机构

• 地理科学与资源研究... [11]
• 成都山地灾害与环境研... [1]
• 遥感与数字地球研究所 [1]

采集方式

• OAI收割 [13]

内容类型

• 期刊论文 [12]
• 会议论文 [1]

发表日期

• 2025 [1]
• 2022 [2]
• 2021 [1]
• 2020 [1]
• 2019 [2]
• 2018 [5]
• 更多

学科主题

筛选

浏览/检索结果: 共13条，第1-10条 帮助 条数/页： 5101520253035404550556065707580859095100 排序方式： 请选择题名升序题名降序提交时间升序提交时间降序作者升序作者降序发表日期升序发表日期降序 An Evaluation of the Capability of Global Meteorological Datasets to Capture Drought Events in Xinjiang 期刊论文  OAI收割 LAND, 2025, 卷号: 14, 期号: 2, 页码: 219 作者:  Xu, Yang;  Yang, Zijiang();  Zhang, Liang;  Zhang, Juncheng   |  收藏  |  浏览/下载：12/0  |  提交时间：2025/04/21 global meteorological datasets  performance evaluation  Xinjiang  meteorological drought  SPEI   Evaluation of Global Historical Cropland Datasets with Regional Historical Evidence and Remotely Sensed Satellite Data from the Xinjiang Area of China 期刊论文  OAI收割 REMOTE SENSING, 2022, 卷号: 14, 期号: 17, 页码: 18 作者:  Li, Meijiao;  He, Fanneng;  Zhao, Caishan;  Yang, Fan   |  收藏  |  浏览/下载：29/0  |  提交时间：2022/10/08 LUCC  historical cropland  global datasets  reliability evaluation  Xinjiang area   Evaluation of Global Historical Cropland Datasets with Regional Historical Evidence and Remotely Sensed Satellite Data from the Xinjiang Area of China 期刊论文  OAI收割 REMOTE SENSING, 2022, 卷号: 14, 期号: 17, 页码: 18 作者:  Li, Meijiao;  He, Fanneng;  Zhao, Caishan;  Yang, Fan   |  收藏  |  浏览/下载：39/0  |  提交时间：2022/10/08 LUCC  historical cropland  global datasets  reliability evaluation  Xinjiang area   Performance comparison among typical open global DEM datasets in the Fenhe River Basin of China 期刊论文  OAI收割 EUROPEAN JOURNAL OF REMOTE SENSING, 2021, 卷号: 54, 期号: 1, 页码: 145-157 作者:  Zhao, Shangmin;  Qi, Danning;  Li, Rongping;  Cheng, Weiming;  Zhou, Chenghu   |  收藏  |  浏览/下载：21/0  |  提交时间：2021/06/10 Performance comparison  typical open global DEM datasets  elevation error  relative error  hydrologic network  Icesat  GLA14   Uncertainty analysis of multiple global GPP datasets in characterizing the lagged effect of drought on photosynthesis 期刊论文  OAI收割 ECOLOGICAL INDICATORS, 2020, 卷号: 113, 页码: 106224 作者:  Xie Xinyao;  Li Ainong;  Tan Jianbo;  Lei Guangbin;  Jin Huaan   |  收藏  |  浏览/下载：154/0  |  提交时间：2020/04/29 Global GPP datasets  Lagged effect of drought  Photosynthesis  LUE-based models  ML-based models  Process-based models   Evaluation of global historical land use scenarios based on regional datasets on the Qinghai-Tibet Area 期刊论文  OAI收割 SCIENCE OF THE TOTAL ENVIRONMENT, 2019, 卷号: 657, 页码: 1615-1628 作者:  Li, Shicheng;  He, Fanneng;  Zhang, Xuezhen;  Zhou, Tianyu   |  收藏  |  浏览/下载：15/0  |  提交时间：2019/05/23 Historical land use  Climate change  Comparisons  Global datasets  Tibetan Plateau  Grazing intensity   Evaluation of global historical land use scenarios based on regional datasets on the Qinghai-Tibet Area 期刊论文  OAI收割 SCIENCE OF THE TOTAL ENVIRONMENT, 2019, 卷号: 657, 页码: 1615-1628 作者:  Li, Shicheng;  He, Fanneng;  Zhang, Xuezhen;  Zhou, Tianyu   |  收藏  |  浏览/下载：16/0  |  提交时间：2019/05/23 Historical land use  Climate change  Comparisons  Global datasets  Tibetan Plateau  Grazing intensity   Evaluating the accuracy of Chinese pasture data in global historical land use datasets 期刊论文  OAI收割 SCIENCE CHINA-EARTH SCIENCES, 2018, 卷号: 61, 期号: 11, 页码: 1685-1696 作者:  He, Fanneng;  Li, Shicheng;  Yang, Fan;  Li, Meijiao   |  收藏  |  浏览/下载：26/0  |  提交时间：2019/05/23 Historical pasture  grassland  Global datasets  Accuracy evaluation  China   Evaluating the accuracy of Chinese pasture data in global historical land use datasets 期刊论文  OAI收割 SCIENCE CHINA-EARTH SCIENCES, 2018, 卷号: 61, 期号: 11, 页码: 1685-1696 作者:  He, Fanneng;  Li, Shicheng;  Yang, Fan;  Li, Meijiao   |  收藏  |  浏览/下载：20/0  |  提交时间：2019/05/23 Historical pasture  grassland  Global datasets  Accuracy evaluation  China   Mapping the spatial distribution of Aedes aegypti and Aedes albopictus 期刊论文  OAI收割 ACTA TROPICA, 2018, 卷号: 178, 页码: 155-162 作者:  Ding, Fangyu;  Fu, Jingying;  Jiang, Dong;  Hao, Mengmeng;  Lin, Gang   |  收藏  |  浏览/下载：37/0  |  提交时间：2019/05/30 Global distribution  Aedes aegypti  Aedes albopictus  Multidisciplinary datasets  Machine learning models