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Validation of MODIS ocean-colour products in the coastal waters of the Yellow Sea and East China Sea
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Lingling Jiang1, *, Xiangyu Guo1, Lin Wang2, Shubha Sathyendranath3, Hayley Evers-King3, Yanlong Chen2, Bingnan Li2
Acta Oceanologica Sinica | 2020, 39(1) : 91 - 101
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Acta Oceanologica Sinica | 2020, 39(1): 91-101
• Physical Oceanography, Marine Meteorology and Marine Physics •
Validation of MODIS ocean-colour products in the coastal waters of the Yellow Sea and East China Sea
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Lingling Jiang1, *, Xiangyu Guo1, Lin Wang2, Shubha Sathyendranath3, Hayley Evers-King3, Yanlong Chen2, Bingnan Li2
Affiliations
  • 1 College of Environmental Science and Engineering, Dalian Maritime University, Dalian 116026, China
  • 2 Ministry of Ecology Environment, National Marine Environmental Monitoring Center, Dalian 116023, China
  • 3 Plymouth Marine Laboratory (PML), Plymouth, Devon , PL1 3DH, UK
Published: 2020-01-25 doi: 10.1007/s13131-019-1522-3
Outline
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An extensive study collected in situ data along the Yellow Sea (YS) and East China Sea (ECS) to assess the radiometric properties and the concentration of the water constituents derived from Moderate Resolution Imaging Spectroradiometer (MODIS). Thirteen high quality match-ups were obtained for evaluating the MODIS estimates of Rrs(λ), chlorophyll a (Chl a) and concentrations of suspended particulate sediment matter (SPM). For MODIS Rrs(λ), the mean absolute percentage difference (APD) was in the range of 20%–36%, and the highest uncertainty appeared at 412 nm, whereas the band ratio of Rrs(λ) at 488 nm compared with that at 547 nm was highly consistent, with an APD of 7%. A combination of near-infrared bands and shortwave infrared wavelengths atmosphere correction algorithm (NIR-SWIR algorithm) was applied to the MODIS data, and the estimation accuracy of Rrs were improved at most of the visible spectral bands except 645 nm, 667 nm and 678 nm. Two ocean-colour empirical algorithms for Chl a estimation were applied to the processed data, the results indicated that the accuracy of the derived Chl a values was obviously improved, the four-band algorithms outperformed the other algorithm for measured and simulated datasets, and the minimum APD was 35%. The SPM was also quantified. Two regional and two coastal SPM algorithms were modified according to the in situ data. By comparison, the modified Tassan model had a higher accuracy for the application along the YS and ECS with an APD of 21%. However, given the limited match-up dataset and the potential influence of the aerosol properties on atmosphere correction, further research is required to develop additional algorithms especially for the low Chl a coastal water.

MODIS  /  turbid waters  /  chlorophyll a  /  SPM  /  retrieval algorithms  /  Yellow and East China Sea
Lingling Jiang, Xiangyu Guo, Lin Wang, Shubha Sathyendranath, Hayley Evers-King, Yanlong Chen, Bingnan Li. Validation of MODIS ocean-colour products in the coastal waters of the Yellow Sea and East China Sea[J]. Acta Oceanologica Sinica, 2020 , 39 (1) : 91 -101 . DOI: 10.1007/s13131-019-1522-3
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1 Introduction
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Satellite ocean-colour remote sensing plays an important role in providing critical ocean information on global, regional and local scales. Typical ocean-colour sensors such as Sea-viewing Wide Field-of-view Sensor (SeaWiFS), Moderate Resolution Imaging Spectroradiometer (MODIS) and Medium Resolution Imaging Spectrometer (MERIS) provide high acquisition frequency bands ideally positioned for the detection of water constituents and primary production estimation.
In coastal waters, chlorophyll a (Chl a) is a good indicator of primary productivity and suspended particulate matter (SPM) can affect the light transmission in water. As key indicators of water quality, remote sensing estimations of these two parameters have achieved many results (Gitelson et al., 2007; Kuchinke et al., 2009; Han et al., 1994; Shen et al., 2010; Cheng et al., 2013). However, some of these satellite Chl a and SPM algorithms tend to fail in case II water due to the optical complexity and overlapping and uncorrelated absorptions by the coloured dissolved organic material (CDOM) and the non-algal particles (Moses et al., 2009; Carder et al., 2004; Darecki and Stramski, 2004). Although the accuracy of Chl a and SPM algorithms for some satellite sensors in certain coastal zones has already been evaluated (Zibordi et al., 2006; Cui et al., 2010), calibration and validation of the various products in different coastal ocean regions are still important for ocean colour missions.
