中国东北地区是我国三大积雪区之一。该地区地处高纬度，冬季太阳高度角低，光照条件差，并且该地区具有很高的森林覆盖度。当前MODISV6积雪产品提供的归一化差值积雪指数（NDSI）在中国东北地区，尤其是东北森林地区存在明显的过度云掩膜问题，严重影响了该产品在积雪时空变化研究中的应用。同时，MODISV6积雪产品采用了低NDSI屏蔽，导致森林地区存在积雪漏分现象。以MODISV6多种数据集作为源数据，分别采用上下午星合成、决策树分类和时空自适应去云的方法，生产了2000-2020年积雪季中国东北地区具有较高精度、时空连续的NDSI逐日无云产品。本数据集可用于我国东北地区积雪面积制图和积雪物候等后续科研工作。
", "ds_source": "在MODIS V6积雪产品中，使用了 MOD10A1/MYD10A1产品中的Raw_NDSI、NDSI_snow_cover数据集和MOD09GA产品中的sur_refl_b04波段数据集。上述产品数据来源于Google Earth Engine (https://code.eartengine.google.com)", "ds_process_way": "
第一步，影像裁剪。在Google Earth Engine 上首先对研究区积雪产品数据集进行裁剪。\n
第二步，上下午星合成，恢复部分像元的NDSI值。算法规则为若原产品上午定义有云，下午定义无云，则将下午星NDSI值赋值给当天；若上下午星都有云，则未被恢复，依然保留该像元为云像元；其余情况则采用上午星的NDSI值。\n
第三步，采用决策树判别方法。首先对Raw_NDSI进行均值滤波，算法规则是像元周围9个像元的值直接平均赋值给中心像元，得到新的数据层Mean_NDSI。若MOD10A1中的云像元对应的Mean_NDSI均值大于0.4，则认为该像元不是云像元，恢复该像元的Raw_NDSI值。当NDSI在0到0.4之间的像元，若绿光波段地表反射率小于或等于0.4，则该像元为非云像元，恢复其Raw NDSI 值；若绿光波段地表反射率大于0.4，则该像元仍标记为云。当NDSI在小于0的像元，仍然认定为云像元（此步骤值只针对上下午星合成后仍标记为云的像元进行判别）。\n
第四步，进行临近日合成。算法规则为若目标像元前后天都是非云像元，则将前后天NDSI值平均后赋值给当天；若前后两天中有一天为非云像元，则直接使用该非云像元的NDSI值赋值给当天；若前后天都为云像元，则需寻找前后两天像元继续判别，算法规则同上；若前后两天同样也都是云像元，则目标像元仍未得到恢复，计算结束，等待下一步去云操作。\n
第五步，采用chen提出的时空自适应去云算法，进行全图层彻底去云。其主要思路是判断相似像元，基于相似像元的NDSI在自适应时空中赋予相应的权重去云。", "ds_quality": "
从数据生产的每个环节进行质量控制，MODIS V6 版本与之前V5相比，有了很大的改进，对水、云、重气溶胶等进行了大气校正。因此，本研究数据来源可靠。\n
从原始数据到最终产品的计算中，每个处理步骤都确保数据准确性。上下午星合成去云是积雪产品去云常用的方法，主要是考虑同一天的地表具有稳定性；决策树中的阈值也是基于文献或者统计特征选取的，并且选取了大量执行决策树之后的结果与假彩色合成影像进行了对比，该步骤可以有效恢复NDSI_snow_cover数据集中一些由森林积雪误分成云的问题。最终采用chen等的方法进行完全去云，该方法也经过了云假设检验证明了其精度。最后对2018年逐日的数据产品均进行了目视检验，结果表明该数据集NDSI分布合理，可以作为东北地区积雪研究的基础数据源。", "ds_acq_start_time": "2000-01-01 00:00:00", "ds_acq_end_time": "2020-12-31 00:00:00", "ds_acq_place": "外兴安岭,大兴安岭,小兴安岭,长白山,东北平原,蒙古高原,朝鲜半岛,地理区域范围涉及到的国家有中国:俄罗斯,朝鲜,韩国,蒙古国等", "ds_acq_lon_east": 136.0, "ds_acq_lat_south": 37.0, "ds_acq_lon_west": 114.0, "ds_acq_lat_north": 54.5, "ds_acq_alt_low": -149.0, "ds_acq_alt_high": 2643.0, "ds_share_type": "apply-access", "ds_total_size": 651856864442, "ds_files_count": 5079, "ds_format": ".tif", "ds_space_res": "500m", "ds_time_res": "日", "ds_coordinate": "WGS84", "ds_projection": "", "ds_thumbnail": "a1d6146d-df7b-43bc-836d-922002130e50.jpg", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "本文以MODIS V6版本数据集为数据源，采用上下午星合成、决策树判断和时空自适应去云算法，综合考虑产品精度和计算时效性，生成了2000-2020年中国东北地区积雪季逐日无云NDSI序列产品。源数据来源于Google Earth Engine (https://code.eartengine.google.com)。中国东北长时间序列逐日无云归一化差值积雪指数（NDSI）数据集，保存为TIFF格式，能够在Arcgis、envi及matlab等相关软件中对数据进行读取、查看、编辑及后续的一系列统计分析工作。需要说明的是本数据集并未对水体进行掩膜。此外，考虑到本数据集服务于后续的积雪产品生产工作中，所以并未使用东北积雪区矢量边界对数据集进行裁剪工作，数据集完全包含了东北积雪区。", "ds_from_station": null, "organization_id": "aba68fe5-65d3-41b1-b036-bc274a834b5e", "ds_serv_man": "李红星", "ds_serv_phone": "0931-4967592", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "10.12072/ncdc.I-SNOW.db0024.2021", "subject_codes": [], "quality_level": 3, "publish_time": "2021-12-06 15:53:34", "last_updated": "2023-03-07 09:18:45", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.ncdc.isnow.db2527.2022", "i18n": { "en": { "title": "Daily cloudless NDSI data set with 500m resolution snow season in Northeast China (2000-2020)", "ds_format": "", "ds_source": "
