Development and Assessment of the SMAP Enhanced Passive Soil Moisture Product.

Launched in January 2015, the National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) observatory was designed to provide frequent global mapping of high-resolution soil moisture and freeze-thaw state every two to three days using a radar and a radiometer operating at L-band frequencies. Despite a hardware mishap that rendered the radar inoperable shortly after launch, the radiometer continues to operate nominally, returning more than two years of science data that have helped to improve existing hydrological applications and foster new ones. Beginning in late 2016 the SMAP project launched a suite of new data products with the objective of recovering some high-resolution observation capability loss resulting from the radar malfunction. Among these new data products are the SMAP Enhanced Passive Soil Moisture Product that was released in December 2016, followed by the SMAP/Sentinel-1 Active-Passive Soil Moisture Product in April 2017. This article covers the development and assessment of the SMAP Level 2 Enhanced Passive Soil Moisture Product (L2_SM_P_E). The product distinguishes itself from the current SMAP Level 2 Passive Soil Moisture Product (L2_SM_P) in that the soil moisture retrieval is posted on a 9 km grid instead of a 36 km grid. This is made possible by first applying the Backus-Gilbert optimal interpolation technique to the antenna temperature (TA) data in the original SMAP Level 1B Brightness Temperature Product to take advantage of the overlapped radiometer footprints on orbit. The resulting interpolated TA data then go through various correction/calibration procedures to become the SMAP Level 1C Enhanced Brightness Temperature Product (LiC_TB_E). The LiC_TB_E product, posted on a 9 km grid, is then used as the primary input to the current operational SMAP baseline soil moisture retrieval algorithm to produce L2_SM_P_E as the final output. Images of the new product reveal enhanced visual features that are not apparent in the standard product. Based on in situ data from core validation sites and sparse networks representing different seasons and biomes all over the world, comparisons between L2_SM_P_E and in situ data were performed for the duration of April 1, 2015 - October 30, 2016. It was found that the performance of the enhanced 9 km L2_SM_P_E is equivalent to that of the standard 36 km L2_SM_P, attaining a retrieval uncertainty below 0.040 m3/m3 unbiased root-mean-square error (ubRMSE) and a correlation coefficient above 0.800. This assessment also affirmed that the Single Channel Algorithm using the V-polarized TB channel (SCA-V) delivered the best retrieval performance among the various algorithms implemented for L2_SM_P_E, a result similar to a previous assessment for L2_SM_P.

The LTAR Grazing Land Common Experiment at Walnut Gulch Experimental Watershed.

The Walnut Gulch Experimental Watershed (WGEW) Long-Term Agroecosystem Research (LTAR) network common experiment addresses the aspirational practice of brush management (BM) to reverse the prevailing condition of woody plant encroachment (WPE) and increase perennial native grass production. Across the western United States, the decision to implement BM includes consideration of management objectives, cost, and the expected impact on a diverse suite of ecosystem services. Maintaining or restoring grass cover will help meet the LTAR sustainable production, economic, and social goals, and averting degradation will meet environmental goals. This common experiment, focused on hydrologic and erosion impacts of BM, aims to inform land management decisions on three major plant communities in the Southwestern United States: creosote bush (Larrea tridentata), mesquite (Prosopis velutina), and pinyon-juniper (PJ, Pinus and Juniperus spp.). On the WGEW, applying tebuthiuron pellets to creosote bush increased grass cover and reduced runoff and erosion. The 2016 BM experiment on the Santa Rita Experimental Range applied a commonly used liquid herbicide cocktail but achieved only 7% mortality on mesquite, probably because of the timing of the aerial application. Experiments manipulating rainfall amount and intensity on plots receiving fire, chemical, or mechanical BM treatments on PJ communities aim to improve process representation in simulation models. The deliverables of these BM experiments will be to (i) improve the performance of runoff and erosion models, (ii) enhance our ability to identify areas most at risk from reduced hydrologic function and soil erosion after shrub proliferation, and (iii) better predict how landscapes will respond to BM interventions. Ranchers, land management agencies, and watershed conservation organizations will benefit from training and availability of improved tools to focus treatments on areas where greatest net benefits might be realized.

Data Assimilation to extract Soil Moisture Information from SMAP Observations.

This study compares different methods to extract soil moisture information through the assimilation of Soil Moisture Active Passive (SMAP) observations. Neural Network (NN) and physically-based SMAP soil moisture retrievals were assimilated into the NASA Catchment model over the contiguous United States for April 2015 to March 2017. By construction, the NN retrievals are consistent with the global climatology of the Catchment model soil moisture. Assimilating the NN retrievals without further bias correction improved the surface and root zone correlations against in situ measurements from 14 SMAP core validation sites (CVS) by 0.12 and 0.16, respectively, over the model-only skill and reduced the surface and root zone ubRMSE by 0.005 m3 m-3 and 0.001 m3 m-3, respectively. The assimilation reduced the average absolute surface bias against the CVS measurements by 0.009 m3 m-3, but increased the root zone bias by 0.014 m3 m-3. Assimilating the NN retrievals after a localized bias correction yielded slightly lower surface correlation and ubRMSE improvements, but generally the skill differences were small. The assimilation of the physically-based SMAP Level-2 passive soil moisture retrievals using a global bias correction yielded similar skill improvements, as did the direct assimilation of locally bias-corrected SMAP brightness temperatures within the SMAP Level-4 soil moisture algorithm. The results show that global bias correction methods may be able to extract more independent information from SMAP observations compared to local bias correction methods, but without accurate quality control and observation error characterization they are also more vulnerable to adverse effects from retrieval errors related to uncertainties in the retrieval inputs and algorithm. Furthermore, the results show that using global bias correction approaches without a simultaneous re-calibration of the land model processes can lead to a skill degradation in other land surface variables.