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The unique geographical location of the land-sea transition makes the ecological environment of the Yellow River Delta very fragile and vulnerable to human activities. As one of the characteristics of anthropogenic activities, monitoring the spatiotemporal changes of impervious surface is of great significance to the protection of the ecological environment in the Yellow River Delta (YRD). Based on the Landsat historical images and computing resources provided by Google Earth Engine (GEE), an impervious surface area (ISA) mapping method was developed through combining spectral, texture features and random forest algorithm, and subsequently was applied to generate the spatiotemporal distribution data of ISA of the YRD for 1992, 1998, 2004, 2010, 2016 and 2021. The experimental results demonstrated that the proposed method achieved satisfactory accuracy, with an average overall accuracy of 92.23% and an average Kappa coefficient of 0.9090. Through further time-series analysis of ISA, it found that the area of ISA in the YRD increased from the initial 394.87 km2 to 1081.74 km2 during study periods, and the annual growth rate broke through new highs, ranging from the initial 1.01 km2/year to 67.87 km2/year. According to the research results, urban development activities in the region should be strictly restricted in order to protect the ecological environment of the Yellow River Delta.

Hyperspectral remote sensing provides apromising solution for estimation of heavy metal concentration in soil. However, few studies have focused on the detection of soil heavy metal concentration b...

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.

Chlorophyll-a (Chl-a) is an important water quality safety evaluation index, and accurate Chl-a concentration monitoring is important for the development of aquaculture, aquatic ecosystem balance, and drinking water safety. Rapid and accurate Chl-a concentration determination in water using hyperspectral remote sensing is an important subject in water ecological environment monitoring. In this study, the spectral reflectance and Chl-a concentration of Nansi Lake were measured, and the time-frequency method of empirical mode decomposition (EMD) analysis was used for the noise reduction and reconstruction of the first-order differential of the spectrum to extract sensitive spectral features. The eXtreme Gradient Boosting (XGBoost) machine learning algorithm was used to establish a Chl-a concentration estimation model, and the best parameters and model combinations for the inversion of the Chl-a concentration in the water column of Nansi Lake were determined. The results show that the combined three-band algorithm combination parameters obtained from the EMD noise-reduced reconstruction of spectral first-order differential (OFODSR-D) data fit the measured Chl-a concentrations better than the original spectral (OSR) and OFODSR data, with a maximum correlation coefficient of 0.8588. Second, the models based on OFODSR-D achieved more satisfactory prediction results, with XGBoost having the highest estimation accuracy (R2 of 0.9024 and root-mean-square error (RMSE) of 1.1312 μg·L−1 for the inverse model), followed by the partial least squares regression (PLSR) model and the linear model (R2 of 0.8474 and 0.8326, and RMSE of 13.3031 and 7.6987 μg·L−1, respectively). This study innovatively introduces the EMD method to the spectral processing of water bodies, obtains optimal parameters for the inversion of the Chl-a concentration, and achieves better results. This study provides a new approach to obtaining optimal inversion parameters for Chl-a monitoring in inland lake water bodies using remote-sensing methods.

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 .

Rapid urbanization leads to changes in surface coverage and landscape patterns. This results in urban heat island (UHI) effects and a series of negative ecological consequences. Considering this concern and taking Shanghai as an example, this paper concentrates on the effects of surface coverage and landscape patterns on urban land surface temperature (LST). The research is based on quantitative retrieval of remote sensing data with consideration of methods in multiple disciplines, including landscape ecology, geographic information systems, and statistical analysis. It concludes that, over time, the thermal environment of Shanghai is becoming critical. The average LST ranking of different surface coverage is as follows: Construction land (CL) &gt, bare land (BL) &gt, green land (GL) &gt, agricultural land (AL) &gt, water body (WB). LST varies significantly with the type of surface coverage. CL contributes the most to the UHI, while WB and GL have obvious mitigation effects on the UHI. The large area, low degree of landscape fragmentation, and complex outlines lead to low LST rankings for GL, WB, and AL and a high LST ranking for CL. The conclusions indicate that CL should be broken down by GL and WB into discrete pieces to effectively mitigate UHI effects. The research reveals UHI features and changes in Shanghai over the years and provides practical advice that can be used by urban planning authorities to mitigate UHI.

