地表温度是地球系统中陆地-大气界面的重要参数，与地表辐射和能量平衡密切相关，广泛应用于冰冻圈、气候学、水文学等地球科学的多个领域。地表温度具有时空异质性，通常通过热红外遥感卫星获取大区域范围的地表温度信息。由于受云、雨等天气状况的影响，加之采样频率、传感器故障、反演算法误差等，导致大量的数据缺失，降低了遥感数据产品的可用性。温度年循环模型是填补地表温度时空缺失的一种稳健方法。
\n当前温度年循环参数多是基于年内不连续数据序列拟合得出，难以反映完整序列下的变化特征。为获取详尽的温度年内变化特征，我们基于最新的EAR5-Land逐时地表温度集，建立一个5参数地表温度年循环模型，采用分布式计算的方式，制备了2000-2023年中国及周边地区地表温度年内循环参数集。\n
该数据地理范围为3°N-54°N，60°E-136°E，覆盖中国、蒙古、巴基斯坦、阿富汗、塔吉克斯坦、吉尔吉斯斯坦等29个国家，时间分辨率为年，空间分辨率为1 km和10 km。数据文件格式为GeoTIFF，命名规则为：CSA_SODskt_APC_[SP1][SP2][SP3]_YYYY.tif，其中SP1表示空间分辨率，有10km和1km两种。SP2标识APC模型拟合的参数和评价指标，共7个。APC模型拟合出的参数有5个，分别是A、T0、d、Theta和deltaT。A 为地表温度年变化的振幅（A≥0）。T0为年平均温度（℃）。d为相对于春分日的相位偏移天数。Theta为春分日所对应的日序数。deltaT为残差，其受气候、植被、土壤等条件以及外部干扰。APC模型的评价指标有2个分别是均方根误差（RMSE）和决定系数（R2）。SP3标识温度数据的类型，共5种，分别是日平均温度、日最高温度、日最低温度、夜间平均温度、白天平均温度，依次用avg、max、min、nighttime和daytime标识。这种大区域范围、多尺度、多变量的参数集可应用于地表温度时空数据集的缺失填补、重建、融合以及气候变化、环境演变等相关研究。
", "ds_source": "基础数据来源于欧洲中期天气预报中心的ERA5-Land逐时地表温度数据。
", "ds_process_way": "数据制备流程主要涉及主要步骤：(1) 从EAR5数据集中提取日最高温度、日最低温度、白天均温、夜间均温、日均温，并进行数据值域检测，若存在异常，则进行校准；(2) 采用正弦函数，逐网格构建5参数的温度年循环模型，基于蒙脱卡洛模拟的方法，求解模型参数；(3) 将参数带入模型，将重建出的温度序列与原始数据进行对比，采用均方根误差和决定系数进行度量每个年份、每个网格的参数精度。
", "ds_quality": "本数据集随附了数据质量评估文件，按照年份，对每种温度类型的年内循环参数拟合质量进行了评估。用户可查看CSA_skt_APC_[SP1]RMSE[SP3]YYYY.tif 和CSA_skt_APC[SP1] R2 [SP3]_YYYY.tif文件，获取每个网格对应的均方根误差和决定系数。
", "ds_acq_start_time": "2000-01-01 00:00:00", "ds_acq_end_time": "2023-12-31 00:00:00", "ds_acq_place": "亚洲", "ds_acq_lon_east": 136.0, "ds_acq_lat_south": 3.0, "ds_acq_lon_west": 60.0, "ds_acq_lat_north": 54.0, "ds_acq_alt_low": 2662.0, "ds_acq_alt_high": 6479.0, "ds_share_type": "apply-access", "ds_total_size": 60707519222, "ds_files_count": 1681, "ds_format": "Geotiff", "ds_space_res": "1km，10km", "ds_time_res": "年", "ds_coordinate": "WGS84", "ds_projection": "GCS_WGS_1984", "ds_thumbnail": "33f116d6-a9b1-4b70-8d19-5d9f059f44db.jpg", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "9de89acc-5714-4927-aba3-ac88067dff8a", "ds_serv_man": "李红星", "ds_serv_phone": "0931-4967592", "ds_serv_mail": "lihongxing@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170", "170.45", "170.4510" ], "quality_level": 3, "publish_time": "2025-11-19 17:32:03", "last_updated": "2026-05-11 16:47:03", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.NIEER.DB7007.2025", "i18n": { "en": { "title": "Annual Surface Temperature Cycle Parameter Set for China and Surrounding Regions (2000–2023)", "ds_format": "Geotiff", "ds_source": "The basic data comes from ERA5 Land hourly surface temperature data from the European Centre for Medium Range Weather Forecasts.
