The current public LIS versions support the following data assimilation (DA) observation dataset types:

• Soil Moisture Active Passive (SMAP) soil moisture products
• NASA/Vrije U. Land Parameter Retrieval Model (LPRM) AMSR-e Soil Moisture data
• NASA/NSIDC AMSR-e Soil Moisture data
• Essential Climate Variable (ECV) Satellite-based Soil Moisture analysis
• TU Wien ASCAT Soil Moisture (retrospective)
• NESDIS/OSPO Soil Moisture Operational Products System (SMOPS) Soil Moisture product
• WindSat satellite-based Soil Moisture Retrievals

One method for reducing systematic biases between the observational data to be assimilated and the model's state estimates to be updated with that data is known as "CDF matching". The cumulative density function, or aka the "CDF", characterizes the cumulative probability of a random variable (say, X) up to a specific point that is equal to the desired area under the associated PDF curve, for example left of that specific point.

Currently, LDT can estimate the statistics required to do a simple bias-correction or scaling approach between similar observational data and model state estimates to reduce bias between the two during assimilation update step. LDT generates the mean, standard deviation and cumulative (probability) density function (CDF) values, which LIS-7 ingests to perform the final CDF "matching" between the observations and the model estimates.