本数据以GIMMS NDVI3g和MOD13Q1 NDVI数据集为基础，利用Python语言调用Arcpy服务合成月度最大值，然后利用python语言的GDAL和sklearn软件包对全年逐月NDVI数据进行萨维茨基-戈莱滤波去噪、回归分析和250米分辨率NDVI数据处理。利用 Savitzky-Golay 滤波器和 sklearn 软件包去除逐月 NDVI 数据中的噪声，对两组数据的重叠年份进行回归，分析数据，并扩展 250 米分辨率的 NDVI 数据集，最终整合出 1981-2020 年青藏高原 250 米分辨率的逐月 NDVI 时间序列数据集。该数据集可反映 1981-2020 年青藏高原 NDVI 的时空变化，可用于提高长时间序列数据的时空分辨率，为青藏高原植被动态和空间格局研究以及生态环境监测提供数据支持。
", "ds_source": "GIMMS NDVI3g和MOD13Q1 NDVI数据集。
", "ds_process_way": "利用Python语言调用Arcpy服务合成月度最大值，然后利用python语言的GDAL和sklearn软件包对全年逐月NDVI数据进行萨维茨基-戈莱滤波去噪、回归分析和250米分辨率NDVI数据处理。利用 Savitzky-Golay 滤波器和 sklearn 软件包去除逐月 NDVI 数据中的噪声，对两组数据的重叠年份进行回归，分析数据，并扩展 250 米分辨率的 NDVI 数据集，最终整合出 1981-2020 年青藏高原 250 米分辨率的逐月 NDVI 时间序列数据集。
", "ds_quality": "首先是对两组数据的重叠年份进行时序分析，20002015年之间具有较好的一致性，因此本文采用重叠年份数据进行相关性分析，并求得逐月相关系数介于0.880.92之间，均通过了0.001的置信度检验，确保了数据的精度和可靠性。为消除年内生长周期的影响，对两组数据的逐月数据进行了SG平滑滤波，将处理后的数据重新进行相关分析，发现相关系数提高0.910.93。
", "ds_acq_start_time": "1981-01-01 00:00:00", "ds_acq_end_time": "2020-12-31 00:00:00", "ds_acq_place": "青藏高原", "ds_acq_lon_east": 73.49888888888889, "ds_acq_lat_south": 39.825833333333335, "ds_acq_lon_west": 104.67222222222223, "ds_acq_lat_north": 25.99361111111111, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 41803223374, "ds_files_count": 2, "ds_format": "tif", "ds_space_res": "250", "ds_time_res": "月", "ds_coordinate": "WGS84", "ds_projection": "", "ds_thumbnail": "1f87b070-e38d-4c86-84f9-20292e8498d7.png", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "d2c052ce-d283-4a48-8962-6a3dbcb03b8e", "ds_serv_man": "敏玉芳", "ds_serv_phone": "09314967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.45" ], "quality_level": 3, "publish_time": "2025-05-29 16:34:32", "last_updated": "2026-01-12 17:38:05", "protected": false, "protected_to": null, "lang": "zh", "cstr": "https://cstr.cn/31253.11.sciencedb.j00001.00856", "i18n": { "en": { "title": "A long time-series 250m resolution monthly NDVI dataset for the Tibetan Plateau based on temporal and spatial data fusion (1981-2020)", "ds_format": "tif", "ds_source": "GIMMS NDVI3g and MOD13Q1 NDVI datasets.
", "ds_quality": "Firstly, a time series analysis was conducted on the overlapping years of the two sets of data. There was good consistency between 2000 and 2015. Therefore, this article used overlapping year data for correlation analysis and obtained monthly correlation coefficients between 0.880.92, all of which passed the 0.001 confidence test, ensuring the accuracy and reliability of the data. To eliminate the influence of the annual growth cycle, SG smoothing filtering was applied to the monthly data of the two groups, and the processed data was re analyzed for correlation. It was found that the correlation coefficient increased by 0.910.93.
", "ds_ref_way": "", "ds_abstract": "This data is based on the GIMMS NDVI3g and MOD13Q1 NDVI datasets. The monthly maximum value is synthesized by calling the Arcpy service in Python language. Then, the Savitsky Golay filter denoising, regression analysis, and 250 meter resolution NDVI data processing are performed on the annual monthly NDVI data using the GDAL and sklearn software packages in Python language. Using Savitzky Golay filter and sklearn software package to remove noise from monthly NDVI data, regressing the overlapping years of the two sets of data, analyzing the data, and expanding the NDVI dataset with a resolution of 250 meters, finally integrating the monthly NDVI time series dataset with a resolution of 250 meters on the Qinghai Tibet Plateau from 1981 to 2020. This dataset can reflect the spatiotemporal changes of NDVI on the Qinghai Tibet Plateau from 1981 to 2020, and can be used to improve the spatiotemporal resolution of long-term series data, providing data support for the study of vegetation dynamics and spatial patterns, as well as ecological environment monitoring on the Qinghai Tibet Plateau.
", "ds_time_res": "月", "ds_acq_place": "Qinghai-Tibet Plateau", "ds_space_res": "250", "ds_projection": "", "ds_process_way": "Using Python language to call Arcpy service to synthesize monthly maximum values, and then using Python language's GDAL and sklearn software packages to perform Savitsky Golay filtering denoising, regression analysis, and 250 meter resolution NDVI data processing on the annual monthly NDVI data. Using Savitzky Golay filter and sklearn software package to remove noise from monthly NDVI data, regressing the overlapping years of the two sets of data, analyzing the data, and expanding the NDVI dataset with a resolution of 250 meters, finally integrating the monthly NDVI time series dataset with a resolution of 250 meters on the Qinghai Tibet Plateau from 1981 to 2020.
", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC BY 4.0", "ds_topic_tags": [ "NDVI", "青藏高原", "高时间和空间分辨率" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "青藏高原" ], "ds_time_tags": [ 1981, 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": "刘峰贵", "email": "lfg_918@163.com", "work_for": "青海师范大学", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "刘峰贵", "email": "lfg_918@163.com", "work_for": "青海师范大学", "country": "中国" } ], "ds_managers": [ { "true_name": "刘峰贵", "email": "lfg_918@163.com", "work_for": "青海师范大学", "country": "中国" } ], "category": "遥感及产品" }