数据集采用基于自动端元提取的多端元谱混合分析算法（MESMA-AGE）和多步时空插值算法（MSTI）对AWT区域（AWT MODIS FSC）的MODIS日云隙填充分数积雪产品进行了研究。AWT MODIS FSC 产品的空间分辨率为 0.005°，跨度从 2000 年到 2022 年。Landsat-8 影像的 2745 个场景用于区域尺度精度评估。分数积雪精度指标，包括决定系数 （R2）、均方根误差 （RMSE） 和平均绝对误差 （MAE） 分别为 0.80、0.16 和 0.10。二元化识别准确率指标，包括总体准确率（OA）、生产者准确率（PA）和用户准确率（UA），分别为95.17%、97.34%和97.59%。在175个气象站观测到的雪深数据用于评估点尺度的精度，得出以下精度指标：OA为93.26%，PA为84.41%，UA为82.14%，Cohen kappa（CK）值为0.79。气象台站的积雪深度观测也用于评估不同天气条件下的积雪分数，MODIS晴空观测（基于MSTI算法的时空重建）的OA为95.36 %（88.96 %），PA为87.75 %（82.26 %），UA为86.86 %（78.86 %），CK为0.84 （0.72）。AWT MODIS FSC 产品可为亚洲水塔地区的山地水文模型、地表模型和数值天气预报提供积雪的定量空间分布信息。", "ds_source": "
MODIS表面反射率数据：使用了 2000 年至 2022 年系列 6 中MOD09GA和 MYD09GA的 MODIS 表面反射率产品。\n
Landsat-8数据：使用谷歌地球引擎（GEE）云平台，选取2013—2021年共2745张云覆盖率小于10%、积雪覆盖率大于30%的Landsat-8影像作为“地面实况”，验证我部分积雪覆盖产品。\n
地面积雪深度数据：使用了中国气象局于2000年2月26日至2019年4月30日在亚洲水塔地区提供的175个原位站数据。\n
辅助数据：亚洲水塔地区的高程和土地覆盖类型。GEE云平台提供了MCD12Q1 V6.1年度国际地圈-生物圈计划（IGBP）分类数据（Sulla-Menashe等人，2019）。利用GEE云平台获取航天飞机雷达地形测量任务（STRM）数字高程模型（DEM）数据。", "ds_process_way": "
根据青藏高原地区MOD10A1、MODSCAG和MODAG部分积雪产品的精度评价，MODAG产品的精度最高。因此，选择MODAAGE部分积雪反演算法（MESMA-AGE算法）对亚洲水塔地区的Terra和Aqua MODIS表面反射率版本6数据进行部分积雪反演。其次，基于Terra/MODIS分数积雪反演结果，利用Aqua/MODIS分数积雪反演结果填补了由于云和缺失观测而导致的数据空白。第三，使用地理空间数据抽象库 （GDAL） 重新投影和镶嵌 12 个 MODIS 瓦片的分数积雪恢复结果。第四，开发了MSTI算法，对具有云量或缺失数据的像素进行时空插值，从而能够生成每日云隙填充的分数积雪产品。最后，利用气象站积雪深度数据和Landsat-8影像，对MESMA-AGE算法和AWT MODIS FSC 产品进行了精度评估和算法优化。", "ds_quality": "
根据青藏高原地区MOD10A1、MODSCAG和MODAG部分积雪产品的精度评价，MODAG产品的精度最高。因此，选择MODAAGE部分积雪反演算法（MESMA-AGE算法）对亚洲水塔地区的Terra和Aqua MODIS表面反射率版本6数据进行部分积雪反演。其次，基于Terra/MODIS分数积雪反演结果，利用Aqua/MODIS分数积雪反演结果填补了由于云和缺失观测而导致的数据空白。第三，使用地理空间数据抽象库 （GDAL） 重新投影和镶嵌 12 个 MODIS 瓦片的分数积雪恢复结果。第四，开发了MSTI算法，对具有云量或缺失数据的像素进行时空插值，从而能够生成每日云隙填充的分数积雪产品。最后，利用气象站积雪深度数据和Landsat-8影像，对MESMA-AGE算法和AWT MODIS FSC 产品进行了精度评估和算法优化。", "ds_acq_start_time": "2000-01-01 00:00:00", "ds_acq_end_time": "2022-12-31 00:00:00", "ds_acq_place": "亚洲水塔区", "ds_acq_lon_east": 106.0, "ds_acq_lat_south": 24.0, "ds_acq_lon_west": 60.0, "ds_acq_lat_north": 54.0, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 39955178352, "ds_files_count": 24, "ds_format": "nc", "ds_space_res": "", "ds_time_res": "日", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "10328ce3-3d5f-403c-b4b5-821d9cb6a5e8.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": [ "170.4510" ], "quality_level": 3, "publish_time": "2024-07-26 17:03:18", "last_updated": "2026-01-14 10:28:54", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.ZENODO.DB6673.2024", "i18n": { "en": { "title": "MODIS Daily Cloud-gap-filled Fractional Snow Cover Dataset of the Asian Water Tower Region (2000-2022)", "ds_format": "nc", "ds_source": "
MODIS surface reflectance data: MODIS surface reflectance products of MOD09GA and MYD09GA from 2000 to 2022 series 6 were used.\n
Landsat-8 data: Using the Google Earth Engine (GEE) cloud platform, a total of 2745 Landsat-8 images with cloud coverage less than 10% and snow coverage greater than 30% from 2013 to 2021 were selected as \"ground truth\" to verify some of our snow coverage products.\n
Surface snow depth data: 175 in-situ station data provided by the China Meteorological Administration in the Asian Water Tower area from February 26, 2000 to April 30, 2019 were used.\n
Auxiliary data: elevation and land cover type of the Asian water tower area. The GEE cloud platform provides MCD12Q1 V6.1 International Geosphere Biosphere Programme (IGBP) classification data (Sulla Menashe et al., 2019). Utilize the GEE cloud platform to obtain digital elevation model (DEM) data for the Space Shuttle Radar Topography Mission (STRM).", "ds_quality": "
