Soil moisture influences forest health, fire occurrence and extent, and insect and pathogen impacts, creating a need for regular, globally extensive soil moisture measurements that can only be achieved by satellite-based sensors, such as NASA’s Soil Moisture Active Passive (SMAP). However, SMAP data for forested regions, which account for ~20% of land cover globally, are flagged as unreliable due to interference from vegetation water content, and forests were underrepresented in previous validation efforts, preventing an assessment of measurement accuracy in these biomes. Here we compare over twelve thousand SMAP soil moisture measurements, representing 88 site-years, to in-situ soil moisture measurements from forty National Ecological Observatory Network (NEON) sites throughout the US, half of which are forested. At unforested NEON sites, agreement with SMAP soil moisture (unbiased RMSD: 0.046 m3 m-3) was similar to previous sparse network validations (which include inflation of the metric due to spatial representativeness errors). For the forested sites, SMAP achieved a reasonable level of accuracy (unbiased RMSD: 0.06 m3 m-3 or 0.053 m3 m-3 after accounting for random representativeness errors) indicating SMAP is sensitive to changes in soil moisture in forest ecosystems. Moreover, we identified that both an index of vegetation water content and canopy height were related to mean difference, which incorporates measurement bias and representativeness bias, and suggests a potential approach to improve SMAP algorithm parameterization for forested regions. In addition, expanding the number and extent of soil moisture measurements at forested validation sites would likely further reduce mean difference by minimizing representativeness errors.

Soil respiration represents one of the dominant fluxes of CO2 from terrestrial ecosystems to the atmosphere, therefore, it is important to understand how it is controlled across a wide variety of sites. As part of developing a soil respiration data product based on freely available National Ecological Observatory Network (NEON) data, soil respiration rates were calculated at 30 sites throughout the USA (from Puerto Rico to North Dakota and Virginia to California) to investigate controls on soil respiration at a continental scale. The sites spanned a wide range of ecosystems, including deserts, grasslands, and forests, as well as managed and wildland sites. Soil respiration was calculated in 30-minute intervals using the gradient method based on soil CO2 concentrations measured at three different depths in conjunction with estimates of soil CO2 diffusivity based on soil physical properties, soil moisture and temperature profiles, and barometric pressure. Inevitably with such a large number of data inputs, the temporal coverage of good quality (i.e., unflagged) soil respiration values was relatively low (8%) because one or more of the input data were flagged, but this was significantly higher for some sites and soil plots (maximum: 58%). Ongoing efforts to increase the quality of input data are expected to substantially improve temporal coverage. Despite these gaps, over 54,000 unflagged half-hourly soil respiration data points were generated for the period of Apr-Jun 2019 (the temporal range and the number of sites will be increased further over coming months). Across all sites and times, soil temperature and soil moisture explained only a moderate amount of variation in soil respiration (20%). However, including site in the model increased the proportions of variation explained to 85%, indicating the importance of site-specific properties, such as vegetation and microbial community composition and the accessibility of soil carbon, in controlling soil respiration rates. Within each site, soil temperature was typically positively correlated with respiration and in the cases where it was not, this was often due to large fluxes of CO2 leaving the soil during snowmelt. The relationship between soil moisture and respiration within each site was more variable, with both positive and negative relationships observed.