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Satellite remote sensing of inland water body requires a high spatial resolution and a multiband narrow spectral resolution, which makes the fusion between panchromatic (PAN) and multi-spectral (MS) images particularly important. Taking the Daquekou section of the Qiantang River as an observation target, four conventional fusion methods widely accepted in satellite image processing, including pan sharpening (PS), principal component analysis (PCA), Gram-Schmidt (GS), and wavelet fusion (WF), are utilized to fuse MS and PAN images of GF-1. The results of subjective and objective evaluation methods application indicate that GS performs the best, followed by the PCA, the WF and the PS in the order of descending. The existence of a large area of the water body is a dominant factor impacting the fusion performance. Meanwhile, the ability of retaining spatial and spectral informations is an important factor affecting the fusion performance of different fusion methods. The fundamental difference of reflectivity information acquisition between water and land is the reason for the failure of conventional fusion methods for land observation such as the PS to be used in the presence of the large water body. It is suggested that the adoption of the conventional fusion methods in the observing water body as the main target should be taken with caution. The performances of the fusion methods need re-assessment when the large-scale water body is present in the remote sensing image or when the research aims for the water body observation.

Combined studies of latitudinal and interannual variations of annual phytoplankton bloom peak in East Asian marginal seas (17°–58°N, including the northern South China Sea (SCS), Kuroshio waters, the Sea of Japan and the Okhotsk Sea) are rarely. Based on satellite-retrieved ten-year (2003–2012) median timing of the annual Chlorophyll a concentration (Chl a) climax, here we report that this annual spring bloom peak generally delays from the SCS in January to the Okhotsk Sea in June at a rate of (21.20±2.86) km/d (decadal median±SD). Spring bloom is dominant feature of the phytoplankton annual cycle over these regions, except for the SCS which features winter bloom. The fluctuation of the annual peak timing is mainly within ±48 d departured from the decadal median peak date, therefore this period (the decadal median peak date ±48 d) is defined as annual spring bloom period. As sea surface temperature rises, earlier spring bloom peak timing but decreasing averaged Chl a biomass in the spring bloom period due to insufficient light is evident in the Okhotsk Sea from 2003 to 2012. For the rest of three study domains, there are no significant interannual variance trend of the peak timing and the averaged Chl a biomass. Furthermore this change of spring phytoplankton bloom timing and magnitude in the Okhotsk Sea challenges previous prediction that ocean warming would enhance algal productivity at high latitudes.

A soil circulation occurs in the south of Cheju Island in the spring. Nutrients and its influence on chlorophyll a (Chl a) around the circulations were studied from April 9 to May 6, 2007. Spring bloom with elevated concentrations of Chl a was observed during the investigation. High concentrations of phosphate, nitrate and silicate at 0.6, 12, and 8 mmol/m3, respectively, were detected. A low water temperature prevented the growth of phytoplankton. Chl a concentrations in the study area might be strongly associated with the high silicate concentration.

Prorocentrum donghaiense is one of the most common red tide causative dinoflagellates in the Changjiang (Yangtze) River Estuary and the adjacent area of the East China Sea. It causes large-scale blooms in late spring and early summer that lead to widespread ecologic and economic damage. A means for distinguishing dinoflagellate blooms from diatom(Skeletonema costatum) blooms is desired. On the basis of measurements of remote sensing reflectance [R rs(λ)] and inherent optical parameters, the potential of using a multispectral approach is assessed for discriminating the algal blooms due to P. donghaiense from those due to S. costatum. The behavior of two reflectance ratios [R 1 = R rs(560)/R rs (532) and R 2 = R rs(708)/R rs(665)], suggests that differentiation of P. donghaiense blooms from diatom bloom types is possible from the current band setup of ocean color sensors. It is found that there are two reflectance ratio regimes that indicate a bloom is dominated by P. donghaiense: (1) R 1 > 1.55 and R 2 < 1.0 or (2) R 1 > 1.75 and R 2 ⩾ 1.0. Various sensitivity analyses are conducted to investigate the effects of the variation in varying levels of chlorophyll concentration and colored dissolved organicmatter (CDOM) as well as changes in the backscattering ratio (b bp/b p) on the efficacy of this multispectral approach. Results indicate that the intensity and inherent optical properties of the algal species explain much of the behavior of the two ratios. Although backscattering influences the amplitude of R rs(λ), especially in the 530 and 560 nm bands, the discrimination between P. donghaiense and diatoms is not significantly affected by the variation of b bp/b p. Since a CDOM(440) in coastal areas of the ECS is typically lower than 1.0 m−1 in most situations, the presence of CDOM does not interfere with this discrimination, even as S CDOM varies from 0.01 to 0.026 nm−1. Despite all of these effects, the discrimination of P. donghaiense blooms from diatom blooms based on multispectral measurements of R rs(λ) is feasible.

This paper proposes the retrieval method of ocean wave spectrum for airborne radar observations at small incidence angles, which is slightly modified from the method developed by Hauser. Firstly, it makes use of integration method to estimate total mean square slope instead of fitting method, which aims to reduce the affects of fluctuations superposed on normalized radar cross-section by integration. Secondly, for eliminating the noise spectrum contained in signal spectrum, the method considers the signal spectrum in certain look direction without any long wave components as the assumed noise spectrum, which would be subtracted from signal spectrum in any look direction for linear wave spectrum retrieval. Estimated ν from the integration method are lower than the one from fitting method and have a standard deviation of 0.004 between them approximately. The assumed noise spectrum energy almost has no big variations along with the wave number and is slightly lower to the high wave number part of signal spectrum in any look direction, which follows that the assumption makes sense. The retrieved directional spectra are compared with the buoy records in terms of peak wavelength, peak direction and the significant wave height. Comparisons show that the retrieved peak wavelength and significant wave height are slightly higher than the buoy records but don’t differs significantly (error less than 10%). For peak direction, the swell waves in first case basically propagate in the wind direction 6 hours ago and the wind-generated waves in second case also propagate in the wind direction, but the 180° ambiguity remains. Results show that the modified method can carry out the retrieval of directional wave spectrum.

Some missions have been carried out to measure wave directional spectrum by synthetic aperture radar (SAR) and airborne real aperture radar (RAR) at a low incidence. Both them have their own advantages and limitations. Scientists hope that SAR and satellite-based RAR can complement each other for the research on wave properties in the future. For this study, the authors aim to simulate the satellite-based RAR system to validate performance for measuring the directional wave spectrum. The principal measurements are introduced and the simulation methods based on the one developed by Hauser are adopted and slightly modified. To enhance the authenticity of input spectrum and the wave spectrum measuring consistency for SAR and satellite-based RAR, the wave height spectrum inversed from Envisat ASAR data by cross spectrum technology is used as the input spectrum of the simulation system. In the process of simulation, the sea surface, backscattering signal, modulation spectrum and the estimated wave height spectrum are simulated in each look direction. Directional wave spectrum are measured based on the simulated observations from 0° to 360°. From the estimated wave spectrum, it has an 180° ambiguity like SAR, but it has no special high wave number cut off in all the direction. Finally, the estimated spectrum is compared with the input one in terms of the dominant wave wavelength, direction and SWH and the results are promising. The simulation shows that satellite-based RAR should be capable of measuring the directional wave properties. Moreover, it indicates satellite-based RAR basically can measure waves that SAR can measure.