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Published July 5, 2024 | Version 9.1
Dataset Open

GAPFILLED Long-term Climate Data Record of Surface Soil Moisture from merged multi-satellite observations

  • 1. TU Wien

Contributors

  • 1. TU Wien

Description

This dataset was produced with funding from the European Space Agency (ESA) Climate Change Initiative (CCI) Plus Soil Moisture Project (CCN 3 to ESRIN Contract No: 4000126684/19/I-NB "ESA CCI+ Phase 1 New R&D on CCI ECVS Soil Moisture").  Project website: https://climate.esa.int/en/projects/soil-moisture/

This dataset contains information on the Surface Soil Moisture (SM) content derived from satellite observations in the microwave domain.

Abstract

ESA CCI Soil Moisture is a multi-satellite climate data record that consists of harmonized, daily observations coming from 19 satellites (as of v09.1) operating in the microwave domain. The wealth of satellite information, particularly over the last decade, facilitates the creation of a data record with the highest possible data consistency and coverage.
However, data gaps are still found in the record. This is particularly notable in earlier periods when a limited number of satellites were in operation, but can also arise from various retrieval issues, such as frozen soils, dense vegetation, and radio frequency interference (RFI). These data gaps present a challenge for many users, as they have the potential to obscure relevant events within a study area or are incompatible with (machine learning) software that often relies on gap-free inputs.
Since the requirement of a gap-free ESA CCI SM product was identified, various studies have demonstrated the suitability of different statistical methods to achieve this goal. A fundamental feature of such gap-filling method is to rely only on the original observational record, without need for ancillary variable or model-based information. Due to the intrinsic challenge, there was until present no global, long-term univariate gap-filled product available. In this version of the record, data gaps due to missing satellite overpasses and invalid measurements are filled using the Discrete Cosine Transform (DCT) Penalized Least Squares (PLS) algorithm (Garcia, 2010). A linear interpolation is applied over periods of (potentially) frozen soils with little to no variability in (frozen) soil moisture content.

Summary

  • Gap-filled global estimates of volumetric surface soil moisture from 1991-2023 at 0.25° sampling
  • Fields of application (partial): climate variability and change, land-atmosphere interactions, global biogeochemical cycles and ecology, hydrological and land surface modelling, drought applications, and meteorology
  • Method: Modified version of DCT-PLS (Garcia, 2010) interpolation/smoothing algorithm
  • More information: ESA CCI SM Algorithm Theoretical Baseline Document [Chapter 7.2.9] (Dorigo et al., 2023)

Technical details

The dataset provides global daily estimates for the 1991-2023 period at 0.25° (~25 km) horizontal grid resolution. Daily images are grouped by year (YYYY), each subdirectory containing one netCDF image file for a specific day (DD), month (MM) in a 2-dimensional (longitude, latitude) grid system (CRS: WGS84). The file name has the following convention:

ESACCI-SOILMOISTURE-L3S-SSMV-COMBINED_GAPFILLED-YYYYMMDD000000-fv09.1.nc

Data Variables

Each netCDF file contains 3 coordinate variables (WGS84 longitude, latitude and time stamp), as well as the following data variables:

  • sm: (float) The Soil Moisture variable reflects estimates of daily average volumetric soil moisture content (m3/m3) in the soil surface layer (~0-5 cm) over a whole grid cell (0.25 degree).
  • sm_uncertainty: (float) The Soil Moisture Uncertainty variable reflects the uncertainty (random error) of satellite observations, on which the interpolation is based (this variable is experimental and will change in future versions of the record). 
  • gapmask: (0 | 1) Indicates grid cells where a satellite observation is available (0), and where the interpolated value is used (1) in the 'sm' field.

Additional information for each variable is given in the netCDF attributes.

Software to open netCDF files

These data can be read by any software that supports Climate and Forecast (CF) conform metadata standards for netCDF files, such as:

  • Xarray (python)
  • netCDF4 (python)
  • esa_cci_sm (python)
  • Similar tools exists for other programming languages (Matlab, R, etc.)
  • Software packages and GIS tools can open netCDF files, e.g. CDONCOQGIS, ArCGIS
  • You can also use the GUI software Panoply to view the contents of each file

References

  • Dorigo, W., Preimesberger, W., Stradiotti, P., Kidd, R., van der Schalie, R., van der Vliet, M., Rodriguez-Fernandez, N., Madelon, R., & Baghdadi, N. (2023). ESA Climate Change Initiative Plus - Soil Moisture Algorithm Theoretical Baseline Document (ATBD) Supporting Product Version 08.1 (version 1.1). Zenodo. https://doi.org/10.5281/zenodo.8320869
  • Garcia, D., 2010. Robust smoothing of gridded data in one and higher dimensions with missing values. Computational Statistics & Data Analysis, 54(4), pp.1167-1178. Available at: https://doi.org/10.1016/j.csda.2009.09.020

Files

2023.zip

Files (17.3 GiB)

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