Coastal phytoplankton blooms expand and intensify in the 21st century

Abstract

Phytoplankton blooms in coastal oceans can be beneficial to coastal fisheries production and ecosystem function, but can also cause major environmental problems1,2—yet detailed characterizations of bloom incidence and distribution are not available worldwide. Here we map daily marine coastal algal blooms between 2003 and 2020 using global satellite observations at 1-km spatial resolution. We found that algal blooms occurred in 126 out of the 153 coastal countries examined. Globally, the spatial extent (+13.2%) and frequency (+59.2%) of blooms increased significantly (P < 0.05) over the study period, whereas blooms weakened in tropical and subtropical areas of the Northern Hemisphere. We documented the relationship between the bloom trends and ocean circulation, and identified the stimulatory effects of recent increases in sea surface temperature. Our compilation of daily mapped coastal phytoplankton blooms provides the basis for global assessments of bloom risks and benefits, and for the formulation or evaluation of management or policy actions.

Full text

280 | Nature | Vol 615 | 9 March 2023
Article
Coastal phytoplankton blooms expand and
intensify in the 21st century
Yanhui Dai1,9, Shangbo Yang1,9, Dan Zhao1, Chuanmin Hu2, Wang Xu3, Donald M. Anderson4,
Yun Li5, Xiao-Peng Song6, Daniel G. Boyce7, Luke Gibson1, Chunmiao Zheng1,8 & Lian Feng1 ✉
Phytoplankton blooms in coastal oceans can be benecial to coastal sheries
production and ecosystem function, but can also cause major environmental
problems1,2—yet detailed characterizations of bloom incidence and distribution are
not available worldwide. Here we map daily marine coastal algal blooms between
2003 and 2020 using global satellite observations at 1-km spatial resolution. We found
that algal blooms occurred in 126 out of the 153 coastal countries examined. Globally,
the spatial extent (+13.2%) and frequency (+59.2%) of blooms increased signicantly
(P < 0.05) over the study period, whereas blooms weakened in tropical and
subtropical areas of the Northern Hemisphere. We documented the relationship
between the bloom trends and ocean circulation, and identied the stimulatory
eects of recent increases in sea surface temperature. Our compilation of daily
mapped coastal phytoplankton blooms provides the basis for global assessments
of bloom risks and benets, and for the formulation or evaluation of management
or policy actions.
Phytoplankton blooms are accumulations of microscopic algae in the
surface layer of fresh and marine water bodies. Although many blooms
can occur naturally, nutrients linked to anthropogenic eutrophication
are expected to intensify their frequency globally
2–4
. Many algal blooms
are beneficial, fixing carbon at the base of the food chain and support-
ing fisheries and ecosystems worldwide. However, proliferations of
algae that cause harm (termed harmful algal blooms (HABs)) have
become a major environmental problem worldwide5–7. For instance,
the toxins produced by some algal species can accumulate in the food
web, causing closures of fisheries as well as illness or mortality of marine
species and humans
8–10
. In other cases, the decay of a dense algal bloom
can deplete oxygen in bottom waters, forming anoxic ‘dead zones’ that
can cause fish and invertebrate die-offs and ecosystem restructuring,
with serious consequences for the well-being of coastal communities
1,11
.
Unfortunately, algal bloom frequency and distribution are projected
to increase with future climate change12,13, with some changes causing
adverse effects on aquatic ecosystems, fisheries and coastal resources.
Owing to substantial heterogeneity in space and time, algal blooms
are challenging to characterize on a large scale
5,14
, and thus present
knowledge does not allow us to answer one of the most fundamen-
tal questions: whether algal blooms have changed in recent decades
on a global basis
6,15,16
. For example, although HAB events have been
compiled into the UNESCO (United Nations Educational, Scientific,
and Cultural Organization) Intergovernmental Oceanographic Com-
mission Harmful Algae Event Database (HAEDAT) globally since 1985,
bloom trends are difficult to resolve, owing to inconsistent sampling
efforts and the diversity of the eco-environmental or socio-economic
effects
6
. Alternatively, satellite data have been used to monitor the
ocean surface continuously since 1997 and have enabled bloom detec-
tion in many coastal regions17–19. However, the technical difficulties in
dealing with complex optical features across different types of coastal
waters have so far prohibited their application globally
20
. To fill this
knowledge gap, we developed a method to map global coastal algal
blooms and used this tool to examine satellite images between 2003
and 2020, addressing three fundamental questions: (1) where and how
frequently global coastal oceans have been affected by phytoplankton
blooms; (2) whether the blooms have expanded or intensified over the
past two decades, both globally and regionally; and (3) the identity of
the potential drivers.
Mapping global coastal phytoplankton blooms
We generated a satellite-based dataset of phytoplankton bloom occur-
rence to characterize the spatial and temporal patterns of algal blooms
in coastal oceans globally. The dataset was derived using global, 1-km
resolution daily observations from the ModerateResolutionImag-
ingSpectroradiometer (MODIS) onboard NASA’s Aqua satellite, and all
0.76 million images acquired by this satellite mission between 2003 and
2020 were used. We developed an automated method to detect phyto-
plankton blooms using MODIS images (Extended Data Fig.1) (Methods).
In this study, we define a phytoplankton bloom as the accumulation of
microscopic algae at the ocean surface that exhibits satellite-detectable
fluorescence signals
21
. However, whether a bloom produces toxins or is
harmful to humans or the marine environment is not distinguishable
from satellite data. We delineated bloom-affected areas (that is, the areas
where algal blooms were detected), and enumerated the bloom count
https://doi.org/10.1038/s41586-023-05760-y
Received: 17 May 2022
Accepted: 25 January 2023
Published online: 1 March 2023
Open access
Check for updates
1School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, China. 2College of Marine Science, University of South Florida, St. Petersburg,
FL, USA. 3Shenzhen Ecological and Environmental Monitoring Center of Guangdong Province, Shenzhen, China. 4Woods Hole Oceanographic Institution, Woods Hole, MA, USA. 5School of
Marine Science and Policy, College of Earth, Ocean, and Environment, University of Delaware, Lewes, DE, USA. 6Department of Geographical Sciences, University of Maryland, College Park,
MD, USA. 7Bedford Institute of Oceanography, Fisheries and Oceans Canada, Dartmouth, Nova Scotia, Canada. 8EIT Institute for Advanced Study, Ningbo, China. 9These authors contributed
equally: Yanhui Dai, Shangbo Yang. ✉e-mail: fengl@sustech.edu.cn
Content courtesy of Springer Nature, terms of use apply. Rights reserved

Nature | Vol 615 | 9 March 2023 | 281
at the 1-km pixel level (that is, the number of detected blooms per pixel)
(Fig.1). We further estimated the bloom frequency (dimensionless) by
integrating the bloom count and affected areas within 1° × 1° grid cells
(seeMethods), and this metric was used to examine temporal dynam
-
ics in bloom intensity. Validation with independent satellite samples
selected via several visual inspection techniques showed an overall accu-
racy level of more than 95% for our method, and comparisons using dis-
crete events in HAEDAT
6
indicated that we successfully identified bloom
counts for 79.3% of the historical HAB events in that database (Extended
Data Figs.2–6). We examined phytoplankton blooms in the exclusive
economic zones(EEZs) of 153 coastal countries and in 54 large marine
ecosystems (LMEs) (Extended Data Fig.7). Our study area encompasses
global continental shelves and outer margins of coastal currents, which
offer the majority of marine resources available for human use (seeMeth-
ods). Out of the 153 coastal countries examined, 126 were observed to
have phytoplankton blooms (Fig.1). The total bloom-affected area was
31.47 million km2, equivalent to approximately 24.2% of the global land
area and 8.6% of the global ocean area, with a median bloom count of
4.3 per year during the past 2 decades (Fig.1b). Europe (9.52 million
km2—30.3% of the total affected area) and North America (6.78 million
km
2
—21.5% of the total affected area) contributed the largest bloom
areas. By contrast, the most frequent blooms were found around Africa
and South America (median bloom counts of more than 6.3 per year).