The MODIS onboard the Aqua satellite (MODIS-Aqua), launched in May 2002, is the current operational, medium resolution mission from NASA. There have been few previous MODIS validation studies in coastal waters (Zibordi et al., 2009; Dall’Olmo et al., 2005; Menon et al., 2006; Tilstone et al., 2013; Gitelson et al., 2008), and MODIS has recently experienced radiometric drift, which has been addressed through frequent vicarious calibration and re-processing of the data (Tilstone et al., 2013; Xiao et al., 2018). But few for the SPM and Chl a products are available from MODIS-Aqua in the YS and ECS (Bian et al., 2013; Zhang et al., 2010; Sun et al., 2010). Therefore, it is necessary to assess the MODIS-Aqua algorithms in these coastal areas to identify the most accurate Chl a and SPM products available for the region in terms of on-going monitoring of phytoplankton biomass.
The principle objective of this study is to assess the performance of the major MODIS-Aqua products in the turbid water along the East China Coast on in-situ measurements and match-up analysis, including Rrs (λ), SPM and Chl a. The effects of potential errors in the atmospheric correction were assessed. Subsequently, the ocean-colour algorithms and spatial-temporal match-up differences were analyzed.
2 Data and methodology
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2.1 Study area
The water mass in the Yellow Sea (YS) and Eest China Sea (ECS) regions is considered typical case II water (Li et al., 2017). The YS and ECS are located within the marginal sea of the northwest Pacific Ocean. This semi-enclosed, wide shelf sea consists of complicated hydrological variations and high sediment concentrations, with clear seasonal changes over a wide region (Fig. 1).
The YS is mainly contaminated by industrial pollution, agricultural runoff, and domestic sewage. The ECS is a marginal sea off the east coast of China. The Changjiang (Yangtze River) is the largest river flowing into the ECS and exports abundant nutrients and large amounts of sediments to the estuary (Gao and Song, 2005). Therefore, the coastal water near the estuary is well known for its extremely high turbidity (Zhang et al., 2010).
2.2 In-situ data
The in-situ dataset in this study contained 156 SPM, Rrs(λ) and Chl a measurements that were collected from May 2012 to May 2014 during eight separate cruises (Fig. 1). Water-leaving Rrs(λ) was measured with a hand-held ASD (Analyti-cal Spectral Device, Inc.) spectrometer following the NASA Ocean Optics protocols (Mueller et al., 2003). The “above-water method” is preferred to determine the value. This specific method was similar to the one utilized by Wang et al. (2012), in which Rrs was calculated from the following formula:
${R_{\rm {rs}}} = \frac{{{L_{\rm w}}\left({\lambda,{0^ + }} \right)}}{{{E_{\rm d}}\left({\lambda,{0^ + }} \right)}} = \frac{{{L_{\rm {sfc}}}\left({\lambda,{0^ + }} \right) - \rho {L_{\rm {sky}}}\left({\lambda,{0^ + }} \right)}}{{{E_{\rm d}}\left({\lambda,{0^ + }} \right)}},$
where ${L_{\rm w}}\left({\lambda,{0^ + }} \right)$ is the water leaving radiance (W/(m2·nm·sr)); ${E_{\rm d}}\left({\lambda,{0^ + }} \right)$ is the above surface downwelling spectral irradiance (W/(m2·nm)); ${L_{\rm {sfc}}}\left({\lambda,{0^ + }} \right)$ is the above-water upwelling radiance; ${L_{\rm {sky}}}\left({\lambda,{0^ + }} \right)$ is the sky diffuse radiance; and ρ is the dimensionless air-water reflectance, which is always in the range of 0.022–0.05 (Lee et al., 1996; Tang et al., 2004) and was set at 0.028 for this study (Mobley, 1999). Among the curves obtained by the measurement, we selected data between 400 nm and 900 nm to calculate the remote sensing reflectance. The median repeated measurement was selected, and the remote sensing reflectance was resampled to 1 nm intervals. The Rrs(λ) spectra (Fig. 2) depicts typical turbid coastal water, with large variability in magnitude, a roughly similar shape, and dominant peaks near 580 nm. Meanwhile, some high values around 700 nm are attributed to the scattering of suspended material.
