The Songnen Plain is an important commodity grain product base in China for which a spatiotemporal pattern of actual evapotranspiration (ETa) would provide critical important information to evaluate crop growth status and water use efficiency. ETa over the Songnen Plain in the 2008 growing season (from May to September) was mapped using the moderate resolution imaging spectroradiometer time-series products based on the surface energy balance algorithm for land model and the Penman-Monteith equation. The estimated ETa was validated using eddy covariance surface data. The calculated and observed ETa values were highly consistent with a total difference of 18.26% in the whole growing season. Therefore, the ETa retrieval method based on remote sensing technology could satisfy the requirements for regional ETa estimation over the Songnen Plain. The total ETa over the Songnen Plain in the 2008 growing season ranged from 182.7 to 1002.4 mm, and the average value for the whole study area was 591.1 ± 122.2 mm (standard deviation). ETa exhibited obvious spatial variation, gradually increasing from low values in the southwest to higher values in the east and northeast. Monthly ETa varied with meteorological conditions, land covers, root-zone soil moisture, and vegetation phenology. Higher monthly ETa values appeared in June, July, and August with a maximum value of 139.5 mm observed in July. The average monthly ETa for water-body, woodland, and wetland was much higher than cropland and grassland during the growing season. Grassland obtained the lowest monthly ETa due to the scarcity of rainfall and lower groundwater level.
Lake Chagan represents a complex situation of major optical constituents and emergent spectral signals for remote sensing analysis of water quality in the Songnen Plain. As such it provides a good test of the combined radiometric correction methods developed for optical remote sensing data to monitor water quality. Landsat thematic mapper (TM) data and in situ water samples collected concurrently with satellite overpass were used for the analysis, in which four important water quality parameters are considered: chlorophyll-a, turbidity, total dissolved organic matter, and total phosphorus in surface water. Both empirical regressions and neural networks were established to analyze the relationship between the concentrations of these four water parameters and the satellite radiance signals. It is found that the neural network model performed at better accuracy than empirical regressions with TM visible and near-infrared bands as spectral variables. The relative root mean square error (RMSE) for the neural network was < 10%, while the RMSE for the regressions was less than 25% in general. Future work is needed on establishing the dynamic characteristic of Chagan Lake water quality with TM or other optical remote sensing data. The algorithms developed in this study need to be further tested and refined with multidate imagery data.
In this study, we present spectral measurements of corn chlorophyll content in Changchun (eight times in 2003) and
Hailun (five time in 2004), both of which lie in the Songnen Plain, China. Corn canopy reflectance and its derivative
reflectance were subsequently used in a linear regression analysis against Chl-a concentration on one by one spectral
reflectance. It was found that determination coefficient for Chl-a concentration was high in blue, red and near infrared
spectral region, and it was low in green and red edge spectral region, however Chl-a concentration obtained its high
determination coefficient in blue, green and red edge spectral region, especially in red edge region with derivative
reflectance. Regression models were established based upon 6 spectral vegetation indices and wavelet coefficient,
reflectance principal components as well. It was found that wavelet transforms is an effective method of hyperspectral
reflectance feature extraction for corn Chl-a estimation, and the best multivariable regressions obtain determination
coefficient (R2) up to 0.87 for Chl-a concentration. Finally, neural network algorithms with both specific band
reflectance and wavelet coefficient as input variables were applied to estimate corn chlorophyll concentration. The
results indicate that estimation accuracy improved with nodes number increasing in the hidden layer, and neural network
performs better with wavelet coefficient than that with specific band reflectance as input variables, determination
coefficient was up to 0.96 for Chl-a concentration. Further studies are still needed to refine the methods for determining
and estimating corn bio-physical/chemical parameters or other vegetation as well in the future.
