从以下位置获取的日平均地表温度 （LST） 极地轨道飞行器对于各种应用至关重要，例如全球和区域气候变化分析。然而，来自极地轨道飞行器只能在非常有限的情况下有效地对表面进行采样 在无云条件下每天的次数。这些限制产生了系统抽样偏差 （ΔTsb） 在每日平均 LST 上 (Tdm） 使用传统方法估计，该方法使用晴空LST观测直接作为Tdm.有几种方法有被提议用于估计Tdm，但它们正在变得越来越少 能够产生时空无缝Tdm遍布全球。 基于MODIS和再分析数据，我们提出了改进的年度和基于昼夜温度循环的框架（称为IADTC框架） 生成全局时空无缝Tdm产品范围从2003年开始 到 2019 年（命名为 GADTC 产品）。
", "ds_source": "MODIS的LST产品和MERRA2（现代回顾分析 研究与应用版本 2） 再分析数据集需要作为 IADTC框架的输入数据。我们还采用了原位 LST 测量 SURFRAD 和 FLUXNET 用于验证 IADTC 框架和 GADTC 产品。MODIS的LST产品，包括MOD11C1和MYD11C1 LST产品 在 2003 年至 2019 年的第 6 个集合中（可在 https://ladsweb.nascom.nasa.gov/ 获得。地表空气温度 （SAT） 用于驱动 ATC 模型的 云下LST的重建，从 https://disc.gsfc.nasa.gov/datasets/M2I1NXLFO_V5.12.4/summary 获得
", "ds_process_way": "OADTC 框架包括两个生成 Tdm的步骤：（1）使用 ATC 模型重建瞬时云下 LST，以确保在每天的四个过境时间有四个有效的LST；（2）使用四参数 DTC 模型模拟昼夜 LST 动态并估算 Tdm。这项研究利用更先进的 ATC 模型改进了 OADTC 框架，并利用 DTC 模型优化了 Tdm的估算。利用这一改进框架（称为 IADTC 框架）生成全球无间隙 Tdm 包括四个步骤：数据预处理、利用先进的 ATC 模型重建云下 LST、对 MODIS 过境时间进行线性插值以及利用 DTC 模型估算 Tdm。
", "ds_quality": "验证表明，IADTC 框架减少了系统化ΔTdm显著。仅使用原位数据进行验证，评估表明平均绝对值 对于 SURFRAD 和 FLUXNET数据和平均偏差均接近于零。 GADTC产品与原位测量之间的直接比较表明 SURFRAD 和FLUXNET 数据集的 MAE 分别为 2.2 和 3.1 K， 这两个的平均偏差分别为 -1.6 和 -1.5K。通过以 GADTC 产品为参考，进一步分析表明，Tdm用传统平均法估计方法产生正系统ΔTsb大于 2.0 K在低纬度和中纬度地区，而在高纬度地区。虽然全球平均LST趋势（2003年至2019年）用传统方法计算，IADTC框架相对关闭（均在 0.025 至 0.029 Kyr-1）、LST的区域差异趋势确实发生LST 中。基于像素的 MAE 趋势介于这两者之间方法达到 0.012 Kyr-1.我们认为IADTC框架可以指导进一步优化Tdm全球估计，以及生成的 GADTC 产品应该在各种应用中很有价值，例如全球和区域变暖分析。
", "ds_acq_start_time": "2003-01-01 00:00:00", "ds_acq_end_time": "2019-12-31 00:00:00", "ds_acq_place": "全球", "ds_acq_lon_east": 150.0, "ds_acq_lat_south": -60.0, "ds_acq_lon_west": -150.0, "ds_acq_lat_north": 60.0, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "open-access", "ds_total_size": 3670425220, "ds_files_count": 2, "ds_format": "tif", "ds_space_res": "1km", "ds_time_res": "年", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "9e664d73-664c-4ca5-9a12-9b8fdabb9222.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": "2024-04-26 14:45:44", "last_updated": "2025-06-30 16:18:07", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.ZENODO.DB6441.2024", "i18n": { "en": { "title": "A global spatiotemporally seamless daily mean land surface temperature from 2003 to 2019", "ds_format": "tif", "ds_source": "The LST product of MODIS and the MERRA2 (Modern Retrospective Analysis Research and Application Version 2) reanalysis dataset need to be used as input data for the IADTC framework. We also used in-situ LST measurements of SURFRED and FLUXNET to validate the IADTC framework and GADTC products. MODIS' LST products, including MOD11C1 and MYD11C1 LST products, were included in the sixth collection from 2003 to 2019 (available in https://ladsweb.nascom.nasa.gov/ get. Surface air temperature (SAT) is used to drive the reconstruction of cloud based LST in ATC models, from https://disc.gsfc.nasa.gov/datasets/M2I1NXLFO_V5.12.4/summary Obtain
