中国科学院机构知识库网格系统: 检索

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	Detection of Periodic Signals With Time-Varying Coefficients From CMONOC Stations in China by Singular Spectrum Analysis 期刊论文 OAI收割 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2023, 卷号: 61, 页码: 5802812 作者: Wu, Shuguang; Li, Zhao; Li, Houpu; Bian, Shaofeng; Ouyang, Hua \| 收藏 \| 浏览/下载：13/0 \| 提交时间：2024/01/04 Crustal Movement Observation Network of China (CMONOC) stations periodic signals with time-varying coefficients (PSTC) root mean square error (RMSE) improve-ment rate singular spectrum analysis (SSA) method
	Trade-off analyses of multiple mountain ecosystem services along elevation, vegetation cover and precipitation gradients: A case study in the Taihang Mountains 期刊论文 iSwitch采集 ECOLOGICAL INDICATORS, 2019, 卷号: 103, 页码: 94-104 作者: Liu, Laibao; Wang, Zheng; Wang, Yang; Zhang, Yatong; Shen, Jiashu 收藏 \| 浏览/下载：113/0 \| 提交时间：2019/10/08 Ecosystem services Trade-off Elevation Precipitation Vegetation cover fraction RMSE Taihang Mountains
	The impact of wave-induced Coriolis-Stokes forcing on satellite-derived ocean surface currents 期刊论文 OAI收割 JOURNAL OF GEOPHYSICAL RESEARCH-OCEANS, 2016, 卷号: 121, 期号: 1, 页码: 410-426 作者: Hui, Zhenli; Xu, Yongsheng 收藏 \| 浏览/下载：32/0 \| 提交时间：2016/08/24 waves surface currents Stokes drift correlation RMSE
	Rectification methods comparison for the ASTER GDEM V2 data using the ICESat/GLA14 data in the Lvliang mountains, China SCI/SSCI论文 OAI收割 2015 作者: Zhao S. M.; Wang, L.; Cheng, W. M.; Liu, H. J.; He, W. C. 收藏 \| 浏览/下载：45/0 \| 提交时间：2015/12/09 ASTER GDEM V2 data ICESat/GLA14 data Rectification method GCP data RMSE Lvliang Mountains spatial interpolation methods digital elevation models high-accuracy srtm dem vertical accuracy level changes tandem-x india performance jharkhand
	The influence of source data density for generating digital elevation models 期刊论文 OAI收割 JOURNAL OF FOOD AGRICULTURE & ENVIRONMENT, 2013, 卷号: 11, 期号: 1, 页码: 709-712 作者: Wang, Lei; Yang, Qinke; Long, Yongqin; Guo, Weiling; Wang, Chunmei 收藏 \| 浏览/下载：19/0 \| 提交时间：2016/01/06 Digital elevation models source data density grid size RMSE of elevation
	Retrieval of snow depth in Northeast China using FY-3B/MWRI passive microwave remote sensing data (EI CONFERENCE) 会议论文 OAI收割 Satellite Data Compression, Communications, and Processing VIII, August 12, 2012 - August 13, 2012, San Diego, CA, United states Ren R.; Gu L.; Chen H.; Cao J. 收藏 \| 浏览/下载：136/0 \| 提交时间：2013/03/25 Comparing with optical remote sensing techniques passive remote sensing data have been proved to be effective for observing snowpack parameters such as snow depth and snow water equivalent which can penetrate snowpack without clouds interferences. The Microwave Radiation Imager (MWRI) loaded on the Chinese FengYun-3B (FY-3B) satellite is gradually used in the global environment research through November 2011. In this paper we proposed a snow depth retrieval algorithm to estimate snow depth in Northeast China using MWRI passive microwave remote sensing data. A decision tree method of snow identification was firstly designed to distinguish different snow cover conditions in order to eliminate other interference signals. After using the proposed decision tree method the processing results were further used to retrieve the snow depth in Northeast China. Finally the practical snow depth data and the MODIS data were collected for the accuracy assessment of the proposed snow depth retrieval method. The experimental results demonstrated that the RMSE of snow depth used the proposed method was approximately 3 cm in Northeast China. 2012 SPIE.
	Validation of AVHRR/MODIS/AMSR-E satellite SST products in the West Pacific 会议论文 OAI收割 Proceedings of the 8th International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences, Vol I: Spatial Uncertainty, Liverpool Guo, Peng; Bo, Yanchen 收藏 \| 浏览/下载：23/0 \| 提交时间：2014/12/07 sea surface temperature validation bias RMSE SEA-SURFACE TEMPERATURE SKIN
	Remote chlorophyll-a retrieval in eutrophic inland waters by concentration classification Taihu Lake case study 会议论文 OAI收割 International Conference on Earth Observation Data Processing and Analysis, ICEODPA,, Wuhan, China, December 28, 2008 - December 30,2008 Du, Cong; Wang, Shixin; Zhou, Yi; Yan, Fuli 收藏 \| 浏览/下载：32/0 \| 提交时间：2014/12/07 In order to improve the precision of phytoplankton chlorophyll-a (chla) concentration retrieval this study classified the data into two groups (the high and the low) by chla concentration with the threshold of 50gA&bullL-1. And then build the statistical models for each group. Particularly a modifying factor OSS/TSS was used to unmixing the spectra in the low model to improve the low relationship between spectral reflectance and chla concentrations. As a result the concentration classification model allowed estimation of chla with a root mean square error (RMSE) of 21.12gA&bullL-1 and the determination coefficient (R2) was 0.92 comparing with RMSE of chla estimation was 35.72gA&bullL-1 and R2=0.72 in the traditional model. It shows that concentration classification is a helpful method for accurate remote chla retrieval in eutrophic inland waters. 2008 SPIE.