. Institute of Geographical Science, Henan Academy of Sciences, Zhengzhou 450052, China . State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography (Ministry of Natural Resources), Hangzhou 310012, China . Editorial Department of Journal of Central China Normal University, Wuhan 430079, China
The chlorophyll-a concentration data obtained through remote sensing are important for a wide range of scientific concerns. However, cloud cover and limitations of inversion algorithms of chlorophyll-a concentration lead to data loss, which critically limits studying the mechanism of spatial-temporal patterns of chlorophyll-a concentration in response to marine environment changes. If the commonly used operational chlorophyll-a concentration products can offer the best data coverage frequency, highest accuracy, best applicability, and greatest robustness at different scales remains debatable to date. Therefore, in the present study, four commonly used operational multi-sensor multi-algorithm fusion products were compared and subjected to validation based on statistical analysis using the available data measured at multiple spatial and temporal scales. The experimental results revealed that in terms of spatial distribution, the chlorophyll-a concentration products generated by averaging method (Chl1-AV/AVW) and GSM model (Chl1-GSM) presented a relatively high data coverage frequency in Case I water regions and extremely low or no data coverage frequency in the estuarine coastal zone regions and inland water regions, the chlorophyll-a concentration products generated by the Neural Network algorithm (Chl2) presented high data coverage frequency in the estuarine coastal zone Case 2 water regions. The chlorophyll-a concentration products generated by the OC5 algorithm (ChlOC5) presented high data coverage frequency in Case I water regions and the turbid Case II water regions. In terms of absolute precision, the Chl1-AV/AVW and Chl1-GSM chlorophyll-a concentration products performed better in Class I water regions, and the Chl2 product performed well only in Case II estuarine coastal zones, while presenting large errors in absolute precision in the Case I water regions. The ChlOC5 product presented a higher precision in Case I and Case II water regions, with a better and more stable performance in both regions compared to the other products.
Online First Date: 06 July 2023Issue Date: 29 September 2024
Cite this article:
Zheng WANG,Qun ZENG,Shike QIU, et al. Assessing the quality of chlorophyll-a concentration products under multiple spatial and temporal scales[J]. Front. Earth Sci.,
2024, 18(3): 463-487.
Tab.1 Parameters of different satellite-inversed chlorophyll-a concentration products
Fig.1 Distribution of in situ measured chlorophyll-a concentration data for accuracy evaluation in this paper. (a) Distribution of all NOMAD (2808), Bio-Argo (9502), and in situ measured (northern South China Sea) data points matched to the remote sensing inversion data (Chl1-AVW, Chl1-GSM, Chl2, and ChlOC5 products). (b) Spatial distribution of measured NOMAD (765) and Bio-Argo (2008) data points under the same criteria after the selection.
Fig.2 The measured chlorophyll-a concentration data from the spring, summer, autumn, and winter voyages in the South China Sea. (b−e) In situ chlorophyll-a concentration data collected during different seasons in the South China Sea region.
Fig.3 Flow chart of the multi-sensor fusion of global chlorophyll-a concentration data.
Fig.4 Average global data coverage frequency of Chl1 product at different time scales. (a)–(b) Daily average Chl1 chlorophyll-a concentration data coverage frequency between the years 1998 and 2018; (c)–(d) 8-d time scale average Chl1 chlorophyll-a concentration data coverage frequency between the years 1998 and 2018; (e)–(f) Monthly average Chl1 chlorophyll-a concentration data coverage frequency between the years 1998 and 2018.
Fig.5 The average global spatial coverage of Chl2 and ChlOC5 products at different time scales. Chlorophyll-a concentration data coverage frequency of the Chl2 (a, c, e) and ChlOC5 (b, d, f) products at daily, 8-d, and monthly time scales, respectively.
