本数据集为塔吉克斯坦重要作物主要生育期旬尺度干旱监测产品集，覆盖塔吉克斯坦全国范围，时间范围为2001—2022年，监测时段为每年3—9月，时间尺度为旬，空间分辨率为1 km。数据基于MODIS遥感数据，在Google Earth Engine（GEE）平台完成预处理、指标计算和产品生成，共形成22年×7个月×3旬=462期干旱监测产品。该数据集形成基础植被—植被状态—温度胁迫—水分状况—综合干旱—干旱等级的完整指标体系，用于主要作物生育期干旱动态监测、空间分布分析、年际变化分析及干旱灾害评估。
", "ds_source": "本数据集的原始数据均通过Google Earth Engine（GEE）数据目录在线调用获取。首先在GEE中检索并调用NASA MODIS相关产品，根据数据集ID、时间范围和塔吉克斯坦国境范围进行筛选；其中MOD09A1用于获取地表反射率，MOD11A2用于获取地表温度。塔吉克斯坦国境范围通过FAO GAUL行政区划数据调用获取，用作空间范围约束和最终产品裁剪边界。\n主要数据源包括：
\nMODIS MOD09A1地表反射率产品\n通过GEE数据目录调用NASA MODIS MOD09A1地表反射率产品（8天合成、原始空间分辨率500 m），按研究区和时间范围筛选获取红光、近红外及质量控制等相关波段；具体云、云影筛选及NDVI计算在“数据加工方法”中说明。\nMODIS MOD11A2地表温度产品\n通过GEE数据目录调用NASA MODIS MOD11A2地表温度产品（8天合成、空间分辨率1 km），按研究区和时间范围筛选获取白天地表温度及QC_Day等相关波段；具体质量筛选、单位转换及旬尺度合成在“数据加工方法”中说明。\nFAO GAUL行政区划数据\n通过GEE数据目录调用FAO GAUL行政区划数据，获取塔吉克斯坦level 0国境范围，用于限定数据处理范围并对最终产品进行空间裁剪。
", "ds_process_way": "本数据集以MODIS MOD09A1地表反射率和MOD11A2地表温度产品为主要数据源，在Google Earth Engine（GEE）平台完成数据加工。\n首先提取塔吉克斯坦国境范围，筛选2001—2022年3—9月的MODIS数据，并按照每月上旬、中旬和下旬进行旬尺度划分，共形成462个旬尺度时相。对MOD09A1数据利用质量控制信息进行云、云影等低质量像元剔除，对红光和近红外反射率进行比例因子转换并计算NDVI，随后进行旬尺度平均合成；对MOD11A2数据利用QC_Day质量控制信息筛选有效地表温度像元，完成比例因子转换和单位换算后进行旬尺度平均合成。\n在此基础上，以2001—2022年同月同旬历史数据建立P5/P95百分位基准，分别计算植被状况指数VCI和温度状况指数TCI，并利用NDVI和地表温度计算植被供水指数VSWI。\n进一步按照VCI和TCI各50%的权重计算植被健康指数VHI，并根据VHI划分正常、轻旱、中旱、重旱和特旱5个干旱等级，形成Drought_Class波段。\n最终将NDVI、VCI、TCI、VSWI、VHI和Drought_Class统一处理为1 km空间分辨率，裁剪至塔吉克斯坦范围，以多波段GeoTIFF格式输出。
", "ds_quality": "本数据集采用NASA MODIS标准遥感产品作为主要输入数据，并使用原始产品自带质量控制信息进行有效观测筛选，数据源具有较好的时间连续性和空间一致性。MOD09A1和MOD11A2分别通过StateQA、QC_Day等质量信息进行低质量观测剔除，并通过旬尺度合成降低云污染、观测噪声和单期异常值影响。VCI和TCI基于2001—2022年同月同旬历史数据的P5/P95百分位建立标准化基准，降低季节变化和极端值对结果的影响；计算过程中进行除零处理、合理范围约束和无效值掩膜。VSWI、VHI及Drought_Class均由质量控制后的基础指标计算得到，保持时间和空间对应关系。最终产品统一为1 km空间分辨率和一致网格，并检查时间完整性、波段完整性、空间范围、空间分辨率及指标取值范围。\nNDVI数据经过云、云影等低质量像元剔除及异常值检查，地表温度数据经过质量控制波段筛选、异常值剔除和单位转换；有效数据按照旬尺度进行合成，以降低单期遥感观测噪声及云污染影响。\nVCI和TCI采用2001—2022年同月同旬历史数据的P5/P95百分位值建立基准，减少季节变化和极端值对干旱监测结果的影响，并对指标进行合理范围约束和除零处理。\nVSWI、VHI及Drought_Class均基于经过质量控制的基础数据计算，确保各指标之间具有一致的空间位置和时间对应关系。\n最终产品统一为1 km空间分辨率和相同空间网格，并对无效像元进行掩膜处理，不进行无依据的数据填补。产品生产完成后对时间完整性、波段完整性、空间范围、空间分辨率及指标取值范围进行检查，保证数据具有较好的完整性、一致性和可用性。
", "ds_acq_start_time": "2001-03-01 00:00:00", "ds_acq_end_time": "2022-09-30 00:00:00", "ds_acq_place": "塔吉克斯坦", "ds_acq_lon_east": 75.23305555555555, "ds_acq_lat_south": 36.669444444444444, "ds_acq_lon_west": 67.51944444444445, "ds_acq_lat_north": 41.08305555555556, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 1322849589, "ds_files_count": 282, "ds_format": "*.tif", "ds_space_res": "1km", "ds_time_res": "旬", "ds_coordinate": "WGS84", "ds_projection": "", "ds_thumbnail": "4ea806c1-f9af-496e-9c60-9abde8c1a8d9.png", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "None", "ds_from_station": null, "organization_id": "b6ee8072-db07-4053-bcbc-78e22d72c5ec", "ds_serv_man": null, "ds_serv_phone": null, "ds_serv_mail": null, "doi_value": "", "subject_codes": [ "210" ], "quality_level": 0, "publish_time": "2026-10-14 15:20:04", "last_updated": "2026-10-14 15:20:04", "protected": false, "protected_to": "2027-09-05 00:00:00", "lang": "zh", "cstr": "11738.11.ncdc.db7835.2026", "i18n": { "en": { "title": "Tajikistan 1 km