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
NDVI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, 对合理施用氮肥具有重要的指导作用。\n
基于MODIS MOD13A2数据集，将覆盖渭河流域的分幅影像，利用MRT工具及Python语言代码，进行批量拼接、投影转换、裁剪等处理，生成2000-2020年渭河流域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.v005 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数据拼接和投影转换的prm文件。\n空间分辨率为1000 m。\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
（3）利用渭河流域矢量边界，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 MOD13A2.005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": "2020-12-31 00:00:00", "ds_acq_place": "渭河流域", "ds_acq_lon_east": 110.27444444444444, "ds_acq_lat_south": 33.69611111111111, "ds_acq_lon_west": 103.9713888888889, "ds_acq_lat_north": 37.40833333333333, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 756359804, "ds_files_count": 1441, "ds_format": "tif", "ds_space_res": "1000m", "ds_time_res": "16天", "ds_coordinate": "WGS84", "ds_projection": "", "ds_thumbnail": "a3d61593-19da-4405-ac8b-bec797f00337.png", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "0d5fea4e-6fd7-4c70-b28b-3f91204c579a", "ds_serv_man": "敏玉芳", "ds_serv_phone": "0931-4967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.4510" ], "quality_level": 3, "publish_time": "2021-09-29 12:51:18", "last_updated": "2025-05-29 17:28:37", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.ncdc.WRiver.2021.20", "i18n": { "en": { "title": "Weihe River Basin MODIS MOD13A2 Normalized Vegetation Index (NDVI) Dataset (2000-2020)", "ds_format": "tif", "ds_source": "
The data source for this dataset is MOD13A2.v005 version data from the official NASA website (https://ladsweb.modaps.eosdis.nasa.gov).", "ds_quality": "
This dataset is consistent in quality with the source dataset MODIS MOD13A2.005. The data has a valid range of -2000 to 10000, a scaling factor of 0.0001, and a fill value of -3000.", "ds_ref_way": "", "ds_abstract": "
Vegetation indices mainly reflect the difference between vegetation reflectance in the visible and near-infrared bands and the soil background, and each of them can be used to quantify the growth status of vegetation under certain conditions. Currently, the normalized vegetation index (NDVI) is an important data source for detecting the growth status of vegetation, vegetation cover and eliminating some of the radiation error related aspects.\n
NDVI reflects the background effects 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, which is an important guide for rational application of nitrogen fertilizer.\n
Based on the MODIS MOD13A2 dataset, the split-format images covering the Weihe River Basin were processed by batch splicing, projection transformation, cropping and other processes using the MRT tool and Python language code to generate the MODIS MOD13A2 NDVI data of the Weihe River Basin for the period of 2000-2020. The spatial resolution of this dataset is 1km, and the temporal resolution is 16 days.
", "ds_time_res": "16天", "ds_acq_place": "Weihe River Basin", "ds_space_res": "1000m", "ds_projection": "", "ds_process_way": "(1) Trial run of the MRT tool to generate prm files for MOD13A2 NDVI data splicing and projection transformation.\nThe spatial resolution is 1000 m.\n
(2) Generate the batch file for calling MRT tool using MATLAB language program and run it.\n
(3) Using the vector boundaries of the Weihe River Basin, steps such as Python batch cropping were used, and finally the output was saved in GeoTIFF format.", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "recommendation_value": 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", "植被覆盖度", "投影转换", "植物冠层", "农作物长势" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "渭河流域" ], "ds_time_tags": [ 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020 ], "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" }