2016年中亚植被净初级生产力（NPP）栅格数据集包含2016年12期数据，数据范围包括中亚哈萨克斯坦、吉尔吉斯斯坦、塔吉克斯坦、土库曼斯坦、乌兹别克斯坦，以及中国新疆范围，空间分辨率10000米，正弦投影。
", "ds_source": "全球逐日NPP模拟数据产品。
", "ds_process_way": "1、数据预处理：读取原始.img格式的逐日NPP数据，解析文件名中的年份与日序信息，标记无效值（如-9999）以避免干扰后续计算。\n2、时间维度聚合：按月份对逐日数据进行分组，通过求和或平均值生成逐月NPP数据。\n3、空间重采样：将原始约8公里（0.072727°）分辨率数据升尺度至10公里（约0.09°）。选择均值聚合（average）或双线性插值方法进行重采样，确保大尺度空间分布的一致性。\n4、结果输出与验证：将处理后的逐月数据保存为GeoTIFF或NetCDF格式，保留地理投影信息；通过统计分布对比（均值、标准差）或实测数据验证精度，确保数据合理性
", "ds_quality": "空间分辨率10000米。
", "ds_acq_start_time": null, "ds_acq_end_time": null, "ds_acq_place": "", "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": 3465756, "ds_files_count": 1, "ds_format": ".Geotiff格式", "ds_space_res": "10000", "ds_time_res": "每月", "ds_coordinate": "无", "ds_projection": "正弦投影", "ds_thumbnail": "f881ec3e-fcce-45f0-885c-46c0f97f8871.png", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "a5877b42-96ea-4f13-af7e-246f355413d6", "doi_value": "", "subject_codes": [ "170.40" ], "quality_level": 1, "publish_time": "2025-05-08 19:28:49", "first_publish_time": null, "last_updated": "2026-01-20 11:25:19", "protected": false, "protected_to": null, "lang": "zh", "cstr": "33110.11.ariddc.00515", "license": null, "extra": null, "files_shape": [ { "name": "2016_CA_NPP.rar", "size": 3465756, "is_dir": false } ], "features": null, "data_level": 0, "i18n": { "en": { "title": "2016 Central Asia vegetation net primary productivity (NPP) raster dataset", "ds_abstract": "The 2016 Central Asia Vegetation Net Primary Productivity (NPP) raster dataset contains 12 periods of data in 2016. The data range includes Central Asia in Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, Uzbekistan, and Xinjiang in China. The spatial resolution is 10000 meters and is sinusoidal projection.
", "ds_source": "Global daily NPP simulation data product.
", "ds_process_way": "1. Data pretreatment: Read daily NPP data in the original.img format, parse the year and day sequence information in the file name, and mark invalid values (such as-9999) to avoid interfering with subsequent calculations.\n2. Time dimension aggregation: Group daily data by month, and generate monthly NPP data by summing or averaging.\n3. Spatial resampling: Upscale the original data with a resolution of about 8 kilometers (0.072727°) to 10 kilometers (about 0.09°). Select average or bilinear interpolation methods for resampling to ensure consistency of large-scale spatial distribution.\n4. Result output and verification: Save the processed monthly data in GeoTIFF or NetCDF format, retaining the geographical projection information; verify the accuracy through statistical distribution comparison (mean, standard deviation) or measured data to ensure the rationality of the data.
", "ds_quality": "The spatial resolution is 10000 meters.
", "ds_format": ".Geotiff format", "ds_projection": "sinusoidal projection", "ds_space_res": "10000", "ds_time_res": "monthly" } }, "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": [ "NPP", "净初级生产力" ], "ds_subject_tags": [ "地图学" ], "ds_class_tags": [], "ds_locus_tags": [ "哈萨克斯坦", "新疆", "吉尔吉斯斯坦", "乌兹别克斯坦", "塔吉克斯坦", "土库曼斯坦" ], "ds_time_tags": [ 2016 ], "ds_contributors": [ "刘铁" ], "ds_meta_authors": [ "李锦" ], "ds_managers": [ "刘铁" ], "category": "遥感再分析数据" }