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Algorithm Description

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The following products are distributed with ESA CCI SM v08.1:

  • ACTIVE: A merged product created from all active data sets
  • PASSIVE: A merged product created from all passive data sets
  • COMBINED: A product created from combined active and passive products

In addition, the following experimental products will available shortly after the main release:

  • Gap-filled: A product free of data gaps, generated from COMBINED
  • Model-independent:  an alternative version of COMBINED which uses a merged L-band soil moisture record as scaling reference, thereby removing any influence of the model


The merging algorithm

The ESA CCI SM algorithm generates consistent, quality-controlled, long-term (1978-2022) soil moisture climate data records by harmonising and merging soil moisture retrievals from multiple satellites into an active-microwave-based only (ACTIVE), a passive-microwave-based only (PASSIVE) and combined active-passive (COMBINED) product.

Since its first release in 2012 the ESA CCI SM merging algorithm has undergone substantial improvements. In essence, the merging algorithm is responsible for merging soil moisture retrievals from various satellites that have finite lifetimes and significantly different instrument characteristics (e.g., frequency, spatial resolution, temporal coverage, polarisation, revisit time) into three consistent multi-decadal data sets. This process faces several scientific challenges and is therefore subject to continuous research and development. The merging scheme of the most recent version is illustrated and streamlined below.

Merging scheme of the ESA CCI SM v08.1 algorithm.  For more information on the product generation please refer to the product documentation found on the Key Documents page.

1. Soil moisture product retrieval and gridding

All ESA CCI SM algorithms merge pre-processed level 3 (L3) data, that is, gridded soil moisture products retrieved from calibrated backscatter or brightness temperature measurements and aggregated on daily intervals. These are later resampled to daily global images on a 0.25o regular grid.

2. L3 data harmonisation

All L2 input data sets are directly scaled against a reference data record to harmonize their climatology. For COMBINED, the GLDAS Noah land surface model estimates are used.

3. Uncertainty estimation

Uncertainties are estimated for each individual product and used to construct error covariance matrices for all merging periods depending on the sensor availability during these periods.

4. Merging

The soil moisture observations from the different sensors are merged into the final Level 4 product via a weighted averaging scheme. This uses the location-dependent error estimates of the individual products to limit the impact of noisy sensors.

5. Break adjustment

Temporal breaks in the time series of the COMBINED product are detected and corrected to improve the homogeneity of the record.