植被指数主要反映植被在可见光、近红外波段反射与土壤背景之间差异的指标，各个植被指数在一定条件下能用来定量说明植被的生长状况。目前，归一化植被指数(NDVI)是检测植被生长状态、植被覆盖度和消除部分辐射误差相关方面的重要数据源。\n
\nNDVI能反映出植物冠层的背景影响，如土壤、潮湿地面、雪、枯叶、粗糙度等，且与植被覆盖有关。是反映农作物长势和营养信息的重要参数之一。根据该参数,可以知道不同季节的农作物对氮的需求量, 也对合理施用氮肥具有重要的指导作用。\n
\n基于MODIS MOD13A2数据集，将覆盖泾河流域的分幅影像，利用MRT工具及Python语言代码，进行投影转换、裁剪处理，生成2000-2021年泾河流域MODIS MOD13A2 NDVI数据。本数据集空间分辨率为1km，时间分辨率为16天。
", "ds_source": "本数据集的数据源为MOD13A2.v006版本数据，数据来源于NASA官网(https://ladsweb.modaps.eosdis.nasa.gov)。", "ds_process_way": "
（1）试运行MRT工具，生成MOD13A2 NDVI投影转换的prm文件，空间分辨率为1000 m；\n
\n（2）利用MATLAB 语言程序生成调用MRT工具的批处理文件，并运行；\n
\n（3）利用泾河流域矢量边界，采用Python批量裁剪等步骤，最后采用GeoTIFF格式输出保存。", "ds_quality": "
本数据集与源数据集MODIS MOD13A2.v006质量一致。数据有效范围-2000 至 10000，缩放因子为0.0001，填充值为-3000。", "ds_acq_start_time": "2000-01-01 00:00:00", "ds_acq_end_time": "2021-12-31 00:00:00", "ds_acq_place": "泾河流域", "ds_acq_lon_east": 108.86583333333333, "ds_acq_lat_south": 34.65, "ds_acq_lon_west": 106.19166666666668, "ds_acq_lat_north": 37.39888888888889, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 296050337, "ds_files_count": 1510, "ds_format": "tif", "ds_space_res": "1000m", "ds_time_res": "16天", "ds_coordinate": "WGS84", "ds_projection": "", "ds_thumbnail": "55e2d873-80d8-4589-bd8b-699c285908f7.jpg", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "用户在使用数据时请在正文中明确声明数据的来源，并在参考文献部分引用本元数据提供的引用方式。", "ds_from_station": null, "organization_id": "bf1ae243-c102-4b2d-a2f6-b698468f4401", "ds_serv_man": "敏玉芳", "ds_serv_phone": " 0931-4967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "10.12072/ncdc.JRiver.db1971.2022", "subject_codes": [ "170.4510" ], "quality_level": 3, "publish_time": "2022-04-08 08:41:00", "last_updated": "2025-06-30 15:52:30", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.ncdc.JRiver.db1971.2022", "i18n": { "en": { "title": "MODIS mod13a2 normalized vegetation index (NDVI) data set of Jinghe River Basin (2000-2021)", "ds_format": "tif", "ds_source": "
The data source of this data set is mod13a2 V006 version data from NASA official website（ https://ladsweb.modaps.eosdis.nasa.gov )。", "ds_quality": "
This data set is the same as the source data set MODIS mod13a2 V006 consistent quality. The valid range of data is - 2000 to 10000, the scaling factor is 0.0001, and the filling value is - 3000.", "ds_ref_way": "", "ds_abstract": "
Vegetation index is an index that mainly reflects the difference between the reflection of vegetation in visible light and near-infrared band and soil background. Each vegetation index can be used to quantitatively describe the growth status of vegetation under certain conditions. At present, normalized vegetation index (NDVI) is an important data source for detecting vegetation growth status, vegetation coverage and eliminating some radiation errors.\n
\nNDVI can reflect the background influence of plant canopy, such as soil, wet ground, snow, dead leaves, roughness, etc., and is related to vegetation cover. It is one of the important parameters reflecting crop growth and nutritional information. According to this parameter, we can know the nitrogen demand of crops in different seasons, and it also plays an important guiding role in the rational application of nitrogen fertilizer.\n
\nBased on modis-2000, modis-2000 and modis-2000 are used to convert the watershed data set, and the watershed data of Jinghe River is generated based on modis-2000. The spatial resolution of this data set is 1km and the temporal resolution is 16 days
", "ds_time_res": "16天", "ds_acq_place": "Jinghe River Basin", "ds_space_res": "1000m", "ds_projection": "", "ds_process_way": "(1) Test run the MRT tool and generate the PRM file converted by mod13a2 NDVI projection, with a spatial resolution of 1000 m;\n
\n(2) Using MATLAB language program to generate batch file calling MRT tool and run it;\n
\n(3) Using the vector boundary of Jinghe River Basin, using Python batch cutting and other steps, and finally using GeoTIFF format to output and save.", "ds_ref_instruction": "When using the data, users should clearly state the source of the data in the text and quote the reference method provided by this metadata in the References section." } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "https://creativecommons.org/licenses/by/4.0/", "doi_reg_from": "reg_local", "cstr_reg_from": "reg_local", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "ds_topic_tags": [ "植被指数", "农作物长势", "泾河流域" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "泾河流域" ], "ds_time_tags": [ 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021 ], "ds_contributors": [ { "true_name": "张耀南", "email": "yaonan@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "李红星", "email": "lihongxing@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" } ], "ds_managers": [ { "true_name": "敏玉芳", "email": "myf@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" } ], "category": "遥感及产品" }