Fig.6 The average data coverage frequency of Chl1 product in the South China Sea and its surrounding waters at different time scales. Panels (a, c e) and panels (b, d, f) present the chlorophyll-a concentration data coverage frequency of the Chl1-GSM and Chl1-AVW products at daily, 8-d, and monthly time scales, respectively.
Fig.7 Data coverage frequency of the Chl2 and ChlOC5 products in the South China Sea and its surrounding regions at different time scales. Panels (a, c, e) and panels (b, d, f) present the average data coverage frequency at daily, 8-d, and monthly scales for the Chl2 and ChlOC5 products.
Fig.8 Daily chlorophyll-a concentration data coverage frequency for different chlorophyll-a concentration inverse algorithm products over 21 years. Panels (a–d) present the regional average daily available chlorophyll-a concentration data obtained through inverting the Chl1-AVW, Chl1-GSM, Chl2, and ChlOC5 products.
Fig.9 The long-time-scale monthly data coverage frequency for the different algorithms. Panels (a–d) present the regionally averaged monthly available chlorophyll-a concentration data through the inversion of the Chl1-AVW, Chl1-GSM, Chl2, and ChlOC5 products, respectively.
Fig.10 Regionally averaged 8-d scale data coverage frequency over 21 years for different algorithms. Panels (a–d) present the average regional 8-d available chlorophyll-a concentration data through the inversion of the Chl1-AVW, Chl1-GSM, Chl2, and ChlOC5 products, respectively.
Fig.11 Comparative evaluation of the chlorophyll-a concentration data retrieved from Bio-Argo and satellite remote sensing. Panels (a–c) compare the Bio-Argo in situ chlorophyll-a concentration data within 5 m of depth, and the satellite observed chlorophyll-a concentration data of Chl1-AVW, Chl1-GSM, and ChlOC5, respectively.
Fig.12 NOMAD?s comparative evaluation of the different satellite inversion products. Panels (a–d) compare the NOMAD in situ chlorophyll-a concentration data and the satellite observed chlorophyll-a concentration data of Chl1-AVW, Chl1-GSM, Chl2, and ChlOC5 products, respectively.
Fig.13 The Bio-Argo data set compares the different satellite inversion products after standardization. Panels (a–c) compare the Bio-Argo in situ chlorophyll-a concentration data within 5 m of depth and the satellite-observed chlorophyll-a concentration data of Chl1-AVW, Chl1-GSM, and ChlOC5, respectively, with the same sample point number and location.
Fig.14 Comparison of the NOMAD data set with the different satellite inversion products (including the Chl2 product) after standardization.
Fig.15 Comparison of the NOMAD data set with the different satellite inversion products (excluding the Chl2 product) after standardization.
Fig.16 Comparative analysis between the measured chlorophyll-a concentration and the data from four satellite remote sensing inversion products (i.e., Chl1-AVW, Chl1-GSM, Chl2, and ChlOC5) during spring voyage in the South China Sea.
Fig.17 Comparative analysis between the measured chlorophyll-a concentration and the data from the four remote sensing inversion products (i.e., Chl1-AVW, Chl1-GSM, Chl2, and ChlOC5) during a summer voyage in the South China Sea.
Fig.18 Comparative analysis between the measured chlorophyll-a concentration and the data from the four satellites remote sensing inversion products (i.e., Chl1-AVW, Chl1-GSM, Chl2, and ChlOC5) during the autumn voyage in the South China Sea.
Fig.19 Comparative analysis between the measured chlorophyll-a concentration and the data from the four remote sensing inversion products (i.e., Chl1-AVW, Chl1-GSM, Chl2, and ChlOC5) during a winter voyage in the South China Sea.