Australia experienced the lowest frequency (2.4 per year) and affected
area (2.84 million km2—9.0% of the total affected area) of blooms.
Phytoplankton blooms occurred frequently in the eastern boundary
current systems (that is, California, Benguela, Humboldt and Canary),
northeastern USA, Latin America, the Baltic Sea, Northern Black Sea and
the Arabian Sea (Fig.1a). Five LMEs were found with the most frequent
blooms (annual median bloom count over 15), including Patagonian
Shelf, Northeast US continental Shelf, the Baltic Sea, Gulf of California
and Benguela Current (Extended Data Fig.7). These hotspots are often
reported as having a high incidence of algal blooms, some of which are
HABs, driven by either coastal upwelling or pronounced anthropogenic
nutrient enrichment
9,22–26
. European LMEs showed mostly large propor-
tions of bloom-affected areas, and some also showed frequent bloom
occurrences. By contrast, Asian LMEs exhibited mainly infrequent
blooms, given their large affected areas. We identified more bloom
events in estuarine regions than along coasts in regions without major
river discharge (P < 0.05; Extended Data Fig.8), highlighting the critical
role of terrestrial nutrient sources in coastal algal blooms3.
Long-term trends
The total global bloom-affected area has expanded by 3.97 million km
2
(13.2%) between 2003 and 2020, equivalent to 0.14 million km2 yr−1
(P < 0.05; Fig.2). Furthermore, the number of countries with significant
bloom expansion was about 1.6 times those with a decreasing trend.
The global median bloom frequency showed an increasing rate of 59.2%
(+2.19% yr
−1
, P < 0.05) over the observed period. Spatially, areas showing
significant increasing trends (P < 0.05) in bloom frequency were 77.6%
larger than those with the opposite trends (Fig.2). Globally, our analysis
revealed overall consistent fluctuations between the bloom-affected
area and bloom frequency between 2003 and 2020 (Fig.2b). However,
there was no significant relationship between bloom extent and fre-
quency in 23 countries and 10 LMEs over the past two decades, under-
scoring the spatial and temporal variability of algal blooms and the
importance of continuous satellite monitoring.
The entire Southern Hemisphere was primarily characterized by
increased bloom frequency, although weakened blooms were also
sometimes found. In the Northern Hemisphere, the low latitude
(<30° N) coasts were mainly featured with strong bloom weakening
(Fig.2a), primarily in the California Current System and the Arabian
Sea. Bloom strengthening was found in the northern Gulf of Mexico and
the East and South China Seas, albeit at smaller magnitudes. At higher
latitudes, weakening blooms were detected mainly in the northeastern
North Atlantic and the Okhotsk Sea in the northwestern North Pacific.
Globally, the largest increases in bloom frequency were observed in six
a
1
10
30
50
SA AF EU NA AS AU Global
0
20
40
60
Bloom counts
b
EU NA AS SA AF AU
0
5
10
Affected area (×106 km2)
c
30.3%
21.5% 18.2%
11.8%
9.2%
9.0%
Global total affected area:
31.47 × 106 km2
Bloom counts
Fig. 1 | Globa l patterns o f coastal phy toplankton bl ooms betwe en 2003 and
2020. a, The spatial dist ribution of annu al mean bloom co unt based on dail y
satellite detections. b, Contine ntal and global s tatistics for a nnual mean
bloom count (South America (SA), n = 3,846, 125; Afric a (AF), n = 2,516, 225;
Europe (EU), n = 17,703,949; North Ameri ca (NA), n = 10,034,286; A sia (AS),
n = 5,371, 158; Australia (AU), n = 2, 781,998 pixel obser vations). The cen tre line
represen ts the median val ue, bottom and to p bounds of boxes are f irst and
third quart iles, and the whis kers show a maximum o f 1.5 times t he interquart ile
range. c, Conti nental stat istics for the lon g-term annual mean o f bloom-
affecte d areas (n = 18 years). The p ercentages s how the corresp onding
contribu tions to the glo bal total. The ba rs represent s .d. Open circle s are
the affec ted areas duri ngdifferentyears. M ap created usin g Python 3. 8.
Content courtesy of Springer Nature, terms of use apply. Rights reserved

282 | Nature | Vol 615 | 9 March 2023
Article
major coastal current systems, including Oyashio (+6.31% yr−1), Alaska
(+5.22% yr−1), Canary (+4.28% yr−1), Malvinas (+3.02% yr−1), Gulf Stream
(+2.42% yr−1) and Benguela (+2.30% yr−1) (Figs.2a and3).
Natural and anthropogenic effects
Increases in sea surface temperature (SST) can stimulate bloom occur-
rence. We found significant positive correlations (P < 0.05) between the
annual mean bloom frequency and the coincident SST (SST data were
averaged over the growth window of algal blooms within a year (Meth-
ods and Extended Data Fig.9)) in several high-latitude regions (>40° N),
such as the Alaska Current (r = 0.44), the Oyashio Current (r = 0.48) and
the Baltic Sea (r = 0.41) (Fig.3). These findings agree with previous stud-
ies, in which the bloom-favourable seasons in these temperate seas
have been extended under warmer temperatures27–29. However, this
temperature-based mechanism did not apply to regions with inconsistent
trends between SST and bloom frequency, particularly for the substantial
bloom weakening in the tropical and subtropical areas (Figs.2a and 3b).
a
–1.0 –0.5 0 0.5 1.0
Slope (104 yr−1)
100 0 100
Fraction (%)
50° S
0°
50° N
Latitude
Positive
(P < 0.05)
(P ≥ 0.05)
Negative
(P < 0.05)
(P ≥ 0.05)
2003 2006 2009 2012
Year
2015 2018
1
2
3
Bloom frequency (×103)
+2.19% yr−1 (P = 0.006)
b
25
30
35
Affected area (×106 km2)
+0.14 × 106 km2 yr−1 (P = 0.001)
Fig. 2 | Trends of glo bal coastal p hytoplankto n blooms bet ween 2003 and
2020. a, Spatial patter ns of the trends in b loom frequen cy at a 1° × 1° grid scal e.
