algorithms

Remote Sensing of Environment ◽

10.1016/j.rse.2013.09.016 ◽

2015 ◽

Vol 162 ◽

pp. 271-294 ◽

Cited By ~ 89

Author(s):

Robert J.W. Brewin ◽

Shubha Sathyendranath ◽

Dagmar Müller ◽

Carsten Brockmann ◽

Pierre-Yves Deschamps ◽

...

Keyword(s):

Climate Change ◽

Round Robin ◽

Change Initiative ◽

Ocean Colour

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Quantifying uncertainty in the ESA Ocean Colour – Climate Change Initiative dataset for assimilation of total chlorophyll and phytoplankton functional types

10.5194/egusphere-egu2020-18684 ◽

2020 ◽

Author(s):

Alison Fowler ◽

Jozef Skákala ◽

Stefano Ciavatta

Keyword(s):

Climate Change ◽

Data Uncertainty ◽

Water Type ◽

Total Chlorophyll ◽

Change Initiative ◽

Ocean Colour ◽

North West ◽

In Situ Data ◽

Mean Square Errors ◽

Climate Studies

Monitoring biogeochemistry in shelf seas is of great significance for the economy, ecosystems understanding and climate studies. Data assimilation can aid the realism of marine biogeochemistry models by incorporating information from observations. An important source of information about phytoplankton groups and total chlorophyll is available from the ESA OC-CCI (ocean colour - climate change initiative) dataset.For any assimilation system to be successful it is important to accurately represent all sources of data uncertainty. For the ocean colour product, the propagation of errors throughout the ocean colour algorithm makes the characterisation of the uncertainty challenging. However, the problem can be simplified by assuming that the uncertainty is a function of optical water type (OWT), which characterises the water column of each observed pixel in terms of their reflectance properties.Within this work we apply the well-known Desroziers et al. (2005) consistency diagnostics to the Met Office&#8217;s NEMOVAR 3D-VAR DA system used to create daily biogeochemistry forecasts on the North-West European Shelf. The derived estimates of monthly ocean colour error covariances stratified by OWT are compared to previously derived estimates of the root mean square errors and biases using in-situ data match ups (Brewin et al. 2017). It is found that the agreement between the two estimates of the error variances have a strong seasonal and OWT dependence. The error correlations (which can only be estimated with the Desroziers&#8217; method) in some instances are found to be significant out to a few 100km particularly for more turbid waters during the spring bloom. The reliability and limitation of these two estimates of the ocean colour uncertainty are discussed along with the implications for the future assimilation of ocean colour products and for ecosystem and climate studies.

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The Ocean Colour Climate Change Initiative: II. Spatial and temporal homogeneity of satellite data retrieval due to systematic effects in atmospheric correction processors

Remote Sensing of Environment ◽

10.1016/j.rse.2015.01.033 ◽

2015 ◽

Vol 162 ◽

pp. 257-270 ◽

Cited By ~ 18

Author(s):

Dagmar Müller ◽

Hajo Krasemann ◽

Robert J.W. Brewin ◽

Carsten Brockmann ◽

Pierre-Yves Deschamps ◽

...

Keyword(s):

Climate Change ◽

Satellite Data ◽

Atmospheric Correction ◽

Data Retrieval ◽

Change Initiative ◽

Ocean Colour ◽

Systematic Effects

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Detection of anthropogenic climate change in satellite records of ocean chlorophyll and productivity

Biogeosciences ◽

10.5194/bg-7-621-2010 ◽

2010 ◽

Vol 7 (2) ◽

pp. 621-640 ◽

Cited By ~ 258

Author(s):

S. A. Henson ◽

J. L. Sarmiento ◽

J. P. Dunne ◽

L. Bopp ◽

I. Lima ◽

...

