黑河流域植被物候数据集提供了2012年至2015年遥感物候产品。其空间分辨率为1km，投影类型为正弦投影。该数据采用MODIS LAI产品MOD15A2作为物候遥感监测数据源，MODIS陆地覆盖分类产品MCD12Q1作为辅助数据集进行提取。
产品算法首先采用时间序列数据重建方法（BISE法）控制输入时间序列的数据质量；然后利用主算法（Logistic函数拟合法）与备用算法（分段线性拟合法）相结合的方式提取植被物候参数，实现算法互补，保证精度的同时提高可反演率。算法可提取一年最多三个生长周期，每个生长周期包含6个数据集，包括植被生长起点、生长峰值起点、生长峰值终点、生长终点、生长最快点、衰落最快点，同时记录了生长周期类型、生长季长度、质量标识等，共25个数据集。该物候产品降低了反演缺失率，提高了产品稳定性，数据集信息丰富，是相对可靠的。
", "ds_source": "该数据采用MODIS LAI产品MOD15A2作为物候遥感监测数据源，MODIS陆地覆盖分类产品MCD12Q1作为辅助数据集进行提取。
", "ds_process_way": "产品算法首先采用时间序列数据重建方法（BISE法）控制输入时间序列的数据质量；然后利用主算法（Logistic函数拟合法）与备用算法（分段线性拟合法）相结合的方式提取植被物候参数，实现算法互补，保证精度的同时提高可反演率。
算法可提取一年最多三个生长周期，每个生长周期包含6个数据集，包括植被生长起点、生长峰值起点、生长峰值终点、生长终点、生长最快点、衰落最快点，同时记录了生长周期类型、生长季长度、质量标识等，共25个数据集。该物候产品降低了反演缺失率，提高了产品稳定性，数据集信息丰富，是相对可靠的。
", "ds_quality": "数据质量良好
", "ds_acq_start_time": "2012-01-15 00:00:00", "ds_acq_end_time": "2015-12-31 00:00:00", "ds_acq_place": "黑河流域,中游人工绿洲试验区,上游寒区水文试验区,下游天然绿洲试验区", "ds_acq_lon_east": 101.75, "ds_acq_lat_south": 37.25, "ds_acq_lon_west": 97.75, "ds_acq_lat_north": 42.1, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 65760363, "ds_files_count": 2, "ds_format": "hdr", "ds_space_res": "/", "ds_time_res": "年", "ds_coordinate": "WGS84", "ds_projection": "/", "ds_thumbnail": "b21c57e5-6a8e-449a-b72e-2985d12f9f56.png", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "本数据由“黑河生态水文遥感试验（HiWATER）”产生，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": "c94b3578-20da-4346-9de9-c702b6ca8983", "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": "2021-08-31 09:41:26", "last_updated": "2025-05-29 16:23:45", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.ncdc.nieer.2020.1637", "i18n": { "en": { "title": "Heihe River eco hydrological remote sensing experiment: vegetation phenology data set of Heihe River Basin (2012-2015)", "ds_format": "hdr", "ds_source": "The data is extracted using MODIS Lai product mod15a2 as phenological remote sensing monitoring data source and MODIS land cover classification product mcd12q1 as auxiliary data set
", "ds_quality": "Good data quality
", "ds_ref_way": "", "ds_abstract": "The vegetation phenological data set of Heihe River basin provides remote sensing phenological products from 2012 to 2015. The spatial resolution is 1km and the projection type is sinusoidal projection. The data is extracted using MODIS Lai product mod15a2 as phenological remote sensing monitoring data source and MODIS land cover classification product mcd12q1 as auxiliary data set
Firstly, the product algorithm uses the time series data reconstruction method (bise method) to control the data quality of the input time series; Then the vegetation phenological parameters are extracted by the combination of the main algorithm (logistic function fitting method) and the standby algorithm (piecewise linear fitting method), so as to realize the complementarity of the algorithm, ensure the accuracy and improve the inversion rate. The algorithm can extract up to three growth cycles in a year. Each growth cycle contains 6 data sets, including vegetation growth start point, growth peak start point, growth peak end point, growth end point, fastest growth and fastest fading. At the same time, 25 data sets are recorded, such as growth cycle type, growth season length and quality identification. The phenological product reduces the inversion missing rate and improves the product stability. The data set is rich in information and is relatively reliable
", "ds_time_res": "年", "ds_acq_place": "Heihe River Basin, artificial oasis test area in the middle reaches, hydrological test area in the upper cold area, and natural oasis test area in the lower reaches", "ds_space_res": "/", "ds_projection": "/", "ds_process_way": "Firstly, the product algorithm uses the time series data reconstruction method (bise method) to control the data quality of the input time series; Then the vegetation phenological parameters are extracted by the combination of the main algorithm (logistic function fitting method) and the standby algorithm (piecewise linear fitting method), so as to realize the complementarity of the algorithm, ensure the accuracy and improve the inversion rate
The algorithm can extract up to three growth cycles in a year. Each growth cycle contains 6 data sets, including vegetation growth start point, growth peak start point, growth peak end point, growth end point, fastest growth and fastest fading. At the same time, 25 data sets are recorded, such as growth cycle type, growth season length and quality identification. The phenological product reduces the inversion missing rate and improves the product stability. The data set is rich in information and is relatively reliable
", "ds_ref_instruction": "This data is generated by \"Heihe eco hydrological remote sensing experiment (hiwater)\". 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 reference part." } }, "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": [ "生长季长度", "生长终点", "MODIS", "生长起点", "卫星遥感产品", "植被物候", "物候期", "植被覆盖度", "生态遥感产品" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "中游人工绿洲试验区", "上游寒区水文试验区", "下游天然绿洲试验区", "黑河流域" ], "ds_time_tags": [ 2012, 2013, 2014, 2015 ], "ds_contributors": [ { "true_name": "李静", "email": "lijing01@radi.ac.cn", "work_for": "中国科学院遥感与数字地球研究所", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "李静", "email": "lijing01@radi.ac.cn", "work_for": "中国科学院遥感与数字地球研究所", "country": "中国" } ], "ds_managers": [ { "true_name": "李静", "email": "lijing01@radi.ac.cn", "work_for": "中国科学院遥感与数字地球研究所", "country": "中国" } ], "category": "遥感及产品" }