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File naming and required software

File Naming Convention: YYYYMMDD.tiff (YYYY: year, MM: month, DD: day)
Data Version：V2
Projection：+proj=longlat +datum=WGS84 +no_defs
Data Format: GeoTIFF, 220 rows ×360 columes
Soil Moisture Unit: cm3/cm3
Soil Moisture Valid Range：0.02-0.5
Filled Value：Nodata

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Data Citations Data citation guideline What's data citation?

Cite as:

Chai, L., Zhu, Z., Liu, S. (2021). Daily 0.05°×0.05° land surface soil moisture dataset of Qilian Mountain area (2019,SMHiRes,V2). A Big Earth Data Platform for Three Poles, (Download the reference： RIS | Bibtex )

Related Literatures:

1. Hu, Z., Chai, L., Crow, W.T., Liu, S., Zhu, Z., Zhou, J., Qu, Y., Liu, J., Yang, S., Lu, Z., 2022. Applying a Wavelet Transform Technique to Optimize General Fitting Models for SM Analysis: A Case Study in Downscaling over the Qinghai–Tibet Plateau. Remote Sensing 14, 3063. https://doi.org/10.3390/rs14133063( View Details | Bibtex)

2. Qu, Y., Zhu, Z., Montzka, C., Chai, L., Liu, S., Ge, Y., Liu, J., Lu, Z., He, X., & Zheng, J. (2021). Inter-comparison of several soil moisture downscaling methods over the Qinghai-Tibet Plateau, China. Journal of Hydrology, 592, 125616. (https://doi.org/10.1016/j.jhydrol.2020.125616)( View Details | Bibtex)

Using this data, the data citation is required to be referenced and the related literatures are suggested to be cited.

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References literature

1.Qu, Y., Zhu, Z., Chai, L., Liu, S., Montzka, C., Liu, J., Yang, X., Lu, Z., Jin, R., & Li, X. (2019). Rebuilding a Microwave Soil Moisture Product Using Random Forest Adopting AMSR-E/AMSR2 Brightness Temperature and SMAP over the Qinghai–Tibet Plateau, China. Remote Sensing, 11, 683. (View Details )

2.Jia, K., Liang, S., Liu, S., Li, Y., et al. (2015). Global land surface fractional vegetation cover estimation using general regression neural networks from MODIS surface reflectance. IEEE Transactions on Geoscience and Remote Sensing, 53(9), 4787 - 4796. (View Details )

3.Zhang, X., Zhou, J., Göttsche, F., Zhan, W., Liu, S., & Cao, R. (2019). A Method Based on Temporal Component Decomposition for Estimating 1-km All-Weather Land Surface Temperature by Merging Satellite Thermal Infrared and Passive Microwave Observations. IEEE Transactions on Geoscience and Remote Sensing, 57, 4670–4691. https://doi.org/10.1109/TGRS.2019.2892417 (View Details | Download )

4.Merlin, O., Escorihuela, M.J., Mayoral, M.A., et al. (2013). Self-calibrated evaporation-based disaggregation of SMOS soil moisture: An evaluation study at 3 km and 100 m resolution in Catalunya, Spain. Remote Sensing of Environment, 130(4), 25-38. (View Details )

5.Srivastava, P.K., Han, D., Ramirez, M.R., et al. (2013). Machine Learning Techniques for Downscaling SMOS Satellite Soil Moisture Using MODIS Land Surface Temperature for Hydrological Application. Water Resources Management, 27(8), 3127-3144. (View Details )

6.Piles, M., Petropoulos, G.P., Sánchez, N., et al. (2016).Towards improved spatio-temporal resolution soil moisture retrievals from the synergy of SMOS and MSG SEVIRI spaceborne observations. Remote Sensing of Environment, 180, 403-417. (View Details )

7.Im, J., Park, S., Rhee, J., et al. (2016). Downscaling of AMSR-E soil moisture with MODIS products using machine learning approaches.Environmental Earth Sciences, 75(15), 1120. (View Details )

8.Qu, Y., Q. Liu, S. L. Liang, L. Z. Wang, N. F. Liu & S. H. Liu (2013). Direct-estimation algorithm for mapping daily land-surface broadband albedo from MODIS data. IEEE. Trans. on Geos. and Remote Sens., doi：10.1109/TGRS.2013.2245670. (View Details )

9.Zhao, W., Sánchez, N., Lu, H. &Li, A. (2018). A spatial downscaling approach for the SMAP passive surface soil moisture product using random forest regression. Journal of Hydrology, 563, 1009-1024. (View Details )

10.Xiao, Z., Liang, S., Wang, J., et al. (2014). Use of general regression neural networks for generating the GLASS leaf area index product from time-series MODIS surface reflectance. IEEE Transactions on Geoscience and Remote Sensing, 52(2), 209 - 223. (View Details )

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Support Program

Pan-Third Pole Environment Study for a Green Silk Road-A CAS Strategic Priority A Program (No:XDA20000000)

Copyright & License

To respect the intellectual property rights, protect the rights of data authors, expand services of the data center, and evaluate the application potential of data, data users should clearly indicate the source of the data and the author of the data in the research results generated by using the data (including published papers, articles, data products, and unpublished research reports, data products and other results). For re-posting (second or multiple releases) data, the author must also indicate the source of the original data.

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License： This work is licensed under an Attribution 4.0 International (CC BY 4.0)

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