According to the accuracy evaluation of MOD10A1, MODSCAG, and MODAG snow products in the Qinghai Tibet Plateau region, MODAG products have the highest accuracy. Therefore, the MODAAGE partial snow inversion algorithm (MESMA-AGE algorithm) was selected to perform partial snow inversion on the Terra and Aqua MODIS surface reflectance version 6 data in the Asian Water Tower region. Secondly, based on the Terra/MODIS fractional snow inversion results, the Aqua/MODIS fractional snow inversion results were used to fill the data gap caused by cloud and missing observations. Thirdly, use the Geospatial Data Abstraction Library (GDAL) to re project and embed the score snow restoration results of 12 MODIS tiles. Fourthly, the MSTI algorithm has been developed to perform spatiotemporal interpolation on pixels with cloud cover or missing data, enabling the generation of daily cloud gap filling score snow products. Finally, accuracy evaluation and algorithm optimization were conducted on the MESMA-AGE algorithm and AWT MODIS FSC product using snow depth data from meteorological stations and Landsat-8 imagery.", "ds_ref_way": "", "ds_abstract": "
The dataset was used to study the MODIS daily gap filling fraction snow products in the AWT region (AWT MODIS FSC) using the Multi Element Spectral Hybrid Analysis algorithm based on automatic endmember extraction (MESMA-AGE) and the Multi Step Spatiotemporal Interpolation algorithm (MSTI). The spatial resolution of AWT MODIS FSC products is 0.005 °, spanning from 2000 to 2022. 2745 scenes from Landsat-8 imagery were used for regional scale accuracy assessment. The accuracy indicators for fractional snow cover, including coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE), are 0.80, 0.16, and 0.10, respectively. The binary recognition accuracy indicators, including overall accuracy (OA), producer accuracy (PA), and user accuracy (UA), are 95.17%, 97.34%, and 97.59%, respectively. The snow depth data observed at 175 meteorological stations were used to evaluate the accuracy of point scale, and the following accuracy indicators were obtained: OA was 93.26%, PA was 84.41%, UA was 82.14%, and Cohen kappa (CK) value was 0.79. The snow depth observation of meteorological stations is also used to evaluate the snow fraction under different weather conditions. The OA of MODIS clear sky observation (spatiotemporal reconstruction based on MSTI algorithm) is 95.36% (88.96%), PA is 87.75% (82.26%), UA is 86.86% (78.86%), and CK is 0.84 (0.72). The AWT MODIS FSC product can provide quantitative spatial distribution information of snow cover for mountain hydrological models, surface models, and numerical weather forecasts in the Asian water tower region.
", "ds_time_res": "日", "ds_acq_place": "Asian Water Tower Area", "ds_space_res": "", "ds_projection": "", "ds_process_way": "According to the accuracy evaluation of MOD10A1, MODSCAG, and MODAG snow products in the Qinghai Tibet Plateau region, MODAG products have the highest accuracy. Therefore, the MODAAGE partial snow inversion algorithm (MESMA-AGE algorithm) was selected to perform partial snow inversion on the Terra and Aqua MODIS surface reflectance version 6 data in the Asian Water Tower region. Secondly, based on the Terra/MODIS fractional snow inversion results, the Aqua/MODIS fractional snow inversion results were used to fill the data gap caused by cloud and missing observations. Thirdly, use the Geospatial Data Abstraction Library (GDAL) to re project and embed the score snow restoration results of 12 MODIS tiles. Fourthly, the MSTI algorithm has been developed to perform spatiotemporal interpolation on pixels with cloud cover or missing data, enabling the generation of daily cloud gap filling score snow products. Finally, accuracy evaluation and algorithm optimization were conducted on the MESMA-AGE algorithm and AWT MODIS FSC product using snow depth data from meteorological stations and Landsat-8 imagery.", "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": [ "MODIS", "亚洲水塔区", "积雪" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "亚洲水塔区" ], "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 ], "ds_contributors": [ { "true_name": "蒋玲梅", "email": "jiang@bnu.edu.cn", "work_for": "北京师范大学", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "蒋玲梅", "email": "jiang@bnu.edu.cn", "work_for": "北京师范大学", "country": "中国" } ], "ds_managers": [ { "true_name": "蒋玲梅", "email": "jiang@bnu.edu.cn", "work_for": "北京师范大学", "country": "中国" } ], "category": "积雪" }