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Table S1: Nineteen spectral indices used in this study; Table S2: Carbon content coefficients of the main tree species considered in this study; Table S3: Allometric biomass equations of each tree species considered in this study; Table S4: Correlation coefficients between the forest carbon storage amount at different classification scales and the spectral indices.

Chlorophyll content can effectively reflect the health (growth condition) of vegetation. At present, chlorophyll retrieval is mostly based on pure species and traditional spectral indices but seldom considers the influence of canopy structural differences caused by interspecific competition of mixed vegetation on the accuracy of chlorophyll retrieval. Therefore, in this research, the native species Phragmites australis and invasive species Spartina alterniflora in the wetland of the Yangtze River Estuary were studied, and the mixed growth of the two species in the field was simulated accurately by setting different competition ratios. The differences in canopy spectral characteristics caused by different competition ratios and the effect of interspecific competition on the retrieval accuracy of canopy chlorophyll content (CCC) were analyzed. It is found that different competition ratios will lead to different spectral characteristics of the vegetation canopy, but these differences poorly reflect the spectral dimension. To solve this problem, the Hilbert-Huang transform (HHT) theory is introduced to decompose the spectrum and obtain frequency domain information, which is combined with spectral information to research the spectral response characteristics and retrieval of CCC under different interspecific competition ratios. Finally, the necessity of considering interspecific competition under mixed growth is analyzed by using the PROSAIL-D model from remote sensing. The results show that (1) the retrieval accuracy of CCC based on pure spectral indices is lower than that based on the mixed indices under different competition ratios, especially under the competition ratio of P:S = 1:1, and the R2 of the pure spectral index model is 0.714, but the R2 of the model increases to 0.803 after the HHT indices are added, with the accuracy of the retrieval model with the mixed index being the highest, with R2 and RMSE values of 0.874 and 17.378 µg.cm-2, respectively. (2) There are differences in the distribution of spectral index values under different competition ratios based on the remote sensing mechanism results of simulation data from the PROSAIL-D model, especially under mixed growth of Phragmites australis and Spartina alterniflora. (3) The HHT indices ESP1 and ESP2 are more sensitive to changes in the competition ratio under different competition conditions, showing a greater range of values. All the above results indicate that the frequency domain indices can reflect the change in canopy structure of mixed vegetation more accurately, and it is necessary to consider the level of interspecific competition in the retrieval of CCC from mixed vegetation.

Salt marshes provide crucial ecological functions and services and are experiencing rapid losses and degradation under global climate changes and high-intensity human activities in coastal zone. However, mapping salt marsh distributions and compositions with high accuracy on the national or global scale remains challenging. Here, we used Sentinel-1 time-series data and knowledge-based automatic decision tree classifiers to produce a 10-m map of the salt marshes in China coastal zones. We established annual synthetic aperture radar (SAR) composite features to address seasonal variations and tidal dynamics in salt marshes and created five partitioned-region classifiers according to the climatic and environmental characteristics. In total, 1729 Sentinel-1 images in 2019 and 3418 in situ sites were included for the classification. The results showed that (1) the annual mean SAR composite is more stable and practical than the seasonal composite for salt marsh classification along the China coastal zones in terms of these features addressing variations in tidal fluctuation, climate zones, and salt marsh compositions; (2) SAR surface roughness can effectively separate salt marshes into sparse density and red phenotype salt marshes; (3) the total area of Chinese coastal salt marshes was 127,477.37 ha with an overall accuracy of 87.30%, it included 48.30% Spartina alterniflora, 29.19% Phragmites australis, 13.76% Suaeda spp. and 8.75% Scirpus spp., and more than 85% of salt marshes were distributed from Shandong Province to Zhejiang Province. This study showed the potential application of time-series Sentinel-1 SAR data, annual composite data and knowledge-based classifiers for large-scale coastal zones, and these data will be valuable for coastal ecological restoration and sustainability management.