The latit udinal profile s show the frac tions of grids w ith signif icant and
insignif icant tren ds (positive or ne gative) along the ea st–west direction.
b, Interannual v ariability an d trends in annua l median bloom f requency and t otal
global bloo m-affected ar ea. The linea r slopes and P-value (two -sided t-test) are
indicate d. The shading a ssociated w ith the bloom fr equency dat a represents
an uncert ainty level of 5% in b loom detect ion. Map creat ed using Py thon 3.8.
a
–2 –1 012
10−6 °C m−1 decade−1
b
–1.0 –0.5 00.5 1.0
°C decade−1
0
6
12
6
8
10
r = 0.81*
S = –4.26
r = –0.83*
1 California Current
26
28
2
5
8
14
19
24
r = 0.61*
S = 2.42
r = –0.02
2 Gulf Stream
12
14
16
0
3
6
8
10
r = 0.84*
S = 4.28
r = –0.63*
3 Canary Current
20
22
24
8
14
20
4 Malvinas Current
7
9
11
r = 0.83*
S = 3.02
r = 0.12
11
13
15
2003 2011 2019
10
25
40
10
13
16
r = 0.73*
S = 2.30
r = 0.16
5 Benguela Current
19
21
2003 2011 2019
0
5
10
12
14
16
6 Oyashio Current
r = 0.58*
S = 6.31 r = 0.48* 10
12
14
2003 2011
Year
2019
0
2
4
6
8
r = 0.09
S = 5.22
r = 0.44*
7 Alaska Current
7
9
11
2003 2011 2019
3
6
9
3
7
11
r = 0.30
S = 2.74 r = 0.41*
8 Baltic Sea
9
11
Bloom frequency (104)
SST (10–6 ഒ per m)
SST (
ഒ
)
c
7
8
6
5
4
3
2
1
2003 2011 2019 2003 2011 2019 2003 2011 2019 2003 2011 2019
Fig. 3 | Effe cts of clim ate change on phy toplankton b looms. a,b, Global
pattern s of trends in SST g radient (a) and SST (b) fro m 2003 to 2020. c, L ong-
term change s in bloom frequ ency in the reg ions labelled i n a and b, and their
relations hip to the SST and SS T gradient. Lin ear slope (S) of bloo m frequency
and the correlation coefficient (r) between b loom frequenc y and the SST and
the SST gra dient (∇SST) are show n. Asteris ks indicate st atisticall y signific ant
(P<0.05) correlation s. Maps create d using ArcMap 10.4 a nd Python 3. 8.
Content courtesy of Springer Nature, terms of use apply. Rights reserved

Nature | Vol 615 | 9 March 2023 | 283
Changes in climate can also affect ocean circulation, altering ocean
mixing and the transport of nutrients that drive the growth of marine
phytoplankton and bloom formation
30–32
. We used the spatial SST gradi-
ent (in °C m
−1
) as a proxy for the magnitude of oceanic mesoscale cur-
rents (the time-varying velocity of kinetic energy (also known as the eddy
kinetic energy (EKE))) by following the methods of a previous study33,
and examined its effects on algal blooms (Methods). The trend in the
SST gradient appeared more spatially aligned to bloom frequency than
SST. We found significant positive correlations (P < 0.05) between the
SST gradient and bloom frequency for various coastal current systems,
including the Canary (r = 0.84), Malvinas (r = 0.83), California (r = 0.81),
Benguela (r = 0.73), Gulf Stream (r = 0.61) and Oyashio (r = 0.58) currents.
Trends in bloom frequency in subtropical eastern boundary upwelling
regions (the California, Benguela and Canary currents) followed the
changes in mesoscale currents (Fig.3a,c). In the California Current Sys-
tem, the decrease in bloom frequency was probably due to the weakened
upwelling (represented by a reduced SST gradient and increased SST)
and thus lower nutrient supply
25
. Conversely, the Canary and Benguela
currents were characterized by strengthened upwelling and increased
bloom frequency. The two western boundary current systems at high
latitudes (Malvinas and Oyashio)—although characterized by less pro-
nounced upwelling
34
—exhibited a similar mechanism to the subtropical
eastern boundary regions. For the subtropical western boundary Gulf
Stream current, the strengthened current jets (manifested as a larger SST
gradient) brought more nutrients from the continental shelf
35
, trigger-
ing more algal blooms. Nevertheless, whether these changes in oceanic
mesoscale activities were responses to wind, stratification, the shear of
ocean currents or other factors
33
requires region-based investigations.
Global climate events, represented as the multivariate El Niño–
Southern Oscillation index36 (MEI), also showed connections with coastal
bloom frequency. The minimum MEI in 2010 (a strong LaNiña year)
was followed by a low bloom frequency in the following year, and the
largest MEI in 2015 (a strong El Niño year) was followed by the strongest
bloom frequency in 2016 (Fig.2b and Extended Data Fig.10a).
Changes in anthropogenic nutrient enrichment may have also con-
tributed to the trends in blooms
37
. For example, the decline in bloom
frequency in the Arabian Sea, without clear links to SST or SST gradient
changes, could result from decreased fertilizer use in the surround-
ing countries (such as Iran) (Extended Data Fig.10). By contrast, the
bloom strengthening in some Asian countries could be attributed to
surges in fertilizer use
38
. We examined trends in fertilizer usage (either
nitrogen or phosphorus) and bloom frequency and found high positive
correlations in China, Iran, Vietnam and the Philippines. Paradoxi-
cally, decreased fertilizer uses and increased bloom frequency were
identifiedin some countries, suggesting that nutrient control efforts
might have been counterbalanced by the stimulatory effects of climate
warming or other factors. Furthermore, the intensified aquaculture
industry in Finland, China, Algeria, Guinea, Vietnam, Argentina, Russia
and Uruguay may also be linked to their increased bloom incidence,
as suggested by the significant positive correlations (r > 0.5, P < 0.05)
between their aquaculture production and bloom frequency. A similar
relationship between aquaculture expansion and positive trends in HAB
incidence was reported from an analysis of HAEDAT data
6
. However,
analogous positive feedbacks for fertilizer or aquaculture were not
found in many other countries. Thus, an ecosystem model incorporat-
ing terrestrial and oceanic nutrient transport and nutrient–plankton
relationships of different species39 is required to quantify the contribu-
tions of natural and anthropogenic factors to algal blooms14.
Future implications
We acknowledge that our criteria for a detectable bloom event is
operationally defined by sensor sensitivities and other factors, and
that the bloom count metric used here may underestimate algal
bloom incidence, particularly compared to harmful events entered in
HAEDAT. For example, in a recent global analysis of the HAEDAT events,
Hallegraeff etal.
6
report a dozen or more events per year for each of nine
regions over a 33-year study period, compared to the global median
bloom count of 4.3 in this study. There are several possible explana-
tions for this discrepancy, such as the many low-cell-concentration
HABs that are not detectable from space but that can still cause harm,
as well as sensor sensitivities and algorithm thresholds. Furthermore,
our bloom count was averaged over all 1-km pixels within the EEZs,
whereas HAEDAT entries are based on discrete sampling regions. This
underestimation does not, however, alter the trends and other conclu
-
sions of this study, as the metrics used here were constant across time
and space. Underestimates would have been uniform across regions
globally. In this regard, it is of note that the study of Hallegraeff etal.6
found a significant link between the number of HAEDAT events over
time and the global expansion of aquaculture production, similar to
findings in our study.
The major contribution of our study is to provide a spatially and
temporally consistent characterization of global coastal algal blooms
between 2003 and 2020. Globally, increasing trends in algal bloom
area and frequency are apparent. Regionally, however, trends were
non-uniform owing to the compounded effects of changes in climate
(such as changes in SST and SST gradient and climate extremes),
anthropogenic eutrophication and aquaculture development. Our
daily mapping of bloom events offers valuable baseline information
to understand the mechanisms underlying the formation, mainte-
nance, and dissipation of algal blooms
5,40
. This could aid in developing
forecasting models (on either global or regional scales) that can help
minimize the consequences of harmful blooms, and can also help in
policy decisions relating to the control of nutrient discharges and
other HAB-stimulatory factors. Noting again that many blooms are
beneficial, particularly in terms of their positive effects on ecosystems
as well as on wild and farmed fisheries, the results here can also con-
tribute toward policies and management actions that sustain those
beneficial blooms.
