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全球最大土壤水分数据库（国际土壤水分网络）——武大是首个高校
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——武汉大学成为我国首个加入该数据库的高校
据介绍，国际土壤水分网络（International Soil Moisture Network ，简称ISMN），是全球最大的地面土壤水分共享数据库，由维也纳科技大学教授沃特·多里戈（WouterDorigo）和沃尔夫冈·瓦格纳（Wolfgang Wagner）牵头，致力于为数值天气预报、洪水预测、农业干旱监测和水资源管理等领域的研究提供重要的基础土壤数据。目前，全球已有超过50个地面土壤水分观测网络、200余个站点的数据加入到ISMN。
国际土壤水分网络不久前发布通知，成功接入武汉大学传感网地面气象和土壤观测数据。武汉大学成为我国首个加入该数据库的高校（The new network "SW-WHU", coordinated by the Wuhan University in China, is now available. Currently, it consists of 7 stations in the province Hubei, 5 stations are located in Wuhan and 2 stations are close to Yichang. Each station measures soil moisture in one depth, soil temperature, precipitation and air temperature. Datasets are currently available from autumn 2014 to June 2015.We would like to thank Chen Nengcheng and Xiang Zhang for providing the data.）。
按该网站说明，如要获取土壤水分有关数据，需要填写比较详细的表格进行申请。
http://ismn.geo.tuwien.ac.at/ismn/
Welcome to the International Soil Moisture Network
The International Soil Moisture Network is an international cooperation to establish and maintain a global in-situ soil moisture database. This database is an essential means of the geoscientific community for validating and improving global satellite observations and land surface models.
Soil moisture, which is the water stored in the upper soil layer, is a crucial parameter for a large number of applications, including numerical weather prediction, flood forecasting, agricultural drought assessment, water resources management, greenhouse gas accounting, civil protection, and epidemiological modeling of water borne diseases. Therefore, the societal benefits of the International Soil Moisture Network are expected to be large.
This international initiative is coordinated by the Global Energy and Water Exchanges Project (GEWEX) in cooperation with the Group of Earth Observation (GEO) and the Committee on Earth Observation Satellites (CEOS). The International Soil Moisture Network has been made possible through the voluntary contributions of scientists and networks from around the world. The International Soil Moisture Network is operated in cooperation with the Global Soil Moisture Databank of the Rutgers University.
http://ismn.geo.tuwien.ac.at/about-us/
Download Instructions
Viewing the data is possible without registration. To download any data you first need to login or register at the bottom left of this page.
In the following paragraphs the data download and viewing process are explained.
To start the data download/viewing application press the "Data Access" button on the bottom left of this page.
Data Download
To download data you have to select the networks and the time range from the menu on the left, you can also specify a latitude/longitude range but that's not mandatory. When the selection process is finished press the "Download" button and select the data format in which you want to receive the data. By pressing the "Create File" Button a .zip file with your data is created and a link to it will be provided as soon as it is finished. (Notice: This can take a while depending on the amount of data selected)
Data Format
Two format types are available (klick for further information):
Data Viewing
Depending on the zoom level the networks or the stations are shown on the map. Clicking on a network (red marker) will show you information about that particular network and a button to zoom to the stations of that network. Clicking on a station (blue marker) will show more information about it and a button to view the data recorded by this station. When clicking the "View Data" button the data that is available, in the date range which is selected in the menu, will be loaded. You can then explore the data by selecting up to 3 variables from the selectors at the bottom of the data viewer. It's also possible to zoom into the data using either the mouse wheel, the buttons at the top or the range selector at the bottom. (Notice: If to much data is selected the data viewing can become slow and the browser may freeze depending on your system specifications. Try to use a modern web browser.)
Advanced Download
By clicking the "Advanced Download" button in the menu, a new window/tab opens and you can enter custom SQL queries. The database structure is displayed at the right side of the screen and shows all the available tables and columns. The "Execute" button executes the query and returns a table with the results or an error. If the result is good the "Create File" button will generate a .zip file with your data.
Automated Download
It will be possible to download data automatically using python scripts that will be provided as soon as they are finished and thoroughly tested.
