本数据包含2016年12个月的中亚五国和中国新疆范围的NPP栅格数据。结合土地利用、温度、植被指数、降雨量、太阳辐射和蒸散发等数据，借助于CASA模型计算反演得出NPP,空间分辨率为10km×10km，时间分辨率为月，每个文件有12个波段，分别对应当年每个月份的NPP结果，投影为正弦投影。在气候变化背景下，可用于气象要素和植被特征相关关系分析，也可以与其它植被数据和生态数据相结合分析土地退化情况。
", "ds_source": "土地利用、温度、植被指数、降雨量、太阳辐射和蒸散发等遥感数据。
", "ds_process_way": "结合土地利用、温度、植被指数、降雨量、太阳辐射和蒸散发等数据，借助于CASA模型计算反演得出NPP。CASA模型（Carnegie-Ames-Stanford Approach Model）是一种基于遥感数据和气象数据的生态系统净初级生产力（NPP，Net Primary Productivity）估算模型。它由美国卡内基研究所、艾姆斯研究中心和斯坦福大学联合开发，主要用于评估植被通过光合作用固定的碳量。
", "ds_quality": "空间分辨率为10km×10km，时间分辨率为月，每个文件有1个波段，对应每个月份的NPP结果。
", "ds_acq_start_time": "2016-01-01 00:00:00", "ds_acq_end_time": "2016-12-31 00:00:00", "ds_acq_place": "哈萨克斯坦；吉尔吉斯坦；塔吉克斯坦；乌兹别克斯坦；土库曼斯坦；中国新疆", "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": "apply-access", "ds_total_size": 3463198, "ds_files_count": 1, "ds_format": "栅格数据", "ds_space_res": "10000", "ds_time_res": "月", "ds_coordinate": "WGS84", "ds_projection": "正弦投影", "ds_thumbnail": "230f0285-c746-43e4-a84c-497cd767ac50.png", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "a5877b42-96ea-4f13-af7e-246f355413d6", "doi_value": "", "subject_codes": [ "170.45" ], "quality_level": 1, "publish_time": "2025-03-07 16:23:20", "first_publish_time": null, "last_updated": "2025-03-22 18:58:06", "protected": false, "protected_to": null, "lang": "zh", "cstr": "33110.11.ariddc.00127", "license": null, "extra": null, "files_shape": [ { "name": "2016_CA_Basic ecological data set（NPP）.rar", "size": 3463198, "is_dir": false } ], "features": null, "data_level": 0, "i18n": { "en": { "title": "2016 Central Asian Great Lakes Basic Ecological Dataset (NPP)", "ds_abstract": "This data includes NPP grid data from five Central Asian countries and Xinjiang in China for 12 months in 2016. Combined with data such as land use, temperature, vegetation index, rainfall, solar radiation and evapotranspiration, the NPP is calculated and inverted with the help of CASA model. The spatial resolution is 10km×10km and the temporal resolution is month. Each file has 12 bands, which correspond to the NPP results for each month of the year, and the projection is a sinusoidal projection. In the context of climate change, it can be used to analyze the correlation between meteorological elements and vegetation characteristics, and can also be combined with other vegetation data and ecological data to analyze land degradation.
", "ds_source": "Remote sensing data such as land use, temperature, vegetation index, rainfall, solar radiation and evapotranspiration.
", "ds_process_way": "Combined with data such as land use, temperature, vegetation index, rainfall, solar radiation and evapotranspiration, the NPP is obtained by calculation and inversion using CASA model. The Carnegie Ames-Stanford Approach Model (CASA) is a model for estimating ecosystem net primary productivity (NPP) based on remote sensing data and meteorological data. It was jointly developed by the Carnegie Institution, the Ames Research Center and Stanford University and is mainly used to assess the amount of carbon fixed by vegetation through photosynthesis.
", "ds_quality": "The spatial resolution is 10km×10km, the temporal resolution is months, and each file has 1 band, corresponding to the NPP results for each month.
", "ds_acq_place": "Kazakhstan; Kyrgyzstan; Tajikistan; Uzbekistan; Turkmenistan; China Xinjiang", "ds_format": "raster data", "ds_projection": "sinusoidal projection", "ds_space_res": "10000", "ds_time_res": "months" } }, "license_type": null, "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": [ "净初级生产力", "NPP" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "中亚" ], "ds_time_tags": [ 2016 ], "ds_contributors": [ "刘铁" ], "ds_meta_authors": [ "刘铁" ], "ds_managers": [ "刘铁" ], "category": "遥感再分析数据" }