To compensate for the limitations of optical remote sensing when restricted by cloud cover, it is worth exploring how to detect cyanobacterial blooms using synthetic aperture radar (SAR), which can penetrate clouds. A satellite–ground synchronous experiment was conducted in Lake Taihu, the third largest freshwater lake in China. A lipopeptide biosurfactant was detected in the algal scum layer, with an average content of 1.8 g/L. The viscosity (1.41 to 332 mPa.s) of the scum was significantly higher than that of scum-free water. The surface tension of the algal scum decreased by 12.5%, and the SAR microwave backscatter was reduced by 7.3 dB. This indicated that the cyanobacterial scum could effectively attenuate capillary waves and appear as dark patches in SAR images. SAR has the potential to be developed as a tool for the remote sensing of algal scum in lake waters.
Colored Dissolved Organic Matter (CDOM, or yellow substance) exists in all natural waters. It can be used as
evaluation indexes for inland water pollution condition. Remote sensing data used for CDOM inversion has its
significant advantages, but the inversion method usually has obvious regional limitations. At present, there is little
CDOM studies have been carried out to the waters in north China. Yuqiao Reservoir, which is in northern Tianjin, was
chosen as the study area, and CDOM was inverted through empirical method for the first time. The data used in this
paper was the spectral reflectance data collected on September 24 and 25, 2013 over the 23 sampling points in Yuqiao
Reservoir and CDOM concentrations (which is represented by the absorption coeffiecnet of CDOM at 440nm,
aCDOM(440)) of each sampling points. Among the 23 sampling points, 16 points were selected randomly as training
samples, and the remaining 7 points were for accuracy test. Four ratios, as Rrs(412)/Rrs(551), Rrs(443)/Rrs(551),
Rrs(490)/Rrs(551) and Rrs(531)/Rrs(551) were used to carry out linear regression with aCDOM(440). At the same time, the
linear regression was also taken between the logs base 10 of the four ratios and log(aCDOM(440)).Then 8 inversion models
were built. The performance of the model based on log(Rrs(490)/Rrs(551)) and log(aCDOM(440)) was the best. The
correlation coefficient R was 0.65. The Root Mean Square Error (RMSE) was 0.088 and the average relative error (σ)
was 11.9%. It showed that the precision of using the ratio of the Remote sensing reflectance of the blue and green band
to build inversion models for Yuqiao Reservoir was good, and the method was worth popularization and utilization.
As the reflectivity is very low in near-infrared spectral band for water, it is easy to extract water bodies in remote
sensing images with the use of NDVI (Normalized Difference Vegetation Index).The problem is that we always have to try
several times to find the appropriate threshold to separate water bodies from land. In this paper, a particular method was
developed to solve this problem by automatically determining the threshold that was used to extract the specific water body.
We select both Chaohu Lake and Taihu Lake as the study regions. First, we generate NDVI image of the study region from
GF-1 data after several pre-processing procedures. Then we resize the NDVI image to make it contain approximately the
same number of water and land pixels. Because the NDVI value is lower for water than that for land, there will appear two
peaks in the histogram which we derived from the resized NDVI image. The threshold locates at the lowest place between
the two peaks can be chosen as the proper threshold used for land/water delineation. With the use of a reasonable threshold
range we can finally get the threshold by calculating the minimum value in it, and extract the water body successfully.
This paper is aiming at the problem that the MODIS surface reflectance product (MOD09) does not offer
an accurate aerosol correction for inland water, for the constraints of MODIS atmospheric correction
algorithm. In-situ data collected in Taihu Lake and Yuqiao Reservoir were used to validate and assess the
quasi-synchronous MOD09 product. The results showed that there is linear relationship on the whole
between MOD09 bands and in-situ data in inland water with acceptable deviation level. The reason for
the deviations is analyzed primarily and a simple correction for MOD09 product in Lake Taihu is
introduced based on bands calculation. The results also illustrated that it is possible to monitor inland
water quality globally with MOD09 product by providing validation evidence in typical inland waters.
And it would be most accurate by using bands ratio algorithm for the water quality retrieval using
MOD09. The validation is also important to improve atmospheric algorithms of MODIS.
Ulva prolifera, a kind of green macroalgae, is nontoxic itself, however, its bloom has bad effects on the marine
environment, coastal scene, water sports and seashore tourism. Monitoring of the Ulva prolifera by remote sensing
technology has the advantages of wide coverage, rapidness, low cost and dynamic monitoring over a long period of time.
