A continental-scale soil evaporation dataset derived from Soil Moisture Active Passive satellite drying rates.

This manuscript describes an observationally-based dataset of soil evaporation for the conterminous U.S. (CONUS), gridded to a 9 km resolution for the time-period of April 2015-March 2019. This product is termed E-SMAP (Evaporation-Soil Moisture Active Passive) in which soil evaporation is estimated from the surface layer, defined by the SMAP sensing depth of 50 mm, between SMAP overpass intervals that are screened on the basis of precipitation and SMAP quality control flags. Soil evaporation is estimated using a water balance of the surface soil that we show is largely dominated by SMAP-observed soil drying. E-SMAP soil evaporation is on average 0.72 mm day-1, which falls within the range of soil evaporation estimates (0.17-0.89 mm day-1) derived from operational land surface models and an alternative remote sensing product. E-SMAP is independent from existing soil evaporation estimates and therefore has the potential to improve understanding of evapotranspiration partitioning and model development.

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.

Comparing land surface phenology derived from satellite and GPS network microwave remote sensing.

The land surface phenology (LSP) start of season (SOS) metric signals the seasonal onset of vegetation activity, including canopy growth and associated increases in land-atmosphere water, energy and carbon (CO2) exchanges influencing weather and climate variability. The vegetation optical depth (VOD) parameter determined from satellite passive microwave remote sensing provides for global LSP monitoring that is sensitive to changes in vegetation canopy water content and biomass, and insensitive to atmosphere and solar illumination constraints. Direct field measures of canopy water content and biomass changes desired for LSP validation are generally lacking due to the prohibitive costs of maintaining regional monitoring networks. Alternatively, a normalized microwave reflectance index (NMRI) derived from GPS base station measurements is sensitive to daily vegetation water content changes and may provide for effective microwave LSP validation. We compared multiyear (2007-2011) NMRI and satellite VOD records at over 300 GPS sites in North America, and their derived SOS metrics for a subset of 24 homogenous land cover sites to investigate VOD and NMRI correspondence, and potential NMRI utility for LSP validation. Significant correlations (P<0.05) were found at 276 of 305 sites (90.5 %), with generally favorable correspondence in the resulting SOS metrics (r (2)=0.73, P<0.001, RMSE=36.8 days). This study is the first attempt to compare satellite microwave LSP metrics to a GPS network derived reflectance index and highlights both the utility and limitations of the NMRI data for LSP validation, including spatial scale discrepancies between local NMRI measurements and relatively coarse satellite VOD retrievals.

Effects of experimental rainfall manipulations on Chihuahuan Desert grassland and shrubland plant communities.

Aridland ecosystems are predicted to be responsive to both increases and decreases in precipitation. In addition, chronic droughts may contribute to encroachment of native C3 shrubs into C4-dominated grasslands. We conducted a long-term rainfall manipulation experiment in native grassland, shrubland and the grass-shrub ecotone in the northern Chihuahuan Desert, USA. We evaluated the effects of 5 years of experimental drought and 4 years of water addition on plant community structure and dynamics. We assessed the effects of altered rainfall regimes on the abundance of dominant species as well as on species richness and subdominant grasses, forbs and shrubs. Nonmetric multidimensional scaling and MANOVA were used to quantify changes in species composition in response to chronic addition or reduction of rainfall. We found that drought consistently and strongly decreased cover of Bouteloua eriopoda, the dominant C4 grass in this system, whereas water addition slightly increased cover, with little variation between years. In contrast, neither chronic drought nor increased rainfall had consistent effects on the cover of Larrea tridentata, the dominant C3 shrub. Species richness declined in shrub-dominated vegetation in response to drought whereas richness increased or was unaffected by water addition or drought in mixed- and grass-dominated vegetation. Cover of subdominant shrubs, grasses and forbs changed significantly over time, primarily in response to interannual rainfall variability more so than to our experimental rainfall treatments. Nevertheless, drought and water addition shifted the species composition of plant communities in all three vegetation types. Overall, we found that B. eriopoda responded strongly to drought and less so to irrigation, whereas L. tridentata showed limited response to either treatment. The strong decline in grass cover and the resistance of shrub cover to rainfall reduction suggest that chronic drought may be a key factor promoting shrub dominance during encroachment into desert grassland.

Convergence across biomes to a common rain-use efficiency.

Water availability limits plant growth and production in almost all terrestrial ecosystems. However, biomes differ substantially in sensitivity of aboveground net primary production (ANPP) to between-year variation in precipitation. Average rain-use efficiency (RUE; ANPP/precipitation) also varies between biomes, supposedly because of differences in vegetation structure and/or biogeochemical constraints. Here we show that RUE decreases across biomes as mean annual precipitation increases. However, during the driest years at each site, there is convergence to a common maximum RUE (RUE(max)) that is typical of arid ecosystems. RUE(max) was also identified by experimentally altering the degree of limitation by water and other resources. Thus, in years when water is most limiting, deserts, grasslands and forests all exhibit the same rate of biomass production per unit rainfall, despite differences in physiognomy and site-level RUE. Global climate models predict increased between-year variability in precipitation, more frequent extreme drought events, and changes in temperature. Forecasts of future ecosystem behaviour should take into account this convergent feature of terrestrial biomes.