利用MOD13Q1、GIMMS NDVI 3g植被指数数据集，结合数字高程模型等数据源，基于随机森林降尺度模型生成青藏高原1982-2020年250 m空间逐月NDVI数据合成产品，并通过验证评价。该数据可为青藏高原草地生态系统研究提供基础数据支持。
", "ds_source": "GIMMS NDVI 3g产品由美国国家和大气管理局（NOAA）极地轨道气象卫星上的高级甚高分辨率辐射计（AVHRR）传感器提供（https://ecocast.arc.nasa.gov/data/pub/gimms）。该产品在1982-2015年间问世，时间分辨率为15 d，空间分辨率为8 km，消除了火山爆发、太阳高度角和传感器灵敏度随时间变化等的影响，在全球范围得到了广泛应用。MODIS NDVI数据来源于NASA MODIS陆地产品组根据统一算法开发的MODIS植被指数产品，从MODIS Web网站（https://modis.gsfc.nasa.gov/）下载本文所采用的时间分辨率为16 d，空间分辨率为250 m的MOD13Q1 NDVI数据集。该产品时间范围为2000年2月28日至2020年12月31日。DEM数据由国家地理空间情报局（NGA）和国家航空航天局（NASA）运营的航天飞机雷达地形测绘任务（SRTM）提供。采用的DEM数据空间分辨率为90 m，可通过http://srtm.csi.cgiar.org/SELECTION/inputCoord.asp访问获取。
", "ds_process_way": "（1）利用MRT工具，对MODIS 13Q1进行数据拼接、投影转换，投影坐标系为WGS84；（2）利用最大值合成（MVC）方法将MODIS和GIMMS NDVI结果整合为每个像元的月时间序列；（3）采用最邻近方法将GIMMS数据、DEM数据重采样到250 m，使其与MODIS NDVI的分辨率相匹配；（4）选取GIMMS和MODIS两种数据集交叉时段（2001-2015）进行模型构建和评估，其中，以奇数年数据构建逐月降尺度模型，以偶数年数据评估降尺度产品的精度。
", "ds_quality": "质量良好
", "ds_acq_start_time": "1982-01-01 00:00:00", "ds_acq_end_time": "2020-12-31 00:00:00", "ds_acq_place": "青藏高原", "ds_acq_lon_east": 104.0, "ds_acq_lat_south": 25.0, "ds_acq_lon_west": 73.81666666666666, "ds_acq_lat_north": 39.0, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 82054523959, "ds_files_count": 469, "ds_format": "*.tif", "ds_space_res": "250m", "ds_time_res": "月", "ds_coordinate": "WGS84", "ds_projection": "", "ds_thumbnail": "a3d695f3-c268-4e47-8fcb-4054ebb9c5ef.png", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "52b7b79b-860c-49a5-9083-9a70cf8bed5a", "ds_serv_man": "李红星", "ds_serv_phone": "0931-4967592", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.4510" ], "quality_level": 3, "publish_time": "2024-11-22 09:53:40", "last_updated": "2024-11-26 15:49:04", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.LZU.DB6634.2024", "license": null, "i18n": { "en": { "title": "Monthly synthetic vegetation index data set for the Tibetan Plateau (1982-2020)", "ds_format": "", "ds_source": "The GIMMS NDVI 3g product is provided by the Advanced Very High Resolution Radiometer (AVHRR) sensor on board the United States National and Atmospheric Administration (NOAA) polar orbiting meteorological satellite (https://ecocast.arc.nasa.gov/data/pub/gimms). Introduced between 1982-2015, this product has a temporal resolution of 15 d and a spatial resolution of 8 km, eliminating the effects of volcanic eruptions, solar altitude angle, and sensor sensitivity variations over time, and is widely used globally.The MODIS NDVI data are derived from the MODIS Vegetation Index (MVI) product, which has been developed by the MODIS Terrestrial Products Group of NASA based on the Harmonised Algorithm (HAP). Download the MOD13Q1 NDVI dataset with 16 d temporal resolution and 250 m spatial resolution used in this paper from the MODIS Web site (https://modis.gsfc.nasa.gov/). The product has a time frame of 28 February 2000 to 31 December 2020.The DEM data were provided by the Shuttle Radar Topography Mapping Mission (SRTM) operated by the National Geospatial-Intelligence Agency (NGA) and the National Aeronautics and Space Administration (NASA). The DEM data used have a spatial resolution of 90 m and are available at http://srtm.csi.cgiar.org/SELECTION/inputCoord.asp访问获取.
", "ds_quality": "good quality
", "ds_ref_way": "", "ds_abstract": "Using MOD13Q1, GIMMS NDVI 3g vegetation index dataset, combined with digital elevation model and other data sources, a 250 m spatial resolution NDVI data product was generated based on the stochastic forest downscaling model for the Tibetan Plateau from 1982 to 2020, and passed the validation evaluation. The data can provide basic data support for grassland ecosystem research on the Tibetan Plateau.
", "ds_time_res": "月", "ds_acq_place": "Qinghai-Tibetan plateau", "ds_space_res": "250m", "ds_projection": "", "ds_process_way": "(1) Using the MRT tool, data splicing and projection transformation of MODIS 13Q1, with the projection coordinate system of WGS84; (2) using the maximum value compositing (MVC) method to integrate the MODIS and GIMMS NDVI results into a monthly time series of each pixel; (3) using the nearest neighbour method to resample the GIMMS data and the DEM data up to 250 m, so as to match the MODIS NDVI resolution; (4) selecting the intersection time period (2001-2015) of the two datasets, GIMMS and MODIS, for model construction and evaluation, in which the month-by-month downscaling model is constructed with data from odd-numbered years, and the accuracy of the downscaling products is evaluated with data from even-numbered years.
", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC BY 4.0", "ds_topic_tags": [ "植被指数", "长时序", "机器学习", "MODIS", "GIMMS" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "中国，青藏高原" ], "ds_time_tags": [ 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020 ], "ds_contributors": [ { "true_name": "黄晓东", "email": "huangxd@lzu.edu.cn", "work_for": "兰州大学草地农业科技学院", "country": "中国" }, { "true_name": "杨霞礼", "email": "220220901510@lzu.edu.cn", "work_for": "兰州大学", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "杨霞礼", "email": "220220901510@lzu.edu.cn", "work_for": "兰州大学", "country": "中国" } ], "ds_managers": [ { "true_name": "黄晓东", "email": "huangxd@lzu.edu.cn", "work_for": "兰州大学草地农业科技学院", "country": "中国" } ], "category": "生态" }