Online content
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edgements, peer review information; details of author contributions
and competing interests; and statements of data and code availability
are available at https://doi.org/10.1038/s41586-023-05760-y.
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Methods
Data sources
MODIS on the Aqua satellite provides a global coverage within 1–2
days. All images acquired by this satellite mission from January 2003
to December 2020 were used in our study to detect global coastal
phytoplankton blooms, with a total of 0.76 million images. MODIS
Level-1A images were downloaded from the Ocean Biology Distributed
Active Archive Center (OB.DAAC) at NASA Goddard Space Flight Center
(GSFC), and were subsequently processed with SeaDAS software (ver-
sion 7.5) to obtain Rayleigh-corrected reflectance (R
rc
(dimensionless),
which was converted using the rhos (in sr
−1
) product (rhos × π) from
SeaDAS)
41
, remote sensing reflectance (R
rs
(sr
−1
)) and quality control
flags (l2_flags). If a pixel was flagged by any of the following, it was then
removed from phytoplankton bloom detection: straylight, cloud, land,
high sunglint, high solar zenith angle and high sensor zenith angle
(https://oceancolor.gsfc.nasa.gov/atbd/ocl2flags/). MODIS level-3
product for aerosol optical thicknesses (AOT) at 869 nm was also
obtained from OB.DAAC NASA GSFC (version R2018.0), which was
used to examine the impacts of aerosols on bloom trends.
We examined the algal blooms in the EEZs of 153 ocean-bordering
countries (excluding the EEZs in the Caspian Sea or around the
Antarctic), 126 of which were found with at least one bloom in the past
two decades. The EEZ dataset is available at https://www.marinere-
gions.org/download_file.php?name=World_EEZ_v11_20191118.zip.
The EEZs are up to 200 nautical miles (or 370 km) away from coast-
lines, which include all continental shelf areas and offer the majority of
marine resources available for human use. Regional statistics of algal
blooms were also performed for LMEs. LMEs encompass global coastal
oceans and outer edges of coastal currents areas, which are defined
by various distinct features of the oceans, including hydrology, pro-
ductivity, bathymetry and trophically dependent populations42.
Of the 66 LMEs identified globally, we excluded the Arctic and Ant-
arctic regions and examined 54 LMEs. The boundaries of LMEs were
obtained from https://www.sciencebase.gov/catalog/item/55c7772
2e4b08400b1fd8244.
We used HAEDAT to validate our satellite-detected phytoplankton
blooms in terms of presence or absence. The HAEDAT dataset (http://
haedat.iode.org) is a collection of records of HAB events, maintained
under the UNESCO Intergovernmental Oceanographic Commission
and with data archives since 1985. For each HAB event, the HAEDAT
records its bloom period (ranging from days to months) and geoloca-
tion. We merged duplicate entries when both the recorded locations
and times of the HAEDAT events were very similar to one another, and a
total number of 2,609 HAEDAT events were ultimately selected between
2003 and 2020.
We used the ¼° resolution National Oceanic and Atmospheric
Administration Optimum Interpolated SST (v. 2.1) data to examine the
potential simulating effects of warming on the global phytoplankton
trends. We also estimated the SST gradients following the method of
Martínez-Moreno33. As detailedin ref.33, the SST gradient can be used
as a proxy for the magnitude of oceanic mesoscale currents (EKE).
We used the SST gradient to explore the effects of ocean circulation
dynamics on algal blooms.
Fertilizer uses and aquaculture production for different countries
was used to examine the potential effects of nutrient enrichment
from humans on global phytoplankton bloom trends. Annual data
between 2003 and 2019 on synthetic fertilizer use, including nitrogen
and phosphorus, are available from https://ourworldindata.org/fer-
tilizers. Annual aquaculture production includes cultivated fish and
crustaceans in marine and inland waters, and sea tanks, and the data
between 2003 and 2018 are available from https://ourworldindata.
org/grapher/aquaculture-farmed-fish-production.
The MEI, which combines various oceanic and atmospheric variables36,
was used to examine the connections between El Niño–Southern
Oscillation activities and marine phytoplankton blooms. The dataset
is available from https://psl.noaa.gov/enso/mei/.
Development of an automated bloom detection method
A recent study by the UNESCO Intergovernmental Oceanographic
Commission revealed that globally reported HAB events have
increased
6
. However, such an overall increasing trend was found to
be highly correlated with recently intensified sampling efforts
6
. Once
this potential bias was accounted for by examining the ratio between
HAB events to the number of samplings5, there was no significant
global trend in HAB incidence, though there were increases in certain
regions. With synoptic, frequent, and large-scale observations, satel-
lite remote sensing has been extensively used to monitor algal blooms
in oceanic environments
17–19
. For example, chlorophyll a (Chla) con-
centrations, a proxy for phytoplankton biomass, has been provided as
a standard product by NASA since the proof-of-concept Coastal Zone
Color Scanner (1978–1986) era
43,44
. The current default algorithm used
to retrieve Chla products is based on the high absorption of Chla at
the blue band45,46, which often shows high accuracy in the clear open
oceans but high uncertainties in coastal waters. This is because, in
productive and dynamic coastal oceans, the absorption of Chla in the
blue band can be obscured by the presence of suspended sediments
and/or coloured dissolved organic matter (CDOM)47. To address this
problem, various regionalized Chla algorithms have been developed48.
Unfortunately, the concentrations of the water constituents (CDOM,
sediment and Chla) can vary substantially across different coastal
oceans. As a result, a universal Chla algorithm that can accurately
estimate Chla concentrations in global coastal oceans is not currently
available.
Alternatively, many spectral indices have been developed to iden-
tify phytoplankton blooms instead of quantifying their bloom bio-
mass, including the normalized fluorescence line height21 (nFLH),
red tide index
49
(RI), algal bloom index
47
(ABI), red–blue difference
(RBD)50, Karenia brevis bloom index50 (KBBI) and red tide detec-
tion index51 (RDI). In practice, the most important task for these
index-based algorithms is to determine their optimal thresholds
for bloom classification. However, such optimal thresholds can be
regional-or image-specific
20
, due to the complexity of optical fea-
tures in coastal waters and/or the contamination of unfavourable
observational conditions (such as thick aerosols, thin clouds, and
so on), making it difficult to apply spectral-index-based algorithms
at a global scale.
To circumvent the difficulty in determining unified thresholds for
various spectral indices across global coastal oceans, an approach
from a recent study to classify algal blooms in freshwater lakes
52
was
adopted and modified here. In that study, the remotely sensed reflec-
tance data in three visible bands (red, green and blue) were converted
into two-dimensional colour space created by the Commission Interna-
tionale del’éclairage (CIE), in which the position on the CIE chromaticity
diagram represented the colour perceived by human eyes (Extended
Data Fig.1a). As the algal blooms in freshwater lakes were manifested
as greenish colours, the reflectance of bloom-containing pixels was
expected to be distributed in the green gamut of the CIE chromaticity
diagram; the stronger the bloom, the closer the distance to the upper
border of the diagram (the greener the water).
