DOI: 10.5281/zenodo.15015557
title: Global daily soil moisture products at 0.1° and 0.25° (including relative and volumetric soil moisture, 2015-2024)

description: Soil moisture plays a key role in numerous applications such as drought monitoring, flood forecasting, crop yield estimation, and landslide warning. This new global soil moisture product is based on SMAP L-band brightness temperature (TB, ~0.25°) and enhanced TB (~0.1°) data from 1 April 2015 to 31 December 2024, by using the newly developed Soil Moisture Index (SMI) method. The SMI is based on two key physical foundations: 1) vegetation and surface roughness have a "depolarization" effect on microwave emission (as these two variables increase, the difference in microwave polarization decreases), while soil moisture enhances the difference in microwave polarization; 2) vegetation and surface roughness are positively correlated with surface emissivity (the larger the values of both variables, the higher the surface emissivity), while soil moisture is negatively correlated with surface emissivity (emissivity decreases with increasing soil moisture). Based on the two physical principles, the effects of soil moisture and those of vegetation and surface roughness can be decoupled in a two-dimensional space independent of vegetation type and roughness condition. The original SMI-derived soil moisture products calculated using SMAP TB/enhanced TB data denote relative soil moisture (dimensionless, ranging from 0-1 where 0 represents extremely dry and 1 represents extremely wet), which are mainly used to capture the temporal dynamics of soil moisture (independent of model/reanalysis datasets, and can be used in data assimilation, etc.). To further obtain the absolute value of soil moisture (i.e., volumetric soil moisture content) without affecting the dynamic information of soil moisture retained in the original SMI soil moisture, the Cumulative Distribution Function (CDF) matching method was used to rescale the CDF of SMI to that of MERRA-2 soil moisture data, and finally obtain the SMI-derived volumetric soil moisture data (unit: m3 m-3, can be used in agriculture, forestry, ecology, environment, as well as comparing/evaluating with other volumetric soil moisture products). The new soil moisture dataset was validated using in situ measurements from the International Soil Moisture Network worldwide. The results indicate SMI can effectively capture the dynamic changes in soil moisture with the highest correlation coefficient compared to other publicly available satellite soil moisture products. The SMI calibrated using the CDF method has a lower unbiased root mean square error (ubRMSE), which is also superior to other state-of-the-art soil moisture products. Therefore, SMI-derived soil moisture products have great potential for various applications.

 
description: Soil moisture data are saved in NetCDF format, and compressed monthly for storage. The naming convention for zip files is as follows: YYYYMM_025deg_SMI.Zip, among them, YYYYMM represents year and month, 025deg represents spatial resolution of 0.25° (if it is 01deg, it represents spatial resolution of 0.1°), SMI represents the used retrieval algorithm. The naming convention for nc files is as follows: SMI_SMAP_SM_025deg_V001_YYYYMMDD.nc, among them, SMI represents the used retrieval algorithm, SMAP represents the source of brightness temperature data, SM represents soil moisture, 025deg represents spatial resolution of 0.25° (if it is 01deg, it means spatial resolution of 0.1°), V001 represents the version of the product, and YYYYMMDD represents year, month, and day. Usage: the data can be read using various programming languages such as MATLAB, Python, IDL, etc., and can also be visualized in software such as Panoply.