附录1:系统底图来源

系统底图数据来自于esri的公开地图服务,您在使用这些数据时,必须遵守许esri使用条款(https://www.esri.com/zh-cn/legal/terms/full-master-agreement

自然资源部第二海洋研究所和浙江大学不承担用户因违反条款对外发布、复制、修改该数据而产生的一切责任。

附录2:数据来源

附录3:数据集列表

附录4:数据文件命名

附录5:系统计算说明

S5.1 球面系统坐标系

SatCO2平台采用WGS84坐标系统,该地理坐标系的设置参数如下:

GEOGCS["WGS 84",

     DATUM["WGS_1984",

            SPHEROID["WGS 84",6378137,298.257223563,

                   AUTHORITY["EPSG","7030"]],

            TOWGS84[0,0,0,0,0,0,0],

            AUTHORITY["EPSG","6326"]],

     PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],

UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9108"]],

   AUTHORITY["EPSG","4326"]]

S5.2 实测数据网格化方法

通常情况下,需要将研究区域一系列的离散点转换为规则的网格数据进行处理,或者和其他网格数据进行合并等处理。从离散的实测数据点创建一个栅格图像需要经过离散数据插值的过程。Fig.S1是网格插值的示意图。

SatCO2平台中的插值方法为最邻近插值法。最邻近插值法是将与目标栅格点空间距离最近的实测数据赋予目标栅格的相应像元。该方法的优点是不会改变原始实测栅格值,而且处理速度快。

涉及的功能见 REF _Ref498872881 \r \h \* MERGEFORMAT 2.5.2节。

 

Fig.S1网格化示意图

 

S5.3 时空匹配方法

SatCO2平台实测数据与遥感影像时空匹配处理的策略为:

1)以实测采样点的经纬度数据,对应遥感影像的经纬度。由于遥感数据空间尺度大,会存在一个遥感网格,对应多个采样点(如相同4km网格内采样);

2)以实测采样点的时间信息,对应遥感影像的时间。由于遥感数据为日/旬或月平均数据,会存在同一时间的遥感数据,对应多个采样点(如10天内采样或者连续站采样);

3)如果采样点匹配不到满足条件的数据,用户可选择匹配气候态数据。

如果匹配到多幅满足要求的遥感影像,则取数值平均值作为匹配结果。

涉及的功能见  2.7.1节。

附录6:海-气CO2通量算法说明

We adopt the commonly used method to calculate the air-sea CO2 flux, which is based on the multiplication of CO2 partial pressure difference between surface sea water and atmosphere and CO2 gas transfer velocity. The equation is as follows:

Flux=∆pCO2×E=WCP-ACP×KHCO2×ρ×C2×k´24×10-2

where,

1ACP: partial pressure of CO2 in atmosphere (μatm)

WCP: wpartial pressure of CO2 in seawater(μatm)

SST: sea surface temperature (oC)

SSS: sea surface salinity (psu)

SSW: sea surface wind speed (m/s)

2KHCO2——Dissolution efficient of CO2(mol·kg-1·atm-1)

ln(KHCO2) = -60.2409 + 93.4517×(100/T) + 23.3585×ln(T/100) + SSS×[0.023517 - 0.023656×(T/100) + 0.0047036×(T/100)2];

T = SST (°C) + 273.15

3)ρ——Sea water density, which can be calculated by the function of surface water temperature and salinity(Millero, 2013)(kg·m-3)

ρ=(ρw+A*S+B*S3/2+C*S2)*10-3

ρw=999.842594+6.793952*10-2*SST-9.09529*10-3*SST2+1.001685*10-4*SST3-1.120083*10-6* SST4+6.536332*10-9* SST5

A=0.824493-4.0899*10-3*SST+7.6438*10-5*SST2-8.2467*10-7*SST3+5.3875*10-9*SST4

B=-5.72466*10-3+1.0227*10-4*SST-1.6546*10-6*SST2

C=4.8314*10-4 

4)C2——Wind speed coefficient C2, which has been calculated and uploaded in the SOED database

To calculate the monthly average flux, it is often necessary to consider the influence of the high-frequency wind speed change (e.g. daily) on the monthly average wind speed, using a coefficient of C2 (Wanninkhof, 2002).The C2 coefficient is not needed when calculate daily flux.

Uj is high-frequency satellite-derived wind speed (e.g. daily), and Umean is satellite-derived monthly average wind speed, both with unit of m·s-1.