Here, the colour of phytoplankton blooms in the coastal oceans
can be greenish, yellowish, brownish, or even reddish53, owing to
the compositions of bloom species (diatoms or dinoflagellates) and
the concentrations of different water constituents. Furthermore, the
Chla concentrations of the coastal blooms are typically lower than
those in inland waters, thus demanding more accurate classification
algorithms. Thus, the algorithm proposed by Hou etal.
52
was modified
when using the CIE chromaticity space for bloom detection in marine
environments. Specifically, we used the following coordinate conver-
sion formulas to obtain the xy coordinate values in the CIE colour space:
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Article
xXXYZ
yYXYZ
XRGB
YRGB
ZRGB
=/(++)
=/(++)
=2.7689+1.7517 +1.1302
=1.0000 +4.5907+0.0601
=0.0000 +0.0565+5.5943
(1
)
where R, G and B represent the R
rc
at 748 nm, 678 nm (fluorescence
band) and 667 nm in the MODIS Aqua data, respectively. By contrast,
the R, G and B channels used in Hou etal.52 were the red, green and blue
bands. We used the fluorescence band for the G channel because, for a
given region, the 678 nm signal increases monotonically with the Chla
concentration for blooms of moderate intensity
21
, which is similar to the
response of greenness to freshwater algal blooms. Thus, the converted
y value in the CIE coordinate system represents the strength of the
fluorescence. In practice, for pixels with phytoplankton blooms, the
converted colours in the chromaticity diagram will be located within
the green, yellow or orange–red gamut (see Extended Data Fig.1a); the
stronger the fluorescence signal is, the closer the distance to the upper
border of the CIE diagram (larger y value). By contrast, for bloom-free
pixels without a fluorescence signal, their converted xy coordinates will
be located in the blue or purple gamut. Therefore, we can determine
a lower boundary in the CIE two-dimensional coordinate system to
separate bloom and non-bloom pixels, similar to the method proposed
by Hou etal.52.
We selected 53,820 bloom-containing pixels from the MODIS R
rc
data
as training samples to determine the boundary of the CIE colour space.
These sample points were selected from nearshore waters worldwide
where frequent phytoplankton blooms have been reported (Extended
Data Fig.2); the algal species included various speciesof dinoflagellates
and diatoms
20
. A total of 80 images was used, which were acquired from
different seasons and across various bloom magnitudes, to ensure that
the samples used could almost exhaustively represent the different
bloom conditions in the coastal oceans.
We combined the MODIS FLHRrc (fluorescence line height based on
Rrc) and enhanced red–green–blue composite (ERGB) to delineate
bloom pixels manually. The FLHRrc image was calculated as:
RFRFRF
RF
FLH =×−[ ×+(×
−×)×(678 −667)/(748 −667)] (2
)
Rrcrc678 678rc667 667 rc748748
rc667 667
where R
rc667
, R
rc678
and R
rc74 8
are the R
rc
at 667, 678 and 748 nm, respec-
tively, and F
667
, F
678
and F
748
are the corresponding extraterrestrial solar
irradiance. ERGB composite images were generated using Rrc of three
bands at 555 (R), 488 (G) and 443 nm (B). Although phytoplankton-rich
andsediment-rich waters have high FLHRrc values, they appear as dark-
ish and bright features in the ERGB images (Extended Data Fig.3),
respectively
21
. In fact, visual examination with fluorescence signals
and ERGB has been widely accepted as a practical way to deline-
ate coastal algal blooms on a limited number of images21,54,55. Note
that the FLHRrc here was slightly different from the NASA standard
nFLH product
56
, as the latter is generated using R
rs
(corrected for both
Rayleigh and aerosol scattering) instead of R
rc
(with residual effects of
aerosols). However, when using the NASA standard algorithm to further
perform aerosol scattering correction over R
rc
, 20.7% of our selected
bloom-containing pixels failed to obtain valid R
rs
(without retrievals
or flagged as low quality), especially for those with strong blooms (see
examples in Extended Data Fig.4). Likewise, we also found various
nearshore regions with invalid Rrs retrievals. By contrast, Rrc had valid
data for all selected samples and showed more coverage in nearshore
coastal waters. The differences between R
rs
and R
rc
were because the
assumptions for the standard atmospheric correction algorithm do
not hold for bloom pixels or nearshore waters with complex optical
properties57. In fact, Rrc has been used as an alternative to Rrs in various
applications in complex waters58,59.
We converted the Rrc data of 53,820 selected sample pixels into
the xy coordinates in the CIE colour space (Extended Data Fig.1a).
As expected, these samples of bloom-containing pixels were located
in the upper half of the chromaticity diagram (the green, yellow and
orange–red gamut) (Extended Data Fig.1a). We determined the lower
boundary of these sample points in the chromaticity diagram, which
represents the lightest colour and thus the weakest phytoplankton
blooms; any point that falls above this boundary represents stronger
blooms. The method to determine the boundary was similar to Hou
etal.
52
: we first binned the sample points according to the x value in
the chromaticity diagram and estimated the 1st percentile (Q1%) of
the corresponding Y for each bin; then, we fit the Q1% using two-order
polynomial regression. Sensitivity analysis with Q
0.3
% (the three-sigma
value) resulted in minor changes (<1%) in the resulting bloom areas for
single images. Notably, sample points were rarely located near white
points (x = 1/3 and y = 1/3, represent equal reflection from three RGB
bands) in the diagram, and we used two polynomial regressions to
determine the boundaries for x values greater and less than 1/3, which
can be expressed as:
yxxx=4.8093−3.0958 +0.8357<
1
3
(3)
12
yxxx=4.9040−3.5759 +0.9862>
1
3
(4)
22
Based on the above, if a pixel’s xy coordinate (converted from R
rc
spectrum) satisfies the conditions of (x < 1/3 AND y > y
1
) or (x > 1/3 AND
y > y2), it is classified as a ‘bloom’ pixel.
Depending on the local region and application purpose, the mean-
ing of ‘phytoplankton bloom’ may differ. Here, for a global applica-
tion, the pixelwise bloom classification is based on the relationship
(represented using the CIE colour space) between Rrc in the 667-, 678-
and 754-nm bands derived from visual interpretation of the 80 pairs
of FLH
Rrc
and ERGB imagery. Instead of a simple threshold, we used a
lower boundary of the sample points in the chromaticity diagram to
define a bloom. In simple words, a pixel is classified as a bloom if its
fluorescence signal is detectable (the associated xy coordinate in the
CIE colour space located above the lower boundary). Histogram of the
nFLH values from the 53,820 training pixels demonstrated the minimum
value of ~0.02 mWcm
−2
μm
−1
(Extended Data Fig.1a), which is in line
with the lower-bound signal of K. brevis blooms on the West Florida
shelf21,47. Note that, such a minimum nFLH is determined from the global
training pixels, and it does not necessarily represent a unified lower
bound for phytoplankton blooms across the entire globe, especially
considering that fluorescence efficiency may be a large variable across
different regions. Different regions may have different lower bounds
of nFLH to define a bloom, and such variability is represented by the
predefined boundary in the CIE chromaticity diagram in our study. Cor-
respondingly, although the accuracy of Chla retrievals may have large
uncertainties in coastal waters, the histogram of the 53,820 training
pixels shows a lower bound of ~1 mg m−3 (Extended Data Fig.1a). Simi-
larly to nFLH, such a lower bound may not be applicable to all coastal
regions, as different regions may have different lower bounds of Chla
for bloom definition.