Spartina alterniflora is the main invasive vegetation in wetland ecosystems, and information on its chlorophyll content is important data for quantitative research on the key ecological functions of wetland ecosystems. The Dongtan wetland of the Yangtze River estuary was used as the experimental research area, and the artificial cultivation field of Eupatorium was taken as the research object. To prevent uncertainty in the calculation of the chlorophyll content inversion factor for Spartina alterniflora leaves, harmonic analysis theory was adopted in this study. First, the original measured spectral data in the range of 400 nm ~ 1000 nm were decomposed and reconstructed by empirical mode decomposition (EMD), and the harmonic characteristic parameters of the EMD reconstruction spectral data were obtained by harmonic analysis (HA). Meanwhile, using the PROSPECT-D radiation transfer model, the simulated data was used to validate the inversion model. Based on these harmonic characteristic parameters, harmonic analysis-back propagation (HA-BP) and stepwise multiple linear regression (SMLR) models were established. Finally, the model was validated with data simulated by the PROSPECT-D model and compared the measured chlorophyll contents using the inversion values of the two models. The results show that the inversion accuracy of the HA-BP model was highest for the measured data. The determination coefficient (R2) and root mean square error (RMSE) of the model were 0.8528 and 6.8968 (μg/cm2), respectively, and the inversion accuracy of the simulated data was slightly lower than that of the measured data. The results show that the original spectral noise could be effectively suppressed by EMD and reconstruction, while harmonic analysis could compress the signal and prevent uncertainty in the spectral parameter calculation. Thus, the harmonic decomposition method could be applied to the inversion of Spartina alterniflora chlorophyll contents. The methods in this research provide a theoretical basis and technical support for the use of frequency domain parameters for the inversion of vegetation pigment contents.

The location of the infections is the basic data for precise prevention and control of dengue fever (DF). However, most studies default to residence address as the place of infection, ignoring the possibility that cases are infected at other places (e.g., workplace address). This study aimed to explore the spatiotemporal patterns of DF in Guangzhou from 2016 to 2018, differentiating workplace and residence. In terms of temporal and spatial dimensions, a case weight assignment method that differentiates workplace and residence location was proposed, taking into account the onset of cases around their workplace and residence. Logistic modeling was used to classify the epidemic phases. Spatial autocorrelation analysis was used to reveal the high and early incidence areas of DF in Guangzhou from 2016 to 2018. At high temporal resolution, the DF in Guangzhou has apparent phase characteristics and is consistent with logistic growth. The local epidemic is clustered in terms of the number of cases and the time of onset and outbreak. High and early epidemic areas are mainly distributed in the central urban areas of Baiyun, Yuexiu, Liwan and Haizhu districts. The high epidemic areas due to commuting cases can be further identified after considering the workplaces of cases. Improving the temporal resolution and differentiating the workplace and residence address of cases could help to improve the identification of early and high epidemic areas in analyzing the spatiotemporal patterns of dengue fever in Guangzhou, which could more reasonably reflect the spatiotemporal patterns of DF in the study area.

Biological invasion by Spartina alterniflora is widespread in coastal wetlands in China, threatening native vegetation. Obtaining the distribution of native and invasive vegetation at the small community scale is important to understand the invasion mechanism of S. alterniflora and the protection of wetland ecosystems. Remote sensing images can quickly and effectively identify vegetation at a large scale, which can have a considerable influence on the protection of biological diversity. Although Landsat-like space borne sensors have been applied in the monitoring of invasive species in wetlands, their spatial resolutions are often coarser than those of small communities of invasive species. To identify small-scale native (Phragmites australis and Scirpus mariqueter) and invasive (S. alterniflora) vegetation communities around Chongming Island in eastern China, we first investigated the performance of four classifiers based on spectral features. Then, textural features of Gaofen-1 images under an appropriate textural window size were added. Furthermore, a comprehensive classification (CC) method generated by multiple classification features was proposed to obtain a reliable classification, and CC was further improved by considering the properties of neighbouring pixels. The results demonstrated that (i) the random forest (RF) classifier reached a higher overall accuracy (OA) than all the other classifiers; (ii) adding textural features slightly increased the OA, but the production accuracy (PA) for P. australis and the user accuracy (UA) for S. alterniflora decreased; (iii) the CC method improved OA by up to 4.12 %, and accounting for the neighbouring pixels improved the OA to 93.96 %. The comprehensive method effectively improved not only OA but also PA and UA for the vegetation communities, and it can be used in other wetlands.

