绿叶含有叶绿素色素，可以收集光进行光合作用，并发出叶绿素荧光作为副产品。叶绿素色素和荧光都可以通过地球轨道卫星传感器进行测量。在这里，我们证明了使用这些测量可以在全球范围内可靠地得出叶片的光合作用能力。这种新的基于卫星的信息克服了全球生态研究的瓶颈，目前缺乏这种空间明确的信息。", "ds_source": "
生长季节的全球分布均值V最大值获得自国美-2 SIF、MERIS LCC、TROPOMI SIF如图1所示跟V最大值根据生态最优理论 （EOT） 计算得出的生长季节平均气温。\n
探讨耕地和草地可能产生的影响灌溉V最大值，我们使用了全球灌溉地图（https://www.fao.org/aquastat/en/geospatial-information/global-maps-irrigated-areas/latest-version/，）在 0.5∘分辨率与相对差值（ΔV最大值) TROPOMI SIF+LCC之间V最大值和基于EOT的V最大值，即 （TROPOMI-EOT）/EOT。", "ds_process_way": "
数据同化方法首先应用于GOME-2 SIF数据和生成全球日报V最大值2007年至2017年在36公里处的地图系列 决议（He et al.， 2019）。在这项研究中，这种方法得到了改进并应用于TROPOMI SIF数据，以产生全球日报V最大值地图 2019 年为 0.1∘分辨率 （≈10 km）。\n
两个RSV最大值本研究中使用的产品是衍生的独立于 SIF 和 LCC 的单独卫星观测，但在建模的量级和空间模式上表现出密切的一致性V最大值.这些遥感的V最大值产品 （https://doi.org/10.5281/zenodo.6466968）\n在大规模上也非常吻合 空间格局与生态最优理论计算的空间格局利用气象变量，为该理论的使用提供支持未来气候下陆地生态系统功能预测模型场景。", "ds_quality": "
数据质量良好。", "ds_acq_start_time": "2019-01-01 00:00:00", "ds_acq_end_time": "2019-12-31 00:00:00", "ds_acq_place": "全球", "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": "login-access", "ds_total_size": 616806, "ds_files_count": 5, "ds_format": "txt,tif,mat", "ds_space_res": "", "ds_time_res": "", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "97da9931-f570-4758-9cc7-77a1bbeb6c42.png", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "0a4269e1-65f4-45f1-aeba-88ea3068eebf", "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": "2024-03-27 15:36:03", "last_updated": "2026-01-14 10:39:01", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.ZENODO.DB6452.2024", "i18n": { "en": { "title": "Three global products of leaf photosynthetic capacity derived from satellite observations", "ds_format": "txt,tif,mat", "ds_source": "
The global distribution mean V maximum value during the growing season is obtained from the Gome-2 SIF, MERIS LCC, TROPOMI SIF as shown in Figure 1, and the average temperature during the growing season is calculated based on the Ecological Optimal Theory (EOT) using V maximum value.\n
To explore the potential impacts of farmland and grassland on irrigation V maximum values, we used a global irrigation map（ https://www.fao.org/aquastat/en/geospatial-information/global-maps-irrigated-areas/latest-version/ The maximum value of V between TROPOMI SIF+LCC with a resolution of 0.5 ∘ and the relative difference value (Δ V maximum), and the maximum value of V based on EOT, i.e. (TROPOMI-EOT)/EOT.", "ds_quality": "
The data quality is good.", "ds_ref_way": "", "ds_abstract": "
Green leaves contain chlorophyll pigments that can collect light for photosynthesis and emit chlorophyll fluorescence as a byproduct. Chlorophyll pigments and fluorescence can both be measured through Earth orbit satellite sensors. Here, we demonstrate that using these measurements can reliably determine the photosynthetic capacity of leaves on a global scale. This new satellite based information overcomes the bottleneck of global ecological research, which currently lacks clear spatial information.
", "ds_time_res": "", "ds_acq_place": "Global", "ds_space_res": "", "ds_projection": "", "ds_process_way": "The data assimilation method was first applied to GOME-2 SIF data and generated a map series resolution of the maximum value of global daily V from 2007 to 2017 at 36 kilometers (He et al., 2019). In this study, this method was improved and applied to TROPOMI SIF data to generate a global daily V-maximum map with a resolution of 0.1 ∘ (≈ 10 km) in 2019.\n
The two RSV maximum values used in this study are derived from separate satellite observations independent of SIF and LCC, but exhibit close consistency in modeling magnitude and spatial patterns with the maximum value of V These remote sensing V-maximum products（ https://doi.org/10.5281/zenodo.6466968 ）\nThe spatial pattern, which is highly consistent with the spatial pattern and ecological optimal theory calculation on a large scale, utilizes meteorological variables to provide support for the use of this theory in future climate based land ecosystem function prediction model scenarios.", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC 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": [ "生态", "地球系统", "叶片", "光合能力" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "全球" ], "ds_time_tags": [ 2019 ], "ds_contributors": [ { "true_name": "陈镜明", "email": "chenjm@fjnu.edu.cn", "work_for": "福建师范大学", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "陈镜明", "email": "chenjm@fjnu.edu.cn", "work_for": "福建师范大学", "country": "中国" } ], "ds_managers": [ { "true_name": "陈镜明", "email": "chenjm@fjnu.edu.cn", "work_for": "福建师范大学", "country": "中国" } ], "category": "生态" }