Introduction, purpose and scope

The purpose of this Algorithm Theoretical Baseline Document (ATBD) is to present the procedure that will be used in the Copernicus Imaging Microwave Radiometer (CIMR) mission to derive Soil Moisture (SM) products from the brightness temperatures (TB) measured from the CIMR radiometer. Initially, the historical background of passive microwave remote sensing for soil moisture is provided, along with the justification for the chosen algorithm. As detailed in the Level-2 product definition section, the output product will contain data structured on a geographical grid (EAE2, Equal Area Cylindrical projection).

The procedure for extracting soil moisture information from CIMR TB observations employs the tau-omega model, widely used in the passive microwave soil moisture community. The proposed algorithm is based on the SMOS-IC algorithm for SMOS and the Multi-Temporal Dual Channel Algorithm for SMAP. This design has been adapted to the particular characteristics of CIMR.

The CIMR Soil Moisture retrieval algorithm takes into consideration the impact of a vegetation layer covering the soil. This layer absorves partially the emission of the soil and adds to the overall radiative flux its own emission. By accounting for this absorption, the algorithm generates a complementary product known as the vegetation optical depth, varying in spatial resolution depending on the CIMR band. Utilizing the L-band brightness temperature measurements enables accurate estimation of soil moisture, while C and X-band are used to sharpen L-band TB spatial resolution. The algorithm provides the soil moisture products at two distinct spatial resolutions: a hydroclimatological scale based on inversion of L-band TB (~60 km) and an enhanced hydrometeorological scale based on inversion of L-band TB at enhanced spatial resolution (~10 to 25 km).

In the “Baseline Algorithm Definition” section, the forward model and CIMR retrieval algorithms are presented, including a flow diagram that outlines all the steps involved in the retrieval of L-band soil moisture and vegetation optical depth. Finally, the Algorithm Input and Output Data Definition section outlines all the necessary input and output data for the algorithm.