Though hyperspectral data can provide more information compared with multi-spectral data, the major problem is the
high dimensionality which needs effective approaches to extract useful information for practical purpose, and requires
large numbers of training samples to meet statistical requirements. The use of Wavelet Transformation (WT) for
analyzing hyperspectral data, particularly for feature extraction from hyperspectral data, has been extremely limited. WT
can decompose a spectral signal into a series of shifted and scaled versions of the mother wavelet function, and that the
local energy variation of a spectral signal in different bands at each scale can be detected automatically and provide some
useful information for further analysis of hyperspectral data. Therefore, in this study, WT techniques was applied to
automatically extract features from soybean hyperspectral canopy reflectance for LAI estimation; and compared the
model prediction accuracy to those based on spectral indices (PCA). 144 samples were collected in 2003 and 2004,
respectively in the Songnen Plain at two study regions. It is found that wavelet transforms is an effective method for
hyperspectral reflectance feature extraction on soybean LAI estimation, and the best multivariable regressions obtain
determination coefficient ( R2) above 0.90 with RMSE less than 0.30 m2/m2. As a comparison study, Vegetation Index (VI)
method applied in this study, and wavelet transform technique performs much better than VI method for LAI estimation.
Further studies are still needed to refine the methods for estimating soybean bio-physical/chemical parameters based on
WT method.
Spectral absorption properties of total suspended matter (TSM) and colored dissolved organic matter (CDOM) are
important for the use of the bio-optical model to estimate water quality parameters. This study aims to investigate the
variation in the absorption coefficients of TSM and CDOM of inland waters. A total of 92 water samples were collected
from Shitoukoumen Reservoir and Songhua Lake in Northeast China, analyzed for TSM and Chl-a, and measured for the
absorption coefficient of TSM, CDOM and total pigments using a laboratory spectrophotometer. The absorption
coefficient of TSM has been decomposed for phytoplankton and inorganic sediments. The results show that for
Shitoukoumen Reservoir, CDOM has strong absorptions with shallow absorption slopes (i.e., the coefficient S in
a(λ)=a(λ0)exp[-S(λ- λ0)]) and large absorption at 355 nm; and for Songhua Lake, CDOM follows similar spectral
absorption curves but less variation in the S value. The results also show TSM has the average absorption coefficient 5.7
m-1 at 440 nm and 0.93 m-1 at 675 nm, and their concentration is well correlated to TSM with R2 larger than 0.85 at 440
nm over both Songhu Lake and Shitoukoumen Reservoir. In summer, CDOM was mainly terrigenous and had a high
proportion of humic acid derived from the decomposition of phytoplankton and there were no obvious difference of S
value. The results indicate that inorganic sediments contributed much more absorption than phytoplankton pigments in
Shitoukoumen Reservoir than that in Songhua Lake, and there is strong association of TSM concentration to absorption
coefficient at 440 nm.
Atmospheric water vapor (AWV) content is closely related to precipitation that in turn has effects on the productivity of
agricultural, forestry and range land. MODIS images have been used for AWV retrieval, and the method uses either two
(0.841-0.876 μm and 0.915-0.965 μm) or three (0.841-0.876, 0.915-0.965 and 1.230-0-1.250 μm) MODIS channel
ratios. We applied both methods to the MODIS data over Northeast China acquired from June to August, 2008 to
retrieve AWV content, and the results were validated on ground observed data from 10 radio sonde stations characterized
by various land cover. The bulk results indicate that the two-channel ratio outperformed the three-channel ratio based on
the coefficient of determination R2 = 0.81 vs. 0.78. The validation results for individual land cover types also support this
observation with R2 = 0.92 vs. 0.84 for woodland, 0.82 vs. 0.79 for cropland, 0.90 vs. 0.86 for grassland and 0.673 vs.
0.669 for urban areas. The spatial distribution of AWV derived using the two-channel ratio method was correlated to
land-use classification data, and a high correlation was evident when other conditions were similar. With the exception
of dry cropland, the amount of average water vapor content over different land use types demonstrates a consistent order:
water-body > paddy-field > woodland > grassland > barren for the analyzed multi-temporal MODIS data. This order
partially matches the evapotranspiration pattern of underlying surface, and future work is required for analyzing the
association of the landscape pattern with AWV in the region.