5k—Gas transfer velocity(cm·h-1)

Based on the relationship between gas transfer velocity (k) and the wind speed at 10m above sea level(U10), the commonly used equations for calculating k are shown in the table below.

No.

Equation

References

1

k660 = 0.31×u102(Instantaneous wind speed)

k660 = 0.39×u10(Long-term average wind speed)

Wanninkhof (1992)( Instantaneous)

Wanninkhof (1992)( Long-term)

2

k660 = 0.27×u102

Sweeney et al. (2007)

3

k600 = 0.266×u102

Ho et al. (2006)

4

k660 = 0.24×u102

Wanninkhof et al. (2009)

5

k600 = 0.17×u10 (u10< 3.6 m/s)

Liss and Merlivat (1986)

6

k660 = 0.0283×u103

Wanninkhof and McGillis (1999)

7

k600 = 2.85×u10 -9.65 (3.6 < u10< 13 m/s)

Liss and Merlivat (1986)

8

k600 = 5.9×u10 -49.3 (u10> 13 m/s)

Liss and Merlivat (1986)

k660 and k600 mean the k with the the Schmidt number (Sc) of 660 and 600, respectively.

k = k600 × (Sc/600)-0.5 and k = k660 × (Sc/660)-0.5

When sea suface temperature (sst) ranging 0-30℃, the Sc can be calculated with the following equation(Wanninkhof, 1992),

Sc = 2073.1−125.62×sst + 3.6276×sst2−0.043219×sst3

where, sst is the sea suface temperature, with the unit of oC.

For the calculation of the satellite-derived air-sea CO2 flux in the Open Ocean, the k-u10equation of #1 (long-term wind speed) and #2 in the above table are commonly used.

Reference

[1]     Bai, Y., Cai, W.-J., He, X., Zhai, W., Pan, D., Dai, M., Yu, P., 2015. A mechanistic semi‐analytical method for remotely sensing sea surface pCO2 in river‐dominated coastal oceans: A case study from the East China Sea. Journal of Geophysical Research : Oceans, 120, doi:10.1002/2014JC010632.

[2]     Song, X., Bai, Y., Cai, W.-J., Chen, C.-T. A., Pan, D., He, X., Zhu Q., 2016, Remote Sensing of Sea Surface pCO2 in the Bering Sea in Summer Based on a Mechanistic Semi-Analytical Algorithm (MeSAA), Remote Sensing, 8, 558; doi:10.3390/rs8070558 .

[3]     Ho, D.T., Law, C.S., Smith, M.J., Schlosser, P., Harvey, M., Hill, P., 2006. Measurements of air-sea gas exchange at high wind speeds in the Southern Ocean: Implications for global parameterizations. Geophysical Research Letters, 33(16): L16611, doi:10.1029/2006GL026817.

[4]     Liss, P.S. and Merlivat, L., 1986. Air-sea gas exchange rates: introduction and synthesis. In: P. Buat-Menard (Editor), The Role of Air-Sea Exchange in Geochemical Cycling. Reidel, Hingham, MA, pp. 113-129.

[5]     Millero, 2013, Chemical Oceanography (4th Edition), CRC Press, Taylor & Francis Group, International Standard Book Number-13: 978-1-4665-1255-9 (eBook - PDF).

[6]     Sweeney, C., Gloor, E., Jacobson, A. R., Key, R. M., McKinley, G., Sarmiento, J. L., Wanninkhof, R., 2007. Constraining global air-sea gas exchange for CO2 with recent bomb C-14 measurements. Global Biogeochemical Cycles, 21(2): GB2015, doi:10.1029/2006GB002784.

[7]     Wanninkhof, R., 1992. Relationship between wind speed and gas exchange over the ocean. Journal of Geophysical Research, 97(C05): 7373-7382.

[8]     Wanninkhof, R., Asher, W.E., Ho, D.T., Sweeney, C. and McGillis, W.R., 2009. Advances in quantifying air-sea gas exchange and environmental forcing, Annual Review of Marine Science. Annual Review of Marine Science, pp. 213-244.

[9]     Wanninkhof, R., Doney, S. C., Takahashi, T., and McGillis, W.: The effect of using time-averaged winds on regional air-sea CO2 fluxes, in: Gas Transfer at Water Surfaces, edited by: Donelan, M., Geophys. Monogr. Ser., Amrican Geophysical Union,Washington, D.C., 127, doi:10.1029/GM127p0351, 351–357, 2002.