附1：http://www.gs.whu.edu.cn/index.php/index-view-aid-7796.html
武大加入全球最大土壤水分数据库
时间：2015年12月01日 发布者： 来源：测绘遥感信息工程国家重点实验室
新闻网讯 国际土壤水分网络发布通知，成功接入武汉大学传感网地面气象和土壤观测数据。武汉大学成为我国首个加入该数据库的高校。
国际土壤水分网络（International Soil Moisture Network ，简称ISMN），是全球最大的地面土壤水分共享数据库，由维也纳科技大学教授沃特·多里戈（WouterDorigo）和沃尔夫冈·瓦格纳（Wolfgang Wagner）牵头，致力于为数值天气预报、洪水预测、农业干旱监测和水资源管理等领域的研究提供重要的基础土壤数据。目前，全球已有超过50个地面土壤水分观测网络、200余个站点的数据加入到ISMN。
武汉大学传感网（Sensor Web-Wuhan University，简称SW-WHU），由测绘遥感信息工程国家重点实验室陈能成教授团队建设，此次ISMN成功接入了该网自2014年8月至2015年6月的地面气象和土壤观测数据。这些数据由分布在武汉和宜昌三峡两地的7个站点、共计54颗传感器提供。观测周期为1小时，土壤观测深度为地表以下10厘米、40厘米和100厘米。这些数据的共享有助于科研人员对武汉和三峡地表环境监测和建模开展更多研究。
据悉，SW-WHU传感网的建设受国家科技部重点基础研究973计划“空天地一体化对地观测传感网的理论与方法”和国家自然科学基金“地学工作流驱动的传感网即时协同制图方法”等项目资助。自2014年以来，陈能成领导团队在武汉豹澥、华中农业大学和三峡野猫面等多地部署了地面传感网，并以武汉大学GeoSensor为中心节点，接入中国矿业大学环境监测传感网、抚顺西露天煤矿传感网和NASA ECHO, NOAA CLASS和USGS Landsat等多个地面和网络数据系统，已形成包含百余种万级数量传感器的分布式传感网试验场。
相关链接：https://ismn.geo.tuwien.ac.at/newsitem/new-network-sw-whu-2015-11-16/
ISMN中SW-WHU的地理位置和摘要图
SW-WHU在武汉豹澥部署的监测站
The International Soil Moisture Network_a data hosting facility for global in si.pdf
附：http://www.igsnrr.ac.cn/xwzx/jxlwtj/201411/t20141127_4261854.html
| 刘苏峡研究小组关于区域土壤水分的研究新进展 |
| 2014-11-27 |
区域土壤水分，是水文循环的重要环节，对区域干旱预测、生态系统管理和水土资源配置均具有重要意义。然而传统的水文模拟更看重降水和径流，视土壤水分为中间环节，缺乏深入研究。另外，土壤水分的观测资料在全球范围内都非常贫乏，资料积累尤为有限。刘苏峡研究小组自1995年至今，一直致力于区域土壤水分的研究。通过多渠道集成实测土壤水分及相关的土壤、植被、地下水位等多源资料，建成了中国统一时间间隔的实测土壤水分数据库，与美国、俄罗斯、蒙古等共建了全球唯一的实测土壤水分数据库，现已融入国际土壤水分网络，具有广泛国际影响,属于国际上较早开展区域土壤水分研究团队。研究成果应用于西北荒漠化的水文效应、华北水文循环和东北水资源优化配置等重要国家需求。最近在土壤水分和土壤物理因子的深表层关系、遥感、实测和过程模型模拟土壤水分的深化比较、区域土壤水分的空间变异及驱动力等方面取得新进展。 1、建立了中国表层和深层土壤水分存在线性总关系。发现该关系随深层厚度增加而降低，不受土壤水分量级本身的影响。对小麦、玉米、花生、油菜等植被类型可以用该总关系来由表层推算深层土壤水分，该关系是这类植被获取深层土壤水分的重要途径。鉴于目前大多数研究均用土壤质地来刻画土壤垂直剖面变异性的现状，采用室内室外观测分析方法，探索了其他土壤物理因子对土壤分层的刻画，发现在一些地方如河南新乡，滞后含水率、饱和含水量、饱和导水率是比土壤质地更重要的分层因子。 宋亚路，刘苏峡，马英，胡超，莫兴国，2014. 土壤分层关键因子确定——以新乡实验农地为例。地理研究. 33(11):2115-2135 刘苏峡，邢博，袁国富，莫兴国，林忠辉, 2013. 中国根层与表层土壤水分关系分析，植物生态学报，37(1):1-17. 2、基于熵理论的互信息原理检验了遥感土壤水分算子的有效性。发现采用指数滤波从遥感观测的表层土壤水分导出的深层土壤水分指数的互信息与实测深层土壤水分的互信息容量相当，给遥感反演深层土壤水分的研究提供了支撑。 Jianxiu Qiu（邱建秀）, Wade T. Crow, Grey S. Nearing, Xingguo Mo（莫兴国）, Suxia Liu（刘苏峡）. 2014. The impact of vertical measurement depth on the information content of soil moisture times series data. Geophysical Research Letters, 41(14), 4997-5004. DOI: 10.1002/2014GL060017. http://onlinelibrary.wiley.com/doi/10.1002/2014GL060017/abstract 3、采用集合卡尔曼滤波同化方法研究了不同观测算子对应的VIP模型模拟的深层土壤水分和用遥感资料(AMSR-E、MODIS)反演的深层土壤水分的自相关系数的差异。