Although the MODIS cloud (generated by SeaDAS with R
rc869
< 0.027)
and associated straylight flags can be used to exclude most clouds, we
found that residual errors from thin clouds or cloud shadows could
affect the spectral shape and cause misclassification for bloom detec-
tions. Thus, we designed two spectral indices to remove such effects:
RR RRIndex1 =n −n −(n−n)×0.5 (5)
rc488rc443 rc555 rc443
RR RRIndex2 =n −n −(n−n)×0.5 (6)
rc555 rc469rc645 rc469
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where Index1 and Index2 were used to remove cloud shadows and
clouds, respectively. The nRrc443, nRrc488 and nRrc555 in index1 are the
normalized R
rc
, obtained by normalizing R
rc488
. Similar calculations
were performed for index2. The purpose of normalizations is to elimi-
nate the effect of the absolute magnitude of the reflectance, so that
the thresholds of these two indices are influenced by only the relative
magnitude (spectral shape). We determined thresholds for Index1
(>0.12) and Index2 (<0.012) through trial-and-error and ensured that
the misclassifications caused by residual errors from clouds and cloud
shadows could be effectively removed. After applying the cloud/cloud
shadow and various other masks that are associated with l2_flags, we
obtained an annual mean valid pixel observation (N
vobs
) of ~2.0 × 10
5
for global 1° × 1° grid cells, and the fluctuation patterns and trends of
N
vobs
, either annually or seasonally, are different from that of the global
bloom frequency and affected areas (see Supplementary Fig.1).
Assessments of the algorithm performance
In addition to phytoplankton blooms, macroalgal blooms (Sargassum
and Ulva) frequently occur in many coastal oceans60–63. To verify
whether our CIE-fluorescence algorithm could eliminate such impacts,
we compared the spectra between micro-and macroalgal blooms (see
Extended Data Fig.1b). We found that the spectral shapes of Sargassum
and Ulva are substantially different from those of microalgae, particu-
larly for the three bands used for CIE coordinate conversion. The con-
verted xy coordinates for macroalgae were located in the purple–red
gamut of the CIE diagram, which was far below the predefined bound-
ary (Extended Data Fig.1). Moreover, our algorithm is not affected by
highly turbid waters for the following two reasons: first, extremely high
turbidity tends to saturate the MODIS ocean bands
64
, which can be eas-
ily excluded; second, without a fluorescence peak, the reflectance of
unsaturated turbid waters, after conversion to CIE coordinates, will be
located below the predefined boundary (see example in Extended Data
Fig.1b). We also confirmed that the spectral shapes of coccolithophore
blooms are different from dinoflagellates and diatoms (see example in
Extended Data Fig.1b), and thus they are excluded from our algorithm.
Three different types of validation methods were adopted to dem-
onstrate the reliability of the proposed CIE-fluorescence algorithm for
phytoplankton bloom detection in global coastal oceans, including
visual inspections of the RGB, ERGB and FLH
Rrc
images, verifications
using independent manually delineated algal blooms, and comparisons
with the reported HAB events from the HAEDAT dataset.
First, we selected MODIS Aqua images from different locations where
coastal phytoplankton blooms have been recorded in the published
literature. We visually compared the RGB, ERGB, and FLH
Rrc
images, and
our algorithm detected bloom patterns (see examples in Extended Data
Fig.3). Comparisons from various images worldwide showed that our
algorithm could successfully identify regions with high FLHRrc values
and brownish-to-darkish features on the ERGB images, which can be
considered phytoplankton blooms.
Second, we delineated additional 15,466 bloom-containing pixels
from 35 images covering different coastal areas, using the same visual
inspection and manual delineation method as for the training sample
pixels. Moreover, we also selected 14,149 bloom-free pixels (bright or
turquoise green colours on ERGB images or low FLHRrc values) within
the same images as bloom-containing images. We applied our algo-
rithm to all these pixels, and compared the algorithm-identified and
manually delineated results. Our CIE-fluorescence algorithm showed
high values in both producer and user accuracies (92.04% and 98.63%)
(Supplementary Table1), and appeared effective at identifying bloom
pixels and excluding false negatives (blooms classified as non-blooms)
and false positives (non-blooms classified as blooms).
Third, we validated the satellite-detected phytoplankton blooms
using insitu reported HAB events from the HAEDAT dataset. For each
HAB event in the HAEDAT dataset, we obtained all MODIS images over
the reported bloom period (from days to months). Within each year, we
estimated the ratio between the number of satellite images with ‘bloom
detected’ against the number of valid images (see definition above)
during the bloom periods across the entire globe (Supplementary
Table1). Moreover, we calculated the number of events with at least
one successful satellite bloom detection (Ns), and then estimated the
ratio between N
s
and the total HAB events for each year. Results showed
that substantial amounts (averaged at 51.2%) of satellite observations
acquired during the HAB event periods were found with phytoplank-
ton blooms by our algorithm. Overall, 79.3% of the global HAB events
were successfully identified by satellite. The discrepancies between
satellite and insitu observations could be explained by the following
reasons: first, our study focused only on the phytoplankton blooms
that are resolvable by satellite fluorescence signals; other types of HAB
events in the HAEDAT dataset may not have been detectable by satellite
observations, such as events with lower phytoplankton biomass but
high toxicity, occurrences at the subsurface layers, or fluorescence
signals overwhelmed by suspended sediments65–67. Second, although
the HAEDAT recorded HAB events could be sustained for long periods,
high biomass of surface algae may not have occurred every day within
this period due to the changes in stratification, precipitation, wind, ver-
tical migration of cells, and many other factors
68
. Third, the spatial scale
of certain HAB events may have been too small to be identified using
the 1-km resolution MODIS observations. Fourth, a reduced MODIS
satellite observation frequency by the contaminations of clouds and
land adjacency effects69. Therefore, we believe the underestimations
of satellite-detected blooms compared to the insitu reported HAB
events were mainly due to inconsistencies between the two observa-
tions rather than uncertainties in our algorithm.
Because R
rc
depends not only on water colour but also on aerosols
(type and concentration) and solar and viewing geometry, a sensitivity
analysis was used to determine whether such variables could impact
bloom detection. Aerosol reflectance (ρ
a
) with different AOTs at 869
nm was simulated using the NASA-recommended maritime aerosol
model (r75f02, with a relative humidity of 75% and a fine-mode frac-
tion of 2%). Then, ρ
a
of each MODIS band was added to R
rc
images, and
the resulting bloom areas with and without added ρ
a
were compared.
Results showed that even with a change of 0.02 in AOT at 869 nm, the
bloom areas showed minor changes (<2%) in the tested images; minor
changes were also found when we used different aerosol models to
conduct ρ
a
simulations
70
. Note that 0.02 represents the high end of
the AOT intra-annual variability in coastal oceans (see Extended Data
Fig.5), and the associated interannual changes are much smaller.
Thus, the use of R
rc
instead of the fully atmospherically corrected
reflectance R
rs
could have limited impacts on our detected global
bloom trend.
We also tried various index-based algorithms developed in previ-
ous studies. However, results showed that all these methods require
image-specific thresholds to accurately determine algal bloom bounda-
ries for different coastal regions (see Extended Data Fig.6). By con-
trast, although our CIE-fluorescence algorithm may lead to different
bloom thresholds for different regions, it can identify bloom pixels
without adjusting the coefficients and, therefore, is more suitable for
global-scale bloom assessment efforts.