Crop residue burning has been proved to have negative impacts on regional atmospheric environment. In this study, an evidence-based statistical modeling framework was established to quantify potential impacts of crop residue burning in the North China Plain (NCP) on summertime tropospheric ozone increase over East Asia during 2004–2016. To assess the intensity of crop residue burning, fire occurrence counts derived from the MODerate-resolution Imaging Spectroradiometer onboard the Terra and Aqua satellites were used as a proxy. Additionally, another six factors were employed as potent explanatory variables. Maximum covariance analysis was first applied to decouple spatiotemporal interactions between tropospheric ozone and each explanatory variable. Based on the decoupled modes, multivariate linear regression (MLR) and artificial neural network (ANN) were used to establish statistical relationships between tropospheric ozone and contributing factors, respectively. The results indicate that the ANN-based modeling scheme enables to approximate the observed tropospheric ozone variations better than MLR. Further investigations reveal that the summertime crop residue burning in the NCP is the predominant factor contributing to the observed additive tropospheric ozone increases over East Asia, yielding extra 8% tropospheric ozone elevation on average in June. Moreover, UV radiation and wind also played critical roles in modulating the observed tropospheric ozone variations therein. In general, the critical role of crop residue burning over the NCP in modulating summertime tropospheric ozone increase over East Asia have been well demonstrated based on the proposed evidenced-based modeling framework.

The composition and distribution of wetland vegetation is critical for ecosystem diversity and sustainable development. However, tidal flat wetland environments are complex, and obtaining effective satellite imagery is challenging due to the high cloud coverage. Moreover, it is difficult to acquire phenological feature data and extract species-level wetland vegetation information by using only spectral data or individual images. To solve these limitations, statistical features, temporal features, and phenological features of multiple Landsat 8 time-series images obtained via the Google Earth Engine (GEE) platform were compared to extract species-level wetland vegetation information from Chongming Island, China. The results indicated that (1) a harmonic model obtained the phenological characteristics of wetland vegetation better than the raw vegetation index (VI) and the Savitzky–Golay (SG) smoothing method; (2) classification based on the combination of the three features provided the highest overall accuracy (85.54%), and the phenological features (represented by the amplitude and phase of the harmonic model) had the greatest impact on the classification; and (3) the classification result from the senescence period was more accurate than that from the green period, but the annual mapping result on all seasons was the most accurate. The method described in this study can be applied to overcome the impacts of the complex environment in tidal flat wetlands and to effectively classify wetland vegetation species using GEE. This study could be used as a reference for the analysis of the phenological features of other areas or vegetation types.

A four-step adaptive ozone trend estimation scheme is proposed by integrating multivariate linear regression (MLR) and ensemble empirical mode decomposition (EEMD) to analyze the long-term variability of total column ozone from a set of four observational and reanalysis total ozone data sets, including the rarely explored ERA-Interim total ozone reanalysis, from 1979 to 2009. Consistency among the four data sets was first assessed, indicating a mean relative difference of 1% and root-mean-square error around 2% on average, with respect to collocated ground-based total ozone observations. Nevertheless, large drifts with significant spatiotemporal inhomogeneity were diagnosed in ERA-Interim after 1995. To emphasize long-term trends, natural ozone variations associated with the solar cycle, quasi-biennial oscillation, volcanic aerosols, and El Nino–Southern Oscillation were modeled with MLR and then removed from each total ozone record, respectively, before performing EEMD analyses. The resulting rates of change estimated from the proposed scheme captured the long-term ozone variability well, with an inflection time of 2000 clearly detected. The positive rates of change after 2000 suggest that the ozone layer seems to be on a healing path, but the results are still inadequate to conclude an actual recovery of the ozone layer, and more observational evidence is needed. Further investigations suggest that biases embedded in total ozone records may significantly impact ozone trend estimations by resulting in large uncertainty or even negative rates of change after 2000.

Air pollutants existing in the environment may have negative impacts on human health depending on their toxicity and concentrations. Remote sensing data enable researchers to map concentrations of various air pollutants over vast areas. By combining ground-level concentrations with population data, the spatial distribution of health impacts attributed to air pollutants can be acquired. This study took five highly populated and severely polluted provinces along the Huaihe River, China, as the research area. The ground-level concentrations of four major air pollutants including nitrogen dioxide (NO2), sulfate dioxide (SO2), particulate matters with diameter equal or less than 10 (PM10) or 2.5 micron (PM2.5) were estimated based on relevant remote sensing data using the geographically weighted regression (GWR) model. The health impacts of these pollutants were then assessed with the aid of co-located gridded population data. The results show that the annual average concentrations of ground-level NO2, SO2, PM10, and PM2.5 in 2016 were 31 &micro, g/m3, 26 &micro, g/m3, 100 &micro, g/m3, and 59 &micro, g/m3, respectively. In terms of the health impacts attributable to NO2, SO2, PM10, and PM2.5, there were 546, 1788, 10,595, and 8364 respiratory deaths, and 1221, 9666, 46,954, and 39,524 cardiovascular deaths, respectively. Northern Henan, west-central Shandong, southern Jiangsu, and Wuhan City in Hubei are prone to large health risks. Meanwhile, air pollutants have an overall greater impact on cardiovascular disease than respiratory disease, which is primarily attributable to the inhalable particle matters. Our findings provide a good reference to local decision makers for the implementation of further emission control strategies and possible health impacts assessment.

