青藏高原中部土壤温湿度多尺度观测网数据集

该数据集记录了青藏高原中部土壤温湿度观测网数据。

青藏高原,土壤温湿度,观测网,温度,湿度
:阳坤 :北京市朝阳区林萃路16号院3号楼 100101
:yangk@itpcas.ac.cn

a. 数据内容(数据文件、表名称,包含的观测指标内容)

(1)57个观测站点

(2)2个观测变量(土壤湿度、土壤温度)

(3)4个观测深度(0-5、10、20和40cm)

(4)3个典型空间尺度,分别对应GCM网格(1°)、被动微波卫星象元(0.3°)、以及雷达卫星象元(0.1°)

 

b. 项目来源:中国气象局行业专项、国家杰出青年科学基金。

 

c. 建设目的

观测网的建立将为一系列水文气象研究提供支持,主要包括:提供三个空间尺度(1°、0.3°、0.1°)的土壤水分和冻融实测数据集;为土壤水分升尺度研究提供数据基础;完善那曲地区中尺度水文气象观测。

 

d. 服务对象

 从事水文气象研究的学生和科研人员。

 

e. 数据的时间范围

2010.08.01-2014.12.31 。

 

f. 数据的空间范围、投影方式

青藏高原中部土壤温湿度观测网位于青藏高原中部10000km2的空间范围,站点平均海拔为4650米。纬度:31°-32°N;经度:91.5°-92.5°E。

 

g. 数据的学科范围

属于水文气象学范围。

 

h. 数据的量

所有数据合计约31MB。

 

i.数据类型

ASCII-text。

 

j. 数据更新的频度

 2次/年。

 

k. 缩略图

 

青藏高原中部土壤温湿度观测网示意图:(a)观测网在高原中部的位置(由小矩形表示);(b)实验区域站点分布状况(灰色曲线为国家/省级公路);(c)-(e)分别指大,中,小三个观测网络。DEM是显示在图(a)-(b)中,土地使用状况是显示在图(c)-(e)中。

a. 数据文件字段描述:

例如 “SM_NQ-30 minutes-05cm.txt”,“ST_NQ-30 minutes-05cm.txt”

其中SM指土壤水分,ST指土壤温度,NQ指那曲,30minutes指代数据时间分辨率,05cm指采样土壤层深度。

 

b. 数据内容字段描述:

(1)30min分辨率

变量1-6:日期(整型:yyyy-mm-dd-hh-mm-ss)

变量7-63:各站点观测数据值(实型,缺测值:-99.00)

(2)daily分辨率

变量1-3:日期(整型:yyyy-mm-dd)

变量4-60:各站点观测数据值(实型,缺测值:-99.00)

 

c. 变量单位描述:

土壤水分体积含量(SM) 单位:%vol(m3/m3

土壤温度(ST) 单位:℃

 

数据来自课题 

负责人:阳坤

单位:中国科学院青藏高原研究所

资助者:中国科学院

30min分辨率温度数据是进行质量控制后的直接采样数据,土壤水分体积含量是以烘干法测量土壤水分为基础的校正值

daily 分辨率数据是在30min分辨率基础上的算术平均值

a.土壤温度测量精度和分辨率:±1℃和0.1℃;

土壤水分测量精度和分辨率:±3%VWC和0.1%VWC。

b. 免责申明:尽管我们已经尽最大努力控制数据误差,但不对数据产品的精确性和可靠性作出任何担保。数据提供者的免责范围包括但不限于以下几个方面:数据质量的可靠性、数据在使用中的表现以及数据对于特殊目的研究的适用性。

1. Yang, K., J. Qin, L. Zhao, Y. Chen, W. Tang, M. Han, Lazhu, Z. Chen, N. Lu, B. Ding, H. Wu, and C. Lin, 2013: A Multi-Scale Soil Moisture and Freeze-Thaw Monitoring Network on the Third Pole, Bull. Amer. Meteor. Soc., 94(12), 1907–1916.

 

2. Qin, J., K. Yang, N. Lu, Y. Chen, L. Zhao, and M. Han, 2013: Spatial upscaling of in-situ soil moisture measurements based on MODIS-derived apparent thermal inertia, Remote Sens. Environ., 138, 1-9.

