This study selects the typical middle and lower reaches of Han River as the study area and focuses on water quality
evaluation methods and water quality evaluation of the surface water of the river basin. On the basis of the field survey,
the author conducted a water quality sampling survey in the study area in spring and summer in 2012. The main
excessive factors in the study area are determined as TN and TP. Using HJ1A/1B CCD multi-spectral data, the multiple
linear regression inversion model and neural network inversion model are established for content of TN and TP. In
accordance with these inversion results, the single factor water quality identification indexes in the study area are
obtained. The results show that, BP neural network model boasts the highest inversion accuracy and that the single factor
water quality identification indexes resulting from its inversion results are highly accurate, reliable and applicable, which
can really reflect the changes in water quality and better realize the evaluation of water quality in the study area. Water
quality evaluation results show that the water pollution in the study area is organic pollution; the water quality of Han
River experiences large differences in different regions and seasons; downstream indexes are superior to upstream
indexes, and the indexes in summer are superior to those in spring; the TN index seriously exceeds the standard in spring
and the TP index seriously exceeds the standard in some regions.
A new algorithm is presented for land fog detection from daytime image of Earth Observation System
Moderate Resolution Imaging Spectroradiometer (EOS/MODIS) data. Due to its outstanding spatial
and spectral resolutions, this image is an ideal data source for fog detection. The algorithm utilizes an
object-oriented technique to separate fog from other cloud types. In this paper, MOD35 product is first
introduced to exclude cloud-free areas, and high clouds are removed with MODIS 26 band, and then a
parameter named Normalized Difference Fog Index (NDFI) is proposed based on Streamer radiative
model and MODIS data for fog detection. Through segmenting NDFI image into regions of pixels, and
computing attributes (e.g. mean value of brightness temperature) for each region to create objects, each
object could be identified based on the attributes selected to determine whether belongs to fog or cloud.
Algorithm's performance is evaluated against ground-based measurements over China in winter. The
algorithm is proved to be effective in detecting fog accurately based on two different test cases.
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