Sea surface temperature (SST) is an important factor that affects the changes of marine fishery resources. In this paper, the characteristics of distribution and variation in sea surface temperature was retrieved in northwestern Pacific Ocean by MODIS from 2008 to 2017. The results showed that the distribution of SST in northwestern Pacific Ocean was found to be characterized by regional and seasonal changes. Annually, periodical changes in SST was found unconspicuously, and spatially, the SST high value area showed a trend of moving from high-latitude to low-latitude. In August each year, there seemed to be a temperature boundary at 40°N, and the boundary will move south in September. Finally, we analyzed the SST distribution of the two main fishing periods of Cololabis saira in August and September each year, and preliminarily explained the cause of the "fish shortage" of saury recently years in Japan. The long-term variations in SST were discussed macroscopically in this paper, and this could give a new insight into fishing industry research in the Northwest Pacific.

People in Huaihe River Basin and Shanghai have been suffering from severe air pollution of nitrogen dioxide and sulfur dioxide due to the development of heavy industry. Traditional ambient monitoring station measurements can provide real-time accurate data, but it is limited due to the less number of monitoring sites. Satellite observation data from remote sensing can provide a wide range opollutants concentrations in long-time sequence. Top-down approaches based on satellite data can be effectively applied to estimate the ground concentrations of pollutants. In this paper, the tropospheric pollutants columns from the Ozone Monitoring Instrument(OMI) were used to analyse the seasonal variation of NO2 and SO2 in 2015. Moreover, the ground-level NO2 and SO2 concentrations of the Huaihe River Basin and Shanghai at this time were estimated by the data and meteorological data. The results show that: the concentrations of NO2 and SO2 are highest in winter, and high-value areas are mainly located in Shandong and Northern Henan. Estimating the ground-level NO2 and SO2 concentrations based on satellite observations is reliable with the validation R2 0.48 and 0.47 respectively. Finally, The spatial distribution of satellite-derived annual mean NO2 and SO2 has a similar characteristics to the satellite columns.

The reliability of the measurement of ultraviolet radiation has always been a hot spot of research. The observation of ultraviolet radiation is not only affected by the solar elevation angle, aerosol thickness, ozone, dioxide, there is also a great connection with the systematic error of the measuring instrument. In fact, in the ultraviolet radiation observation, due to the lack of routine maintenance and periodic calibration, the radiation meter will obviously decline after a period of time, and the longer the use time, the more obvious the attenuation. Therefore, in order to obtained the consistent time series of the stable observational values, some reasonable methods must be adopted to correct the measured values. The data source of this research was part of the UV-MFRSR type ultraviolet radiometer observations from 2003 to 2010. These data were obtained by these daily time series calibration method. In theory, these time series points represent the response time of the instrument, and they should be stable for several months or even years. However, the performance of the in-situ calibration method was influenced by the aerosol / ozone loading mode in practice. The purpose of this study was to get a smooth observation curve by eliminating some observational anomalies. In addition, the actual data in the observation process, some date data is missing, so the reasonable prediction model is used to estimate the value of these data. In this paper, the ARIMA and GARCH models were used to predict the missing data and compared between the predicted value and the true value, it is found that the fitting degree of the predicted value and the true value based on the AR-GARCH model is higher.

Air pollutants can cause serious effects on human health, especially fine particles in air, which can easily cause respiratory diseases, such as asthma and bronchitis, and also increase the probability of lung cancer and heart disease. The disease surveillance of the residents around the chemical plant is an effective means to understand the effects of the discharged pollutant on the health of the surrounding residents. The research area, Shanghai Chemical Industry Park is located at the junction of Jinshan and Fengxian, as the South Center of the six industrial bases of Shanghai. Applying the spatial distribution of population obtained from the extraction of residential land by using GF-2 data, and the disease data after cleaning treatment, can reach to the spatial distribution of the above two diseases. Through the integration of spatial characteristics and attribute characteristics, the disease surveillance of the surrounding residents can be realized, which directly reflects the impact of chemical industry on the health of the surrounding residents.