", "ds_quality": "Verification shows that the IADTC framework significantly reduces systematic Δ Tdm. Only in-situ data was used for validation, and the evaluation showed that the mean absolute value and mean deviation for SURFRED and FLUXNET data were close to zero. The direct comparison between GADTC products and in-situ measurements shows that the MAEs of SURFRED and FLUXNET datasets are 2.2 and 3.1 K, respectively, with average deviations of -1.6 and -1.5K. Further analysis using GADTC products as a reference shows that Tdmusing traditional averaging methods produces positive systems with Δ Tsbgreater than 2.0 K in low and mid latitude regions, while in high latitude regions. Although the global average LST trend (from 2003 to 2019) was calculated using traditional methods, the IADTC framework was relatively closed (all between 0.025 and 0.029 Kyr-1), and regional differences in LST trends did indeed occur in LST. The pixel based MAE trend falls between these two methods, reaching 0.012 Kyr-1. We believe that the IADTC framework can guide further optimization of Tdmglobal estimation, and the generated GADTC products should be valuable in various applications, such as global and regional warming analysis.
", "ds_ref_way": "", "ds_abstract": "The daily average surface temperature (LST) obtained from the following locations is crucial for various applications, such as global and regional climate change analysis, for polar orbiting spacecraft. However, polar orbiting spacecraft can only effectively sample the surface in very limited circumstances, under cloudless conditions, on a daily basis. These limitations result in systematic sampling bias (Δ Tsb) on the daily average LST (Tdm) estimated using traditional methods that directly use clear sky LST observations as Tdm. Several methods have been proposed for estimating Tdm, but they are becoming increasingly rare and can produce spatiotemporal seamless Tdmacross the globe. Based on MODIS and reanalysis data, we propose an improved annual and diurnal temperature cycle based framework (referred to as the IADTC framework) to generate global spatiotemporal seamless Tdmproducts ranging from 2003 to 2019 (named the GADTC product).
", "ds_time_res": "年", "ds_acq_place": "Global", "ds_space_res": "1km", "ds_projection": "", "ds_process_way": "The OADTC framework includes two steps for generating Tdm: (1) using the ATC model to reconstruct the instantaneous cloud LST, ensuring that there are four valid LSTs at the four transit times each day; (2) Simulate day night LST dynamics using a four parameter DTC model and estimate Tdm. This study improved the OADTC framework using more advanced ATC models and optimized the estimation of Tdmusing the DTC model. The use of this improved framework (known as the IADTC framework) to generate global seamless Tdm involves four steps: data preprocessing, reconstruction of cloud LST using advanced ATC models, linear interpolation of MODIS transit time, and estimation of Tdmusing DTC models.
", "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": [ "地表温度", "昼夜温度循环", "MODIS" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "全球" ], "ds_time_tags": [ 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019 ], "ds_contributors": [ { "true_name": "占文凤", "email": "zhanwenfeng@nju.edu.cn", "work_for": "南京大学国际地球系统科学研究所", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "占文凤", "email": "zhanwenfeng@nju.edu.cn", "work_for": "南京大学国际地球系统科学研究所", "country": "中国" } ], "ds_managers": [ { "true_name": "占文凤", "email": "zhanwenfeng@nju.edu.cn", "work_for": "南京大学国际地球系统科学研究所", "country": "中国" } ], "category": "生态" }