黄河流域大部分属于干旱、半干旱气候，先天水资源条件不足，是中国各大流域中受干旱影响最为严重的流域。随着全球环境和气候变化，黄河流域的干旱愈加频繁，对黄河流域的干旱监测研究已经成为当下的热点。本数据集基于MODIS植被和地表温度产品，通过对逐年数据进行去云、重构等质量控制，分别生产了2003–2022年黄河流域逐年的温度条件指数（Temperature Condition Index，TCI）、植被状态指数（Vegetation Condition Index，VCI）、植被健康指数（Vegetation Health Index，VHI）、温度植被干旱指数（Temperature Vegetation Dryness Index，TVDI）数据集。本数据集空间范围为32°10′N–41°50′N，95°53′E–119°05′E，数据格式为GeoTiff，空间分辨率为1 km。同其他干旱指数数据集相比。\n
本数据集可以在逐年时间尺度上表现黄河流域的干旱格局，在时间序列上反映黄河流域干旱变化趋势，为黄河流域干旱灾害监测提供基础数据支撑。
", "ds_source": "本干旱指数数据集包含4个压缩文件，分别为TCI_YellowRiverBasin.zip、VCI_YellowRiverBasin.zip、VHI_YellowRiverBasin.zip、TVDI_YellowRiverBasin.zip。压缩文件内包含四种干旱指数2002-2023逐年黄河流域数据，格式为.tif图像文件，tif文件命名格式为：干旱指数.YYYY.1_km_year.tif。
", "ds_process_way": "利用Google Earth Engine（GEE）获取MODIS产品的NVDI和LST数据，按照时间跨度和区域范围进行筛选、裁剪获得2003–2022年间黄河流域数据。使用MODIS产品的质量控制波段（QC bands）对数据进行质量控制，如去云等，获得初步数据。同时，采用最大值合成法对年内的NDVI数据进行最大值合成。
", "ds_quality": "数据产品的质量与NDVI和LST产品的质量有关，本研究中主要通过控制所使用的NVDI与LST数据产品的质量来保证所生产数据产品的质量。
", "ds_acq_start_time": "2003-01-01 00:00:00", "ds_acq_end_time": "2022-12-31 00:00:00", "ds_acq_place": "黄河流域", "ds_acq_lon_east": 119.01666666666667, "ds_acq_lat_south": 32.11666666666667, "ds_acq_lon_west": 95.81666666666666, "ds_acq_lat_north": 41.81666666666667, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "open-access", "ds_total_size": 492581301, "ds_files_count": 5, "ds_format": "tiff", "ds_space_res": "1 km", "ds_time_res": "", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "8b05becd-12cd-47ad-acdf-12ecf7158ba5.png", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "b412fac3-1f10-4eb6-9d10-c51bcea30d0c", "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": "2023-12-28 13:35:35", "last_updated": "2025-05-29 11:38:53", "protected": false, "protected_to": null, "lang": "zh", "cstr": "https://cstr.cn/31253.11.sciencedb.09116", "i18n": { "en": { "title": "Temperature Condition Index (TCI), Vegetation Condition Index (VCI), Vegetation Health Index (VHI), and Temperature Vegetation Dryness Index (TVDI) Annual 1 km Resolution Dataset for the Yellow River Basin (2003-2022)", "ds_format": "tiff", "ds_source": "This drought index dataset consists of four compressed files, namely TCI_YellowRiverBasin.zip, VCI_YellowRiverBasin.zip, VHI_YellowRiverBasin.zip, and TVDI_YellowRiverBasin.zip. The compressed files contain annual data for the Yellow River Basin from 2002 to 2023 for the four drought indices, in the form of .tif image files. The naming format for the tif files is: Drought_Index.YYYY.1_km_year.tif.
", "ds_quality": "The quality of the data product is related to the quality of the NDVI and LST data. In this study, the quality of the produced data product is mainly ensured by controlling the quality of the NVDI and LST data used.
", "ds_ref_way": "", "ds_abstract": "The Yellow River Basin is mostly characterized by arid and semi-arid climates, with inherently inadequate water resources. It is the most severely affected basin by drought among the major river basins in China. With global environmental and climatic changes, droughts in the Yellow River Basin have become more frequent, and research on drought monitoring in this basin has become a hot topic of current interest. This dataset is based on MODIS vegetation and land surface temperature products. Through quality control processes such as cloud removal and reconstruction for annual data, annual datasets of Temperature Condition Index (TCI), Vegetation Condition Index (VCI), Vegetation Health Index (VHI), and Temperature Vegetation Dryness Index (TVDI) for the Yellow River Basin from 2003 to 2022 have been produced. The spatial scope of this dataset is 32°10′N–41°50′N, 95°53′E–119°05′E, with a data format of GeoTiff and a spatial resolution of 1 km. Compared to other drought index datasets.
\nThis dataset can represent the drought patterns in the Yellow River Basin on an annual timescale and reflect the trends of drought changes in the basin over a time series, providing basic data support for drought disaster monitoring in the Yellow River Basin.
", "ds_time_res": "", "ds_acq_place": "Yellow River basin", "ds_space_res": "1 km", "ds_projection": "", "ds_process_way": "Using Google Earth Engine (GEE), NVDI and LST data from MODIS products were obtained, and then filtered and clipped according to the time span and regional scope to acquire data for the Yellow River Basin from 2003 to 2022. Quality control was performed on the data using the quality control bands (QC bands) of the MODIS products, such as cloud removal, to obtain preliminary data. Additionally, the maximum value composite method was adopted to synthesize the maximum NDVI data within the year.
", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC BY 4.0", "doi_reg_from": "reg_outside", "cstr_reg_from": "reg_outside", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "ds_topic_tags": [ "黄河流域", "植被状态指数", "温度条件指数" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "黄河流域" ], "ds_time_tags": [ 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022 ], "ds_contributors": [ { "true_name": "夏浩铭", "email": "xiahm@vip.henu.edu.cn", "work_for": "河南大学地理与环境学院", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "沙寅涛", "email": "sytforreas@henu.edu.cn", "work_for": "河南大学地理与环境学院", "country": "中国" }, { "true_name": "刘戈", "email": "liuge@henu.edu.cn", "work_for": "河南大学", "country": "中国" }, { "true_name": "赵晓阳", "email": "104754200207@henu.edu.cn", "work_for": "河南大学地理与环境学院", "country": "中国" }, { "true_name": "靳宁", "email": "jinn.13b@igsnrr.ac.cn", "work_for": "山西能源学院", "country": "中国" }, { "true_name": "夏浩铭", "email": "xiahm@vip.henu.edu.cn", "work_for": "河南大学地理与环境学院", "country": "中国" }, { "true_name": "董光华", "email": "dgh605@163.com", "work_for": "山西能源学院", "country": "中国" } ], "ds_managers": [ { "true_name": "夏浩铭", "email": "xiahm@vip.henu.edu.cn", "work_for": "河南大学地理与环境学院", "country": "中国" } ], "category": "生态" }