发现同化后的模型估算的土壤水分跟实测的差别比未开展同化的模型估算和实测的差别要小；越深层次的土壤水分的估算受观测误差的影响越大；水分交换活跃的土层受遥感观测误差的影响比水分交换缓慢的土层所受影响要小。 Jianxiu Qiu（邱建秀）, Wade T. Crow, Xingguo Mo（莫兴国）, Suxia Liu（刘苏峡）.2014. The impact of temporal auto-correlation mismatches on the assimilation of satellite-derived surface soil moisture retrievals. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. Vol.7,issue 8. DOI: 10.1109/JSTARS.2014.2349354.http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&arnumber=6902782 4、比较了VIP生态水文模型模拟的区域土壤水分、遥感(ASCAT和AMSR-E)土壤水分和实测土壤水分的差异。发现ASCAT、AMSR-E 两组遥感数据在白洋淀流域的均方根误差分别为0.044 和0.054 m3 m-3，略低于SMOS、SMAP遥感土壤水分的目标精度0.040 m3 m-3 。实测土壤水分及ASCAT、AMSR-E遥感土壤水分时间序列基本都包含在模型的不确定区间内，表明这三种数据源尽管在绝对值上有差异，但都能准确地捕捉到表层土壤水分动态，其距平值信息互补。 Jianxiu Qiu（邱建秀）, Xingguo Mo（莫兴国）, Suxia Liu（刘苏峡）, Zhonghui Lin(林忠辉), Lihu Yang(杨丽虎), Xianfang Song(宋献方), Guangying Zhang, Vahid Naeimi, Wolfgang Wagner, 2013. Intercomparison of microwave remote sensing soil moisture datasets based on distributed eco-hydrological model simulation and in-situ measurements over the North China Plain. International Journal of Remote Sensing, 34 (19), 6587-6610. DOI:10.1080/01431161.2013.788799.http://www.tandfonline.com/doi/abs/10.1080/01431161.2013.788799#.U5-zJPmSzuo MO XINGGUO(莫兴国), JIANXIU QIU(邱建秀), SUXIA LIU(刘苏峡), VAHID NAEIMI.2011. Estimating root-layer soil moisture for North China from multiple data sources In: Mohsin Hafeez （ed.）Grace, Remote sensing and ground-based methods in multi-scale hydrology, Melbourne，Australia,29 June-5 July.2011. IAHS publication 343: 118-124. 5、根据VIP模型模拟、AMSR-E被动微波遥感反演、全球陆面模式模拟和实测等多源数据，采用经验正交函数法(EOF)，从白洋淀流域、华北区域到全国多个尺度，研究了土壤水分的空间变异及驱动力。发现土壤水分空间变化的驱动力随研究尺度而变。在流域尺度，地形因素是主要驱动力。在区域尺度，土壤质地比地形作用强。在全国尺度，地形和土壤性质是共同驱动力。随研究尺度增大，降水的驱动作用越强，遥感产品展示的空间变异程度越弱。 Jianxiu Qiu（邱建秀）, Xingguo Mo（莫兴国）, Suxia Liu（刘苏峡）, Zhonghui Lin(林忠辉), 2014. Exploring spatiotemporal patterns and physical controls of soil moisture at various spatial scales. Theoretical and Applied Climatology. Volume 118, Issue 1-2, pp 159-171.DOI: 10.1007/s00704-013-1050-6. http://link.springer.com/article/10.1007%2Fs00704-013-1050-6 |
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