SPM concentration was measured by the weighing method, where the water sample was filtered through a 0.45 μm vacuum filtration system. The filter pad was flushed with 50 cm3 of distilled water 3 times to remove the salt. The dry-weight of the filter-pad was weighed by an electronic analytic scale with 0.01 g/m3. The blank filter and sampled filter-pad were scaled several times, and two successive weight readings were within 0.01 g/m3 (Tang et al., 2004). We found that the SPM of the study area was in the range of 1.33 g/m3 and 958 g/m3 . The histogram of SPM measurements is shown in Fig. 3 and indicates that the typical SPM value for this study area is less than 50 g/m3 .
The Chl a concentration was determined by a fluorometric method (Cui et al., 2010), where the water samples were filtered through Whatman GF/F glass microfibre filters under a vacuum of less than 5×104 Pa. The volumes of the water samples were 200 mL for the mesotrophic area and 300–500 mL for the relatively clear area. The filters were analyzed immediately with a TD-700 fluorometer. Chl a concentration varied from 0.129 mg/m3 to 21.6 mg/m3 (Fig. 4), with the typical Chl a concentration less than 5.25 mg/m3. Analysis of the replicate measurements gave an average difference of about ±10%.
2.3 Match-up analysis
MODIS-Aqua level-2 data, corresponding to the days of in situ sampling, were obtained from the NASA Ocean Biology Processing Group where the spatial resolution is approximately 1 km by 1 km (http://oceandata.sci.gsfc.nasa.gov). The data included remote sensing reflectance Rrs at bands of 412, 443, 469, 488, 531, 547, 555, 645, 667 and 678 nm, Chl a (by OC3M algorithm), aerosol optical thickness at band 869 nm and Kd (490).
Of the in-situ dataset, a total of 13 match-ups were obtained. These were extracted by a 3×3 pixel box around the sampled station, within ±3 h of sampling. The match-up dataset was established according to the procedure used by Cui et al. (2010). The dates of the MODIS image satisfying the match-up criteria are July 29, 2012; October 18, 2012 and May 26, 2014. The spatial distribution of the match-ups are shown in Fig. 5.
To evaluate the algorithm performance, we used the average of the absolute percentage difference (APD), average of relative percentage difference (RPD), root mean square error (RMS), median and semi-interquartile range of satellite to in-situ ratios (Ratio, SIQR) (Bailey et al., 2006). These quantities were calculated as follows:
$APD = \frac{1}{N}\mathop \sum \limits_{i = 1}^N \frac{{\left| {{y_i} - {x_i}} \right|}}{{{x_i}}} \times 100\%,$
$RPD = \frac{1}{N}\mathop \sum \limits_{i = 1}^N \frac{{{y_i} - {x_i}}}{{{x_i}}} \times 100\%,$
$RMS = \sqrt {\frac{{\displaystyle\mathop \sum \limits_{i = 1}^N {{\left({{y_i} - {x_i}} \right)}^2}}}{N}},$
$SIQR = \frac{{\left({{Q_3} - {Q_1}} \right)}}{2},$
where xi is the in situ value, yi is the satellite-retrieved value, N is the number of match-up data points. Q1 and Q3 are the first and third quartiles, respectively. All the statistics were calculated using a linear scale.
3 Results
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3.1 Rrs assessment
Table 1 shows the comparison results between MODIS Rrs(λ) and in-situ Rrs(λ), while the scatter plots are shown in Fig. 6. The APD for the individual bands ranged from 20% to 36%, the lowest uncertainties appeared at 531 nm and 547 nm, and the highest APD was at 412 nm. For the band combination, Rrs(488)/Rrs(547) and Rrs(488)/Rrs(555) yield a lower uncertainties with an APD of 7% and 8%, respectively. Rrs(555)/Rrs(667) and Rrs(443)/Rrs(555) also have better accuracy, both with an APD of 17%. The ratio (<1) and the RPD (<0) indicated that the MODIS Rrs(λ) underestimated the in-situ measurements at all visible bands. This underestimation is more pronounced at 412 nm. The coefficient of determination R2 was lower in the red bands than that in the green and blue bands, and the slope was 0.953 for Rrs(443) and 0.887 for Rrs(469), but 0.408 and 0.392 for Rrs(667) and Rrs(678), respectively. For the band combination, Rrs(488)/Rrs(547) and Rrs(488)/Rrs(555) have a better performance with an APD of 7% and 8%, and Rrs(455)/Rrs(667) and Rrs(443)/Rrs(555) also have higher accuracy, both with an APD of 17%.
