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991.
992.
黑土区土壤有机质和全氮含量遥感反演研究 总被引:1,自引:0,他引:1
以东北典型黑土区耕地为研究区,以Sentinel-2A(全球环境与安全监测计划的第二颗卫星,于2015年6月23日发射)影像作为数据源,构建光谱指数,分别采用多元逐步线性回归(Multiple Stepwise Linear Regression, MSLR)和随机森林(Random Forest, RF)算法建立土壤有机质(SOM)和土壤全氮(STN)预测模型,并采用十折交叉验证方法评估模型的性能。研究对比分析了不同气候、土壤类型和地形下土壤有机质和全氮的空间分布差异。研究表明:① 海伦示范区的SOM和STN含量最高,其年均温最低,高程最高,年降水量多,SOM含量升高,其年均温最低,年降水量多,STN含量升高;② 与基于多元逐步线性回归算法建立的SOM和STN预测模型相比,随机森林算法建立的SOM和STN预测模型,有着更高的精度和稳定性;③ 运用RF算法建立的SOM反演模型的R2为0.96,均方根误差为5.49 g/kg,STN反演模型的R2为0.95,均方根误差为0.27 g/kg; ④ 不同示范区统一建立SOM和STN预测模型,有助于提高预测精度,实现跨区域建模与制图。 相似文献
993.
为探究不同类型锡矿床所反映出的自然重砂矿物组合特征,统计了云南、福建、江西、浙江、湖南、青海和广西7省共35个典型锡矿床的自然重砂情况,通过计算和分析各自然重砂矿物在所对应类型矿床中的报出频率,得出矽卡岩型、热液脉型和斑岩型3种锡矿所对应的自然重砂矿物组合。所反映出的重砂矿物组合既有相似之处,如均出现锡石+白钨矿(或黑钨矿)的矿物组合,亦反映出不相同的地方,如矽卡岩型锡矿石榴子石报出率高,斑岩型锡矿锆石报出率高等。因此,按照矿床类型建立的自然重砂矿物组合对于建立自然重砂找矿模型具有重要意义。 相似文献
994.
995.
Huaiyong Shao Meng Liu Qiufang Shao Xiaofei Sun Jinhui Wu Zhiying Xiang Wunian Yang 《Environmental Earth Sciences》2014,72(5):1555-1568
The Anning River Basin is located in the transitional zone of the Qinghai-Tibet Plateau, Yunnan-Kweichow Plateau and Sichuan Basin. This transitional zone is an important ecological barrier of the upper reaches of the Yangtze River and plays a significant role in the ecological security and ecological construction of Sichuan Province. However, the innate vulnerability of the eco-environment combined with the unreasonable development and use of minerals, hydropower, agriculture and animal husbandry resources contribute to prominent eco-environmental problems. In support of remote sensing and geographical information system, this study uses the spatial principal component analysis (SPCA) method to build the evaluation model for the vulnerability evaluation and analysis of the eco-environment in the Anning River Basin. The following indicators are selected for the SPCA: elevation, slope, vegetation index, land use, soil type, soil erosion, precipitation, temperature, and population density. Thereafter, the first four principal components are selected and their corresponding weights are determined. The eco-environmental vulnerability comprehensive index of the Anning River Basin is calculated by using these data. According to the calculated results, the eco-environmental vulnerability of the Anning River Basin is divided into five levels, namely, potential vulnerability, slight vulnerability, light vulnerability, moderate vulnerability, and high vulnerability. Eco-environmental changes for the past 20 years (from 1990 to 2010) are discussed and analyzed as well as the driving forces. The analysis shows that the eco-environmental vulnerability of the Anning River Basin is at the moderate level, and exhibits obvious vertical distribution characteristics. The main reasons that cause eco-environmental changes are mainly human factors, socioeconomic factors, and environmental protection policies like “Natural Forests Protection” and “Grain-for-Green”. Based on the vulnerability classification results, the Anning River Basin is divided into three partitions for different degrees of eco-environmental reconstruction and protection, which provides foundation for the local eco-environmental reconstruction so as to reconstruct in order of the importance and urgency. 相似文献
996.