Measured Chl-a concentration
Inversion products of Chl-a
Number of matching points
R
RMSE
MAD
Bio-Argo
Chl1-AVW
2008
0.73
0.53
0.1996
Chl1-GSM
2008
0.85
0.41
0.1662
ChlOC5
2008
0.84
0.42
0.1835
NOMAD
Chl1-AVW
77
0.71
2.79
1.8674
Chl1-GSM
77
0.62
3.1
1.4998
Chl2
77
0.73
2.7
1.6745
ChlOC5
77
0.85
0.41
1.3459
Chl1-AVW
765
0.72
2.07
0.9122
Chl1-GSM
765
0.67
2.2
0.9158
ChlOC5
765
0.76
1.9
0.7687
SCS-Spring
Chl1-AVW
32
0.72
0.076
0.0445
Chl1-GSM
32
0.74
0.073
0.0463
Chl2
32
0.58
0.09
0.1706
ChlOC5
32
0.78
0.068
0.0426
SCS-Summer
Chl1-AVW
41
0.94
0.07
0.0355
Chl1-GSM
41
0.4
0.18
0.0742
Chl2
41
0.59
0.16
0.0668
ChlOC5
41
0.74
0.14
0.0463
SCS-Autumn
Chl1-AVW
13
0.44
0.22
0.2684
Chl1-GSM
13
0.05
0.26
0.2582
Chl2
13
0.03
0.28
1.9822
ChlOC5
13
0.64
0.64
0.2593
SCS-Winter
Chl1-AVW
26
0.78
0.12
0.1426
Chl1-GSM
26
0.8
0.11
0.1408
Chl2
26
0.5
0.2
0.2991
ChlOC5
26
0.74
0.13
0.1586
Tab.2 Comparative statistical evaluation of the remote sensing inversion chlorophyll-a concentration (Chl_a) with the actual measured chlorophyll-a concentration
1
ACRI-ST GlobColour Team (2017). GlobColour Product User Guide, GC-UM-ACR-PUG-01, Version 4.1. (Sophia-Antipolis)
2
R J W, Brewin F, Mélin S, Sathyendranath F, Steinmetz A, Chuprin M Grant (2014). On the temporal consistency of chlorophyll products derived from three ocean-colour sensors.ISPRS J Photogramm Remote Sens, 97: 171–184 https://doi.org/10.1016/j.isprsjprs.2014.08.013
3
P, Cai D, Zhao L, Wang B, Huang M Dai (2015). Role of particle stock and phytoplankton community structure in regulating particulate organic carbon export in a large marginal sea.J Geophys Res Oceans, 120(3): 2063–2095 https://doi.org/10.1002/2014JC010432
4
C Chen, F Shiah, S Chung, K Liu (2006). Winter phytoplankton blooms in the shallow mixed layer of the South China Sea enhanced by upwelling. J Mar Syst, 59(1–2): 97–110 https://doi.org/10.1016/j.jmarsys.2005.09.002
5
Y L, Chen H, Chen I, Lin M, Lee J Chang (2007). Effects of cold eddy on Phytoplankton production and assemblages in Luzon Strait bordering the South China Sea.J Oceanogr, 63(4): 671–683 https://doi.org/10.1007/s10872-007-0059-9
P H C Eilers, J C H Peeters (1988). A model for the relationship between light intensity and the rate of photosynthesis in phytoplankton. Ecol Modell, 42(3–4): 199–215 https://doi.org/10.1016/0304-3800(88)90057-9
8
S, Gai H, Wang G, Liu L, Huang X Song (2012). Chlorophyll a increase induced by surface winds in the northern South China Sea.Acta Oceanol Sin, 31(4): 76–88 https://doi.org/10.1007/s13131-012-0222-z
9
F, Gohin J N, Druon L Lampert (2002). A five channel chlorophyll concentration algorithm applied to SeaWiFS data processed by SeaDAS in coastal waters.Int J Remote Sens, 23(8): 1639–1661 https://doi.org/10.1080/01431160110071879
10
J, Grosse D, Bombar H N, Doan L N, Nguyen M Voss (2010). The Mekong River plume fuels nitrogen fixation and determines phytoplankton species distribution in the South China Sea during low- and high-discharge season.Limnol Oceanogr, 55(4): 1668–1680 https://doi.org/10.4319/lo.2010.55.4.1668
11