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.

The aim of this work is to use narrow band normalized difference vegetation indices to compare the estimations of chlorophyll contents at foliar level and canopy level, through a large number of simulated canopy reflectance spectra under different chlorophyll contents based on PROSPECT model and SAIL model. 10 narrow band NDVIs were selected at the identified ranges that can effectively assess foliar chlorophyll content. We analyzed the correlations between canopy chlorophyll contents and the ten narrow band NDVIs firstly, and then analyze these indices’ sensitivities to all canopy parameters, the adaptation of the 10 narrow band NDVIs used in assessing the canopy chlorophyll content were evaluated finally. We found that only two narrow band NDVIs (i.e., NDVI(875, 725) and NDVI(900,720)) can be applied for the estimation of chlorophyll contents at canopy level.

To provide a reference for canopy parameters inversion, sensitivity analysis of plant canopy parameters based on remote sensing model is a prerequisite for the inversion. Because the local sensitivity analysis do not consider the coupling effect among the parameters, the EFAST (i.e., Extended Fourier Amplitude Sensitivity Test), a global sensitivity analysis, can be used not only for the analysis of each parameter, but also consider the interacted effect among each parameter. Based on PROSAIL model, the paper focused on the parameters’ sensitivity by using simulated data and EFAST method. The results showed that the EFAST considered not only the contribution of single parameter, but also the interactive effects among each parameter, and four parameters, leaf area index (LAI), leaf mesophyll structure (N), the controller factor of the average leaf slope (LIDFa) and soil moisture condition (psoil) had great effect on the canopy reflectance in the whole wavelength from 400 to 2500 nm than other canopy parameters, and the EFAST method enlarged the contribution of some parameters that had little effects.

This paper uses PROSAIL model to simulate vegetation canopy reflectance under different chlorophyll contents and Leaf area index (LAI). The changes of NDVIs with different LAIs and chlorophyll contents are analyzed. A simulated spectral dataset was built firstly by using PROSIAL vegetation radiative transfer model with various vegetation chlorophyll concentrations and leaf area index. The responses of NDVIs to LAIs are quantitatively analyzed further based on the dataset. The results show that chlorophyll contents affect canopy reflectance mainly in visible band. Canopy reflectance decreases with an increasing chlorophyll content. Under the same LAI value, NDVI values increase with an increase chlorophyll contents. Under constant content of chlorophyll, NDVIs increases with an increasing LAI. When the value of LAI is less than5, the canopy reflectance is significantly affected by soil background. When value of LAI is higher than5, the earth surface is almost completely covered with vegetation. The increase in LAI has little effect on canopy reflectance and NDVIs consequently. NDVIs increases with the adding of chlorophyll content, when chlorophyll is higher than 40, the rangeability of NDVIs is becoming stable.

Remote sensing is an effective tool to estimate foliar pigments contents with the analysis of vegetation index. The crucial issue is how to choose the optimal bands-combination to conduct the vegetation index. In this study, RVI, a vegetation index computed by the reflectance of Red and NIR bands, has been used to estimate the contents of chlorophyll and carotenoid. The reflectance of the two bands forming the narrow band RVI was simulated by the PROSPECT model. The possible combinations of narrow band RVI were examined from 400 nm to 800 nm. The results showed that: At the leaf level, estimation of chlorophyll content can be identified in narrow band RVI. Ranges for these bands included: (1) 549-589nm, 616-636nm or 729-735nm combined with 434-454nm; (2) 663-688nm, 710-717nm, 719-728nm or 730- 739nm combined with 549-561nm; (3) 663-688nm combined with 569-615nm. However, no valid narrow-band RVI for the estimation of carotenoid content was successfully identified. Our results also showed that two rules should be followed when choosing optimal bands-combination: (1) the selected bands must have minimal interference from other biochemical constituents; (2) there should be distinct differences between the sensitivities of the bands selected for particular pigments.