Figure 7 shows the comparison between the in-situ measurements and satellite retrievals for the 13 match-ups, indicating high levels of agreement at the majority of stations.
3.2 SPM and Chl a concentration assessment
The results of Chl a from MODIS products are shown in Table 2 as well as Fig. 10a. The derived Chl a value overestimated the in-situ ones, with the ratios of MODIS values to those of in-situ values being 2.72. Uncertainties in the satellite Chl a were 132% and 2.49 in RMS, and the determination coefficient was 0.05, with a slope of 0.007 6.
There is no standard MODIS SPM product. To identify the source of the uncertainty, two regional SPM retrieval algorithms for the YS and ECS were evaluated and modified according to our in situ data. One is the Tang model (Tang et al., 2004), which utilizes the reflectance at bands of 488, 555, and 667 nm. The other is the Zhang model (Zhang et al., 2010), which utilizes the reflectance at bands of 488, 555, and 645 nm. From Table 3 and Fig. 8, it is obvious that the performance of the adjusted models are better than the original ones. Therefore, the modified model were applied to the MODIS data. The APDs were 49% and 33%, respectively (Table 2). The average relative error between the inversed SPM when using the Tang model and the measured SPM was higher than the results of Cui et al. (2014a). And we obtained a higher APD than that of Zhang et al. (2010) (26%). The retrieval accuracy of the SPM was significantly better than that of Chl a for the MODIS data, but the improved model is still needed for this region.
4 Discussion
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4.1 Atmospheric correction
Compared with Rrs(λ) validation results in the study area, it is noted that Cui et al. (2014) and Sun et al. (2010) obtained similar results, with RMS values of 0.002 4–0.003 9 sr–1 and 0.002 4–0.003 6 sr–1, and APDs of 18%–28% and 21%–40%, respectively. While the RMS range is slightly larger in our study, in the range between 0.002 7 sr–1 and 0.009 3 sr–1. In addition, the largest uncertainty occurred at the blue band 412 nm and the satellite Rrs(λ) were underestimated, whereas we found the values were overestimated at this band in Cui’s study, which may be attributed to the spatial distribution of the match-ups, the sediment-dominated water, and/or the elevated turbidity, which triggers many bio-optical models within the atmospheric correction process and accounts for non-negligible near-infrared Rrs(λ) (Bailey et al., 2010). The short wave infrared method (SWIR) was used for the East China Sea, Zhang et al. (2010) found that this algorithm is also invalid in highly turbid waters. Wang and Shi (2007) proposed a combination of the near-infrared bands (NIR) algorithm and SWIR algorithm (NIR-SWIR algorithm) that can effectively remove the atmospheric contribution over highly turbid coastal and clear waters (Wang et al., 2009).
Thus, the NIR-SWIR method has been applied to our data. The results were shown in Table 5 for the comparison between in situ and inversion Rrs. It is clearly that the estimation accuracy of Rrs was improved at most of the visible spectral bands except 645, 667 and 678 nm. That is still a legitimate question (Chen et al., 2015). High turbidity levels may cause non-negligible Rrs(1 240) so that atmosphere correction using SWIR bands may be wrong from the beginning to the subsequent extrapolation (Knaeps et al., 2012; Shi and Wang, 2014; Doxaran et al., 2006). Furthermore, SWIR bands have lower signals-to-noise ratio (SNR) than the NIR, which often makes processed products to be poor (Vanhellemont and Ruddick, 2015; Wang and Shi, 2012; Werdell et al., 2010). Therefore, improving the performance of the atmospheric correction is still an important task in studying the coastal water.
