A global dataset of remote sensing-based soil critical point and permanent wilting point

Description
This long-term global soil hydraulic properties dataset is based on a neural network soil moisture product (Yao et al., 2021). Using McColl's (2019) method to calculate drydown timescales, there are six parameters related to soil moisture drydown at two sampling frequencies: the global medians of estimated parameters, namely τS (short-term timescale of drydown), annual drydown times (ndd), τL (long-term timescale of drydown), the global average results of soil moisture before (θC) and after drydown (θW), and the fitting coefficient (R2) of soil moisture drydown characteristics. This dataset contains global soil moisture drydown characteristics at a 36km resolution from 2002 to 2023. Each file contains 406 (latitude) * 964 (longitude) grid points. All files can be opened in MATLAB. We believe it can help us gain more knowledge of land-atmosphere feedback and provide practical input parameters for land surface models.

Term of use
Spatial resolution: 36km x 36km
Time range: 2002.6.1-2023.12.31
Files type: .mat
Filename: "Parameter""Sample frequency""Year".mat
Parameter:

tauS: τS [day], Short-term timescale of drydown
ndd: ndd [times], Annual times of drydown events
tauL: τL [day], Long-term timescale of drydown
thetaC: θC [m³/m³], Initial soil moisture value at the beginning of drydown events - long-term median/mean may serve as the critical point
thetaW: θW [m³/m³], Minimum soil moisture value during drydown events - long-term median/mean may serve as the permanent wilting point
R2: R2[-], fitting coefficient of the drydown curve. Range is [0-1]. The closer to 1, the better.
Sample frequency:
f1: one-day interval
f3: three-day interval
Year: From 2002 to 2023
Reference
McColl, K. A., Q. He, H. Lu, et al., “Short-Term and Long-Term Surface Soil Moisture Memory Time Scales Are Spatially Anticorrelated at Global Scales,” J. Hydrometeor., Amer. Meteorological Soc., Boston, vol. 20, no. 6, pp. 1165-1182, Jun. 2019. https://doi.org/10.1175/jhm-d-18-0141.1
Yao, P., H. Lu, J. Shi, et al., “A long-term global daily soil moisture dataset derived from AMSR-E and AMSR2 (2002-2019),” Sci. Data, Springer Nature, New York, vol. 8, no. 1, pp. 143, Dec. 2021. https://doi.org/10.1038/s41597-021-00925-8