", "ds_quality": "This dataset is accompanied by a data quality assessment file, which evaluates the fitting quality of annual cycle parameters for each temperature type by year. Users can view the CSA_skt_APC_ [SP1]RMSE[SP3]YYYY.tif and CSA_skt_APC[SP1]R2[SP3] _YYYY.tif files to obtain the root mean square error and determination coefficient corresponding to each grid.
", "ds_ref_way": "", "ds_abstract": "Surface temperature is an important parameter at the land atmosphere interface in the Earth system, closely related to surface radiation and energy balance, and widely used in multiple fields of Earth science such as cryosphere, climatology, hydrology, etc. The surface temperature has spatiotemporal heterogeneity, and it is usually obtained through thermal infrared remote sensing satellites to obtain large-scale surface temperature information. Due to the influence of weather conditions such as clouds and rain, as well as sampling frequency, sensor failures, and inversion algorithm errors, a large amount of data is missing, which reduces the availability of remote sensing data products. The temperature annual cycle model is a robust method for filling the spatial and temporal gaps in surface temperature.
\r\nThe current temperature annual cycle parameters are mostly based on fitting discontinuous data sequences within the year, which makes it difficult to reflect the changing characteristics under the complete sequence. To obtain detailed annual temperature variation characteristics, we established a 5-parameter annual surface temperature cycle model based on the latest EAR5 Land hourly surface temperature set. Using distributed computing, we prepared the annual surface temperature cycle parameter set for China and surrounding areas from 2000 to 2023.\r\n
The geographical range of this data is 3 ° N-54 ° N, 60 ° E-136 ° E, covering 29 countries including China, Mongolia, Pakistan, Afghanistan, Tajikistan, Kyrgyzstan, etc. The time resolution is years, and the spatial resolutions are 1 km and 10 km. The data file format is GeoTIFF, and the naming convention is CSA_SODskt_APC_ [SP1] [SP2] [SP3] _YYYY.tif, where SP1 represents spatial resolution and there are two types: 10km and 1km. SP2 identifies 7 parameters and evaluation indicators for fitting the APC model. The APC model fits 5 parameters, namely A, T0, d, Theta, and deltaT. A is the amplitude of annual variation in surface temperature (A ≥ 0). T0 is the annual average temperature (℃). D is the number of days of phase shift relative to the vernal equinox. Theta is the day number corresponding to the vernal equinox. DeltaT is a residual that is affected by climate, vegetation, soil conditions, and external disturbances. There are two evaluation metrics for the APC model, namely root mean square error (RMSE) and coefficient of determination (R2). SP3 identifies five types of temperature data, namely daily average temperature, daily maximum temperature, daily minimum temperature, nighttime average temperature, and daytime average temperature, which are identified by avg, max, min, nighttime, and daytime in sequence. This large-scale, multi-scale, and multivariate parameter set can be applied to fill in missing data, reconstruct, fuse, and conduct research on climate change, environmental evolution, and other related aspects of surface temperature spatiotemporal datasets.
", "ds_time_res": "", "ds_acq_place": "Asia", "ds_space_res": "", "ds_projection": "", "ds_process_way": "The data preparation process mainly involves the following steps: (1) extracting the daily highest temperature, daily lowest temperature, daytime average temperature, nighttime average temperature, and daily average temperature from the EAR5 dataset, and conducting data range detection. If there are abnormalities, calibration is performed; (2) Using a sine function, a 5-parameter temperature annual cycle model is constructed grid by grid, and the model parameters are solved based on the Monte Carlo simulation method; (3) Substitute the parameters into the model, compare the reconstructed temperature series with the original data, and measure the parameter accuracy for each year and grid using root mean square error and coefficient of determination.
", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "recommendation_value": 0, "license_type": "https://creativecommons.org/licenses/by/4.0/", "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": [ "地表温度", "中国", "中国周边地区", "年内循环", "参数集", "2000-2023", "温度" ], "ds_subject_tags": [ "地球科学", "地理学", "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "亚洲", "中国", "巴基斯坦", "蒙古", "阿富汗", "塔吉克斯坦", "吉尔斯斯坦", "中巴经济走廊", "一带一路", "黄河流域", "青藏高原", "中亚" ], "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, 2023 ], "ds_contributors": [ { "true_name": "赵国辉", "email": "zhgh@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "赵国辉", "email": "zhgh@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" } ], "ds_managers": [ { "true_name": "赵国辉", "email": "zhgh@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" } ], "category": "气象" }