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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.

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.

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.

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.

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.

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.

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 .

Over the past decade, China has experienced a rapid increase in urbanization. The urban built-up areas (population) of Shanghai increased by 16.1% (22.9%) from 2006 to 2015. This study aims to analyze the variations of tropospheric NO2 over Yangtze River Delta region and the impacts of rapid urbanization during 2006-2015. The results indicate that tropospheric NO2 vertical column density (VCD) of all cities in the study area showed an increasing trend during 2006-2011 whereas a decreasing trend during 2011-2015. Most cities showed a lower tropospheric NO2 VCD value in 2015 compared to that in 2006, except for Changzhou and Nantong. Shanghai and Ningbo are two hotspots where the tropospheric NO2 VCD decreased most significantly, at a rate of 22% and 19%, respectively. This effect could be ascribed to the implementation of harsh emission control policies therein. Similar seasonal variability was observed over all cities, with larger values observed in the summer and smaller values shown in the winter. Further investigations show that the observed increasing trend of tropospheric NO2 during 2006-2011 could be largely explained by rapid urbanization linked to car ownership, GDP, power consumption, population and total industrial output. Such effect was not prominent after 2011, mainly due to the implementation of emission control strategies.

The 3D model is an important part of simulated remote sensing for earth observation. Regarding the small-scale spatial extent of DART software, both the details of the model itself and the number of models of the distribution have an important impact on the scene canopy Normalized Difference Vegetation Index (NDVI).Taking the phragmitesaustralis in the Yangtze Estuary as an example, this paper studied the effect of the P.australias model on the canopy NDVI, based on the previous studies of the model precision, mainly from the cell dimension of the DART software and the density distribution of the P.australias model in the scene, As well as the choice of the density of the P.australiass model under the cost of computer running time in the actual simulation. The DART Cell dimensions and the density of the scene model were set by using the optimal precision model from the existing research results. The simulation results of NDVI with different model densities under different cell dimensions were analyzed by error analysis. By studying the relationship between relative error, absolute error and time costs, we have mastered the density selection method of P.australias model in the simulation of small-scale spatial scale scene. Experiments showed that the number of P.australias in the simulated scene need not be the same as those in the real environment due to the difference between the 3D model and the real scenarios. The best simulation results could be obtained by keeping the density ratio of about 40 trees per square meter, simultaneously, of the visual effects.

Fine particulates less than 2.5 microns in aerodynamic diameter (PM2.5) has been widely considered to be one of the main pollutant threating human health. Ground-level PM2.5 monitoring can provide accurate point data, but its value is hard to scale up to large scale. In this respects, satellite data with large coverage areas and long term range, could enhance our ability to estimate PM2.5 concentration. In this study, a Multilinear correlation model (MLC) based on MODIS AOD level 2 data was developed to estimate PM2.5 concentration in Northeastern China from 2013-2016, then ground-level PM2.5 monitoring data from 15 stations covering study area were used for validation. Results showed that 1) the annual PM2.5 is 63.98μg/m2, AOD values agreed well with estimated PM2.5 concentration, 2) the spatial variations of PM2.5 were not clear, while the temporal dynamic of PM2.5 were observed, the highest values were observed in winter, opposite to what were observed in fall. 3) the MLC model coupled with meteorological data could improve the precision of PM2.5 estimations. Therefore, we suggest that the developed MLC model is useful for the PM2.5 estimations in northeastern China.

Coastal wetland is a net carbon sink with a high carbon density. However, coastal reclamation directly changes the structure of coastal wetland ecosystem and consequent carbon sink function. The aim of this work was to estimate the reclamation-induced carbon loss in coastal wetlands using time series GF-1 WVF data. For this purpose, GF-1 WVF imageries of 2013 (before reclamation) and 2017 (after reclamation) in the Yangtze Estuary were collected and analyzed combined with field monitoring. Results showed that the converted coastal wetland area occupied up to 61.60% between 2013 and 2017. Carbon estimation indicated that the coastal wetland before reclamation had greater potential contribution to the global warming mitigation than the wetland reclamation to other land cover types. Finally the vulnerability of carbon stores and uncertain analysis with remote sensing technology in coastal wetlands environment were discussed. We emphasized that long-term monitoring of coastal wetlands and its carbon dynamic are urgently needed, because so many uncertain factors exist in short-term monitoring.

Phragmites australis is a native dominant specie in the Yangtze Estuary, which plays a key role in the structure and function of wetland ecosystem. One key question is how to estimate the Chlorophyll content quickly and effectively at large scales, which could be used to reflect the growth condition and calculate the vegetation productivity. The aim of this work was to estimate Chlorophyll content of P. australis based on the PROSPECT and DART (Discrete Anisotropic Radiative Transfer) model. A total of 6 widely used Vegetation indices (VIs) were chosen (i.e., Normalized Difference Vegetation Index (NDVI), Structure Insensitive Pigment Index (SIPI), Colouration Index (COI), Simple Ratio Index (SR), Cater Index (CAI), and Red-edge Position Linear Interpolation (REP_Li)) and calculated, and then the relationship between VIs and Cab were analyzed. Results showed that COI and SIPI were sensitive to the leaf chlorophyll content as the chlorophyll content changes at the leaf scale. Meanwhile, no obvious saturation phenomenon was observed for these two indices compared to other indices.