Paddy field is a major source of methane (CH4) emission. Methane emission in paddy fields accounts for 31.5% of agricultural methane emissions in China. Double-rice cropping system is a part of the major paddy systems in China for rice production, accounting for only 27% of the national rice planting area while CH4 emission accounting for 60% of the national CH4 emission. Given the importance of reducing CH4 emission from double rice to mitigate climate warming, it is necessary to investigate the impact of climate change on CH4 emission of double cropping paddy field in the future. In this study, the denitrification–decomposition (DNDC-a process-based biogeochemistry model) model is employed to simulate the CH4 emission from double-rice cropping system in southern China based on the historical meteorological data of the past 50 years (1966-2015) and the observational data of rice agricultural stations in the study area. Then we combined the outputs with Geographic Information System (GIS) technology to analyze the impact of climate change on CH4 emissions from the double rice paddy. The results indicate that change of the average temperature is associated with the change of CH4 emission across the growing period of double rice paddy. Methane has increased by 8.4% in the main producing provinces of double cropping rice in southern China. Zhejiang has increased by up to 20.8%. Anhui, Hubei, Hunan has increased by 10.6%, 10.2% and 11.4%. The relatively small increase in Fujian and Yunnan is only 5%. However, in the low latitudes of Guangxi, and Guangdong province, there was a slight reduction in CH4 emission.

Fine particles less than 2.5 microns in aerodynamic diameter (PM2.5) has found to threat human health and environment. The formation and diffusion of PM2.5 are closely related to the meteorological elements. Many scholars have studied the influence mechanism of meteorological elements to PM2.5. However, most of these researches mainly focus on some serious short-term atmospheric pollution, long-term research is rare. In addition, the impact of meteorological elements on PM2.5 has regional characteristics. This paper takes Shanghai as study area, applying PM2.5 concentrations from China environmental monitoring stations and reanalysis meteorological data from 2014 to 2016.. Through qualitative and quantitative analysis, this paper got the change characteristics of PM2.5 in Shanghai in recent three years, and the correlation between PM2.5 and relative humidity, temperature, wind and boundary layer height. Relative humidity is positively correlated with PM2.5, while U wind is negatively correlated with PM2.5. And there are seasonal differences in the correlation between PM2.5 and temperature, V wind and boundary layer height.

For the past few years, the aerosol pollution in Shanghai is getting worse, leading to the haze weather and air quality deterioration as well. This paper is a comparative study on reliability and applicability of the spatial interpolation methods for the regional air quality evaluation, the daily data of the air quality indices (AQI, PM2.5 and PM10) comes from the Shanghai automatic monitoring stations, which helps us to compare the different interpolation methods in testing and measuring various air pollutants in Shanghai. Inverse Distance Weighted (IDW), Spline and Kriging were respectively used for the calculation of spatial interpolation. With the aforementioned methods we can compare the interpolation methods and gain the four indices, such as the mean error (ME), the mean relative error (MRE), the root mean squared error (RMSIE), and the correlation coefficient (R2) , which help us make a comprehensive comparative analysis of the spatial interpolation methods for the Shanghai regional air quality. The result shows that the IDW method is optimal for PM2.5 concentration and AQI, while Kriging Method is the Best for the concentration of PM10. We can also find that Seasonal characteristics and different spatial aggregation characteristics have a significant impact on the interpolated results of air pollutants.

With the rapid development of urbanization, the dynamic evolution of urban expansion has become one of the hot topics throughout the world. Thus, modeling and predicting the urban expansion in the future is one of the effective methods for the study of urban growth. Based on the rapid urbanization in Shanghai, our study uses four years of land use data (1995, 2000, 2005 and 2010), DEM and two years of traffic roads data (2005 and 2010) to obtain the optimal parameters of urban growth through model calibration. And the results of calibration were used to simulate and predict the land use change in 2040 under different scenarios of excluded layers. The results show that the urban growth in Shanghai is more often grow along the edge of existing urban centers and the transportation network with the relatively high spread coefficient (43) and road coefficient (66), while the dispersion, breed and slope coefficient are relatively low. The SLEUTH simulation with these five parameters possessed satisfactory capability of predicting land use changes with the kappa coefficient of 0.8628 and an appropriate Lee-Sallee index of 0.8139. The result shows that the urban areas in Shanghai increase significantly in 2040, while the rural area, grass and other construction area are decreased. Therefore, SLEUTH can better predict the spatial changes of land use and provide some theoretical support and decision-making basis for the urban-rural planning in Shanghai.