Landslide susceptibility analysis based on ArcGIS and Artificial Neural Network for a large catchment in Three Gorges region,China 总被引:1,自引:0,他引:1
Renneng Bi Markus Schleier Joachim Rohn Dominik Ehret Wei Xiang 《Environmental Earth Sciences》2014,72(6):1925-1938
A landslide susceptibility map is very important and necessary to efficiently prevent and mitigate the losses brought by natural hazard for a large area. For the purpose of landslide susceptibility analysis for the whole Xiangxi catchment (3,209 km2), Artificial Neural Network (ANN) analysis was applied as the main method. The whole catchment was divided into two parts: the training area and the implementation area. The backwater area (559 km2) of Xiangxi catchment was used as the training area for the ANN method. In the training area the correlations between the landslide distribution and its causative factors, which includes lithology, slope angle, slope curvature and river network, have been analyzed based on the geological map and digital elevation model (DEM). The back-propagation training algorithm in ANN was selected to train the sample data from the training area, which were composed of input data (causative factors) and target output data (landslide occurrence), in order to find the correlations between them. Based on these correlations and input data in the implementation area (causative factors), the network output data were obtained for the implementation area. In the end, a map of landslide susceptibility, which was established by network output data, was presented for Xiangxi catchment. ArcGIS was applied to extract and quantify input information from a DEM for susceptibility analysis and also to present the result visually. As a result, a landslide susceptibility map, in which 70 % of all landslides are rightly classified in the training area (backwater area), was created for Xiangxi catchment. 相似文献
997.
原生的石膏脉通常呈层、片状分布,在准噶尔盆地南部四棵树河地区野外地质考察中发现有龟壳状结构的石膏脉晶体。通过对周边地区露头实测、探勘、取样及盆地腹部钻井取心资料分析,结合前人研究成果,对龟壳状结构石膏脉形成的古环境背景、盆内沉积证据、龟裂成因和充填石膏成因进行系统研究。准噶尔盆地在三叠—新近纪为内陆浅水湖盆沉积,南部山前地区新近系沙湾组发育辫状河三角洲相和湖泊相,岩相古地理特征受湖平面升降及构造综合控制。研究区发育大量红层沉积,盆地岩石的"先成岩、后胶结"为龟壳状结构石膏脉形成提供了必要条件。综合认为,龟壳状结构石膏脉的形成可能是经"两期盐湖、四个阶段"的作用而成,主要分为一期盐湖作用的原始状态阶段和龟裂状态阶段、二期盐湖作用的龟裂注溶盐水状态和龟裂充填石膏状态。龟壳状结构石膏脉的发现可推测盆地腹部存在天然储层裂缝改造区,为预测有利区带提供地质依据,并对中国西北地区新近纪干旱沉积环境指示具一定参考意义。 相似文献
998.
Ocean is a highly complex and nonlinear dynamical system. The inevitable errors in both data and numerical models lead to uncertainties in ocean numerical prediction. By understanding features and properties in the ocean on multiple scales, it is important to quantify and estimate the predictability of the ocean, and analyze the reasons and mechanism of error growth. The efforts focus on investigating the method to reduce the uncertainties and errors in forecasting and increase the time limit of ocean predictability. The advances will result in improved marine forecasting models and forecasting skill. Understanding limitations and identifying the research needed to increase accuracy will lead to fundamental progress in ocean forecast, which is of great significance. The present study described and illustrated the mechanics and computations involved in modeling and predicting uncertainties for ocean prediction and its modern applications. Firstly, it discussed the fundamental concept and classification of the ocean predictability. The research status of ocean predictability is introduced including the dynamics methodologies and the ocean ensemble prediction. Three of the dynamical computational methodologies including the singular vector, Lyapunov exponent and bred vector method were introduced. Three ocean ensemble prediction methods including initial condition ensemble, multi-model ensemble and atmospheric forcing ensemble were described and illustrated. Finally, this paper gave a future prospective of ocean predictability and its application. 相似文献
999.
1000.