At the same time, aerosol properties play an important role in the atmospheric correction. More than 50% of the resolved aerosol mass is contributed by anthropogenic sources. The anthropogenic contribution is influenced to a large extent by East Asian monsoons, with a significantly higher effect in spring and winter than in summer. This effect is associated with the change of prevailing wind directions from north/northwest (off the continent) to south/southeast (Wang et al., 2016). Furthermore, the aerosol optical depth (AOD) is an important parameter for the best use of satellite data. Previous research has shown that the MODIS-retrieved AODs are lower than those of in-situ data in turbid water. This is likely caused by the errors in aerosol model assumptions or overestimation of single scattering albedo (Prasad and Singh, 2007; He et al., 2010). The visible reflectance affects the aerosol properties, while the shortwave infrared or so-called near infrared reflectance affects the surface reflectance estimates in MODIS AOD retrieval, these effects could be a source of error for the MODIS-aqua data calibration (Kang et al., 2016; Xiong et al., 2007). If in-situ aerosol measurements synchronous with ocean colour data were available, it could provide the auxiliary aerosol information for the atmospheric correction. Additionally, this is helpful in providing a better understanding of the error source of Rrs(λ).
4.2 Development of regional ocean-colour algorithm
Compared with the in-situ Rrs(λ) (Table 1, Fig. 6), the MODIS-Aqua Rrs(λ) was more accurate at 488 and 547 nm than it was at 412 and 443 nm. This indicates the possible errors in the atmospheric correction, affecting the blue bands (Tilstone et al., 2013). Of the 13 MODIS in situ Chl a match-ups obtained, OC3M uses Rrs(488)/Rrs(547) which seems less affected by the atmospheric correction; however, compared with the in situ ratio, the determination coefficient R2 was only 0.075. This is due to the tendency of MODIS-Aqua to under-estimate Rrs(488) at high values (i.e., when Chl a is low). The use of the Rrs(488)/Rrs(547) would result in a higher than expected ratio and therefore reflect higher Chl a values (Table 2).
We tested the red-NIR MODIS algorithm using our in situ dataset. Figure 9 depicts the relationship between Chl a and the Rrs(748)/Rrs(667). However, the correlation coefficient is significantly lower (R2=0.13) than those achieved by Gons et al. (2002, 2005) and others (Carswell et al., 2017; Yacobi et al., 2011; Gilerson et al., 2010; Moses et al., 2009). Besides the influence of the atmospheric correction, the reflectance spectra at the red and NIR bands were affected by possible variations in phytoplankton specific absorption coefficient, and perhaps were also influenced by the temporal variation of the water quality. Therefore, the NIR-Red models have substantial uncertainties when applied to multi-temporal data.
To improve the accuracy of the Chl a retrievals, Sun et al. (2010) and Shen et al. (2010) built a four-band algorithms and a SCI Chl a inversion method respectively for the turbid waters of Changjiang Estuary. The performance of these algorithms was compared with that of the MODIS-OC3M and MERIS-C2P. The accuracies yielded by the algorithms of Sun and Shen were better than those yielded by the MODIS and MERIS data. Thus, these two methods were also evaluated in our study, and the NIR-SWIR combined with the atmospheric correction was applied to the MODIS data (https://oceandata.sci.gsfc.nasa.gov/) (see Table 6 and Fig. 10a). Obviously, the accuracy was improved compared with the MODIS-derived Chl a value for the match-ups, especially when using the Sun algorithm. The APD was 31%, while the RMS was 1.527, which were higher than those in the study of Sun et al. Furthermore, the linear relationship between the in-situ and model estimated Chl a is still not obvious according to a low correlation coefficient R2. We obtained results similar to that of Salem et al.(2017), who had assessed seven Chl a algorithms in Tokyo Bay, and found that two-band and four-band algorithms outperformed the other algorithms for the measured and simulated datasets, and the SCI algorithms showed the highest error for both datasets(Salem et al., 2017). For a Chl a concentration below 10 mg/m3, the output of the algorithm is sensitive to variation in concentrations of total suspended solids and colored dissolved organic matter (Dall’Olmo and Gitelson, 2006). Thus, only single algorithm cannot provide outstanding accuracy for Chl a retrieval and multi-algorithms should be included and developed for different sea areas.
