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Algorithm Description

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A total of four ECV soil moisture products are provided (Algorithm Theoretical Baseline Document, ATBD):

  • A merged product created from all active data sets 
  • A merged product created from all passive data sets
  • A product created from combined active and passive products
  • A product created from the break-adjustment of thecombined product

The merging algorithm

The ESA CCI SM algorithm generates consistent, quality-controlled, long-term (1978-2019) 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 (frequency, spatial resolution, temporal coverage, polarisation, revisit time etc) into three consistent multi-decadal data sets. This process faces innumerable scientific challenges and is therefore subject to continuous research and development. The merging scheme of the most recent version is illustrated and described below.

Merging scheme of the ESA CCI SM v06.1 algorithm.  For more information on the product generation please refer to the product documentation.

1. Soil moisture product retrieval and gridding

All ESA CCI SM algorithms merge pre-processed level 2 (L2) data, that is, gridded soil moisture products retrieved from radiometrically calibrated backscatter or brightness temperature measurements. These are later resampled to daily global images on a 0.25o regular grid.

2. Level 2 data harmonisation into COMBINED product

All L2 input data sets are directly scaled against GLDAS Noah land surface model to harmonize their climatology. 

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. Uncertainty estimates and hence merging weights largely follow VOD patterns.

4. Quality control

For the generation of both the PASSIVE and the COMBINED product, correlation significance levels are used to completely mask out individual L2 input products that are deemed unreliable at a particular location and during a particular merging period (where more than one product is available for merging).