 

3. Chen, Y., K. Yang, J. Qin, L. Zhao, W. Tang, and M. Han, 2013: Evaluation of AMSR-E retrievals and GLDAS simulations against observations of a soil moisture network on the central Tibetan Plateau, J. Geophys. Res. Atmos., 118(10), 4466-4475,.

 

4. Zhao, L., K. Yang, J. Qin, Y. Chen, W. Tang, C. Montzka, H. Wu, C. Lin, M. Han, and H. Vereecken., 2013: Spatiotemporal analysis of soil moisture observations within a Tibetan mesoscale area and its implication to regional soil moisture measurements, J. Hydrol., 482, 92-104.

 

5. Zhao, L., K. Yang, J. Qin, Y. Chen, W. Tang, H. Lu, and Z. Yang, 2014: The scale-dependence of SMOS soil moisture accuracy and its improvement through land data assimilation in the central Tibetan Plateau, Remote Sens. Environ., 152, 345-355.

 

6. Han, M., K. Yang, J. Qin, R. Jin, Y. Ma, J. Wen, Y. Chen, L. Zhao, Lazhu and W. Tang, 2015: An algorithm based on the standard deviation of passive microwave brightness temperatures for monitoring soil surface freeze/thaw state on the Tibetan Plateau, IEEE Trans. Geosci. Remote Sens., 53(5), 2775-2783, doi:10.11-09/TGRS.2014.2364823.

 

7. Qin, J., L. Zhao, Y. Chen, K. Yang, Y. Yang, Z. Chen, and H. Lu, 2015: Inter-comparison of spatial upscaling methods for evaluation of satellite-based soil moisture, J. Hydrol., 523, 170-178, doi:10.1016/j.jhydrol.2015.01.061.

 

8. Yang, K., Lazhu, Y. Chen, L. Zhao, J. Qin, H. Lu, W. Tang, M. Han, B. Ding, and N. Fang, 2016: Land surface model calibration through microwave data assimilation for improving soil moisture simulations, J. Hydrol., 533, 266–276, doi:10.1016/j.jhydrol.2015.12.018. 

 

9. Zhao, T., Shi, J., Lin, M., Yin, X., Liu, Y., Lan, H. and Xiong, C., 2014. Potential soil moisture product from the Chinese HY-2 scanning microwave radiometer and its initial assessment. Journal of Applied Remote Sensing, 8(1), pp.083560-083560.

 

10. 叶勤玉, 柴琳娜, 蒋玲梅 and 赵天杰, 2014. 利用 AMSR2 和 MODIS 数据的土壤冻融相变水量降尺度方法. 遥感学报, 18(6), pp.1147-1157.

 

11. Bi, H., Ma, J. and Wang, F., 2014, July. Soil moisture estimation using an improved particle filter assimilation algorithm. 2014 IEEE Geoscience and Remote Sensing Symposium, pp. 3770-3773.

 

12. Zeng, J., Li, Z., Chen, Q., Bi, H. and Zou, P., 2014, July. Land surface temperature estimates in the Tibetan Plateau from passive microwave observations. In 2014 IEEE Geoscience and Remote Sensing Symposium, pp. 2558-2561.

 

13. Zeng, J., Li, Z., Chen, Q., Bi, H., Qiu, J. and Zou, P., 2015. Evaluation of remotely sensed and reanalysis soil moisture products over the Tibetan Plateau using in-situ observations. Remote Sensing of Environment, 163, pp.91-110.

 

14. Bi, H., Ma, J. and Wang, F., 2015. An improved particle filter algorithm based on ensemble Kalman filter and Markov chain Monte Carlo method. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(2), pp.447-459.

 

15. Zeng, J., Li, Z., Chen, Q. and Bi, H., 2015. Method for soil moisture and surface temperature estimation in the Tibetan Plateau using spaceborne radiometer observations. IEEE Geoscience and Remote Sensing Letters, 12(1), pp.97-101.

 

16. Yang, Y., Guan, H., Long, D., Liu, B., Qin, G., Qin, J. and Batelaan, O., 2015. Estimation of surface soil moisture from thermal infrared remote sensing using an improved trapezoid method. Remote Sensing, 7(7), pp.8250-8270.