Leaf pigments are key elements for plant photosynthesis and growth. Traditional manual sampling of these pigments is labor-intensive and costly, which also has the difficulty in capturing their temporal and spatial characteristics. The aim of this work is to estimate photosynthetic pigments at large scale by remote sensing. For this purpose, inverse model were proposed with the aid of stepwise multiple linear regression (SMLR) analysis. Furthermore, a leaf radiative transfer model (i.e. PROSPECT model) was employed to simulate the leaf reflectance where wavelength varies from 400 to 780 nm at 1 nm interval, and then these values were treated as the data from remote sensing observations. Meanwhile, simulated chlorophyll concentration (C ab ), carotenoid concentration (C ar ) and their ratio (C ab /C ar ) were taken as target to build the regression model respectively. In this study, a total of 4000 samples were simulated via PROSPECT with different C ab , C ar and leaf mesophyll structures as 70% of these samples were applied for training while the last 30% for model validation. Reflectance (r) and its mathematic transformations (1/r and log (1/r)) were all employed to build regression model respectively. Results showed fair agreements between pigments and simulated reflectance with all adjusted coefficients of determination ( R 2 ) larger than 0.8 as 6 wavebands were selected to build the SMLR model. The largest value of R 2 for Cab, Car and Cab/Car are 0.8845, 0.876 and 0.8765, respectively. Meanwhile, mathematic transformations of reflectance showed little influence on regression accuracy. We concluded that it was feasible to estimate the chlorophyll and carotenoids and their ratio based on statistical model with leaf reflectance data.

Accurate regional crop growth monitoring and yield prediction is very critical for the national food security assessment and sustainable development of agriculture, especially for China, which has the largest population in the world. Remote sensing data and crop growth model have been successfully used in the crop production prediction. However, both of them have inherent limitation and uncertainty. The data assimilation method which combines crop growth model and remotely sensed data has been proven to be the most effective method in regional yield estimation. The aim of this paper is to improve the estimation of regional winter wheat yield of crop growth model by using data assimilation schemes with Ensemble Kalman Filter (EnKF) algorithm. WOrld FOod STudies (WOFOST) crop growth model was chosen as the crop growth model which was calibrated and validated by the field measured data. MODIS Leaf Area Index (LAI) values were used as remote sensing observations to adjust the LAI simulated by the WOFOST model based on EnKF. The results illustrate that the EnKF algorithm has significantly improved the regional winter wheat yield estimates over the WOFOST simulation without assimilation in both potential and water-limited modes. Although this study clearly implies that the assimilation of the remotely sensed data into crop growth model with EnKF algorithm has the potential to improve the prediction of regional crop yield and has great potential in agricultural applications, high resolution meteorological data and detailed crop field management are necessary to reach a high accuracy of regional crop yield estimation.

Aerosol optical depth (AOD) data from Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) were inter-compared and validated against ground-based measurements from Aerosol Robotic Network (AERONET) as well as Moderate Resolution Imaging Spectroradiometer (MODIS) over China during June 2006 to December 2012. We have compared the AOD between CALIOP and AERONET site by site using quality control flags to screen the AOD data. In general, CALIOP AOD is lower than AERONET due to cloud effect detected algorithm and retrieval uncertanty. Better agreement is apparent for these sites: XiangHe, Beijing, Xinglong, and SACOL. Low correlations were observed between CALIPSO and ground-based sunphotometer data in in south or east China. Comparison results show that the overall spatio-temporal distribution of CALIPSO AOD and MODIS AOD are basically consistent. As for the spatial distribution, both of the data show several high-value regions and low-value regions in China. CALIPSO is systematically lower than MODIS over China, especially over high AOD value regions for all seasons. As for the temporal variation, both data show a significant seasonal variation: AOD is largest in spring, then less in summer, and smallest in winter and autumn. Statistical frequency analysis show that CALIPSO AOD and MODIS AOD was separated at the cut-off points around 0.2 and 0.8, the frequency distribution curves were almost the same with AOD between 0.2 and 0.8, while AOD was smaller than 0.4, CALIPSO AOD gathered at the low-value region (0-0.2) and the frequency of MODIS AOD was higher than CALIPSO AOD with AOD greater than 0.8. CALIOP AOD values show good correlation with MODIS AOD for all time scales, particularly for yearly AOD with higher correlation coefficient of 0.691. Seasonal scatterplot comparisons suggest the highest correlation coefficient of 0.749 in autumn, followed by winter of 0.665, summer of 0.566, and spring of 0.442. Evaluation of CALIOP AOD retrievals provides prospect application for CALIPSO data.

Estimating leaf chlorophyll contents through leaf reflectance spectra is efficient and nondestructive, but the actual dataset always based on a single or a few kinds of specific species, has a limitation and instability for a common use. To address this problem, a combination of multiple spectral indices and a model simulated dataset are proposed in this paper. Six spectral indices are selected, including Blue Green Index (BGI), Photochemical Reflectance Index (PRI_5), Triangle Vegetation Index (TVI), Chlorophyll Absorption Ratio Index (CARI), Carotenoid Reflectance Index (CRI) and the green peak reflectance (R525). Both stepwise linear regression (SLR) and back-propagation artificial neural network (ANN) are used to combine the six spectral indices for the estimation of chlorophyll content (Cab). In addition, to overcome the limitation of actual dataset, a “big data” is applied by a within-leaf radiation transfer model (PROSPECT) to generate a large number of simulated samples with varying biochemical and biophysical parameters. 30% of the simulated dataset (SIM30) and an experimental dataset are used for validation. Compared with linear regression method, NN yields better result with R2 = 0.96 and RMSE = 5.80ug.cm−2 for Cab if validated by SIM30, while R2 = 0.95 and RMSE = 6.39ug.cm−2 for SLR. NN also gives satisfactory result with R2 = 0.80 and RMSE = 5.93ug.cm−2 for Cab if validated by LOPEX dataset, however, the SLR only gets 0.72 of R2 and 12.20ug.cm−2 of RMSE. The results indicate that integrating multiple spectral indices can improve the Cab estimating accuracy with a better stability in different kind of species and the model simulated dataset can make up the shortfall of actual measured dataset.

Recently, the air quality has been continuing to deteriorate and threaten public health in the Pearl River Delta. China, the host country for the 2010 Asian Games, faced the great challenge of air quality issues, particularly in the Pearl River Delta, where the Asian Games were held. The major aim of this study is to reveal the spatial and temporal characteristics of NO 2 in the Pearl River Delta during October 2004 to December 2010. The long-term characteristics and variations of the NO 2 column concentration before and during the 2010 Asian Games were analyzed by using the NO 2 product OMNO2e from the Ozone Monitoring Instrument (OMI). Results show that the annual average of the NO 2 column concentration has a significant downward trend from 2005 to 2010 in the Pearl River Delta: the total column concentration of NO 2 (TotNO 2 ) in the atmosphere decreased from 9.207×10 15 molec/cm 2 to 8.173×10 15 molec/cm 2 , with an average annual rate of -2.247%; the tropospheric column concentration of NO 2 (TropNO 2 )decreased from 6.685×10 15 molec/cm 2 to 5.646×10 15 molec/cm 2 , with an average annual rate of -3.109%. The ratio TropNO 2 /TotNO 2 indicating the amount of NO 2 exhausted by human activities also decreased from 0.726 in 2005 to 0.691 in 2010. During the 2010 Asian Games, the weekly average of the TropNO 2 in Pearl River Delta was maintained at a low level. The NO 2 average distribution in the Pearl River Delta is characterized by the maximum in the geometric center, outwardly smaller, and the shrinking areas with high TropNO 2 concentration from 2005 to 2010. Foshan, Jiangmen and Kwangchowan were severely polluted cities during the Games. However, the air quality of the Pearl River Delta was improved compared to its historical periods due to governmental preventive/control measures during the 2010 Asian Games.