太阳诱导的叶绿素荧光（SIF）是光合作用的重要替代物。哥白尼哨兵-5P 任务所搭载的 TROPOspheric Monitoring Instrument（TROPOMI）提供了几乎覆盖全球的精细光谱分辨率，可进行可靠的 SIF 检索。然而，目前卫星获得的 SIF 数据集只有较粗的空间分辨率，限制了其在精细尺度上的应用。在此，我们利用加权叠加算法，从 TROPOMI 卫星上生成了 2000 年至 2022 年的中国高空间分辨率 SIF 数据集（500 米，8 天）（HCSIF），其空间分辨率为 3.5 千米乘 5.6-7 千米。我们的算法在验证数据集上表现出很高的准确性（R2 = 0.87，RMSE = 0.057 mW/m2/nm/sr）。HCSIF 数据集对照 OCO-2 SIF、基于塔的 SIF 测量值以及通量塔的总初级生产力（GPP）进行了评估。我们的数据集可促进对精细尺度陆地生态过程的了解，从而对生态系统的生物多样性进行监测，并对作物健康、生产力和压力水平进行精确的长期评估。比例因子为 0.0001。", "ds_source": "
Sentinel-5P", "ds_process_way": "
使用加权叠加集合算法，以 8 天的间隔对中国上空 500 米分辨率的 TROPOMI SIF（HCSIF）进行长时间（2000-2022 年）的降尺处理，中国是拥有 2 公顷以下土地的小农户最多的国家（2.3 亿）。HCSIF 数据来自加州理工学院 TROPOMI SIF 数据 11、中分辨率成像光谱仪 (MODIS) 植被指数38,39、fPAR 数据40、数字高程模型 (DEM) 数据41 和 ERA5-Land 数据42。这项研究的三个目标是：(1)开发一个基于堆叠的降尺度模型，并与地形相关变量相结合；(2)为中国制作一个长期的高分辨率 SIF 数据集（HCSIF）；(3)分别用通量塔、OCO-2 SIF 和 GOME-2 SIF 的 SIF、总初级生产力（GPP）的塔基测量数据对 HCSIF 进行评估。我们预计，这一新型数据集有望监测生态过程、碳循环，并评估气候多变性对农业和林业在精细尺度上的影响。", "ds_quality": "
HCSIF 数据集是使用与 SR、LST、fPAR、气象因素和地形相关的多个特征生成的。采用包含三个基本学习器（CatBoost、RF 和 GBDT）的加权集成学习算法来构建缩小模型。HCSIF 数据集显著提高了 TROPOMI SIF 的空间分辨率，这有助于更好地表示空间细节。这种增强导致了 SIF 和 GPP 验证的高精度，无论是在站点级别和卫星观测。", "ds_acq_start_time": "2000-01-01 00:00:00", "ds_acq_end_time": "2022-12-31 00:00:00", "ds_acq_place": "中国", "ds_acq_lon_east": null, "ds_acq_lat_south": null, "ds_acq_lon_west": null, "ds_acq_lat_north": null, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "open-access", "ds_total_size": 460795004119, "ds_files_count": 3420, "ds_format": "*.tif,*.nc", "ds_space_res": null, "ds_time_res": "", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "ef1309aa-2564-4d5f-9c64-3259351c0179.png", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "d2c052ce-d283-4a48-8962-6a3dbcb03b8e", "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": "2025-03-28 19:01:05", "last_updated": "2025-05-29 11:06:27", "protected": false, "protected_to": null, "lang": "zh", "cstr": "https://cstr.cn/31253.11.sciencedb.16910", "i18n": { "en": { "title": "A high-resolution satellite-based solar-induced chlorophyll fluorescence dataset for China from 2000 to 2022", "ds_format": "*.tif,*.nc", "ds_source": "
Sentinel-5P", "ds_quality": "
The HCSIF dataset is generated using multiple features related to SR, LST, fPAR, meteorological factors, and terrain. We use a weighted ensemble learning algorithm consisting of three basic learners (CatBoost, RF, and GBDT) to construct a scaled down model. The HCSIF dataset significantly improves the spatial resolution of TROPOMI SIF, which helps to better represent spatial details. This enhancement leads to high accuracy in SIF and GPP validation, both at the site level and satellite observations.", "ds_ref_way": "", "ds_abstract": "
Solar induced chlorophyll fluorescence (SIF) is an important substitute for photosynthesis. The TROPOSphere Monitoring Instrument (TROPOMI) carried by the Copernicus Sentinel-5P mission provides almost global coverage of fine spectral resolution, enabling reliable SIF retrieval. However, the SIF dataset currently obtained by satellites only has a relatively coarse spatial resolution, which limits its application at fine scales. Here, we used the weighted superposition algorithm to generate the Chinese High Spatial Resolution SIF dataset (500 meters, 8 days) (HCSIF) from TROPOMI satellite from 2000 to 2022, with a spatial resolution of 3.5 kilometers by 5.6-7 kilometers. Our algorithm demonstrates high accuracy on the validation dataset (R2=0.87, RMSE=0.057 mW/m2/nm/sr). The HCSIF dataset was evaluated against OCO-2 SIF, tower based SIF measurements, and total primary productivity (GPP) of flux towers. Our dataset can facilitate the understanding of fine scale terrestrial ecological processes, enabling the monitoring of biodiversity in ecosystems and accurate long-term assessments of crop health, productivity, and stress levels. The scaling factor is 0.0001.
", "ds_time_res": "", "ds_acq_place": "China", "ds_space_res": "", "ds_projection": "", "ds_process_way": "Using the weighted superposition set algorithm, TROPOMI SIF (HCSIF) with a resolution of 500 meters over China was downscaled for a long period of time (2000-2022) at 8-day intervals. China has the largest number of small farmers with less than 2 hectares of land (230 million). The HCSIF data comes from TROPOMI SIF data 11 from the California Institute of Technology, MODIS vegetation indices 38,39, fPAR data 40, digital elevation model (DEM) data 41, and ERA5 Land data 42. The three objectives of this study are: (1) to develop a stack based downscaling model and combine it with terrain related variables; (2) Develop a long-term high-resolution SIF dataset (HCSIF) for China; (3) Evaluate HCSIF using SIF measurements from flux tower, OCO-2 SIF, GOME-2 SIF, and total primary productivity (GPP) tower base. We expect that this new dataset has the potential to monitor ecological processes, carbon cycling, and assess the impact of climate variability on agriculture and forestry at a fine scale.", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "https://creativecommons.org/licenses/by/4.0/", "doi_reg_from": "reg_outside", "cstr_reg_from": "reg_outside", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "ds_topic_tags": [ "SIF", "光合作用", "机器学习", "遥感" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "中国" ], "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, 2022 ], "ds_contributors": [ { "true_name": "张兆英", "email": "zhaoying_zhang@nju.edu.cn", "work_for": "南京大学", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "张兆英", "email": "zhaoying_zhang@nju.edu.cn", "work_for": "南京大学", "country": "中国" } ], "ds_managers": [ { "true_name": "张兆英", "email": "zhaoying_zhang@nju.edu.cn", "work_for": "南京大学", "country": "中国" } ], "category": "生态" }