This dataset contains daily 0.05°×0.05° land surface soil moisture products in Qilian Mountain Area in 2020. The dataset was produced by utilizing the optimized wavelet-coupled-RF downscaling model (RF-OWCM) to downscale the SMAP L3 Radiometer Global Daily 36 km EASE-Grid Soil Moisture (SMAP L3, V8). The auxiliary datasets participating in the downscaling model include GLASS Albedo, MUSES LAI/FVC, Daily 1-km all-weather land surface temperature dataset for Western China (TRIMS LST-TP; 2000-2021) V2 and Lat/Lon information.

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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Cite as:

Chai, L., Zhu, Z., Liu, S. (2022). Daily 0.05°×0.05° land surface soil moisture dataset of Qilian Mountain area (2020,SMHiRes,V2). A Big Earth Data Platform for Three Poles, DOI: 10.11888/Terre.tpdc.272375. CSTR: 18406.11.Terre.tpdc.272375. (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.

References literature

1.Liu, Q., Wang, L. Z., Qu, Y., Liu, N. F., Liu, S. H., Tang, H. R., and Liang, S. L. (2013) Preliminary Evaluation of the Long-term GLASS Albedo Product, International Journal of Digital Earth, doi: 10.1080/17538947.2013.804601 (View Details )

2.Xiao, Z.Q., Song, J.L., Yang, H., Sun, R., & Li, J. (2022). A 250 m resolution global leaf area index product derived from MODIS surface reflectance data. International Journal of Remote Sensing, 43(4), 1199-1225. (https://doi.org/10.1080/01431161.2022.2039415) (View Details )

3.Liu, J., Chai, L., Dong, J., Zheng, D., Wigneron, J., Liu, S., & Zhou, J. (2021). Uncertainty analysis of eleven multisource soil moisture products in the third pole environment based on the three-corned hat method. Remote Sensing of Environment, 255, 112225. (https://doi.org/10.1016/j.rse.2020.112225) (View Details )

4.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 )

5.Xiao, Z.Q., Liang, S.L., Wang, J.D., Chen, P., Yin, X.J., Zhang, L.Q., & Song, J.L. (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, vol.52, no.1, pp. 209-223. (https://doi.org/10.1109/TGRS.2013.2237780) (View Details )

6.Xiao, Z.Q., Liang, S.L., Wang, J.D., Xiang, Y., Zhao, X., & Song, J.L. (2016). Long Time-Series Global Land Surface Satellite (GLASS) Leaf Area Index Product Derived from MODIS and AVHRR Data, IEEE Transactions on Geoscience and Remote Sensing, 54(9), 5301-5318. (https://doi.org/10.1109/TGRS.2016.2560522) (View Details )

7.Liu, J., Chai, L., Lu, Z., Liu, S., Qu, Y., Geng, D., & Wang, J. (2019). Evaluation of SMAP, SMOS-IC, FY3B, JAXA, and LPRM soil moisture products over the Qinghai-Tibet Plateau and its surrounding area. Remote Sensing, 11, 792. (https://doi.org/10.3390/rs11070792) (View Details )

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