C, Hu Z, Lee B Franz (2012). Chlorophyll-a algorithm for oligotrophic oceans: a novel approach based on three-band reflectance difference.J Geophys Res Oceans, 117(C1): C01011 https://doi.org/10.1029/2011jc007395
12
H T, Huynh A, Alvera-Azcárate A, Barth J Beckers (2016). Reconstruction and analysis of long-term satellite-derived sea surface temperature for the South China Sea.J Oceanogr, 72(5): 707–726 https://doi.org/10.1007/s10872-016-0365-1
13
O, Isoguchi H, Kawamura K Ku-Kassim (2005). El Niño–related offshore phytoplankton bloom events around the Spratley Islands in the South China Sea.Geophys Res Lett, 32(21): L21603 https://doi.org/10.1029/2005GL024285
14
T, Kim K, Lee R, Duce P Liss (2014). Impact of atmospheric nitrogen deposition on phytoplankton productivity in the South China Sea.Geophys Res Lett, 41(9): 3156–3162 https://doi.org/10.1002/2014GL059665
15
A, Lewandowska U Sommer (2010). Climate change and the spring bloom: a mesocosm study on the influence of light and temperature on phytoplankton and mesozooplankton.Mar Ecol Prog Ser, 405: 101–111 https://doi.org/10.3354/meps08520
16
G, Li Y, Wu K Gao (2009). Effects of Typhoon Kaemi on coastal phytoplankton assemblages in the South China Sea, with special reference to the effects of solar UV radiation.J Geophys Res Oceans, 114(G4): G04029 https://doi.org/10.1029/2008JG000896
17
I I, Lin G T F, Wong C, Lien C, Chien C, Huang J Chen (2009). Aerosol impact on the South China Sea biogeochemistry: an early assessment from remote sensing.Geophys Res Lett, 36(17): L17605 https://doi.org/10.1029/2009GL037484
18
I, Lin C, Lien C, Wu G T F, Wong C, Huang T Chiang (2010). Enhanced primary production in the oligotrophic South China Sea by eddy injection in spring.Geophys Res Lett, 37(16): L16602 https://doi.org/10.1029/2010GL043872
19
J, Lin W, Cao G, Wang S Hu (2014). Satellite-observed variability of phytoplankton size classes associated with a cold eddy in the South China Sea.Mar Pollut Bull, 83(1): 190–197 https://doi.org/10.1016/j.marpolbul.2014.03.052
20
F Liu, C Chen (2014). Seasonal variation of chlorophyll a in the South China Sea from 1997–2010. Aquat Ecosyst Health Manage, 17(3 3SI): 212–220 https://doi.org/10.1080/14634988.2014.942211
21
K K, Liu C M, Tseng T Y, Yeh L W Wang (2010). Elevated phytoplankton biomass in marginal seas in the low latitude ocean: a case study of the South China Sea.Adv Geosci, 18: 1–17 https://doi.org/10.1142/9789812838148_0001
22
X, Liu M Wang (2019). Filling the Gaps of Missing Data in the Merged VIIRS SNPP/NOAA-20 Ocean Color Product Using the DINEOF Method.Remote Sens (Basel), 11(2): 178 https://doi.org/10.3390/rs11020178
23
C, Ma J, Zhao B, Ai S Sun (2021). Two-decade variability of sea surface temperature and chlorophyll-a in the northern South China Sea as revealed by reconstructed cloud-free satellite data.IEEE Trans Geosci Remote Sens, 59(11): 9033–9046 https://doi.org/10.1109/TGRS.2021.3051025
24
S, Maritorena O H F, d’Andon A, Mangin D A Siegel (2010). Merged satellite ocean color data products using a bio-optical model: characteristics, benefits and issues.Remote Sens Environ, 114(8): 1791–1804 https://doi.org/10.1016/j.rse.2010.04.002
25