 

17. Li, D., Zhao, T., Shi, J., Bindlish, R., Jackson, T.J., Peng, B., An, M. and Han, B., 2015. First Evaluation of Aquarius Soil Moisture Products Using In Situ Observations and GLDAS Model Simulations. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(12), pp.5511-5525.

 

18. Li, Y., Shi, J. and Zhao, T., 2015. Effective vegetation optical depth retrieval using microwave vegetation indices from WindSat data for short vegetation. Journal of Applied Remote Sensing, 9(1), pp.096003-096003.

 

19. Zeng, J., Li, Z., Chen, Q. and Bi, H., 2015, July. Assessment of the newest ECV soil moisture product over the Tibetan plateau using ground-based observations. In 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 665-668.

 

20. Kou, X., Jiang, L., Bo, Y., Yan, S. and Chai, L., 2016. Estimation of Land Surface Temperature through Blending MODIS and AMSR-E Data with the Bayesian Maximum Entropy Method. Remote Sensing, 8(2), p.105.

 

21. Wang, G., Hagan, D.F.T., Lou, D. and Chen, T., 2016. Evaluation of soil moisture derived from FY3B microwave brightness temperature over the Tibetan Plateau. Remote Sensing Letters, 7(9), pp.817-826.

 

22. Bi, H., J. Ma, W. Zheng, and J. Zeng (2016), Comparison of soil moisture in GLDAS model simulations and in situ observations over the Tibetan Plateau, J. Geophys. Res. Atmos., 121, 2658–2678, doi:10.1002/2015JD024131

 

23. Sun, S., Chen, B., Chen, J., Che, M. and Zhang, H., 2016. Comparison of remotely-sensed and modeled soil moisture using CLM4. 0 with in situ measurements in the central Tibetan Plateau area. Cold Regions Science and Technology. 129, 31–44

 

24. Wang, L., Li, X., Chen, Y., Yang, K., Chen, D., Zhou, J., Liu, W., Qi, J. and Huang, J., 2016. Validation of the global land data assimilation system based on measurements of soil temperature profiles. Agricultural and Forest Meteorology, 218, pp.288-297.

 

25. Xiao, Z., Jiang, L., Zhu, Z., Wang, J. and Du, J., 2016. Spatially and Temporally Complete Satellite Soil Moisture Data Based on a Data Assimilation Method. Remote Sensing, 8(1), p.49.

 

使用本数据仅限于科学研究。使用该数据发表学术论文的作者应向数据提供者发送一份论文拷贝。建议引用文献:

Zhao, L., K. Yang, J. Qin, Y. Chen, W. Tang, C. Montzka, H. Wu, C. Lin, M. Han, and H. Vereecken., 2013: Spatiotemporal analysis of soil moisture observations within a Tibetan mesoscale area and its implication to regional soil moisture measurements, J. Hydrol., 482, 92-104.

 

Chen, Y., K. Yang, J. Qin, L. Zhao, W. Tang, and M. Han, 2013: Evaluation of AMSR-E retrievals and GLDAS simulations against observations of a soil moisture network on the central Tibetan Plateau, J. Geophys. Res. Atmos., 118(10), 4466-4475,.

 

Qin, J., K. Yang, N. Lu, Y. Chen, L. Zhao, and M. Han, 2013: Spatial upscaling of in-situ soil moisture measurements based on MODIS-derived apparent thermal inertia, Remote Sens. Environ., 138, 1-9. DOI: 10.11888/AtmosphericPhysics.tpe.249400.file

 

Yang., K., J. Qin, L. Zhao, Y. Y. Chen, W. J. Tang, M. L. Han, Lazhu., Z. Q. Chen, N. Lv, B. H. Ding, H. Wu, C. G. Lin,. 2013. A Multi-Scale Soil Moisture and Freeze-Thaw Monitoring Network on the Third Pole, Bull. Am. Meteorol. Soc., DOI: 10.1175/BAMS-D-12-00203.1.

 
DOI: 10.11888/AtmosphericPhysics.tpe.249400.file
2027
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