{"id":"b117d43c-b826-4efc-a639-62ea334924df","title":"祁连山地区基于MODIS的逐日地表蒸散发数据（2018)（ETHi-merge V1）","title_en":"Daily MODIS-based land surface evapotranspiration dataset in Qilian Mountain Area (ETHi-merge V1) (2018)","serial":"","user_limit":"为尊重知识产权、保障数据作者的权益、扩展数据中心的服务、评估数据的应用潜力，请数据使用者在使用数据所产生的研究成果中（包括公开发表的论文、论著、数据产品和未公开发表的研究报告、数据产品等成果），明确注明数据来源和数据作者。对于转载（二次或多次发布）的数据，作者还须注明原始数据来源。","instructions":"文件命名约定：YYYYMMDD.tiff\t（YYYY：年，MM：月，DD：日）； \n数据版本号：V1.0； \n投影：+proj=longlat +datum=WGS84 +no_defs；\n数据格式：GeoTIFF, 1079行×1791列； \n数据产品单位：W/m2； \n数据产品有效值范围：0~500； \n填充值：-300。","description":"本数据集包括祁连山地区2018年逐日地表蒸散发产品，产品分辨率为0.01°。采用高斯过程回归（Gaussian Process Regression，GPR）算法，实现对RS-PM (Mu et al., 2011)、SW (Shuttleworth and Wallace., 1985)、PT-JPL (Fisher et al., 2008)、MS-PT (Yao et al., 2013)、SEMI-PM (Wang et al., 2010a)、SIM (Wang et al.2008) 等6种蒸散发产品的集成。参与蒸散发产品生产的驱动数据包括MODIS（NDVI、Albedo、LAI、PAR），中国区域高时空分辨率地面气象要素驱动数据集（何杰, 阳坤. 中国区域高时空分辨率地面气象要素驱动数据集. 寒区旱区科学数据中心, 2011. doi:10.3972/westdc.002.2014.db）等。","doi":"10.11888/Geogra.tpdc.270192","projection":"WGS84","language":"zh","share_type":"online","east":107.02,"west":89.11,"south":34.2,"north":44.99,"scale":null,"crs":null,"filesize":5300.0,"share_policy":"a","ts_created":"2019-06-02T08:56:00.534721+08:00","doi_type":null,"ts_updated":"2021-04-19T03:49:27.811962+08:00","time_description":"","datas_group":3,"time_description_en":null,"data_format":null,"project":[11],"specials":["4e7b97a6-ee1f-4b51-83e5-04a100993973","836a1603-47d0-494e-88c1-96723fd8e0bc","836a1603-47d0-494e-88c1-96723fd8e0bc","f2a78cde-1c98-4241-94dc-7532eba32b80","f2a78cde-1c98-4241-94dc-7532eba32b80"],"cite":null,"review_status":5,"review_invite":[{"user_name":"马伟强","username":"wqma","email":"wqma@itpcas.ac.cn","is_cancel_expert":false}],"review_editor":{"id":21054,"last_login":"2022-04-10T10:49:12.540341+08:00","username":"tingting","email":"panxd@itpcas.ac.cn","is_active":true,"is_staff":true,"user_name":"潘小多"},"data_sources":"该数据集是利用MODIS（NDVI、Albedo、LAI、PAR）和中国区域高时空分辨率地面气象要素驱动数据集（何杰, 阳坤. 中国区域高时空分辨率地面气象要素驱动数据集. 寒区旱区科学数据中心, 2011. doi:10.3972/westdc.002.2014.db）基于不同算法得出多种蒸散发产品，然后通过高斯过程回归（Gaussian Process Regression，GPR）算法集成获取。\n算法中用到的数据分别来自：\nMODIS数据产品：MODIS全球数据门户网站\n(https://ladsweb.modaps.eosdis.nasa.gov/)\n中国区域高时空分辨率地面气象要素驱动数据集：寒区旱区科学数据中心\n(http://westdc.westgis.ac.cn/data/7a35329c-c53f-4267-aa07-e0037d913a21)","temporal_resolution":"Daily","spatial_resolution":"km","license":"Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)","fund":[6],"subtopics":{"metadata":"b117d43c-b826-4efc-a639-62ea334924df","three_pole":null,"third_pole":"XDA20100101"},"username":"XDA20100101","review_time":"2019-06-06T14:17:34.303165+08:00","protected_duration":null,"protected_describe":null,"is_important":false,"hit_count":2240}