S, Maritorena D A Siegel (2005). Consistent merging of satellite ocean color data sets using a bio-optical model.Remote Sens Environ, 94(4): 429–440 https://doi.org/10.1016/j.rse.2004.08.014
26
E, Martinez T, Gorgues M, Lengaigne C, Fontana R, Sauzede C, Menkes J, Uitz E, Lorenzo R Fablet (2020). Reconstructing global chlorophyll-a variations using a non-linear statistical approach.Front Mar Sci, 7: 464 https://doi.org/10.3389/fmars.2020.00464
27
X, Ning X, Peng F, Le Q, Hao J, Sun C, Liu Y Cai (2008). Nutrient limitation of phytoplankton in anticyclonic eddies of the northern South China Sea.Biogeosci Discuss, 5(6): 4591–4619 https://doi.org/10.5194/bgd-5-4591-2008
28
J E, O’Reilly P J Werdell (2019). Chlorophyll algorithms for ocean color sensors – OC4, OC5 & OC6.Remote Sens Environ, 229: 32–47 https://doi.org/10.1016/j.rse.2019.04.021
29
J E O’Reilly, S Maritorena, D A Siegel, M C O’Brien, Toole D, Mitchell B G, Kahru M, Chavez F P, Strutton P, Cota S G F, Hooker S B, McClain C R, Carder K L, Müller-Karger F, Harding L, Magnuson A, Phinney D, Moore G F, Aiken J, Arrigo K R, Letelier R, M Culver (2000). Ocean color chlorophyll a algorithms for SeaWiFS, OC2, and OC4: Version 4. In: Hooker S B, Firestone E R, eds. SeaWIFS Postlaunch Calibration and Validation Analyses, Part 3, NASA Technical Memorandum, 2000-206892, vol. 11, NASA Goddard Space Center, Greenbelt, MD(2000), 9–27
30
G, Shan W Hui (2008). Seasonal and spatial distributions of phytoplankton biomass associated with monsoon and oceanic environments in the South China Sea.Acta Oceanol Sin, 27(6): 17–32
31
S, Shang L, Li F, Sun J, Wu C, Hu D, Chen X, Ning Y, Qiu C, Zhang S Shang (2008). Changes of temperature and bio-optical properties in the South China Sea in response to Typhoon Lingling, 2001.Geophys Res Lett, 35(10): L10602 https://doi.org/10.1029/2008GL033502
32
X, Shang H, Zhu G, Chen C, Xu Q Yang (2015). Research on cold core eddy change and phytoplankton bloom induced by typhoons: case studies in the South China Sea.Adv Meteorol, 2015: 1–19 https://doi.org/10.1155/2015/340432
33
P P, Shen Y H, Tan L M, Huang J L, Zhang J Q Yin (2010). Occurrence of brackish water phytoplankton species at a closed coral reef in Nansha Islands, South China Sea.Mar Pollut Bull, 60(10): 1718–1725 https://doi.org/10.1016/j.marpolbul.2010.06.028
34
T, Shropshire Y, Li R He (2016). Storm impact on sea surface temperature and chlorophylla in the Gulf of Mexico and Sargasso Sea based on daily cloud-free satellite data reconstructions.Geophys Res Lett, 43(23): 12199–12207 https://doi.org/10.1002/2016GL071178
35
D L, Tang I H, Ni D R, Kester F E Müller-Karger (1999). Remote sensing observations of winter phytoplankton blooms southwest of the Luzon Strait in the South China Sea.Mar Ecol Prog Ser, 191(3): 43–51 https://doi.org/10.3354/meps191043
36
S Tang, Q Dong, F Liu (2011). Climate-driven chlorophyll-a concentration interannual variability in the South China Sea. Theor Appl Climatol, 103(1–2): 229–237 https://doi.org/10.1007/s00704-010-0295-6
37
J Wang, D Tang, Y Sui (2010). Winter phytoplankton bloom induced by subsurface upwelling and mixed layer entrainment southwest of Luzon Strait. J Mar Syst, 83(3–4): 141–149 https://doi.org/10.1016/j.jmarsys.2010.05.006