Compared with the Chl a Satellite retrieval algorithms, the performance of SPM regional models is better, but that still cannot meet the requirement for the SPM retrievals. Other popular models for coastal ocean, including the Tassan model (Tassan, 1993) and the D’Sa model (D’Sa et al., 2007), were also applied to the MODIS data which were processed by the NIR-SWIR atmospheric correction (see Table 4, Table 7 and Fig. 10b). Reasonable retrieval results were found by using the Tassan model in the range of approximately 1.7–26 g/m3, which is similar to the SPM range for the match-ups. The RMS values calculated by using the Tassan model and the D’Sa model were 3.275 and 5.309, lower than those calculated by the Tang method and the Zhang method. The R2 coefficients were 0.867 and 0.844, respectively. From our study, the modified Tassan coast model is more suitable for the SPM retrieval in the YS and ESC region. The average relative error was only 21%.
Therefore, large variability in the SPM and Chl a distributions exists along the coast, substitute algorithms are still needed, especially for those using the blue-green and NIR bands. A large number of match-ups between concurrent in situ and satellite data are also useful for Chl a and SPM estimation.
5 Conclusions
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MODIS ocean-colour products along the YS and ECS were assessed based on a strict match-up analysis. The APDs of the MODIS Rrs(λ) retrievals were in the range between 20% and 36%, and the highest uncertainty appeared at 412 nm. Rrs(λ) at bands of 443 and 547 nm performed better. The band ratio of Rrs(λ) at 488 to 547 nm showed high consistency with an APD of 7%. MDOIS Chl a product was found to be overestimated with a higher APD of 132%. Besides, two regional SPM retrieval algorithms were modified according to the in situ measurements and applied to the MODIS data, the derived SPM values underestimated the in situ ones, with the APDs of 49% and 33%, respectively.
By developing a better regional ocean-colour model, parameterized where possible to data from the YS and ECS, we were able to adjust the two ocean-colour empirical algorithms for Chl a estimation and used NIR-SWIR atmospheric correction to the MODIS data, and the accuracy was improved when compared with the MODIS-derived Chl a value. However, the linear correlation coefficients between in-situ and model estimated Chl a were still very small. In coastal waters enriched with nonalgal absorbing particles, the derivation of Chl a using the reflectance band ratio approach is subject to large errors owing to nonalgal materials that influence Rrs(λ) but do not contribute to Chl a. Otherwise, the colored dissolved organic matter plays an important role for the water absorption and affects the reflectance band-ratios. Therefore, only single algorithm cannot provide outstanding accuracy for Chl a retrieval and that multi-algorithms should be included and developed for different sea area.
At the same time, the SPM modified algorithms were also applied to the MODIS data processed by NIR-SWIR atmospheric correction algorithm, and a comparison between the models estimated values and the measured SPM showed that the modified Tassan model obtained higher accuracy than other models along the YS and ECS, which utilizes the reflectance at bands of 488, 555, and 645 nm, the average relative error is only 21%. Given the limited match-up dataset and the potential influence of the aerosol properties on atmosphere correction, the model uncertainties also associated with the particle composition and size distribution, further research is required to develop additional algorithms for coastal water. Furthermore, a dedicated in situ data collection effort is highly needed for validating ocean-colour sensors.
Acknowledgements
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This work was initiated when Lingling Jiang visited Plymouth Marine Laboratory funded by Dalian Maritime University and got many help from team members there for data processing. We thank all crew members on the cruises for their hard work in collecting and analyzing the in situ data. We also thank the NASA for their help with providing MODIS data.
Funding
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  • The National Natural Science Foundation of China under contract Nos 41506197 and 41406199; the Doctoral Scientific Research Foundation of Liaoning Province under contract No. 201501190; the Fundamental Research Funds for the Central Universities under contract No. 3132017110.
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doi: 10.1007/s13131-019-1522-3
  • Receive Date：2018-12-29
  • Online Date：2026-03-27
  • Published：2020-01-25
Article Data
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History
  • Received：2018-12-29
  • Accepted：2019-05-05
Funding
The National Natural Science Foundation of China under contract Nos 41506197 and 41406199; the Doctoral Scientific Research Foundation of Liaoning Province under contract No. 201501190; the Fundamental Research Funds for the Central Universities under contract No. 3132017110.
Affiliations
    1 College of Environmental Science and Engineering, Dalian Maritime University, Dalian 116026, China
    2 Ministry of Ecology Environment, National Marine Environmental Monitoring Center, Dalian 116023, China
    3 Plymouth Marine Laboratory (PML), Plymouth, Devon , PL1 3DH, UK

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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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