We acknowledge that our satellite-detected algal blooms represent
only high amounts of phytoplankton biomass on the ocean surfaces
without distinguishing whether such blooms produce toxins or are
harmful to marine environments. Furthermore, with only limited
spectral information from MODIS, it is difficult to discriminate the
phytoplankton species of algal blooms; such information could help
to improve our understanding of the impacts of these phytoplankton
blooms. However, we expect these challenges to be addressed soon with
the scheduled launch ofthe Plankton,Aerosol,Cloud, oceanEcosystem
(PACE)mission by NASA in 2024, where the hyperspectral measure-
ments over a broad spectrum (350–885 nm) will make species-level
classifications possible71.
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Article
Exploring the patterns and trends of global coastal
phytoplankton blooms
We applied the CIE-fluorescence algorithm to all MODIS Aqua level-2 R
rc
images, and a total number of 0.76 million images between 2003 and
2020 were processed. We mapped all detected blooms into 1-km daily
scale level-3 composites. The number of bloom counts within a year
for each location can be easily enumerated, and the long-term annual
mean values were then estimated (Fig.1a). We further calculated the
total global bloom-affected area (the areas where algal blooms were
detected at least once) for each year and examined their changes over
time (Fig.2b).
We defined bloom frequency (dimensionless) to represent the den-
sity of phytoplankton blooms for a year by integrating the bloom count
and bloom-affected areas within 1°×1° grid cells within that year, which
is expressed as:
∑
n
NMBloom frequency= (7
)
i
n
i
=1
where M
i
is the enumerated bloom count for each 1-km resolution pixel
in a year within one 1° × 1° grid cell, and n represents the associated
number of bloom-affected pixels in the same cell (the number of pixels
with M
i
> 0), and N is the total number of 1-km MODIS pixels in this grid
cell. We estimated the bloom frequency for each year between 2003 and
2020, and determined the long-term trend over global EEZs through a
linear least-squares regression (see Fig.2a).
Continental and country-level statistics were performed for bloom
count, bloom-affected areas, and bloom frequency (Fig.1b,c and
Supplementary Table2), using boundaries for the EEZs of different
ocean-bordering countries (see above). Similar statistics were also con-
ducted for 54 LMEs (Extended Data Fig.7 and Supplementary Table3).
Correlations with SST and SST gradient
To assess the impacts of climate change on long-term trends in coastal
phytoplankton blooms, we correlated the annual mean bloom fre-
quency and the associated SST and SST gradient in various coastal
current systems for grid cells with significant changes in bloom fre-
quency (Fig.3c). The SST and SST gradient were averaged over the
growth window within a year, assuming that the changes within the
growth window, either in water temperatures or ocean circulations,
play more important roles in the bloom trends compared to other
seasons32.
We determined the growth window of phytoplankton blooms for
each 1° × 1° grid cell (Extended Data Fig.9a) using the following method:
first, we estimated the proportion of cumulative bloom-affected pixels
within the grid cells for a year. Second, a generalized additive model72
was used to determine the shape of the phenological curves (Extended
Data Fig.9b), where a log link function and a cubic cyclic regression
spline smoother were applied73,74. Third, the timing of maximum
bloom-affected areas (TMBAA) was then determined by identifying
the inflection point on the bloom growth curve (Extended Data Fig.9c).
To facilitate comparisons across Northern and Southern Hemispheres,
the year in the Southern Hemisphere was shifted forward by 183 days
(Extended Data Fig.9c). We characterized the similarity of the bloom
growth curve between different grid cells and grouped them into three
distinct clusters using a fuzzy c-means cluster analysis method
75,76
.
We found uniform distributions of the clusters over large geographic
areas. Cluster I is mainly distributed in mid-low latitudes (<45° N and
<30° S), where the maximum bloom-affected areas were expected in the
early period of the year. Cluster II was mostly found in higher latitudes,
with bloom developments (quasi-) synchronized with increases in SST.
Cluster III was detected along the coastlines, where the bloom-affected
areas increase throughout the entire year. In practice, the growth win-
dow for clusters I and III was set as the entire year, and that for cluster II
was set from day 150 to day 270 within the year. We further found that
the TMBAA for cluster II showed small changes over the entire period
(Extended Data Fig.9d), indicating relatively stable phenological cycles
for those phytoplankton blooms32,77.
Reporting summary
Further information on research design is available in theNature Port-
folio Reporting Summary linked to this article.
Data availability
The satellite-based dataset of global coastal algal bloom at 1-km resolu-
tion and the associated code are available at https://doi.org/10.5281/
zenodo.7359262.Source data are provided with this paper.
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Acknowledgements We thank NASA for providing global MODIS satellite images, and the
Intergovernmental Oceanographic Commission (IOC) of UNESCO for providing the HAEDAT
dataset. L.F. was supportedby the National Natural Science Foundation of China (no.
41890852, 42271322 and 41971304). D.M.A. was supportedby the Woods Hole Center for
Oceans and Human Health (National Science Foundation grant OCE-1840381 and National
Institutes of Health grants NIEHS-1P01-ES028938-01).
Author contributions Y.D. and S.Y.: methodology, data processing and analyses, and writing.
L.F.: conceptualization, methodology, funding acquisition, supervision and writing. D.Z.: data
processing and analysis. C.H., W.X., D.M.A., Y.L., X.-P.S., D.G.B., L.G. and C.Z. participated in
interpreting the results and reining the manuscript.
Competing interests The authors declare no competing interests.
Additional information
Supplementary information The online version contains supplementary material available at
https://doi.org/10.1038/s41586-023-05760-y.
Correspondence and requests for materials should be addressed to Lian Feng.
Peer review information Nature thanks Bryan Franz, Bingkun Luo and the other, anonymous,
reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are
available.
Reprints and permissions information is available at http://www.nature.com/reprints.
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Article
Extende d Data Fig. 1 | Devel opment of the C IE-fluor escence al gorithm to
detect phytoplankton blooms using MODIS satellite imagery. (a). A1: The
density p lot of manually deli neated bloom -containing p ixels in the CIE
coordinate system (n = 53,820), and the ir distributi on in the CIE color sp ace
(box in A2). A3 : Histograms o f nFLH and Chla for the del ineated pixels , obtained
using NAS A standard algori thms47,57. (b) MODIS true color c omposites an d
selected spect ra for phytoplankton blooms, macroalgal blooms (Ulva and
Sargassum), coccolitho phore blooms, an d sediment-rich turb id waters. The x-y
numbers in dicate their co rresponding p ositions in th e CIE coordinate s ystem.
The black re ctangular b oxes in the three lower p anels highligh t different
spectr al shapes bet ween phytopl ankton blooms an d other features n ear the
fluore scence band . Maps created u sing ArcMap 10.4.
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Extende d Data Fig. 2 | MOD IS-det ected bloo m count withi n certain yea rs
for several coas tal region s with frequ ently repor ted blooms . The MODIS
obser vational year is ann otated with in each panel, an d overlaid points ind icate
insitu re corded harmf ul algal bloom event s from the Harmf ul Algae Event
Databas e (HAEDAT) within the s ame year. The lower rig ht panel shows the
locatio ns of all the HAEDAT record s that were used for algo rithm validatio ns in
this study (Sup plementar y Table1), which also d emonstrate s the increas e in
sampling ef fort in the mos t recent years. Cr eated using Ar cMap 10.4.