<pre><code> <pre><code>\nIn MODIS V6 snow products, raw in mod10a1 / myd10a1 products is used_ NDSI、NDSI_ snow_ Cover dataset and sur in mod09ga products_ refl_ B04 band dataset. The above product data comes from Google Earth engine（ https://code.eartengine.google.com )", "ds_quality": "
<pre><code> <pre><code>\nQuality control is carried out from each link of data production. MODIS V6 version is greatly improved compared with previous V5, and atmospheric correction is carried out for water, cloud and heavy aerosol. Therefore, the data source of this study is reliable.\n
From the original data to the calculation of the final product, each processing step ensures the accuracy of the data. In the morning and afternoon, star synthesis cloud removal is a common method for snow products, mainly considering the stability of the surface on the same day; The threshold in the decision tree is also selected based on literature or statistical features, and a large number of results after executing the decision tree are selected and compared with false color synthetic images. This step can effectively restore NDSI_ snow_ There are some problems of mistakenly dividing forest snow into clouds in cover data set. Finally, Chen's method is used for complete cloud removal, and its accuracy is proved by cloud hypothesis test. Finally, visual inspection was conducted on the daily data products in 2018. The results show that the NDSI distribution of the data set is reasonable and can be used as the basic data source for snow cover research in Northeast China.", "ds_ref_way": "", "ds_abstract": "
<pre><code> <pre><code>\nNortheast China is one of the Three Snow areas in China. The region is located in high latitude, with low solar altitude angle in winter, poor lighting conditions, and high forest coverage. At present, the normalized difference snow index (NDSI) provided by MODIS V6 snow product has an obvious problem of excessive cloud mask in Northeast China, especially in northeast forest areas, which seriously affects the application of this product in the study of temporal and spatial variation of snow. At the same time, MODIS V6 snow products adopt low NDSI shielding, resulting in snow leakage in forest areas. Taking various MODIS V6 data sets as the source data, using the methods of morning and afternoon star synthesis, decision tree classification and spatio-temporal adaptive cloud removal respectively, the NDSI daily cloudless products with high precision and spatio-temporal continuity in Northeast China during the snow season from 2000 to 2020 were produced. This data set can be used for snow area mapping and snow phenology in Northeast China.
