增强型植被指数(EVI)是在归一化植被指数(NDVI)改善出来的，根据大气校正所包含的影像因子、大气分子、气溶胶、薄云、水汽和臭氧等因素进行全面的大气校正。EVI大气校正分三步，第一步是去云处理。第二步是大气校正处理，校正内容除了NDVI已有的瑞利散射和臭氧外，还包括大气分子、气溶胶、水汽等。第三步是进一步处理残留气溶胶影响，方法是借助蓝光和红光通过气溶胶的差异。由于输入的NIR、Red、Blue都经过比较严格的大气校正，所以在设计植被指数算式时，无须为了消除乘法性噪音而采用基于NIR/Red比值的植被指数，因此也就解决了由此引起的植被指数容易饱和以及与实际植被覆盖缺乏线性关系的问题。\n
\nEVI对冠层结构变化包括叶面积指数（LAI），冠层类型、植被相和冠层结构更加敏感。\n
\n基于MODIS MOD13A2.v006增强型植被指数EVI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 EVI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 EVI投影转换的prm文件，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）利用泾河流域矢量边界，采用Python批量裁剪，最后用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": 297607336, "ds_files_count": 1510, "ds_format": "tif", "ds_space_res": "1000m", "ds_time_res": "16天", "ds_coordinate": "WGS84", "ds_projection": "", "ds_thumbnail": "f4075caa-7037-4df5-8643-5cbc5895f93b.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.db1967.2022", "subject_codes": [ "170.4510" ], "quality_level": 3, "publish_time": "2022-04-07 16:21:46", "last_updated": "2025-06-30 15:52:31", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.ncdc.JRiver.db1967.2022", "i18n": { "en": { "title": "MODIS mod13a2 enhanced vegetation index (EVI) 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": "
The enhanced vegetation index (EVI) is improved from the normalized vegetation index (NDVI). It carries out a comprehensive atmospheric correction according to the image factors, atmospheric molecules, aerosols, thin clouds, water vapor and ozone contained in the atmospheric correction. Evi atmospheric correction is divided into three steps. The first step is cloud removal. The second step is atmospheric correction, which includes atmospheric molecules, aerosols, water vapor, etc. in addition to the existing Rayleigh scattering and ozone of NDVI. The third step is to further deal with the impact of residual aerosols by means of the difference between blue and red light passing through aerosols. Since the input NIR, red and blue are subject to relatively strict atmospheric correction, it is unnecessary to use the vegetation index based on NIR / red ratio in order to eliminate multiplicative noise when designing the vegetation index formula. Therefore, the problem of easy saturation of vegetation index and lack of linear relationship with actual vegetation coverage caused by this is solved.\n
\nEvi is more sensitive to the changes of canopy structure, including leaf area index (LAI), canopy type, vegetation phase and canopy structure.\n
\nBased on MODIS mod13a2 V006 enhanced vegetation index evi data set will cover the framing image of Jinghe River Basin, use MRT tool and python language code to carry out projection conversion and clipping processing, and generate MODIS mod13a2 evi data of Jinghe River Basin from 2000 to 2021. 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 evi 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, python batch cutting is adopted, and finally GeoTIFF format is output and saved", "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": "CC 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": [ "增强型植被指数", "冠层结构", "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" }