Product development is tightly linked with quality assessment. Remote sensing observations, like all measurements, are inherently uncertain, as are all model predictions. Ensuring that data be used appropriately, therefore, requires that their uncertainties are properly understood and quantified. This, however, is not easy to do for instruments orbiting in space.
The CLIMERS group has dedicated efforts to develop good-practice guidelines for how to best quantify uncertainties in Earth observation data sets. These guidelines are developed in close collaboration with international experts including both data users and the science community, and are endorsed by global Earth observation authorities. Bringing these guidelines to use, we work with in situ (on the ground), satellite-based, and modeled data to fulfill quality requirements imposed for our own data products, striving to establish ourselves as an acknowledged reference data point within the scientific community.
Lastly, we are engaged in maintaining and developing fiducial reference data sets with automated and transparent quality control, harmonizing and automizing validation procedures, implementing protocols and proven standards, and ensuring the traceability of product uncertainties throughout processing chains.
The Fiducial Reference Measurements for Soil Moisture (FRM4SM) project is a European Space Agency-funded project focused on establishing methods, protocols, and tools required for traceable in situ measurements of soil moisture to support the validation of satellite retrieval products.
Read more on the FRM4SM page
The QA4SM project provides an operational online validation service for soil moisture products. The easy-to-use service allows users to quickly generate traceable validation results using an open access valdidation framework.
Read more on the Q44SM page
he International Soil Moisture Network is an unique international cooperation to establish and maintain a harmonized, quality controlled global in situ soil moisture database with long time series, global distributed stations, making data open and freely available worldwide.
Read more on the ISMN page