38
S, Wang N C, Hsu S, Tsay N, Lin A M, Sayer S, Huang W K Lau (2012). Can Asian dust trigger phytoplankton blooms in the oligotrophic northern South China Sea?.Geophys Res Lett, 39(5): L05811 https://doi.org/10.1029/2011GL050415
39
S, Wang D Tang (2010). Remote sensing of day/night sea surface temperature difference related to phytoplankton blooms.Intern J Remote Sens, 31(17–18): 4569–4578
40
Y, Wang D Liu (2014). Reconstruction of satellite chlorophyll-a data using a modified DINEOF method: a case study in the Bohai and Yellow seas, China.Int J Remote Sens, 35(1): 204–217 https://doi.org/10.1080/01431161.2013.866290
41
Z, Wang J, Du J, Xia C, Chen Q, Zeng L, Tian L, Wang Z Mao (2020). An effective method for detecting clouds in GaoFen-4 images of coastal zones.Remote Sens (Basel), 12(18): 3003 https://doi.org/10.3390/rs12183003
42
P J, Werdell S W Bailey (2005). An improved in-situ bio-optical data set for ocean color algorithm development and satellite data product validation.Remote Sens Environ, 98(1): 122–140 https://doi.org/10.1016/j.rse.2005.07.001
43
M, Winder S A, Berger A, Lewandowska N, Aberle K, Lengfellner U, Sommer S Diehl (2012). Spring phenological responses of marine and freshwater plankton to changing temperature and light conditions.Mar Biol, 159(11): 2491–2501 https://doi.org/10.1007/s00227-012-1964-z
44
H, Xi S N, Losa A, Mangin M A, Soppa P, Garnesson J, Demaria Y, Liu O H F, d’Andon A Bracher (2020). Global retrieval of phytoplankton functional types based on empirical orthogonal functions using CMEMS GlobColour merged products and further extension to OLCI data.Remote Sens Environ, 240: 111704 https://doi.org/10.1016/j.rse.2020.111704
45
H, Xu H W, Paerl B, Qin G, Zhu G Gao (2010). Nitrogen and phosphorus inputs control phytoplankton growth in eutrophic Lake Taihu, China.Limnol Oceanogr, 55(1): 420–432 https://doi.org/10.4319/lo.2010.55.1.0420
46
K Yamada, J Ishizaka, S Yoo, H Kim, S Chiba (2004). Seasonal and interannual variability of sea surface chlorophyll a concentration in the Japan/East Sea (JES). Prog Oceanogr, 61(2–4): 193–211 https://doi.org/10.1016/j.pocean.2004.06.001
47
Y, Yang T, Xian L, Sun Y Fu (2012). Summer monsoon impacts on chlorophyll-a concentration in the middle of the South China Sea: climatological mean and annual variability.Atmos Ocean Sci Lett, 5(1): 15–19 https://doi.org/10.1080/16742834.2012.11446961
48
X, Yuan K, Yin P J, Harrison J Zhang (2011). Phytoplankton are more tolerant to UV than bacteria and viruses in the northern South China Sea.Aquat Microb Ecol, 65(2): 117–128 https://doi.org/10.3354/ame01540
49
M, Zhang Y, Zhang Q, Shu C, Zhao G, Wang Z, Wu F Qiao (2018). Spatiotemporal evolution of the chlorophyll a trend in the North Atlantic Ocean.Sci Total Environ, 612: 1141–1148 https://doi.org/10.1016/j.scitotenv.2017.08.303
50
H, Zhao D, Tang Y Wang (2008). Comparison of phytoplankton blooms triggered by two typhoons with different intensities and translation speeds in the South China Sea.Mar Ecol Prog Ser, 365: 57–65 https://doi.org/10.3354/meps07488