KEYWORDS: System on a chip, Soil science, Carbon, Statistical analysis, Data modeling, Statistical modeling, Chemical analysis, Geographic information systems, Climatology, Spherical lenses
In this paper, a total of 191 topsoil samples were taken in Tongyu County, a typical area of farming-pastoral ecotone in the Northeast China, and soil organic carbon (SOC) concentrations were investigated using statistics, geostatistics and GIS techniques. Mean concentration of SOC in surface soil of Tongyu County was 0.76%, which was a very low level. The coefficient of variation
(Cv) (0.23) indicated the moderate variability of SOC. Significant positive correlations existed between SOC and total N, total P, available N, silt, clay, respectively; negative correlations between SOC and sand, SOC and elevation were observed. The linear regression model of SOC was built based on other soil properties in order to comparing with interpolation results. To obtain an unbiased assessment on the spatial structure of SOC, the spatial outliers were detected using local Moran's I index. The parameters of experimental model fitted for the dataset excluded spatial outliers were better than those for all samples, but the difference was not significant at the regional scale. Based on Kriging interpolation, the spatial distribution of SOC showed a broad regional pattern, with higher values in the eastern part, and lower values in the middle and western part. This spatial pattern was mainly controlled by structural factors, such as climate, parent material and topography.
In terms of the importance and challenge of accurate Chlorophyll-a (Chla) estimation in inland Case-II waters, many empirical or semi-empirical algorithms are established to extract information on Chla concentrations from remote sensing reflectance . However, the assumption that the optical parameter of Chla specific absorption coefficient a*ph(λ) remains constant usually restrains their estimating accuracy, including the conceptual three-band model [Rrs-1(λ1)-(λ2)]×Rrs(λ3) developed originally for estimating Chla amounts in terrestrial vegetation recently. Therefore, in this paper, an improved conceptual three-band model with the correction of Chla specific absorption coefficient [Rrs-1(λ1)-Rrs-1(λ2)]×Rrs(λ3×)×a*ph-1(λ1) was presented to estimate Chla contents for Shitoukoumen Reservoir, as a typical example of inland Case-II waters. According to the optical characteristics of waters studied, spectral regions included in the model were tuned to eliminate other interferences such as the variability of Chla fluorescence quantum yield, which resulted in the optimal positions for , and at 668nm, 678nm and 717nm respectively. Compared with the previous three-band model, the improve model gave out a much better estimating performance with a high coefficient determination (0.92) and a low root mean squared error (0.88μgl-1). Although the findings underline the rationale behind the improved model, an extensive database containing data in different water conditions and water types is required to test the accuracy of the model.
In recent years, the bio-optical model has been paid more and more attention. In order to validate its applicability in the
near-infrared wavelengths to Case II waters, two simply parameterized equations employing reflectance at 808nm and
873nm were established to estimate total suspended matter (TSM) concentrations in the Shitoukoumen Reservoir that
represented a turbid inland water condition. It was showed that both equations gave out comparative good performance
with coefficient determination (R2) larger than 0.85 and root mean squared error (RMSE) much lower than data span for
both training and test data. Based on the transfer of radiation in waters, the bio-optical model could integrate well
apparent optical properties (AOPs) with inherent optical properties (IOPs). However, further investigation is needed to
upgrade the bio-optical dataset and to refine the model for the universal applications.
Based on in situ water sampling and field spectral measurement from June to September 2004 in Lake Chagan, this
paper partly addressed to develop a new approach named inverse continuum removal to isolate fluorescence peak for the
comparison of water reflectance spectra with different Chl-a concentration during the summer. Next, an attempt was
made to link the reflectance changes including band depth and band area with Chl-a concentration and evaluate the
potential of remote sensing data for inversion. Results show that the Chl-a determined from band depth and band area of
fluorescence peak with the determination coefficient (R2) higher than 0.74. The study also proves that inverse continuum
removal analysis can be used to effectively determine the Chl-a concentration of Lake Chagan in Northeast China.
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