NASA’s Soil Moisture Active Passive (SMAP) mission has been validating its soil moisture (SM) products since the start of data production on March 31, 2015. Prior to launch, the mission defined a set of criteria for core validation sites (CVS) that enable the testing of the key mission SM accuracy requirement (unbiased root-mean-square error <0.04 m3/m3). The validation approach also includes other (“sparse network”) in situ SM measurements, satellite SM products, model-based SM products, and field experiments. Over the past six years, the SMAP SM products have been analyzed with respect to these reference data, and the analysis approaches themselves have been scrutinized in an effort to best understand the products’ performance. Validation of the most recent SMAP Level 2 and 3 SM retrieval products (R17000) shows that the L-band (1.4 GHz) radiometer-based SM record continues to meet mission requirements. The products are generally consistent with SM retrievals from the ESA Soil Moisture Ocean Salinity mission, although there are differences in some regions. The high-resolution (3-km) SM retrieval product, generated by combining Copernicus Sentinel-1 data with SMAP observations, performs within expectations. Currently, however, there is limited availability of 3-km CVS data to support extensive validation at this spatial scale. The most recent (version 5) SMAP Level 4 SM data assimilation product providing surface and root-zone SM with complete spatio-temporal coverage at 9-km resolution also meets performance requirements. The SMAP SM validation program will continue throughout the mission life; future plans include expanding it to forested and high-latitude regions.

Newer satellite platforms, such as NISAR, are poised to produce huge amounts of data that require large computational resources. Currently, researchers typically download datasets for analysis on local computer resources. This paradigm is no longer practical given the volumes of data from new sensing platforms. While cloud computing services offer a potential solution for accessing and managing large computational resources, there remains a significant barrier to entry. Levering cloud services requires users to: navigate new terminology without appropriate documentation; optimize settings for services to reduce costs; and maintain software dependencies, upgrades, and allocated hardware resources. A more accessible approach for migrating earth scientists to the cloud is needed. To address this problem, we are developing the open source Python library PODPAC (Pipeline for Observational Data Processing Analysis and Collaboration), with the goal of helping to address NASA’s rapidly growing observational data volume and variety needs. PODPAC enables earth scientists to seamlessly transition between processing on a local workstation (their current paradigm) to distributed remote processing on the cloud. It does this by leveraging a text-based JSON format automatically generated for any plug-and-play algorithm developed using PODPAC (e.g., in a Jupyter Notebook). This text format describes data provenance, and is used in RESTful web requests to preconfigured PODPAC cloud deployments, allowing scalable, massively distributed processing. We demonstrate the seamless transition to the cloud by developing a simplified soil moisture downscaling algorithm in Python using PODPAC. Data for this algorithm uses NASA Soil Moisture Active Passive (SMAP) sensor retrieved from the National Snow and Ice Data Center using OpenDAP, and fine-scale topographic data retrieved via Open Geospatial Consortium (OGC) Web Coverage Service (WCS) calls. We then use a serverless AWS Lambda function to run the same algorithm using the automatically-generated text format. Our generic preconfigured environment can handle a wide variety of processing pipelines, and scale up to 1024 parallel processes. This approach enables incremental adoption of cloud services by researchers, significantly lowering the barrier to entry.

To determine hydrologic changes in a warmer climate, we impose precipitation and potential evaporation perturbations on hydrologic response functions constructed from precipitation and satellite soil moisture observations across the United States. Despite nonlinearities in the evaporation (E) and drainage (D) responses and opposing-sign perturbations, changes in individual fluxes are superposable. Empirical frameworks (Budyko) can misrepresent changes in E/D partitioning by neglecting shifts/trends in hydrologic regime and subseasonal precipitation dynamics. E/D both increase to balance mean precipitation (P) increases, and increased E reduces soil moisture. E and D are generally more elastic to changes in P than E. The results suggest that (1) the impacts of regional hydrologic perturbations may allow for simple superposition/scaling, (2) changes in timing/intensity of precipitation may have substantial impacts on mean moisture states and fluxes, and (3) changes to the distribution of surface moisture states are likely more relevant for E/D partitioning than common aridity indices.

A common viewpoint across the Earth science community is that global soil moisture estimates from satellite L-band (1.4 GHz) measurements represent moisture only in the shallow soil layers (0-5 cm) and are of limited value for studying global terrestrial ecosystems because plants use water from deeper rootzones. Here, we argue that such a viewpoint is flawed for two reasons. First, microwave soil emission theory and statistical considerations of vertically correlated soil moisture information together indicate that L-band measurements are typically representative of soil moisture within at least the top 15-25 cm, or 3-5 times deeper than commonly thought. Second, in reviewing isotopic tracer field studies of plant water uptake, we find a global prevalence of vegetation that primarily draws moisture from these upper soil layers. This is especially true for grasslands and croplands covering more than a third of global vegetated surfaces. While shrub and tree species tend to draw deeper soil moisture, these plants often still preferentially or seasonally draw water from the upper soil layers. Therefore, L-band satellite soil moisture estimates are more relevant to global vegetation water uptake than commonly appreciated, and we encourage their application across terrestrial hydrosphere and biosphere studies.