Aerosol optical depth (AOD) is a key indicator of atmospheric environment. Aerosol remote sensing is the most efficient way to obtain the temporal and spatial distributions of AOD. In this paper, the data from Environment Satellite (HJ-1) CCD camera were employed to retrieve AOD by using deep blue algorithm over the Yangtze River Delta. The third band (in blue) was firstly extracted from the MODIS land surface reflectance product (MOD09) and then converted to the first band of CCD/HJ-1. According to the characteristics of the study area and CCD data, a multi-dimension look up table was then built by the Second Simulation of the Satellite Signal in the Solar Spectrum (6S). AOD over the Yangtze River Delta were finally retrieved from the radiance of the first band of CCD/HJ-1. After the retrieved AOD were validated by the MODIS AOD product (MOD04), the correlation coefficient (R) is 0.64 by regression of all cloud screened pixels (1147). The retrieved AOD has a higher spatial resolution than the MODIS AOD and thus can provide more detailed information. Compared with the AERONET ground observation data, the retrieved AOD is closer to the ground-based data than the MODIS AOD.
Poyang Lake is the largest shallow lake wetlands in China, and which vegetation succession is rapid under high
changeable hydrological regimes. This study measured the fluxes of carbon dioxide and methane simultaneously by
opaque static chamber-gas chromatography technique for typical wetland vegetation ecosystems in the growing season.
In view of the advantages both in temporal and spatial, HJ-1 satellite images were chosen as the data source for
vegetation cover classification and area estimates. And based on the areas in different vegetation, carbon flux for the
entire study area was estimated during the growing season. Results indicated that carbon dioxide flux has closer
relationship with vegetation change than methane flux does.
Methane (CH4), a significant atmospheric trace-gas, controls numerous chemical processes and species in the
troposphere and stratosphere and is also a strong greenhouse gas with significantly adverse environmental impacts. Since
the SCIAMACHY on the Envisat was in orbit since 2002, CH4 measurements at a regional scale became available. This
study (1) firstly improved the spatial resolution of 0.5°×0.5° lat/lon grid data provided by University of Bremen IUP/IFE
SCIAMACHY near-infrared nadir measurements using the scientific retrieval algorithm WFM-DOAS to 0.1°×0.1°
lat/lon with the ordinary Kriging method, (2) then analyzed the spatial-temporal characteristics of atmospheric CH4
concentration in the Yangtze River basin (YRB), China from 2003 to 2005, (3) finally analyzed the relations with the
main environmental factors: the precipitation from GSMaP MVK+ 0.1x 0.1 lat/lon degree grid data and the temperature
from 147 meteorological stations in the YRB. The analysis shows that atmospheric methane concentration has significant
and obvious characteristics of the spatial distribution of the inter-annual cycle fluctuations and seasonal characteristics
during the year, and points out that the temperature is the main impact factor.
Land surface temperature (LST) is one of the key parameters in the atmosphere-land energy and water transfers. An
understanding of the spatial and temporal variations of land surface temperature is important to broad research fields,
including climate, vegetation, hydrology, etc. In this paper, the cloud contamination of MODIS LST product was
analyzed first, and showed that there are numerous data gaps in MODIS 8-day composite LST product, indicating the
necessity of data interpolation. Then the Harmonic Analysis of Time-Series (HANTS) algorithm was applied to the LST
time-series to rebuild cloud-free images and to distill harmonic components. According to the harmonic characters and
reconstruct LST, the spatial and temporal variations of land surface temperature in the Yangtze River Delta were studied.
Desertification in the arid and semiarid regions directly influences the density and growth status of vegetation, NDVI
(Normalized Difference Vegetation Index) has been widely used to monitor vegetation changes. This study analyzed the
spatial patters of vegetation activity and its temporal variability in Tarim Basin, Xinjiang, China since 1998 to 2007 with
NDVI data derived from SPOT4 Vegetation. The coefficient of variation (CoV) of the NDVI was used as a parameter to
characterize the change of vegetation and to compare the amount of variation in different sets of sample data. The
method of quantifying changes in CoV values for each pixel was based on linear regression. The slope of linear
regression was acted as the criterion for the change direction: pixels with a negative slope are considered to represent
ground area with decreasing amounts of vegetation, vice versa. In this paper, We calculated (1) the inter-annual CoV
based on the yearly ONDVI, the sum of the monthly NDVI in the growing season (from April to October), for each pixel
between 1998-2007 to reveal the spatial patterns of vegetation activity, (2) the intra-annual CoV based on monthly NDVI
by MVC to reflect vegetation seasonal dynamics, (3) the slope (ê) of the intra-annual CoV regression line for each pixel
to identify the overall long-term trend of vegetation dynamics. This experiment demonstrated the feasibility of applying
the CoV and its regression analysis based on long term SPOT-VGT NDVI time-series data for vegetation dynamics monitoring.
During the last two decades, the Yangtze River Delta, one of the most economically developed areas of China,
experienced rapid urban expansion, and accordingly, masses of cropland have been converted into human buildings. To
analyze the influence of landscape change, it is important to provide up-to-date land cover information of this area. This
paper describes the development of Land cover map of the Yangtze River Delta using 250m MODIS data, and the main
satellite data used in this study were MODIS EVI data, MODIS reflectance data and DEM. A filter method based on
time series was applied to eliminate EVI noise, and a PCA analysis was performed to reduce the volume of data. Besides,
homogeneity was calculated to present spatial texture information. Therefore, a compositive classification matrix was
generated. Considering the natural and artificial conditions of the study area, a 9-type classification scheme was defined.
ROIs (Region of Interest) were selected from Landsat ETM+ images by human interpretation consulting the Vegetation
Atlas of China. Then the land cover map was generated using MLC method. After correction by buffering analysis, we
got the final land cover classification of the Yangtze River Delta. The classification accuracy was assessed using fineresolution
Landsat images, with an overall accuracy of 95.98%. In addition, our classification result was compared with
the MODIS-IGBP land cover production and showed better accuracy. The good result indicated the good behavior of the
synthetic classification features and technical processing used in our research, and also suggested the advantage of 250m
MODIS data in regional land cover mapping.
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