Dekadal-Scale Drought Monitoring Dataset for Major Crops During Critical Growth Periods (2001–2022)", "ds_format": "*.tif", "ds_source": "", "ds_quality": "This dataset uses NASA MODIS standard remote sensing products as the main input data, and uses the original product's own quality control information for effective observation and screening. The data source has good temporal continuity and spatial consistency. MOD09A1 and MOD11A2 use quality information such as StateQA and QC_Day to eliminate low-quality observations, respectively, and use ten-day scale synthesis to reduce cloud pollution, observation noise and single-period outliers. VCI and TCI establish a standardized benchmark based on the P5/P95 percentile of historical data for the same month from 2001 to 2022 to reduce the impact of seasonal changes and extremes on the results; zero-division processing, reasonable range constraints and invalid value masking are carried out during the calculation process. VSWI, VHI and Drought_Class are calculated from the basic indicators after quality control, maintaining temporal and spatial correspondence. The final product is unified into a 1-km spatial resolution and a consistent grid, and time integrity, band integrity, spatial range, spatial resolution and indicator value range are checked.\r\nNDVI data is removed from low-quality pixels such as clouds and cloud shadows and checked for outliers. Surface temperature data is filtered through quality control bands, outliers are removed and unit converted. Effective data is synthesized on a ten-day scale to reduce single-period remote sensing observation noise and cloud pollution impact.\r\nVCI and TCI use the P5/P95 percentile values of historical data for the same month from 2001 to 2022 to establish a benchmark to reduce the impact of seasonal changes and extremes on drought monitoring results, and reasonably restrict the indicators and eliminate zero.\r\nVSWI, VHI and Drought_Class are all calculated based on basic data that has undergone quality control to ensure consistent spatial position and time correspondence among indicators.\r\nThe final product is unified to a spatial resolution of 1 km and the same spatial grid, and invalid pixels are masked to avoid unfounded data filling. After product production is completed, the time integrity, band integrity, spatial range, spatial resolution and indicator value range are checked to ensure that the data has good integrity, consistency and usability.", "ds_ref_way": "", "ds_abstract": "This dataset is a ten-day drought monitoring product set for the main growth period of important crops in Tajikistan. It covers the whole country of Tajikistan. The time range is from 2001 to 2022. The monitoring period is from March to September every year. The time scale is ten-day, and the spatial resolution is 1 km. The data is based on MODIS remote sensing data. Pre-processing, indicator calculation and product generation are completed on the Google Earth Engine (GEE) platform, forming a total of 22 years ×7 months ×3 ten-days =462 drought monitoring products. This dataset forms a complete indicator system