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Article
Extende d Data Fig. 3 | Per formance of t he CIE-fl uorescen ce algorithm for
phytoplankton bloom detection in 12 selected coastal oceans. From left
to right are th e RGB-true color co mposite, ERGB c omposite, FLH Rrc, and the
bloom area ( green pixels) det ected by the CI E-fluoresce nce algorithm . Created
using ArcM ap 10.4.
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Extende d Data Fig. 4 | Exa mples showi ng disadvant ages of usi ng NASA
standard Rrs (i.e., with t he removal of both Rayl eigh and aer osol scatt ering)
in algal bloom detection. From left to righ t are the RGB compos ites, ERGB,
nFLH, and th e bloom areas ( green pixels) dete cted by the CIE-f luorescen ce
algorithm ( based on Rrc, w ithout the removal o f aerosol scat tering). Sub stantial
amounts of i nvalid Rrs retrieval s can be obser ved in the red-en circled areas in
which severe blo oms can be found . Additionally, nFLH show s high values at
cloud edge s (yellow-encircl ed areas), making it chal lenging to use a s imple
threshold to c lassify bloo ms. However, such problem s can be circumvent ed in
our CIE-flu orescence al gorithm. Creat ed using ArcMap 10.4.
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Article
Extende d Data Fig. 5 | Se nsitivit y analysis of th e impacts of a erosols on
bloom detection. (a) Response s of bloom area (BA) to ch anges in aeroso l
optical thi ckness (AOT). Aero sol reflec tance (ρa) w ith AOTs of 0.01 and 0.02 at
869-nm is simula ted and added to th e MODIS image s, and the resulti ng bloom
areas (g reen pixels) with and w ithout adde d ρa are compared. T he left colum ns
show the RGB com posites, and t he right three c olumns show the blo om areas
under diffe rent AOTs. The percent ages of BA changes a re annotated in t he
panels. (b) The stand ard deviation be tween the 12 m onthly mean valu es of AOT
in global coa stal waters (i.e ., 66.7% of the int ra-annual variab ility), and the
histogra m is shown in (c). Maps create d using ArcMap 10.4.
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Extende d Data Fig. 6 | Com parison of d ifferent i ndex-based algo rithms in
algal bloom detection in various coastal regions. Image-specific thresholds
(annotated w ithin the panel s) are required (label ed within the pa nels) for RI50,
ABI (estima ted with FLHRrc)48, R BD51, KBBI51, and RDI52 to delineate accurate
bloom area s (i.e., high nFLH valu es, which appe ar as bright and d arkish feature s
on the ERGB ima ges). The left panel s are the bloom area s (green pi xels)
extract ed using our CIE-f luorescen ce algorithm. T he RGB-true colo r and ERGB
composit es are shown in Ex tended Data Fi g.3. Created using A rcMap 10.4.
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Article
Extende d Data Fig. 7 | An nual median b loom count an d the proport ion of
bloom-af fected ar eas for large ma rine ecosys tems (LMEs). (a) Annu al
median bloom count, (b) proportion of blo om-affected a reas. The dat a are
ordered from t he largest to the sm allest. The L MEs are groupe d by continent,
and their nam es, numbers, a nd location s are shown in (a) and (b). Map create d
using Py thon 3.8.
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Extende d Data Fig. 8 | Com parison of b loom counts i n the estuari ne and
non-estuarine regions. Boxplot s for long-term mean b loom count in the
estuarin e (n = 13,622 pixel ob servation s) and non-estuari ne (n = 361,604 pixel
obser vations) regions . Compariso n analysis was per formed by two sid ed
Welch’s t-test (P < 0.001).Upper an d lower bounds are f irst and third qua rtiles,
the bar in the mi ddle represent s the median valu e, and the whiskers sh ow the
minimum and ma ximum values . Sixty-two es tuarine zones f rom large rivers
were selec ted, and the bou ndary of each zo ne was manually de lineated
according to high-resolution satellite images.
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Article
Extende d Data Fig. 9 | Clu sters of dif ferent bloo m growth paths . (a) The
spatial distribution of different clusters. The fractions of different clusters
across dif ferent latitude s are summarize d. (b) The developm ent of the
maximum blo om-affecte d areas within a ye ar within 1° × 1° grid c ells, where
all global gr id cells are group ed into three dis tinct cluste rs according to the
similarit y of the bloom grow th curve. Th e colored bond cur ves represe nt the
mean value s of all the grid cell s, and their mean S ST and associ ated standard
deviation s are shown with da shed lines and g ray shading. T he proportion s of
different c lusters in the glo bal bloom-affe cted areas are a nnotated. (c) and (f)
The mean t iming of the maxi mum bloom-affec ted areas (T MBAA) and the
associa ted standard de viations bet ween 2003 an d 2019. The whole year in the
Souther n Hemisphere is s hifted forwar d by 183 days in (c). Maps created u sing
Python 3.8.
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Extende d Data Fig. 10 | Cha nges in clim ate extremes , global ferti lizer uses ,
and fi shery prod uction over the pa st two decad es. (a) Changes in t he
bi-monthly Mu ltivariate El Niñ o–Southern O scillation (ENS O) index (MEI)
betwee n 2002 and 2020. Po sitive and negat ive MEI values repre sent EI Niño
and La Niña even ts, respec tively. The dots show a nnual mean value s.
(b–c) Trends of nitrogen an d phosphorus f rom 2003 to 2019 for differe nt
countries. (d) Trends of fisher y production f rom 2003 to 2018. G ray indicates
no data. Ma ps created usin g ArcMap 10.4.
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Corresponding author(s): Lian Feng
Last updated by author(s): Nov 23, 2022
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Data collection The satellite data were obtained from the U.S. National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (GSFC).
Data analysis SeaDAS (Version 7.5) were used to analyze the satellite images.
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Study description This study developed a novel method to map global coastal algal blooms and used this tool to examine satellite images between
2003 and 2020, addressing three fundamental questions: 1) where and how frequently have global coastal oceans been affected by
phytoplankton blooms? 2) have the blooms expanded or intensified over the past two decades, both globally and regionally? and 3)
what are the potential drivers?
Research sample Three separate samples were selected. 1) MODIS Aqua images were used to develop the phytoplankton bloom extraction algorithm,
2) MODIS Aqua images and were used to verify the reliability of the algorithm and the accuracy of the phytoplankton bloom
extraction results, and 3) in situ reported HAB events from the HAEDAT dataset were used to validate the accuracy of the
phytoplankton bloom extraction results.
Sampling strategy A total of 115 MODIS Aqua images were selected from the different locations where coastal phytoplankton blooms have been
recorded in the published literature, of which 80 were used for algorithm development and 35 were used for algorithm validation. A
total number of 2609 HAB events that occurred in the coastal area were selected from the HAEDAT dataset.
Data collection The HAEDAT dataset is a collection of records of harmful algal bloom (HAB) events , maintained under the UNESCO
Intergovernmental Oceanographic Commission and with data archives since 1985.
Timing and spatial scale The satellite data were acquired from different seasons and across various phytoplankton bloom magnitudes between 2003 and
2020, and HAB data from 2003 to 2020 in the HAEDAT dataset were used.
Data exclusions No data were excluded from analysis.
Reproducibility Our results could easily be reproduced with existing datasets.
Randomization Excluding data affected by clouds, a total of 0.76 million MODIS Aqua images from 2003 to 2020 were used to extract phytoplankton
blooms in global coastal area.
Blinding Not applicable in our study.
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