With characteristics of rapidness, non-destructiveness and high precision in detecting plant leaves, hyperspectral technology is promising in assessing the contents of leaf pigments and other biochemical components. Because the spectral absorption features of carotenoid and chlorophyll are overlapped in visible light region and that foliar carotenoid content is far lower than chlorophyll content, studies about constructing vegetation indices (VIs) for carotenoid is rare at home and abroad though carotenoid is one of the most important photosynthetic pigments. Hyperspectral data has abundant spectral information, so this paper proposed a multiple spectral indices collaborative algorithm to construct VIs on the basis of band-combination traversal and correlation analysis. Through a large number of simulated leaf reflectance spectra under different biochemical components contents run on PROSPECT model, a radiative transfer model, we successfully constructed a new kind of stable vegetation index (VI) for assessing carotenoid content at leaf level: RVIDNDVI. Our results indicate that RVIDNDVI is composed of two parts: (1)Narrow band NDVI constructed with 532 and 405 nm is high correlated with both carotenoid content and chlorophyll content while narrow band NDVI constructed with 548 and 498 nm is highly correlated with carotenoid content. The influence of chlorophyll content on RVIDNDVI can be eliminated with the ratio combination of these two indices. (2) The influence of mesophyll structure parameter can be weakened by subtracting the reflectance at 916 nm, which has strong correlation with mesophyll structure parameter. RVIDNDVIonly has high sensitivity to carotenoid content (the correlation coefficient is -0.94) at leaf level and R2 of its exponential fit is 0.834 4. The estimation of RVIDNDVIto carotenoid content can be verified with the validations of both simulated data and measured data.

The invasive species Spartina alterniflora and native species Phragmites australis display a significant co-occurrence zonation pattern and this co-exist region exerts most competitive situations between these two species, competing for the limited space, directly influencing the co-exist distribution in the future. However, these two species have different growth ratios in this area, which increase the difficulty to detect the distribution situation directly by remote sensing. As chlorophyll content is a key indicator of plant growth and physiological status, the objective of this study was to reduce the effect of interspecies competition when estimating Cab content; we evaluated 79 published representative indices to determine the optimal indices for estimating the chlorophyll a and b (Cab) content. After performing a sensitivity analysis for all 79 spectral indices, five spectral indices were selected and integrated using an artificial neural network (ANN) to estimate the Cab content of different competition ratios: the Gitelson ratio green index, the transformed chlorophyll absorption ratio index/optimized soil-adjusted vegetation index, the modified normalized difference vegetation index, the chlorophyll fluorescence index, and the Vogelmann chlorophyll index. The ANN method yielded better results (R 2 = 0.7110 and RMSE = 8.3829 μg cm-2) on average than the best single spectral index (R 2 = 0.6319 and RMSE = 9.3535 μg cm-2), representing an increase of 10.78% in R 2 and a decrease of 10.38% in RMSE. Our results indicated that integrating multiple vegetation indices with an ANN can alleviate the impact of interspecies competition and achieve higher estimation accuracy than the traditional approach using a single index.

Spartina alterniflora , one of most successful invasive species in the world, was firstly introduced to China in 1979 to accelerate sedimentation and land formation via so-called “ecological engineering”, and it is now widely distributed in coastal saltmarshes in China. A key question is how to retrieve chlorophyll content to reflect growth status, which has important implication of potential invasiveness. In this work, an estimation model of chlorophyll content of S. alterniflora was developed based on hyper-spectral data in the Dongtan Wetland, Yangtze Estuary, China. The spectral reflectance of S. alterniflora leaves and their corresponding chlorophyll contents were measured, and then the correlation analysis and regression (i.e., linear, logarithmic, quadratic, power and exponential regression) method were established. The spectral reflectance was transformed and the feature parameters (i.e., “san bian”, “lv feng” and “hong gu”) were extracted to retrieve the chlorophyll content of S. alterniflora . The results showed that these parameters had a large correlation coefficient with chlorophyll content. On the basis of the correlation coefficient, mathematical models were established, and the models of power and exponential based on SDb had the least RMSE and larger R 2 , which had a good performance regarding the inversion of chlorophyll content of S. alterniflora .