基于2000-2021年的MODO9A1、MOD11A2、MOD13A1数据产品，选取7-9月份云量小于5%的数据，基于WET,NDVI,LST,NDBSI指标计算遥感生态环境指数RSEI，采用平均值的方法获取2000-2021年逐年RSEI指数。", "ds_source": "
数据来源：\n
MODO9A1 500m GEE ( https://developers.google.cn/earth-engine)\n
MOD11A2 1000m GEE ( https://developers.google.cn/earth-engine)\n
MOD13A1 500m GEE ( https://developers.google.cn/earth-engine)", "ds_process_way": "
GEE平台", "ds_quality": "
基于GEE平台，2000-2021年的MODO9A1、MOD11A2、MOD13A1数据产品，选取7-9月份云量小于5%的数据，基于WET,NDVI,LST,NDBSI指标计算遥感生态环境指数RSEI，去除水体部分，采用平均值的方法获取2000-2021年逐年RSEI指数,数据采用重采样的方法，分辨率为250m。", "ds_acq_start_time": "2000-01-01 00:00:00", "ds_acq_end_time": "2021-12-31 00:00:00", "ds_acq_place": "黄河流域", "ds_acq_lon_east": 119.21555555555555, "ds_acq_lat_south": 32.1575, "ds_acq_lon_west": 95.89527777777778, "ds_acq_lat_north": 41.83916666666667, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "apply-access", "ds_total_size": 4995570738, "ds_files_count": 89, "ds_format": "*.tif", "ds_space_res": "250m", "ds_time_res": "年", "ds_coordinate": "WGS84", "ds_projection": "Albers_Conic_Equal_Area", "ds_thumbnail": "c0af6c93-13df-4f0e-8f8f-fce0fc35de7d.png", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "52b7b79b-860c-49a5-9083-9a70cf8bed5a", "ds_serv_man": "敏玉芳", "ds_serv_phone": "0931-4967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.45" ], "quality_level": 3, "publish_time": "2025-10-27 11:06:50", "last_updated": "2026-04-27 11:52:44", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.NIEER.DB6977.2025", "i18n": { "en": { "title": " RSEI of the Yellow River Basin", "ds_format": "*.tif", "ds_source": "
Data source:\n
MODO9A1 500m GEE ( https://developers.google.cn/earth-engine )\n
MOD11A2 1000m GEE ( https://developers.google.cn/earth-engine )\n
MOD13A1 500m GEE ( https://developers.google.cn/earth-engine )", "ds_quality": "
Based on the GEE platform, MODO9A1, MOD11A2, and MOD13A1 data products from 2000 to 2021 were used to select data with cloud cover less than 5% from July to September. The remote sensing ecological environment index (RSEI) was calculated using WET, NDVI, LST, and NDBSI indicators, and the water body was removed. The RSEI index for each year from 2000 to 2021 was obtained using the average method. The data was resampled with a resolution of 250m.", "ds_ref_way": "", "ds_abstract": "
Based on the MODO9A1, MOD11A2, and MOD13A1 data products from 2000 to 2021, data with cloud cover less than 5% from July to September were selected. The remote sensing ecological environment index (RSEI) was calculated using WET, NDVI, LST, and NDBSI indicators, and the RSEI index for each year from 2000 to 2021 was obtained using the average method.
", "ds_time_res": "年", "ds_acq_place": "Yellow River Basin", "ds_space_res": "250m", "ds_projection": "", "ds_process_way": "GEE platform", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC BY 4.0 (Creative Commons Attribution 4.0 International License)", "doi_reg_from": "reg_local", "cstr_reg_from": "reg_local", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "ds_topic_tags": [ "遥感", "生态环境指数", "RSEI" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "黄河流域" ], "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": "何振芳", "email": "hezhenfang@lcu.edu.cn", "work_for": "聊城大学", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "何振芳", "email": "hezhenfang@lcu.edu.cn", "work_for": "聊城大学", "country": "中国" } ], "ds_managers": [ { "true_name": "何振芳", "email": "hezhenfang@lcu.edu.cn", "work_for": "聊城大学", "country": "中国" } ], "category": "生态" }