[10] Weiss, E.F. and Price, R.A., 1980. Nitrous oxide solubility in water and sea water. Marine Chemistry, 8: 347-359.


附录7:赤潮识别反演算法说明

A rapid increase in the number of microalgae is considered as an algal bloom. Harmful algal bloom (HAB) is a bloom that results in negative impact on plants and animals. Over the past decade, significant achievements were made to synoptically detect and characterize the location and extent of HABs from ocean color satellite sensors. A lot of greatly improved algorithms have been developed to more accurately retrieve phytoplankton proxies in coastal waters. Therefore, the SatCO2 software includes 5 HAB detection modules which have been successfully in some typical coastal regions such as the East China Sea, the Gulf of Mexico, and so on. The detailed descriptions of these modules are as following.

S7.1. The HAB algorithm in the East China Sea

This module integrates two HAB algorithms in the East China Sea. The first algorithm is a multispectral approach for discriminating P. donghaiense blooms from other water types based on MODIS Rrs spectral shape discrimination. Its procedure is separated into two steps. First, the bloom waters are identified by the lowRrs(555) and high RAB. Second, two new indices of P. donghaiense index (PDI) and diatom index (DI) are developed for discriminating P. donghaiense from diatom blooms. The second algorithm is a VIIRS based approach for detecting HAB waters. The detailed method is schematically illustrated in Fig S2.

(1)MODIS HAB algorithm description

Algal bloom ratio (RAB) is defined as:


P. donghaiense index(PDI):


where


Diatom index(DI):


(2)VIIRS HAB algorithm description

Algal bloom ratio (RAB) is defined as:


Bloom index (BI) is defined as:



 

Fig. S2 Schematic procedure of MODIS multispectral method for the identification of

P. donghaiense.

S7.2. The RBD based HAB algorithm

This module integrates the technique which was developed by Amin et al., 2009 primarily for detecting Karenia brevis (K. brevis) blooms throughout the Gulf of Mexico. This detection technique for blooms with low backscatter, which we name the Red Band Difference (RBD) technique These techniques take advantage of the relatively high solar induced chlorophyll fluorescence and low backscattering of K. brevis blooms. The techniques are applied to the detection and classification of K. brevis blooms from Moderate Resolution Imaging Spectroradiometer (MODIS) ocean color measurements.

(1)MODIS RBD algorithm description

Red Band Difference (RBD) is defined as:


Threshold of Algal bloom waters:

RBD > 0.15W/m2/μm/sr


Fig.S3 MODIS (Aqua) bloom image from 13 November 2004 for the WFS (a) FLH

(W/m2/μm/sr) image, (b) RBD (W/m2/μm/sr) image and (c) Normalized-water leaving radiance spectra taken from the bloomed and turbid waters indicated by “circle” and “squares” respectively in the FLH image.

 

S7.3. The RGCI based HAB algorithm

This module integrates a novel empirical Chla algorithm based on a Red-Green-Chorophyll-Index (RGCI) which was developed by Le et al., 2013, and validated for MODIS and VIIRS observations. The algorithm showed robust performance in coastal waters such as Tampa Bay and the northeastern Gulf of Mexico. Oi et al.(2015) used RGCI from VIIRS to observe Karenia brevis blooms in the Northeastern Gulf of Mexico, which can overcome deficiency rising from absence of a Fluorescence Band.

(1)MODIS RGCI algorithm description

Red-Green-Chorophyll-Index (RGCI) is defined as:

RGCI = Rrs(667)/ Rrs (547)

The empirical Chla algorithm:

Chla = 0.86 ∗ exp(5.1 ∗ RGCI)

(2)VIIRS RGCI algorithm description

Red-Green-Chorophyll-Index (RGCI) is defined as:

RGCI = Rrs(672)/ Rrs (551)

The empirical Chla algorithm:

Chla = 0.10 ∗ exp(11.8 ∗ RGCI)

 

Fig.S4 Comparisons between (a and d) VIIRS OC3 Chla (mg m−3), (b and e) VIIRS RGCI Chla (mg m−3), and (c and f) MODISA nFLH (mW cm−2 μm−1 sr−1) for (a–c) July 30, 2014, and (d–f) August 27, 2014. The annotate times are GMT hours and minutes. Black is land, and gray represents clouds or invalid data.