The aim of this work was to identify the coastal wetland plants between Bayes and BP neural network using hyperspectral data in order to optimize the classification method. For this purpose, we chose two dominant plants (invasive S. alterniflora and native P. australis) in the Yangtze Estuary, the leaf spectral reflectance of P. australis and S. alterniflora were measured by ASD field spectral machine. We tested the Bayes method and BP neural network for the identification of these two species. Results showed that three different bands (i.e., 555 nm，711 nm and 920 nm) could be identified as the sensitive bands for the input parameters for the two methods. Bayes method and BP neural network prediction model both performed well (Bayes prediction for 88.57% accuracy, BP neural network model prediction for about 80% accuracy), but Bayes theorem method could give higher accuracy and stability.

Spartina alterniflora is an aggressive invasive plant species that replaces native species, changes the structure and function of the ecosystem across coastal wetlands in China, and is thus a major conservation concern. Mapping the spread of its invasion is a necessary first step for the implementation of effective ecological management strategies. The performance of a phenology-based approach for S. alterniflora mapping is explored in the coastal wetland of the Yangtze Estuary using a time series of GaoFen satellite no. 1 wide field of view camera (GF-1 WFV) imagery. First, a time series of the normalized difference vegetation index (NDVI) was constructed to evaluate the phenology of S. alterniflora. Two phenological stages (the senescence stage from November to mid-December and the green-up stage from late April to May) were determined as important for S. alterniflora detection in the study area based on NDVI temporal profiles, spectral reflectance curves of S. alterniflora and its coexistent species, and field surveys. Three phenology feature sets representing three major phenology-based detection strategies were then compared to map S. alterniflora: (1) the single-date imagery acquired within the optimal phenological window, (2) the multitemporal imagery, including four images from the two important phenological windows, and (3) the monthly NDVI time series imagery. Support vector machines and maximum likelihood classifiers were applied on each phenology feature set at different training sample sizes. For all phenology feature sets, the overall results were produced consistently with high mapping accuracies under sufficient training samples sizes, although significantly improved classification accuracies (10%) were obtained when the monthly NDVI time series imagery was employed. The optimal single-date imagery had the lowest accuracies of all detection strategies. The multitemporal analysis demonstrated little reduction in the overall accuracy compared with the use of monthly NDVI time series imagery. These results show the importance of considering the phenological stage for image selection for mapping S. alterniflora using GF-1 WFV imagery. Furthermore, in light of the better tradeoff between the number of images and classification accuracy when using multitemporal GF-1 WFV imagery, we suggest using multitemporal imagery acquired at appropriate phenological windows for S. alterniflora mapping at regional scales.

Fractional Vegetation Cover (FVC) is one of the most important variables in monitoring the changes of terrestrial ecosystems. Based on the Two-endmember model, FVC from 2000 to 2012 in Xinjiang was derived from MODIS Normalized Difference Vegetation Index (MODIS NDVI)) (16-Day). The spatio-temporal vegetation changes were analyzed, and the results showed that: Vegetation cover in Inner-Mongolia was higher than that in Mongolia. In the year of 2000, the FVC in Inner-Mongolia is 0.557, and 0.516 in Mongolia; while in the year of 2012, the FVC in Inner-Mongolia is 0.663, and 0.593 in Mongolia.

The aim of this study was to analyze the urbanization in Shanghai from 1990 to 2005. For this purpose, a time series in 1990, 2000 and 2005 of remote sensing images were collected and interpreted.Land Use/ Cover Change information (i.e. speed and direction and spatial variations) were extracted and the main driving factors were analyzed. Results showed that (1) Urbanization in Shanghai increased from 10.77% in 1990 to 32.96% in 2005 with a total increment value of 22.19%. Land use for city construction, industry and traffic contribute mostly to the change in Land Use/Cover Change, and the average growth rate was 0.43% during 1990-2005 and 1.99% during 2000-2005; (2) The direction in urbanization were different during 1990-2005, mainly expanding in Northeast, Southeast and Southwest. (3) Spatial variations were observed in the urbanization during 1990-2005, and were mainly distributed in the city center (i.e. 0-12 km) in 1990, and then extended to 20 km in 2000 and 40 km in 2005. (4) Economic development and population growth were the main driving factors accounting for the urbanization in Shanghai during 1990-2005.

