Validation Assessment for the Soil Moisture Active Passive (SMAP) Level 4 Carbon (L4_C) Data Product Version 8

The NASA Soil Moisture Active Passive (SMAP) mission has recently completed its first decade of continuous, global measurements of Earth’s L-band (1.4 GHz) microwave emission (or brightness temperature), which began on 31 March 2015. Starting even before launch, periodic calibration and validation activities have been performed with the objectives: 1) To calibrate, verify, and improve the performance of the science algorithms; and 2) To validate the accuracy of the science data products as specified in the SMAP Level 1 mission science requirements.

This report provides an assessment of Version 8 (V8) of the SMAP Level 4 Carbon (L4_C) product, which was released in 2025. Since the initial L4_C release in 2015, the L4_C operational algorithm underwent eight major updates, with each update including a full reprocessing of the entire SMAP mission record. These updates include various algorithm refinements and calibration adjustments to account for similar refinements to upstream SMAP brightness temperature observations, the GEOS land model assimilation system, and the SMAP Level 4 Soil Moisture (L4_SM) inputs used for L4_C processing. In addition, the L4_C V8 update includes major changes to the L4_C calibration process, the soil organic carbon (SOC) model logic, and the soil heterotrophic respiration (RH) sub-model.

The L4_C algorithm estimates a global, daily terrestrial carbon budget, informed by daily surface and root-zone soil moisture information from the L4_SM product, along with optical remote sensing-based land cover from the Moderate Resolution Imaging Spectroradiometer (MODIS) and canopy fraction of absorbed photosynthetically active radiation (fPAR) from the Visible Infrared Imaging Radiometer Suite (VIIRS), along with other ancillary biophysical data. The L4_C product provides estimates of global, daily net ecosystem CO2 exchange (NEE) and component carbon fluxes, including vegetation gross primary production (GPP) and soil RH. Other L4_C product elements include surface (ca. 0-5 cm depth) SOC stocks and associated environmental constraints to the carbon fluxes, including soil moisture-related controls on GPP and ecosystem respiration (RECO).

The L4_C product addresses SMAP carbon cycle science objectives by: 1) Providing a direct link between terrestrial carbon fluxes and underlying freeze/thaw and soil moisture-related constraints to these processes; 2) Documenting primary connections between terrestrial water, energy and carbon cycles; and 3) Improving understanding of terrestrial carbon sink activity.

L4_C is calibrated against eddy covariance (EC) tower CO2 flux measurements, which are a proxy for terrestrial ecosystem NEE. The L4_C product has self-imposed performance requirements related to NEE, the primary product element for validation, although the other L4_C product elements (namely GPP, RH, and SOC) have demonstrated utility for carbon science applications. The L4_C targeted accuracy requirement is a mean unbiased root-mean-square error (ubRMSE, or standard deviation of the error) for NEE of no more than 1.6 g C m-2 d-1 or 30 g C m-2 yr-1, emphasizing northern (≥45°N) boreal and arctic ecosystems; this accuracy is similar to that of EC tower flux measurements. The methods used for L4_C performance and validation assessment have been established in the SMAP Calibration and Validation Plan and previous studies, and are applied here for L4_C V8.

Our primary validation of L4_C V8 compares L4_C estimates of NEE, GPP, and RECO to tower flux measurements at 26, globally distributed Core Validation Sites. We also compared the L4_C V8 mean annual fluxes, interannual variability, and short-term trends to the recent literature and to independent reference datasets, including: solar-induced fluorescence (SIF) data from the Orbiting Carbon Observatory-2 (OCO-2) mission; global, up-scaled EC tower fluxes from an ensemble of machine-learning models; global soil carbon inventory records; and an ensemble of dynamic global vegetation models.