A long term global daily soil moisture dataset derived from AMSR-E and AMSR2 (2002-2022)

A long term global daily soil moisture dataset derived from AMSR-E and AMSR2 (2002-2022)

Long term surface soil moisture (SSM) data with stable and consistent quality are critical for global environment and climate change monitoring. L band radiometers onboard the recently lunched Soil Moisture Active Passive (SMAP) Mission can provide the state-of-the-art accuracy SSM, but the short temporal coverage of the data records has limited its applications in long-term studies. While Advanced Microwave Scanning Radiometer for EOS (AMSR-E) and AMSR2 series provide long term observational records of multi-frequency radiometers (C, X, and K bands). This dataset contains 20 years (2002-2022) global spatio-temporal consistent surface soil moisture. The resolution is 36 km at daily scale, the projection is EASE-Grid2, and the data unit is m3 / m3. This dataset adopts the soil moisture neural network retrieval algorithm developed by Yao et al. (2017). This study transfers the merits of SMAP to AMSR-E/2 through using an Artificial Neural Network (ANN) in which SMAP standard SSM products serve as training targets with AMSR-E/2 brightness temperature (TB) as input. Finally, long term soil moisture data are output. This dataset can reproduce the spatial and temporal distribution of SMAP soil moisture, with comparable accuracy as SMAP soil moisture product. This dataset also compares well with in situ SSM observations at 14 dense validation networks globally, with accuracy of 5% volumetric water content, and outperforms AMSR-E/2 standard SSM products from JAXA and LPRM. This global observation-driven dataset spans nearly two decades at present, and is extendable though the ongoing AMSR2 and upcoming AMSR3 missions for long-term studies of climate extremes, trends, and decadal variability.

File naming and required software

File name:
The soil moisture data is stored in netcdf format, and the file name is“ yyyyddd.nc ”, where yyyy stands for year and ddd stands for Julian date. For example, 2003001.nc represents this document describe the global soil moisture distribution on the first day of 2003.
How to read data:
The data is EASE-grid equal-area projection data (with varying latitude and longitude intervals), rather than usual equal-latitude-longitude data. (for more information about EASE-grid projection, please see https://nsidc.org/data/ease)
The NC file of data stores three variables: latitude matrix, longitude matrix and soil moisture matrix, which are latitude (406*1), longitude(964*1) and soil_moisture (406*964) respectively. Projection information is not stored.
A. NC file can be directly read using software such as Matlab. For more information about netcdf, please see http://www.unidata.ucar.edu/software/netcdf.
B. If you want to convert NC file to TIF format, you need a .tif template data with EASE-grid 36km. We provide this data named EASEGrid2_36km.tif, please see the data folder. Here is a tutorial transferring EASE_grid file to TIF, written by a student in our group. https://blog.csdn.net/weixin_38953602/article/details/101158084

Data Citations Data citation guideline What's data citation?
Cite as:

Yao, P., Lu, H. (2020). A long term global daily soil moisture dataset derived from AMSR-E and AMSR2 (2002-2022). National Tibetan Plateau Data Center, DOI: 10.11888/Soil.tpdc.270960. CSTR: 18406.11.Soil.tpdc.270960. (Download the reference: RIS | Bibtex )

Related Literatures:

1. Yao, P.P., Shi, J.C., Zhao, T.J., Lu, H. & Al-Yaari, A. (2017). Rebuilding Long Time Series Global Soil Moisture Products Using the Neural Network Adopting the Microwave Vegetation Index. Remote Sensing 9(1), 35.( View Details | Bibtex)

2. Yao, P.P., Lu, H., Shi, J.C., Zhao, T.J., Yang K., Cosh, M.H., Gianotti, D.J.S., & Entekhabi, D. (2021). A long term global daily soil moisture dataset derived from AMSR-E and AMSR2 (2002-2019). Scientific Data, 8, 143 (2021). https://doi.org/10.1038/s41597-021-00925-8( View Details | Bibtex)

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

Support Program

Second Tibetan Plateau Scientific Expedition Program

National Key Research and Development Program of China (No:2017YFA0603703)

Strategic Priority Research Program of Chinese Academy of Sciences (No:XDA20100103)

Copyright & License

To respect the intellectual property rights, protect the rights of data authors,expand servglacials 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.

Example of acknowledgement statement is included below: The data set is provided by National Tibetan Plateau Data Center (http://data.tpdc.ac.cn).

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

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Geographic coverage
East: 179.82 West: -179.82
South: -83.64 North: 83.64
  • Temporal resolution: Daily
  • Spatial resolution: 10km - 100km
  • File size: 21,514 MB
  • Views: 13,647
  • Downloads: 1,587
  • Access: Open Access
  • Temporal coverage: 2002-07-27 To 2022-08-31
  • Updated time: 2022-09-13
: YAO Panpan   LU Hui  

Distributor: National Tibetan Plateau Data Center

Email: data@itpcas.ac.cn

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