The GF-1 satellite was launched in April 2013, which provides a new suitable remote sensing data source for monitoring
the Ulva prolifera. At present, segmenting image with a threshold is the most widely used method in Ulva prolifera
extraction by remote sensing data, because it is simple and easy to operate. However, the threshold value is obtained
through visual analysis or using a fixed statistical value, and could not be got automatically. Facing this problem, we
proposed a new method, which can obtain the segmentation threshold automatically based on the local maximum gradient
value. This method adopted the average NDVI value of local maximum gradient points as the threshold, and could get an
appropriate segmentation threshold automatically for each image. The preliminary results showed that this method works
well in monitoring Ulva prolifera by GF-1 WFV data.
With the deterioration of water pollution, monitoring of water environment is becoming more and more urgent.
However, there is no professional water environmental monitoring system in China. To overcome these problems, we
have developed a Surface water environmental monitoring System (WATERS for short) by VISUAL C++6.0 IDE.
WATERS is designed for the four kinds of remote sensing data of HJ-1 satellites, which are multi-spectral camera, ultraspectral
imager, infrared camera, and SAR. Besides, WATERS can also support other satellite remote sensing data. We
use some simulated HJ-1 satellites remote sensing data, as well as remote sensing data of similar satellite sensors, to test
the operation of WATERS. The operation results by these remote sensing data show that WATERS works well, and both
the efficiency and the precision of water quality monitoring are high.
Aerosol model is a major obstacle for passive remote sensing of aerosol properties. For the continent or urban aerosol
model does not fit the needs for more accurate retrieval, and the user defined aerosol model may be more near to the
realistic condition in Taihu region. A method has been developed for retrieving the aerosol model and optical properties
including polarization, i.e., scattering coefficient, asymmetry factor, single scattering albedo, scattering phase function
and polar phase function. In Lake Taihu, the study area of our two combined remote sensing observations in the winter and summer 2006, we got water surface spectral data of ASD (Analytical Spectral Devices) by the ship, atmospheric data of CE318 sunphotometer on the shore, and the MODIS (Moderate Resolution Imaging Spcectroradiometer) image data on the TERRA Satellite. By using the nearly synchronous measurements of the data from surface spectrum and the sunphotometer with the image, and by use of the radiative transfer model 6S(Second Simulation of a Satellite Signal in the Solar Spectrum), varying the components of the aerosol type, a LUT (look up table) is made for the radiance on the satellite. When the total relative error of the new defined parameter for relative error is getting to the least, the aerosol type will be decided. Then, based on the determination of aerosol model, the atmospheric aerosol properties over Lake Taihu have been computed by using Mie theory and analyzed with the typical continental and urban aerosol models available in 6S. These results show that the user-defined aerosol model is a mix model of continent and urban which corresponds with previous studies. Moreover, they may be useful for resolving the vector RTE (radiative transfer equation). In this paper we tried to provide a method with the combination of remote sensing data to obtain the optical properties of atmospheric aerosol over inland water in different seasons. We respect it will be helpful for accurate atmospheric correction in the future.
Atmospheric correction, which can retrieve water-leaving radiance, is an important preprocess in monitoring water quality from remote sensing data. The atmospheric correction algorithms developed by Gordon (1993, 1994) assume that water-leaving radiance of ocean waters in near infrared is zero. However, such an assumption is not applicable to inland waters, and usually leads to failure in atmospheric correction of remote sensing data of inland waters. Some scientists, based on some other assumptions, have developed some improved atmospheric correction algorithms which can be applied to coastal and inland waters. However, these algorithms can only get good results in specific areas. In order to get good results of atmospheric correction of remote sensing data of inland waters in China, an improved atmospheric correction algorithm is developed in this paper. This improved atmospheric correction algorithm assumes that water-leaving radiance in short-wave infrared is zero, which is based on the analysis of absorption and scattering characteristics of inland waters. This atmospheric correction algorithm is validated to have high applicable potentials by applied to concurrent MODIS data and in-situ measured reflectance spectra in Guanting Reservoir in North China.
To meet the demand of monitoring water pollution in China, Information Center of State Environmental Protection of China (ICSEP) and Institute of Remote Sensing Applications, Chinese Academy of Sciences (IRSA,CAS) have carried out a project to utilize the data extracted from Environment and Hazard Monitoring Constellation. This project is to build the first Remote-sensing and Environmental Monitoring System (REMS) in China. The most important component of REMS is the Hyperspectral-Environmental Database (HED). This paper is to describe the architecture and mechanism of HED. HED consists of five parts: Environmental backgrounds, Spectrums, Hyperspectral images, Basic geographic information and Environmental quality data. The interactions and relationships among the five parts are depicted. The workflow of HED assisting REMS is delineated. A preliminary research in Taihu Lake based on HED is also described in this paper.