Keyword(s):

Climate Change ◽

Time Series ◽

Primary Production ◽

Global Climate Change ◽

Decadal Variability ◽

Global Climate ◽

Warming Trend ◽

Ocean Colour ◽

Ocean Productivity ◽

The Impact

Abstract. Global climate change is predicted to alter the ocean's biological productivity. But how will we recognise the impacts of climate change on ocean productivity? The most comprehensive information available on its global distribution comes from satellite ocean colour data. Now that over ten years of satellite-derived chlorophyll and productivity data have accumulated, can we begin to detect and attribute climate change-driven trends in productivity? Here we compare recent trends in satellite ocean colour data to longer-term time series from three biogeochemical models (GFDL, IPSL and NCAR). We find that detection of climate change-driven trends in the satellite data is confounded by the relatively short time series and large interannual and decadal variability in productivity. Thus, recent observed changes in chlorophyll, primary production and the size of the oligotrophic gyres cannot be unequivocally attributed to the impact of global climate change. Instead, our analyses suggest that a time series of ~40 years length is needed to distinguish a global warming trend from natural variability. In some regions, notably equatorial regions, detection times are predicted to be shorter (~20–30 years). Analysis of modelled chlorophyll and primary production from 2001–2100 suggests that, on average, the climate change-driven trend will not be unambiguously separable from decadal variability until ~2055. Because the magnitude of natural variability in chlorophyll and primary production is larger than, or similar to, the global warming trend, a consistent, decades-long data record must be established if the impact of climate change on ocean productivity is to be definitively detected.

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A Comparison of Burned Area Time Series in the Alaskan Boreal Forests from Different Remote Sensing Products

Forests ◽

10.3390/f10050363 ◽

2019 ◽

Vol 10 (5) ◽

pp. 363 ◽

Cited By ~ 1

Author(s):

Moreno-Ruiz ◽

García-Lázaro ◽

Arbelo

Keyword(s):

Climate Change ◽

Remote Sensing ◽

Time Series ◽

Spatial Resolution ◽

Reference Data ◽

Global Climate ◽

Fire Service ◽

Burned Area ◽

Spatial And Temporal Patterns ◽

Modis Data

Alaska’s boreal region stores large amounts of carbon both in its woodlands and in the grounds that sustain them. Any alteration to the fire system that has naturally regulated the region’s ecology for centuries poses a concern regarding global climate change. Satellite-based remote sensors are key to analyzing those spatial and temporal patterns of fire occurrence. This paper compiles four burned area (BA) time series based on remote sensing imagery for the Alaska region between 1982–2015: Burned Areas Boundaries Dataset-Monitoring Trends in Burn Severity (BABD-MTBS) derived from Landsat sensors, Fire Climate Change Initiative (Fire_CCI) (2001–2015) and Moderate-Resolution Imaging Spectroradiometer (MODIS) Direct Broadcast Monthly Burned Area Product (MCD64A1) (2000–2015) with MODIS data, and Burned Area-Long-Term Data Record (BA-LTDR) using Advanced Very High Resolution Radiometer LTDR (AVHRR-LTDR) dataset. All products were analyzed and compared against one another, and their accuracy was assessed through reference data obtained by the Alaskan Fire Service (AFS). The BABD-MTBS product, with the highest spatial resolution (30 m), shows the best overall estimation of BA (81%), however, for the years before 2000 (pre-MODIS era), the BA sensed by this product was only 44.3%, against the 55.5% obtained by the BA-LTDR product with a lower spatial resolution (5 km). In contrast, for the MODIS era (after 2000), BABD-MTBS virtually matches the reference data (98.5%), while the other three time series showed similar results of around 60%. Based on the theoretical limits of their corresponding Pareto boundaries, the lower resolution BA products could be improved, although those based on MODIS data are currently limited by the algorithm’s reliance on the active fire MODIS product, with a 1 km nominal spatial resolution. The large inter-annual variation found in the commission and omission errors in this study suggests that for a fair assessment of the accuracy of any BA product, all available reference data for space and time should be considered and should not be carried out by selective sampling.

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