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

Summary 

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-present) global spatio-temporal consistent surface soil moisture, and will be updated in near real-time . 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, 2021). 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.

No co-authorship is required for use of this data in publications. However, to properly acknowledge the dataset when publishing any research using this dataset, we ask data users to (1) cite the DOI as an in-text citation and/or in the data acknowledgements in any publication and (2) reference  Yao et al. (2021) when referring to the dataset in the text. Feel free to send us an email at [e-mail] to let us know how you are using the data. 

Data Update

We have successfully implemented near real-time data updates. We will update the data every 3 months here. For near real-time updates, please visit the following website https://doi.org/10.11888/Soil.tpdc.270960.  

Contact 

For questions, please email  Panpan Yao at [e-mail]  and Hui Lu at [e-mail]

Data Formatting and File Names

Formating: The soil moisture data is stored in netcdf format and Tiff format. 

File name:  the file name is“ yyyyddd.nc ” or “ yyyyddd.tif ”, 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-grid2 equal-area projection data (with varying latitude and longitude intervals), rather than usual equal-latitude-longitude data. (for more information about EASE-grid2 projection, please see  https://nsidc.org/data/ease/ease_grid2.html. ) 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. 

Reference way

Reference of data

Yao, P., Lu, H. (2020). A long term global daily soil moisture dataset derived from AMSR-E and AMSR2 (2002-2022). National Tibetan Plateau / Third Pole Environment Data Center.  https://doi.org/10.11888/Soil.tpdc.270960.  https://cstr.cn/18406.11.Soil.tpdc.270960.

Article citation

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. 

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