of basic vegetation-vegetation status-temperature stress-water status-comprehensive drought-drought level, which is used for drought dynamic monitoring, spatial distribution analysis, interannual change analysis and drought disaster assessment during the growth period of major crops.", "ds_time_res": "", "ds_acq_place": "Tajikistan", "ds_space_res": "1km", "ds_projection": "", "ds_process_way": "This dataset uses MODIS MOD09A1 surface reflectance and MOD11A2 surface temperature products as the main data sources, and data processing is completed on the Google Earth Engine (GEE) platform.\r\nFirst, the border range of Tajikistan is extracted, MODIS data from March to September from 2001 to 2022 is screened, and divided into ten-day scales according to the first, middle and last ten days of each month, forming a total of 462 ten-day scales. Quality control information is used to remove low-quality pixels such as clouds and cloud shadows on MOD09A1 data, scale factor conversion is performed on red light and near-infrared reflectance and NDVI is calculated, and then ten-day scale average synthesis is performed; QC_Day quality control information is used to screen effective surface temperature pixels for MOD11A2 data, and scale factor conversion and unit conversion are completed and ten-day scale average synthesis is performed.\r\nOn this basis, a P5/P95 percentile benchmark was established based on historical data from the same month from 2001 to 2022, the vegetation condition index VCI and the temperature condition index TCI were calculated respectively, and the vegetation water supply index VSWI was calculated using NDVI and surface temperature.\r\nThe vegetation health index VHI was further calculated according to the weight of 50% of VCI and TCI, and five drought levels were divided according to VHI: normal, light drought, moderate drought, severe drought and extreme drought, forming the Drought_Class band.\r\nFinally, NDVI, VCI, TCI, VSWI, VHI and Drought_Class will be uniformly processed to a spatial resolution of 1 km, clipped to Tajikistan, and output in multi-band GeoTIFF format.", "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, "belong_to_nieer": false, "allow_update_data": false, "created_from": "xmhj", "ds_topic_tags": [ "遥感", "干旱监测" ], "ds_subject_tags": [ "农学" ], "ds_class_tags": [], "ds_locus_tags": [ "塔吉克斯坦" ], "ds_time_tags": [ 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022 ], "ds_contributors": [ { "true_name": "钱静", "email": "jing.qian@siat.ac.cn", "work_for": "中国科学院深圳先进技术研究院", "country": "中国" }, { "true_name": "孙博", "email": "sunbo@siat.ac.cn", "work_for": "中国科学院深圳先进技术研究院", "country": "中国" }, { "true_name": "魏树杰", "email": "sj.wei@siat.ac.cn", "work_for": "中国科学院深圳先进技术研究院", "country": "中国" }, { "true_name": "邢秀为", "email": "xw.xing@siat.ac.cn", "work_for": "中国科学院深圳先进技术研究院", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "魏树杰", "email": "sj.wei@siat.ac.cn", "work_for": "中国科学院深圳先进技术研究院", "country": "中国" } ], "ds_managers": [ { "true_name": "魏树杰", "email": "sj.wei@siat.ac.cn", "work_for": "中国科学院深圳先进技术研究院", "country": "中国" } ], "category": "水文" }