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.
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.
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