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
EVI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
基于MODIS MOD13A2.005 增强型植被指数EVI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 EVI数据。本数据集空间分辨率为1 km，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 EVI数据拼接和投影转换的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": 756342832, "ds_files_count": 1441, "ds_format": "tif", "ds_space_res": "1000m", "ds_time_res": "16天", "ds_coordinate": "WGS84", "ds_projection": "", "ds_thumbnail": "e1bcfbda-4e37-4e5c-b87e-14321daddfbd.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 12:52:14", "last_updated": "2025-05-29 17:28:37", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.ncdc.WRiver.2021.21", "i18n": { "en": { "title": "Weihe River Basin MODIS MOD13A2 Enhanced Vegetation Index (EVI) 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": "
Enhanced Vegetation Index (EVI) is improved out of Normalized Vegetation Index (NDVI), a comprehensive atmospheric correction based on the image factors included in the atmospheric correction atmospheric molecules, aerosols, thin clouds, water vapor, and ozone, etc., EVI atmospheric correction is divided into three steps, and the first step is the de-clouded processing. The second step is the atmospheric correction processing, the correction includes atmospheric molecules, aerosols, water vapor and so on, in addition to the Rayleigh scattering and ozone that already exist in NDVI. The third step is further processing of residual aerosol effects by means of the difference between blue and red light passing through the aerosol. Since the input NIR, Red, and Blue have been subjected to relatively stringent atmospheric corrections, there is no need to use a vegetation index based on the NIR/Red ratio in order to eliminate multiplicative noise when designing the vegetation index equations, and thus the resulting problems of easy saturation of the vegetation index and the lack of a linear relationship with the actual vegetation cover have been solved.\n
EVI is more sensitive to changes in canopy structure including leaf area index (LAI), canopy type, vegetation phase and canopy structure.\n
Based on the MODIS MOD13A2.005 Enhanced Vegetation Index (EVI) dataset, the split-format images covering the Weihe River Basin were processed by batch splicing, projection transformation, cropping, etc., using the MRT tool and the Python language code, to generate the MODIS MOD13A2 EVI data of the Weihe River Basin for the years 2000-2020. The spatial resolution of this dataset is 1 km. The spatial resolution of this dataset is 1 km, 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 the MRT tool to generate prm files for MOD13A2 EVI data splicing and projection conversion with 1000 m spatial resolution.\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, "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", "植被覆盖", "冠层结构", "叶面积指数" ], "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" }