S7.4. The ABI based HAB algorithm

This module integrates new bio-optical algorithm has been developed by Shanmugam et al., 2011, to provide accurate assessments of chlorophyll a (Chl a) concentration for detection and mapping of algal blooms from satellite data in optically complex waters, where the presence of suspended sediments and dissolved substances can interfere with phytoplankton signal and thus confound conventional band ratio algorithms. This algorithm that uses the normalized water‐leaving radiance ratios along with an algal bloom index (ABI) between three visible bands to determine Chl a concentrations, which derived using Moderate Resolution Imaging Spectroradiometer (MODIS) data and can be could be extensively used for quantitative and operational monitoring of algal blooms in various regional and global waters.

(1)MODIS ABI algorithm description

Algal bloom index (ABI) is defined as:



The empirical Chla algorithm:



Fig.S5 (top) A MODIS/Aqua true color composite on 18 February 2010 in the Arabian Sea and Gulf of Oman. The corresponding Chl a images derived using (middle) the OC3 and (bottom) ABI algorithms. The combined SeaDAS algorithm and CCS scheme was used for atmospheric correction of MODIS/Aqua data.

S7.5. The FLH based HAB algorithm

This module integrates a MODIS fluorescence line height (FLH) based algorithm developed by Hu et al., 2005 to detect and trace a harmful algal bloom (HAB), or red tide, in SW Florida coastal waters. Because MODIS fluorescence line height (FLH in W/m2/μm/sr) data showed the highest correlation with near-concurrent in situ chlorophyll-a concentration (Chl in mg m-3, the results show that the MODIS FLH data provide an unprecedented tool for research and managers to study and monitor algal blooms in coastal environments.

(1)MODIS FLH based Chla algorithm description

The FLH based Chla algorithm:

Chl=1.255 * (FLH * 10)0.86

 

Fig.S6. MODIS/Aqua imagery for SW Florida coastal waters. Left column: Fluorescence Line Height (FLH; W/m2/μm/sr); the color scale includes negative values. Middle column: band-ratio chlorophyll concentration (OC3M Chl; mg m-3). Right column: enhanced RGB (ERGB) composite images from water-leaving radiance in three MODIS wavelengths: 551 nm (R), 488 nm (G), and 443 nm (B).

 

Reference

[1]     Tao Bangyi, Mao Zhihua, Lei Hui, et al. A semianalytical MERIS green-red band algorithm for identifying phytoplankton bloom types in the East China Sea[J]. Journal of Geophysical Research: Oceans, 2017,122(3):1772-1788.

[2]     Tao Bangyi, Mao Zhihua, Lei Hui, Pan Delu, ShenYuzhang, Bai Yan, Zhu Qiankun, Li Zhien, A novel method for discriminating Prorocentrum donghaiense from diatom blooms in the East China Sea using MODIS measurements[J]. Remote Sensing of Environment, 2015,158(0):267-280.

[3]     Tao Bangyi, Mao Zhihua, Pan Delu, ShenYuzhang, Zhu Qiankun, Chen Jianyu, Influence of bio-optical parameter variability on the reflectance peak position in the red band of algal bloom waters[J]. Ecological Informatics, 2013,16(0):17-24.

[4]     Tao Bangyi, Mao Zhihua, Pan Delu, ShenYuzhang, Optical detection of Prorocentrum donghaiense blooms based on multispectral reflectances[J]. Acta Oceanologica Sinica. 2013,32(10):48-56.

[5]     Ruhul Amin, Jing Zhou, Alex Gilerson, et al. Novel optical techniques for detecting and classifying toxic dinoflagellate Karenia brevis blooms using satellite imagery [J]. Optics Express, 2009,17(11): 9126.

[6]     Chengfeng Le, Chuanmin Hu, David English, et al. Climate-driven chlorophyll-a changes in a turbid estuary: Observations from satellites and implications for management [J]. Remote Sensing of Environment, 2013,130: 11-24.

[7]     Lin Qi, Chuanmin Hu, Jennifer Cannizzaro, et al. VIIRS Observations of a Karenia brevis Bloom in the Northeastern Gulf of Mexico in the Absence of a Fluorescence Band [J]. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2015,12(11): 2213-2217.

[8]     Palanisamy Shanmugam. A new bio-optical algorithm for the remote sensing of algal blooms in complex ocean waters [J]. JOURNAL OF GEOPHYSICAL RESEARCH, 2011, 116:C04016.

[9]     Chuanmin Hu, Frank E. Muller-Karger, Charles (Judd) Taylor et al. Red tide detection and tracing using MODIS fluorescence data: A regional example in SW Florida coastal waters [J]. Remote Sensing of Environment, 2005, 97:311-321.



下一篇: