[Seasonal Variability of Greenhouse Gas Emissions in the Urban Lakes in Changchun, China].

The partial pressures of carbon dioxide p(CO2) and methane p(CH4) in the surface water of seven urban lakes in Changchun city, China (Nan Lake, Bei Lake, Yanming Lake, Shengli, Dilisuo, Changchun, Tianjia) , have been studied in both summer and autumn related to the environment and water quality parameters. The results indicated that both in summer and autumn, CH4 of seven lakes was all supersaturated, and CO2 was all supersaturated except in Nan lake and Shengli lake. For either p( C02) or p( CH4), there was a significant difference among different urban lakes (P < 0.05), and there was almost no obvious difference in the same lake between summer and autumn. The gas flux also had a significant difference among different urban lakes (P <0. 05). Except for Nan Lake and Shengli, all other lakes were the sources of atmospheric CO2 and CH4 both in summer and fall, and the discharge of CO2 to atmosphere by lakes was more than that of CH4. According to the correlation analysis, there was a significant negative relationship between p(CO2, CH4) and sunshine duration in summer (r p(CO2) = -0.48, P <0. 05; rp(CH4) = -0.63, P < 0.01). The sunshine duration could affect the concentrations of CO2 and dissolved oxygen in the water by influencing the photosynthesis of aquatic plants. There was also a significant negative relationship between p(CH4) and precipitation in summer (r p(CH4) = 0.44, P < 0.05), and between p (CO2) and air temperature in autumn (r p(cO2) = -0.39, P < 0.05). The correlation analysis between water quality parameters and p(CO2, CH4) showed that both p(CO2) and p(CH4) were negatively correlated with pH (r(Co2) = -0.51, r P(C4) = -0.82, P < 0.01), while they were positively correlated with salinity (r p(CO2) = 0.38, P < 0.05; r p(CH4) = 0.75, P < 0.01). The results suggested that the levels of nutrients in these urban lakes, which were related to the phytoplankton biomass, were not the main influencing factors for p(CO2) in surface water, and sunshine duration, pH, and salinity all had a greater impact on p (CO2) and p(CH4) in summer and autumn.

[Absorption Characteristics of Particulates and CDOM in Waters of Chagan Lake and Xinlicheng Reservoir in Autumn].

Field surveys and laboratory analysis were carried out in Chagan Lake and Xinlicheng Reservoir under different salinity conditions in September 2012. In the laboratory, the absorption coefficients of particulates and chromophoric dissolved organic matter (CDOM) were measured, aiming to compare the absorption features, source of optical active substances and relative contribution of optical active constituents over the range of PAR (400-700 nm) in Chagan Lake and Xinlicheng Reservoir. The results showed that the Chagan Lake and Xinlicheng Reservoir were water bodies with medium eutrophication in autumn by TAL nutrient index and the absorption spectra of particulates matters were similar to those of phytoplankton. For the Chagan Lake with high salinity( EC = 988. 87 micro S x cm(-1)), the total particulate absorption was dominated by the nonalgal particles, and the contribution rate was in the order of nonalgal particles > phytoplankton > CDOM. For the Xinlicheng Reservoir with low salinity (EC = 311.67 microS x -cm(-1)), the total particulate absorption was dominated by the phytoplankton, and the contribution rate was ranked as phytoplankton > nonalgal particles > CDOM. Positive correlation was observed between a(p) (440), a(p) (675), a(d) (440) and total suspended matter (TSM), inorganic suspended matter (ISM), organic suspended matter (OSM) and Chl-a respectively in Chagan Lake, with correlation coefficients all above 0.55. Positive correlation was observed between a(p)(440), a(p) (675) and Chl-a (0.77 and 0.85, P < 0.05) , so did a(d) (440) and ISM (0.74, P < 0.01), while negative correlation was observed between a(p) (440) and OSM in the Xinlicheng Reservoir. In terms of Chagan Lake, negative correlation was merely observed between a(g) (440) and OSM (-0.54, P < 0.05) , but not in the Xinlicheng Reservoir. Both Sg, which was calculated by the fitting absorption curve from 250 to 400 nm, and relative molecular weight M showed that Sg[ (0.021 +/- 0.001) m(-1)] in Chagan Lake was greater than that in the Xinlicheng Reservoir [(0.0176 +/- 0.001) m(-1)], and Mr, in Chagan Lake was 11.44 +/- 2.00 (7.5-15.09), which was greater than that in Xinlicheng Reservoir 7.53 +/- 0.79 (6.17-8.89), indicating that the relative molecular weight of CDOM in the Chagan Lake was less than that in the Xinlicheng Reservoir. The Chagan Lake was greatly affected by wind speed and shore collapse to produce suspended mineral and sediment particles. Thereby the total particulate absorption was dominated by the nonalgal particles. The waters in the Xinlicheng Reservoir were greatly impacted by terrestrial inorganic matter, and the growth of phytoplankton was weakened and microbes activities were strengthened simultaneously, which led to the negative correlations between a(g)(lamda) and OSM.

[Seasonal variation in the absorption and fluorescence characteristics of CDOM in downstream of Liaohe River].

Chromophoric dissolved organic matter (CDOM), which is an important part of dissolved organic matter (DOM), is considered as the largest storage of dissolved organic carbon in the aquatic environment. Liaohe River is the seventh largest river in China with annual runoff of 1.48 billion m3. As a result, studying on CDOM of Liaohe River is very important in estimating the organic carbon flux into sea. Seasonal optical characteristics of CDOM in the downstream of Liaohe River were investigated using absorbance spectroscopy and fluorescence excitation-emission matrices (EEMs). CDOM absorption coefficient at 355 nm [aCDOM (355)] in spring was lower than that in autumn and winter while low molecular weight substances were found in autumn and high molecular weight substances in spring based on the absorption coefficient and absorption slope (S) of CDOM. Samples in different seasons all exhibited fairly strong protein-like fluorophore (fluorophore B and fluorophore T) in the EEMs but the values showed apparent temporal variations. Based on the analysis of the relationships between different fluorophores, strong correlations (R2 > 0. 9) were observed between fluorophore A and C in spring, fluorophore B and T in autumn and winter, which illustrated that they had similar CDOM originalsources. However, a weak relationship (R2 = 0.21) was found between fluorophore B and T in spring, demonstrating the complexity and diversity of CDOM sources. Starting from autumn to winter and the subsequent spring, humic-like fluorophores exhibited enhanced fluorescent intensity, which could be ascribed to exogenous input. Furthermore, linear relationship between aCDOM (355) and Fn (355) in different seasons was examined in the study, and the strongest relationship was obtained in winter (R2 = 0.75), followed by autumn (R2 = 0.48) and spring (R2 = 0.01). This study indicated that fluorophore B in autumn and winter (R = 0.66; R = 0.89) as well as humic-like fluorophores (A and C, R = 0.74; R = 0.82) in spring were the main contributors to the CDOM optical characteristics.

[Research on hyperspectral remote sensing in monitoring snow contamination concentration].

Contaminants in the snow can be used to reflect regional and global environmental pollution caused by human activities. However, so far, the research on space-time monitoring of snow contamination concentration for a wide range or areas difficult for human to reach is very scarce. In the present paper, based on the simulated atmospheric deposition experiments, the spectroscopy technique method was applied to analyze the effect of different contamination concentration on the snow reflectance spectra. Then an evaluation of snow contamination concentration (SCC) retrieval methods was conducted using characteristic index method (SDI), principal component analysis (PCA), BP neural network and RBF neural network method, and the estimate effects of four methods were compared. The results showed that the neural network model combined with hyperspectral remote sensing data could estimate the SCC well.

[Comparison of methods for estimating soybean chlorophyll content based on visual/near infrared reflection spectra].

The estimation of crop chlorophyll content could provide technical support for precision agriculture. Canopy spectral reflectance was simulated for different chlorophyll levels using radiative transfer models. Then with multiperiod measured hyperspectral data and corresponding chlorophyll content, after extracting six wavelet energy coefficients from the responded bands, an evaluation of soybean chlorophyll content retrieval methods was conducted using multiple linear regression, BP neural network, RBF neural network and PLS method. The estimate effects of the three methods were compared afterwards. The result showed that the three methods based on wavelet analysis have an ideal effect on the chlorophyll content estimation. R2 of validated model of multiple linear regression, BP neural network, RBF neural network and PLS method were 0. 634, 0. 715, 0. 873 and 0.776, respectively. PLS based on Gaussian kernel function and RBF NN methods were better with higher precision, which could estimate chlorophyll content stably.

[Light absorption by suspended particulate matter in Chagan Lake, Jilin].

Spectral characteristics and the magnitudes of light absorption by suspended particulate matter were determined by spectrophotometry in this optically complex Lake Chagan waters for the purpose of surveying the natural variability of the absorption coefficients to parameterize the bio-optical models for converting satellite or in-situ water reflectance signatures into water quality information. Experiments were carried out on seasonal frozen Lake Chagan, one representative inland case-2 water body in Northeast of China. Particulate absorption properties analyzed using the field data on July 15th and October 12th 2009 were measured using the quantitative filter technique to produce absorption spectra containing several fractions that could be attributed to two main optical active constituents (OACs) phytoplankton pigments and non-algal particulates (mineral sediments, and organic detritus). Results suggested that the suspended particulate matter (SPM) concentration was higher while phytoplankton biomass (chlorophyll-a concentration) was lower in July and that in October. The spectral shape of total suspended particulate matter resembled that of non-algal particulates which contributed greater than phytoplankton in total particulate absorption during both periods. An obvious absorption peak occurring at around 440 nm exhibited an increase in phytoplankton contribution in October. Non-algal particulate absorption at 440 nm (a(NAP) (440)) had better correlation with total suspended particulate matter concentration than that with chlorophyll-a over the two periods. Light absorption by phytoplankton pigments in the Chagan lake region was generally lower than that of non-algal components. Chl. a dominating phytoplankton pigment composition functioned exponentially with its absorption coefficients at 440 and 675 nm specifically, the average values of which in July were 0.146 8 m2 x mg(-1) and 0.050 3 respectively while in October they were 0.153 3 and 0.013 2 m2 x mg(-1) varying regionally and seasonally due to the changes in specific composition, light and nutrient conditions.

[Spatial characteristics of soil organic carbon and nitrogen storages in Songnen Plain maize belt].

By using the data of 382 typical soil profiles from the second soil survey at national and county levels, and in combining with 1:500000 digital soil maps, a spatial database of soil profiles was established. Based on this, the one meter depth soil organic carbon and nitrogen storage in Songnen Plain maize belt of China was estimated, with the spatial characteristics of the soil organic carbon and nitrogen densities as well as the relationships between the soil organic carbon and nitrogen densities and the soil types and land use types analyzed. The soil organic carbon and nitrogen storage in the maize belt was (163.12 +/- 26.48) Tg and (9.53 +/- 1.75) Tg, respectively, mainly concentrated in meadow soil, chernozem, and black soil. The soil organic carbon and nitrogen densities were 5.51-25.25 and 0.37-0.80 kg x m(-2), respectively, and the C/N ratio was about 7.90 -12.67. The eastern and northern parts of the belt had much higher carbon and nitrogen densities than the other parts of the belt, and upland soils had the highest organic carbon density [(19.07 +/- 2.44) kg x m(-2)], forest soils had the highest nitrogen density [(0.82 +/- 0.25) kg x m(-2)], while lowland soils had the lower organic carbon and nitrogen densities.

[Soil taxonomy on the basis of reflectance spectral characteristics].

Soil spectral reflectance is the comprehensive representation of soil physical and chemical parameters, and its study is the physical basis for soil remote sensing and provides a new way and standard for soil properties themselves' research. Soil room spectra significantly correlate with that derived from hyperspectral images. So the room spectra are very important for soil taxonomy and investigation. To seek for the feasibility of soil taxonomy on the basis of topsoil reflectance spectral characteristics, and provide the theory foundation for quick soil taxonomy based on remote sensing methods, the spectral reflectance in the visible and near infrared region (400-2 500 nm) of 248 soil samples (black soil, chernozem, meadow soil, blown soil, alluvial soil) collected from Nongan county, Jilin province was measured with a hyperspectral device in room, and the soil spectral characteristics were determined with continuum removal method, and soil spectral indices (spectral absorption area, depth and asymmetry) were computed, which were introduced into BP network models as external input variables. The models consist of three layers (input, output and hidden layer), the training function is "TRAINLM", learning function "LEARNGDM", and transferring function "TAN SIG". The results showed that: (1) There are some differences among different soils in their spectral characteristics, but with similar parental matrix and climate, the spectral differences of soils in Nongan county are not significant. So it's difficult to analyze soil spectral characteristics based on soil reflectance. (2) The curves after continuum removal strengthened soil spectral absorption characteristics, and simplified soil spectral analysis. The soil spectral curves in Nongan county mainly have five spectral absorption vales at 494, 658, 1 415, 1 913 and 2 206 nm, and the former two vales are caused by soil organic matter, Fe and mechanical composition, the latter three are due to soil moisture; the differences of the latter three vales among different soils are not apparent, and the significant differences are in the former two vales region. (3) Soil reflectance is sensitive to organic matter, soil moisture, Fe, mechanical composition, roughness, and so on. The sensitivity of soil spectral indices derived with continuum removing method is decreased. Then the models with these indices as input variables are more stable and general. As the input variables were external, the BP network model based on the former two vales' shape characteristics was better than that based on reflectance values or all five vales, the classifying accuracy of the main three soils (chernozem, meadow soil, blown soil) was bigger than 60%, and the model could be used for soil taxonomy. However, this work still needs further study, and to improve classifying accuracy, auxiliary data, such as topography, vegetation, and land use should be introduced.

[Black soil organic matter content prediction based on reflectance simulation models].

The hyperspectral reflectance characteristics of black soil in Heilongjiang province were analyzed quantitatively, and then the main characteristic controlling points of reflectance were determined and used to build soil reflectance prediction models; the relationship between organic matter content and reflectance and the coefficients of simulating models were studied, Black soil organic matter content spectral prediction models were built, and the feasibility of hyperspectral reflectance simulatiib method was discussed. The results are as follows (1) Organic matter content is the determining factor of black soil reflectance characteristics in the range less than 1000 nm. When the content is low, the covering effect of organic matter on the black soil parent matrix reflectance characteristics is very weak, there are two absorption vales at 500 and 640 rim; when the content reaches a certain content (about 5%), the reflectance characteristics of black soil parent matrix are totally covered by organic matter, and there is only one large absorption vale in the region caused by organic matter. (2) The spectral characteristic controlling points of black soil hyperspectral reflectance in the range of 450-930 om are located at 450, 500, 590, 660 and 930 nrn, and divide the black soil reflectance into four parts. (3) Simulation models (linear, quadratic) rightly describe the characteristics of black soil hyperspectral reflectance, and the linear piecewise model shows a better performance. (4) The organic matter content prediction models with the coefficients of reflectance simulation models as independent variables are more precise than that based on soil reflectance and its derivate, which indicates that the characteristic controlling points for reflectance simulation models are selected reasonably and representatively, and the simulation models partly solve the data redundancy problem of soil hyperspectral reflectance, and improve the precision of black soil organic matter content prediction models with remote sensing methods. Reflectance simulating method can be used for data simplification and compression, data redundancy removal, organic matter and other soil pararneters remote sensing studies.

[Comparison of methods for estimating soybean leaf area index].

Leaf area index (LAI) is an important biophysical parameter, and is the critical variable in many ecology models, productivity models and carbon circulation study. Based on the field experiment data, an evaluation of soybean LAI retrieval methods was conducted using NDVI (normalized difference vegetation index) and RVI (ratio vegetation index), principle component analysis (PCA) and neural network (NN) methods, and the estimate effects of three methods were compared. The results showed that the three methods have an ideal effect on the LAI estimation. R2 of validated model of vegetation indices, PCA, NN were 0.753 (NDVI), 0.758 (RVI), 0.883, 0.899. PCA and NN methods were better with higher precision, and PCA method was the best, as its RMSE (0.202) was slower than the two vegetation indices (RMSEs of NDVI and RVI were 0.594 and 0.616) and NN (RMSE was 0.413) method. While the LAI was small, vegetation indices were obvious for removing the noise from soil and atmospheric effect and obtained the good evaluation result. PCA showed better effect for all LAI. LAI affected the estimating result of NN method moderately. As for the NN method, modeled LAI value and measured LAI regression formula slope was the nearest to 1 with R2 of 0.949, which showed a great potential for LAI estimating. As a whole, PCA and NN methods were the prior selection for LAI estimation, which should be attributed to the application of hyperspectral information of many bands.

[Bio-optical model of total suspended matter based on reflectance in the near infrared wave band for case-II waters].

From August to October, 2006, reflectance spectra were measured in a turbid Case-Il waters condition with an ASD FieldSpec spectrometer for a total of 58 samples. Based on the observation of reflectance curves, spectral analysis was carried out over 400-1200 nm. Showing the typical character of Case-II waters, the reflectance values were generally higher than those in other similar studies. Strong backscattering of high concentration total suspended matter (TSM) contributed considerably to the total reflectance spectra in water. Two obvious TSM reflectance peaks were observed in the near infrared wave bands, i.e., 808 and 1067 nm, especially the latter one that was never reported before. The highest correlation coefficient between reflectance and concentrations of TSM existed at 873 nm. Based on the simplification of water inherent optical parameters in the near-infrared wave band, including absorption of TSM, Chlorophyll-a (Chl-a) and chromophoric dissolved organic matter (CDOM), and backscattering of pure water, Chl-a and CDOM, three empirical equations of the bio-optical model using reflectance at 808, 873 and 1067 nm respectively were established to estimate the concentrations of TSM. Compared with linear and exponential models, the bio-optical model showed fairly good performance with comparatively high determination coefficient (r2) and low root mean squared error (RMSE), which confirmed the applicability of the bio-optical model to retrieve concentrations of TSM effectively in turbid Case-II waters.