", "ds_time_res": "日", "ds_acq_place": "The outer Xing'an Mountains, the Greater Xing'an Mountains, the lesser Xing'an Mountains, the Changbai Mountains, the Northeast Plain, the Mongolian Plateau, the Korean Peninsula, and the geographical region covers China: Russia, North Korea, South Korea, Mongolia, etc", "ds_space_res": "500m", "ds_projection": "", "ds_process_way": " <pre><code> <pre><code>\nThe first step is image clipping. Firstly, the snow product data set in the study area is cut on Google Earth engine.\n
The second step is star synthesis in the morning and afternoon to recover the NDSI values of some pixels. The algorithm rule is that if the original product is defined with cloud in the morning and no cloud in the afternoon, the NDSI value of the afternoon star will be assigned to the current day; If the stars are cloudy in the morning and afternoon, it is not restored, and the pixel is still retained as a cloud pixel; In other cases, the NDSI value of morning star is used.\n
The third step is to use the decision tree discrimination method. First, raw_ NDSI performs mean filtering. The algorithm rule is that the values of 9 pixels around the pixel are directly averaged and assigned to the central pixel to obtain a new data layer mean_ NDSI。 If the cloud pixel in mod10a1 corresponds to mean_ If the NDSI mean is greater than 0.4, it is considered that the pixel is not a cloud pixel, and the raw of the pixel is restored_ NDSI value. When the NDSI of a pixel is between 0 and 0.4, if the surface reflectance in the green band is less than or equal to 0.4, the pixel is a non cloud pixel, and its raw NDSI value is restored; If the surface reflectance in the green band is greater than 0.4, the pixel is still marked as a cloud. When the NDSI is less than 0, it is still recognized as a cloud pixel (this step value is only used to judge the pixel still marked as a cloud after star synthesis in the morning and afternoon).\n
The fourth step is to synthesize the adjacent day. The algorithm rule is that if the target pixel is a non cloud pixel before and after the day, the NDSI values of the before and after the day are averaged and assigned to the day; If one of the two days is a non cloud pixel, the NDSI value of the non cloud pixel is directly assigned to the current day; If the front and back days are cloud pixels, it is necessary to find the pixels in the front and back days to continue discrimination, and the algorithm rules are the same as above; If there are also cloud pixels in the first two days and the second two days, the target pixel has not been recovered. The calculation ends and waits for the next cloud operation.\n
In the fifth step, the spatiotemporal adaptive cloud removal algorithm proposed by Chen is used to completely remove the cloud in the whole image layer. The main idea is to judge similar pixels, and the NDSI based on similar pixels gives corresponding weight to cloud removal in adaptive space-time.", "ds_ref_instruction": " Taking MODIS V6 version data set as the data source, using morning and afternoon star synthesis, decision tree judgment and spatio-temporal adaptive cloud removal algorithm, comprehensively considering product accuracy and computational timeliness, this paper generates daily cloudless NDSI series products in snow season in Northeast China from 2000 to 2020. The source data comes from Google Earth engine（ https://code.eartengine.google.com )。 The daily cloudless normalized difference snow index (NDSI) data set of long-time Series in Northeast China is saved in TIFF format, which can read, view, edit and follow-up a series of statistical analysis in ArcGIS, envi, MATLAB and other related software. It should be noted that this data set does not mask the water body. In addition, considering that this data set serves the subsequent production of snow products, the vector boundary of northeast snow area is not used to cut the data set, and the data set completely includes northeast snow area." } }, "submit_center_id": "ncdc", "data_level": 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": [ "中国东北", "2000-2020", "NDSI", "遥感产品" ], "ds_subject_tags": [], "ds_class_tags": [], "ds_locus_tags": [ "中国东北", "外兴安岭", "大兴安岭", "小兴安岭", "长白山", "东北平原" ], "ds_time_tags": [ 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020 ], "ds_contributors": [ { "true_name": "韩超", "email": "hanch20@lzu.edu.cn", "work_for": "兰州大学", "country": "中国" }, { "true_name": "沈言龙", "email": "shenyl19@lzu.edu.cn", "work_for": "兰州大学", "country": "中国" }, { "true_name": "欧阳志棋", "email": "ouyzhq21@lzu.edu.cn", "work_for": "兰州大学", "country": "中国" }, { "true_name": "谢佩瑶", "email": "xiepy19@lzu.edu.cn", "work_for": "兰州大学", "country": "中国" }, { "true_name": "郭惠", "email": "hguo2021@lzu.edu.cn", "work_for": "兰州大学", "country": "中国" }, { "true_name": "陈思勇", "email": "chensy18@lzu.edu.cn", "work_for": "南京大学", "country": "中国" }, { "true_name": "王晓艳", "email": "wangxiaoy@lzu.edu.cn", "work_for": "兰州大学", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "韩超", "email": "hanch20@lzu.edu.cn", "work_for": "兰州大学", "country": "中国" } ], "ds_managers": [ { "true_name": "王晓艳", "email": "wangxiaoy@lzu.edu.cn", "work_for": "兰州大学", "country": "中国" } ], "category": "积雪" }