海索面（Nemalion halminthoides vermiculte（Sur）Tseng）的采集与标本制作方法

本文介绍了海索面（Ｎｅｍａｌｉｏｎｈａｌｍｉｎｔｈｏｌｄｅｓｖｅｒｍｉｃｕｌａｔｓ（Ｓｕｒ．）Ｔｓｅｎｇ）的采集和对海索面腊叶标本的制作过程及其注意事项。

海索面腊叶标本的最新制做方法

海索面腊叶标本的最新制做方法海索面藻类腊叶标本的最新制做方法，是从１９８６年开始研究的，连续几年的制做试验，取得了可喜的成果。实践证明，这种方法比传统的制做方法省工、省力，并能保证质量，为制做标本的工厂开创出新的途径。又因此方法简单易行，采集人员可自...

Retrieval algorithm of sea surface wind vectors for WindSat based on a simple forward model

WindSat/Coriolis is the first satellite-borne polarimetric microwave radiometer, which aims to improve the potential of polarimetric microwave radiometry for measuring sea surface wind vectors from space. In this paper, a wind vector retrieval algorithm based on a novel and simple forward model was developed for WindSat. The retrieval algorithm of sea surface wind speed was developed using multiple linear regression based on the simulation dataset of the novel forward model. Sea surface wind directions that minimize the difference between simulated and measured values of the third and fourth Stokes parameters were found using maximum likelihood estimation, by which a group of ambiguous wind directions was obtained. A median filter was then used to remove ambiguity of wind direction. Evaluated with sea surface wind speed and direction data from the U.S. National Data Buoy Center （NDBC）, root mean square errors are 1.2 rn/s and 30~ for retrieved wind speed and wind direction, respectively. The evaluation results suggest that the simple forward model and the retrieval algorithm are practicable for near-real time applications, without reducing accuracy.

A Method for Sea Surface Wind Field Retrieval from SAR Image Mode Data

To retrieve wind field from SAR images, the development for surface wind field retrieval from SAR images based on the improvement of new inversion model is present. Geophysical Model Functions （GMFs） have been widely applied for wind field retrieval from SAR images. Among them CMOD4 has a good performance under low and moderate wind conditions. Although CMOD5 is developed recently with a more fundamental basis, it has ambiguity of wind speed and a shape gradient of normalized radar cross section under low wind speed condition. This study proposes a method of wind field retrieval from SAR image by com-bining CMOD5 and CMOD4 Five VV-polarisation RADARSAT2 SAR images are implemented for validation and the retrieval re-suits by a combination method （CMOD5 and CMOD4） together with CMOD4 GMF are compared with QuikSCAT wind data. The root-mean-square error （RMSE） of wind speed is 0.75 m s-1 with correlation coefficient 0.84 using the combination method and the RMSE of wind speed is 1.01 m s-1 with correlation coefficient 0.72 using CMOD4 GMF alone for those cases. The proposed method can be applied to SAR image for avoiding the internal defect in CMOD5 under low wind speed condition.

A new algorithm for sea-surface wind-speed retrieval based on the L-band radiometer onboard Aquarius

Aquarius is the second satellite mission to focus on the remote sensing of sea-surface salinity from space and it has mapped global sea-surface salinity for nearly 3 years since its launch in 2011. However,benefiting from the high atmospheric transparency and moderate sensitivity to wind speed of the L-band brightness temperature(TB),the Aquarius L-band radiometer can actually provide a new technique for the remote sensing of wind speed. In this article,the sea-surface wind speeds derived from TBs measured by Aquarius' L-band radiometer are presented,the algorithm for which is developed and validated using multisource wind speed data,including Wind Sat microwave radiometer and National Data Buoy Center buoy data,and the Hurricane Research Division of the Atlantic Oceanographic and Meteorological Laboratory wind field product. The error analysis indicates that the performance of retrieval algorithm is good. The RMSE of the Aquarius wind-speed algorithm is about 1 and 1.5 m/s for global oceans and areas of tropical hurricanes,respectively. Consequently,the applicability of using the Aquarius L-band radiometer as a near all-weather wind-speed measuring method is verified.

Analysis of Cost Functions for Retrieving Sea Surface Salinity

Two kinds of Bayesian-based cost functions (i.e., the unconstrained cost function and parameter-constrained cost function) are investigated for retrieving the sea surface salinity (SSS). In low SSS regions, we have analyzed the sensitivity of the two cost functions to geophysical parameters. The results show that the unconstrained cost function is valid for retrieving several parameters (including SSS, wind speed and significant wave height), and the constrained cost function, which largely depends on the accuracy of reference values, may lead to large retrieval biases. Furthermore, as a retrieval parameter, the sea surface temperature (SST) can re-sult in the divergence of other geophysical parameters in an unconstrained cost function due to the strong sensitivity of brightness temperature to SST. By using the unconstrained cost function and the simulated brightness temperature TB with white noises, the retrieval biases of SSS are discussed with the following two procedures. Procedure a): the simulated TB values are first averaged, and then SSS is retrieved. Procedure b): the SSS is directly retrieved from the simulated TB , and then the retrieved SSS values are aver-aged. The results indicate that, for low SSS and SST distributions, the SSS retrieval by procedure a) has less biases compared with that by procedure b), while the two procedures give almost the same retrieval results for high SSS and SST sea regions.

Preliminary validation of SMOS sea surface salinity measurements in the South China Sea

The SMOS(soil moisture and ocean salinity) mission undertaken by the European Space Agency(ESA) has provided sea surface salinity(SSS) measurements at global scale since 2009.Validation of SSS values retrieved from SMOS data has been done globally and regionally.However,the accuracy of SSS measurements by SMOS in the China seas has not been examined in detail.In this study,we compared retrieved SSS values from SMOS data with in situ measurements from a South China Sea(SCS) expedition during autumn 2011.The comparison shows that the retrieved SSS values using ascending pass data have much better agreement with in situ measurements than the result derived from descending pass data.Accuracy in terms of bias and root mean square error(RMS) of the SSS retrieved using three different sea surface roughness models is very consistent,regardless of ascending or descending orbits.When ascending and descending measurements are combined for comparison,the retrieved SSS using a semi-empirical model shows the best agreement with in situ measurements,with bias-0.33 practical salinity units and RMS 0.74.We also investigated the impact of environmental conditions of sea surface wind and sea surface temperature on accuracy of the retrieved SSS.The SCS is a semi-closed basin where radio frequencies transmitted from the mainland strongly interfere with SMOS measurements.Therefore,accuracy of retrieved SSS shows a relationship with distance between the validation sites and land.