Multiscale soil moisture and temperature monitoring network in the Central Tibetan Plateau (CTP-SMTMN)

The multi-scale monitoring network consists of 57 stations in the central Tibetan Plateau to measure two state variables (soil moisture and temperature) at three spatial scales (1.0, 0.3, 0.1 degree) and four soil depths (0~5, 10, 20, and 40 cm). Elevations of these stations vary over 4470~4950 m. The experimental area is characterized by low biomass, high soil moisture dynamic range, and typical freeze-thaw cycle. As auxiliary parameters of this network, soil texture and soil organic carbon content were measured at each station to support further studies. All the sensors have been calibrated by taking account of the impact of soil texture and soil organic carbon content on the measurements.

dense monitoring network,central Tibetan Plateau,soil moisture,soil temperature,Central Tibetan Plateau,CTP-SMTMN
:Kun Yang :Building 3, Courtyard 16, Lin Cui Road, Chaoyang District, Beijing 100101 P.R. China
:yangk@itpcas.ac.cn

Data content: The multi-scale monitoring network consists of 57 stations in the central Tibetan Plateau to measure two state variables (soil moisture and temperature) at three spatial scales (1.0, 0.3, 0.1 degree) and four soil depths (0~5, 10, 20, and 40 cm). Elevations of these stations vary over 4470~4950 m. The experimental area is characterized by low biomass, high soil moisture dynamic range, and typical freeze-thaw cycle. As auxiliary parameters of this network, soil texture and soil organic carbon content were measured at each station to support further studies. All the sensors have been calibrated by taking account of the impact of soil texture and soil organic carbon content on the measurements.

Temporal cover range: 2010.08.01-2014.12.31

File type: ASCII-text

File name:

e.g. “SM_NQ-30 minutes-05cm.txt”,

   “ST_NQ-30 minutes-05cm.txt”

where SM is soil moisture,ST is soil temperature,NQ is NaQu region, 30minutes is the temporal resolution,05cm is the soil depth;

 

File content:

1) 30min resolution:

Variables 1-6:date(integer:yyyy-mm-dd-hh-mm-ss)

Variables 7-63:soil moisture or temperature at 57 stations(float,missing value:-99.00)

2) daily resolution:

Variables 1-3:date(integer:yyyy-mm-dd)

Variables 4-61:soil moisture or temperature at 57 stations(float,missing value:-99.00)

 

Unit: 

Soil temperature(ST): ℃

Soil moisture(SM): %vol(m3/m3

 

Figure    The configuration of the CTP-SMTMN: (a) network position (denoted by the small rectangle) on the central Plateau, (b) the experimental area and station locations (the grey curves are the national/provincial roads), (c)-(e) the large, medium, and small networks. DEM is shown in (a)-(b), land use is shown in (c)-(e).

The temperature data at 30min resolution are direct measurements after the quality control; the soil moisture data at 30min resolution are calibrated with measured dielectricity according to soil texture and soil organic carbon content.

The data of daily resolution is daily mean of 30min data.

(1) The accuracy of ST:  ±1℃;

(2) The accuracy of SM:  ±3%VWC

Although we have made tremendous efforts to maintain the network, to calibrate soil moisture sensors, and to carefully process the data, unexpected instrument behaves may occur. The data provider disclaims any kind of liability for quality, performance, and fitness for a particular purpose arising out of the use.

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.

 

The data can only be utilized for academic research. Use of the data for other purposes (e.g. commercial use) is prohibited. No user is allowed to transfer the data to any third party. 

The authors request all users make appropriate references to the use of this dataset. Users are suggested to refer to the following publications:

 

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
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CTP-SMTMN