This paper presents a new drought assessment method by modifying the NDVI-Ts space, which named NDVI-Ts general space. Based on this method, the general dry side and wet side equation were established for the period of 2000 and 2010 in the Mongolian Plateau. The results showed that: 1) the NDVI-Ts general space was more stable for monitoring drought than that for the single time Remote Sensing data; 2) Drought mainly distributed in the Mongolian Plateau, In Mongolian Plateau, there was about 75% area of drought; 3) Drought changed in the period of 2000 and 2010. In the year of 2003, the area of severe drought is the smallest. In 2001, the drought is the most serious. The results showed that, the distribution of drought was different in different year. There may be close correlation between the occurrence of drought and precipitation.

BACKGROUND: Rice paddies have been identified as major methane (CH(4)) source induced by human activities. As a major rice production region in Northern China, the rice paddies in the Three-Rivers Plain (TRP) have experienced large changes in spatial distribution over the recent 20 years (from 1990 to 2010). Consequently, accurate estimation and characterization of spatiotemporal patterns of CH₄ emissions from rice paddies has become an pressing issue for assessing the environmental impacts of agroecosystems, and further making GHG mitigation strategies at regional or global levels. METHODOLOGY/PRINCIPAL FINDINGS: Integrating remote sensing mapping with a process-based biogeochemistry model, Denitrification and Decomposition (DNDC), was utilized to quantify the regional CH(4) emissions from the entire rice paddies in study region. Based on site validation and sensitivity tests, geographic information system (GIS) databases with the spatially differentiated input information were constructed to drive DNDC upscaling for its regional simulations. Results showed that (1) The large change in total methane emission that occurred in 2000 and 2010 compared to 1990 is distributed to the explosive growth in amounts of rice planted; (2) the spatial variations in CH₄ fluxes in this study are mainly attributed to the most sensitive factor soil properties, i.e., soil clay fraction and soil organic carbon (SOC) content, and (3) the warming climate could enhance CH₄ emission in the cool paddies. CONCLUSIONS/SIGNIFICANCE: The study concluded that the introduction of remote sensing analysis into the DNDC upscaling has a great capability in timely quantifying the methane emissions from cool paddies with fast land use and cover changes. And also, it confirmed that the northern wetland agroecosystems made great contributions to global greenhouse gas inventory.

Shanghai is a metropolis with the fastest growing economy and the largest economic aggregate in China. In this paper, some change detection methods were used for assessment of urbanization and ecosystem changes. The urbanization area index (UAI) which is derived from land cover is used to reflect the speed of urban expansion, while the fraction of vegetation cover (FVC) which is retrieved from NDVI is used to represent the status of urban ecosystem. The NDVI time series were derived from MOD13Q1 by using an annual stacking approach. Land cover maps were retrieved from annual NDVI time series from 2000 to 2009. This paper focused on assessment study of urbanization level and ecosystem changes in Shanghai municipality. Results indicated following: 1) the urban area of Shanghai increased continuously in the past 10 years; 2) the UAI increased by an annual average rate more than 1.84%, its peak value was 4.36% during 2008-2009; 3) the urbanization degree of Shanghai ran on a high speed in the past decade; 4) on the whole, FVC decreased continuously over the past decade, while the FVC of urban area increased slightly and the FVC of some islands and outer suburbs increased slightly too; 5) the urban ecosystem of Shanghai became more and more "green" but at the cost of decreased cropland and natural vegetation cover. The assessment of urbanization and ecosystem changes suggests that suburban ecosystem protection is an import and urgent problem for government to implement more effective environmental management policies.

Aerosol optical depth(AOD) is one of the most important parameters in describing aerosol optical characteristics.It is widely used in researched on air quality monitoring and atmospheric correction based on radiative transfer model.A wide range of aerosol information can be fast obtained and retrieved using satellite remote sensing technology.While,satellite retrieved aerosol product has low spatial coverage because of the limitation of cloud coverage and dark target algorithm,and the time phase of the product is limited by satellite overpass time.Horizontal meteorological visibility(HMV) is another very important parameter in describing aerosol optical characteristics.It is observed 8 fixed times one day by widely distributed meteorological sites in China.It can be a good supplement of satellite retrieved aerosol optical depth data by building the convention relationship between aerosol optical depth data and horizontal meteorological visibility data.In this study,MODIS aerosol optical depth product from 2001 to 2009 and horizontal meteorological visibility data from 71 meteorological sites located in Eastern China were chosen as the research data.The research data was used to optimize Peterson model which describes the relationship between aerosol optical depth and horizontal meteorological visibility.Aerosol scale height is the main parameter that influences the conversion accuracy between aerosol optical depth and horizontal meteorological visibility.By regional Gaussian fitting method,the temporal trends of aerosol scale height were fitted.The aerosol optical depth data and horizontal meteorological visibility data in 2010 were used to validate the accuracy and the regional applicability of the optimized model.The result shows the RMSE of aerosol optical depth estimation of optimized model is 0.31,which is lower than that of Peterson model.Compared with the single site optimized model,the accuracy is similar,while the application of regional optimized model is better than the single site optimized model.