“许穆夫人归卫”辨

在诗意的研究中,《载驰》诗意为何?许穆夫人是否归卫?一直以来是学界争论的焦点。在对《载驰》进行章句分析的基础上,我们从人称的转换、时间地点的暗示等方面可以证明许穆夫人成功归卫的可能性是很大的。

Combined Method of Datum Transformation Between Different Coordinate Systems

The similarity transformation model between different coordinate systems is not accurate enough to describe the discrepancy of them.Therefore,the coordinate transformation from the coordinate frame with poor accuracy to that with high accuracy cannot guarantee a high precision of transformation.In this paper,a combined method of similarity transformation and regressive approximating is presented.The local error accumulation and distortion are taken into consideration and the precision of coordinate system is improved by using the recommended method

Retrieve Sea Surface Salinity Using Principal Component Regression Model Based on SMOS Satellite Data

The sea surface salinity(SSS) is a key parameter in monitoring ocean states. Observing SSS can promote the understanding of global water cycle. This paper provides a new approach for retrieving sea surface salinity from Soil Moisture and Ocean Salinity(SMOS) satellite data. Based on the principal component regression(PCR) model, SSS can also be retrieved from the brightness temperature data of SMOS L2 measurements and Auxiliary data. 26 pair matchup data is used in model validation for the South China Sea(in the area of 4?–25?N, 105?–125?E). The RMSE value of PCR model retrieved SSS reaches 0.37 psu(practical salinity units) and the RMSE of SMOS SSS1 is 1.65 psu when compared with in-situ SSS. The corresponding Argo daily salinity data during April to June 2013 is also used in our validation with RMSE value 0.46 psu compared to 1.82 psu for daily averaged SMOS L2 products. This indicates that the PCR model is valid and may provide us with a good approach for retrieving SSS from SMOS satellite data.

STUDY ON THE INTERACTION BETWEEN NDVI PROFILE AND THE GROWING STATUS OF CROPS

Daily and ten-day Normalized Difference Vegetation Index( NDVI) of crops were retrieved from meteorological satellite NOAA AVHRR images. The temporal variations of the NDVI were analyzed during the whole growing season, and thus the principle of the interaction between NDVI profile and the growing status of crops was discussed. As a case in point, the relationship between integral NDVI and winter wheat yield of Henan Province in 1999 had been analyzed. By putting integral NDVI values of 60 sample counties into the winter wheat yield-integral NDVI coordination, scattering map was plotted. It demonstrated that integral NDVI had a close relation with winter wheat yield. These relation could be described with linear, cubic polynomial, and exponential regression, and the cubic polynomial regression was the best way. In general, NDVI reflects growing status of green vegetation, so crop monitoring and crop yield estimation could be realized by using remote sensing technique on the basis of time serial NDVI data together with agriculture calendars.

Application of Remote Sensing for Mangrove Mapping： A Case Study of AI-Dhakira, the State of Qatar

In recent years, the pressure of increasing coastal industries and tourism activities has, in some areas, led to the clearing of many coastal habitats along the Qatar＇s shorelines for the construction of tourist resorts, tourism-related development and industrial facilities. Such threats are leading to the increasing demand for detailed mangrove maps for the purpose of measuring the extent of decline in mangrove ecosystems. Detailed mangrove maps at the community or species level are, however, not easy to produce, mainly because mangrove forests are very difficult to access. Without doubt, remote sensing is a serious alternative to traditional field-based methods for mangrove mapping, as it allows information to be gathered from the forbidding environment of mangrove forests, which otherwise, logistically and practically speaking, would be extremely difficult to survey. Remote sensing applications for mangrove mapping at the fundamental level are already well established but, surprisingly, a number of advanced remote sensing applications have remained unexplored for the purpose of mangrove mapping at a finer level. Consequently, the aim of this paper is to unveil the potential of some of the unexplored remote sensing techniques for mangrove studies. Temporal Landsat TM image of 1986, Landsat ETM image of 2000 and Resourcesat-1 LISS 3 image of 2008 are used to calculate percentage change in mangrove cover at AI Dhakira site using geometrically registered and radiometrically corrected historical Landsat and Resourcesat-1 images. Region masks are employed to isolate the unwanted area from the images. NDVI （normalized difference vegetation index） is used to detect mangroves using near-infrared and red bands which are computed from the satellite images. The ground-truthing visit to AI Dhakira site is conducted to confirm the results of the analysis. Change detection is applied and mangrove in the study area is found to have decreased by about 8.79% from 2000 to 2008.

Response of the South China Sea summer monsoon onset to air-sea heat fluxes over the Indian Ocean

We objectively define the onset date of the South China Sea (SCS) summer monsoon, after having evaluated previous studies and considered various factors. Then, interannual and interdecadal characteristics of the SCS summer monsoon onset are analyzed. In addition, we calculate air-sea heat fluxes over the Indian Ocean using the advanced method of CORARE3.0, based on satellite remote sensing data. The onset variation cycle has remarkable interdecadal variability with cycles of 16 a and 28 a. Correlation analysis between air-sea heat fluxes in the Indian Ocean and the SCS summer monsoon indicates that there is a remarkable lag correlation between them. This result has important implications for prediction of the SCS summer monsoon, and provides a scientific basis for further study of the onset process of this monsoon and its prediction. Based on these results, a linear regression equation is obtained to predict the onset date of the monsoon in 2011 and 2012. The forecast is that the onset date of 2011 will be normal or 1 pentad earlier than the normal year, while the onset date in 2012 will be 1-2 pentads later.