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 MOD13A2.005 增强型植被指数EVI数据集，将覆盖甘肃省的分幅影像，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2018-2022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
\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）利用甘肃省矢量边界，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": "2018-01-01 00:00:00", "ds_acq_end_time": "2022-12-31 00:00:00", "ds_acq_place": "甘肃省", "ds_acq_lon_east": 108.7075, "ds_acq_lat_south": 32.596111111111114, "ds_acq_lon_west": 92.33777777777777, "ds_acq_lat_north": 42.79333333333333, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 8915485662, "ds_files_count": 808, "ds_format": "tif", "ds_space_res": "1000m", "ds_time_res": "16天", "ds_coordinate": "WGS84", "ds_projection": "/", "ds_thumbnail": "81fc9fa8-0306-4497-a9db-fc2c0362f5b0.png", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "526ff655-4cd4-4650-bb86-6fd3481dfb65", "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": "2023-12-27 09:26:54", "last_updated": "2025-05-29 11:45:31", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.GSEER.DB4130.2023", "i18n": { "en": { "title": "Gansu Province MOD13A2 Enhanced Vegetation Index (EVI) Dataset (2018-2022)", "ds_format": "TIFF", "ds_source": "
The data source of this dataset is MOD13A2-v005 version data, which is sourced from the NASA official website（ https://ladsweb.modaps.eosdis.nasa.gov ）.", "ds_quality": "
This dataset has the same quality as the source dataset MODIS MOD13A2.005. The effective range of data is -2000 to 10000, with a scaling factor of 0.0001 and a padding value of -3000.", "ds_ref_way": "", "ds_abstract": "
Enhanced Vegetation Index (EVI) is an improvement of Normalized Difference Vegetation Index (NDVI), which comprehensively corrects the atmosphere based on imaging factors such as atmospheric molecules, aerosols, thin clouds, water vapor, and ozone. EVI atmospheric correction is divided into three steps, with the first step being cloud removal. The second step is atmospheric correction processing, which includes not only the existing Rayleigh scattering and ozone of NDVI, but also atmospheric molecules, aerosols, water vapor, etc. The third step is to further address the impact of residual aerosols by utilizing the difference between blue and red light through aerosols. Due to the strict atmospheric correction of the input NIR, Red, and Blue, there is no need to use vegetation indices based on the NIR/Red ratio to eliminate multiplicative noise when designing vegetation index equations. This solves the problem of vegetation index saturation and lack of linear relationship with actual vegetation coverage caused by this.\n
\nEVI is more sensitive to changes in canopy structure, including leaf area index (LAI), canopy type, vegetation phase, and canopy structure.\n
\nBased on the MODIS MOD13A2.005 Enhanced Vegetation Index (EVI) dataset, the segmented images covering Gansu Province were processed using MRT tools and Python language code for batch stitching, projection conversion, cropping, etc., to generate MODIS MOD13A2 EVI data for the Wei River Basin from 2018 to 2022. The spatial resolution of this dataset is 1 km, and the temporal resolution is 16 days.
", "ds_time_res": "16天", "ds_acq_place": "Gansu Province", "ds_space_res": "1000m", "ds_projection": "/", "ds_process_way": "(1) Trial run the MRT tool to generate prm files for MOD13A2 EVI data concatenation and projection conversion, with a spatial resolution of 1000 m.\n
\n(2) Generate a batch file using MATLAB language program to call the MRT tool and run it; \n
\n(3) Utilize the vector boundary of Gansu Province, perform batch cropping using Python, and finally output and save 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": [ "甘肃省" ], "ds_time_tags": [ 2018, 2019, 2020, 2021, 2022 ], "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" }