Assessing global surface water inundation dynamics using combined satellite information from SMAP, AMSR2 and Landsat.

A method to assess global land surface water (fw) inundation dynamics was developed by exploiting the enhanced fw sensitivity of L-band (1.4 GHz) passive microwave observations from the Soil Moisture Active Passive (SMAP) mission. The L-band fw (fwLBand ) retrievals were derived using SMAP H-polarization brightness temperature (Tb ) observations and predefined L-band reference microwave emissivities for water and land endmembers. Potential soil moisture and vegetation contributions to the microwave signal were represented from overlapping higher frequency Tb observations from AMSR2. The resulting fwLBand global record has high temporal sampling (1-3 days) and 36-km spatial resolution. The fwLBand annual averages corresponded favourably (R=0.85, p-value<0.001) with a 250-m resolution static global water map (MOD44W) aggregated at the same spatial scale, while capturing significant inundation variations worldwide. The monthly fwLBand averages also showed seasonal inundation changes consistent with river discharge records within six major US river basins. An uncertainty analysis indicated generally reliable fwLBand performance for major land cover areas and under low to moderate vegetation cover, but with lower accuracy for detecting water bodies covered by dense vegetation. Finer resolution (30-m) fwLBand results were obtained for three sub-regions in North America using an empirical downscaling approach and ancillary global Water Occurrence Dataset (WOD) derived from the historical Landsat record. The resulting 30-m fwLBand retrievals showed favourable spatial accuracy for water (commission error 31.46%, omission error 30.20%) and land (commission error 0.87%, omission error 0.96%) classifications and seasonal wet and dry periods when compared to independent water maps derived from Landsat-8 imagery. The new fwLBand algorithms and continuing SMAP and AMSR2 operations provide for near real-time, multi-scale monitoring of global surface water inundation dynamics and potential flood risk.

Satellite microwave detection of boreal forest recovery from the extreme 2004 wildfires in Alaska and Canada.

The rate of vegetation recovery from boreal wildfire influences terrestrial carbon cycle processes and climate feedbacks by affecting the surface energy budget and land-atmosphere carbon exchange. Previous forest recovery assessments using satellite optical-infrared normalized difference vegetation index (NDVI) and tower CO(2) eddy covariance techniques indicate rapid vegetation recovery within 5-10 years, but these techniques are not directly sensitive to changes in vegetation biomass. Alternatively, the vegetation optical depth (VOD) parameter from satellite passive microwave remote sensing can detect changes in canopy biomass structure and may provide a useful metric of post-fire vegetation response to inform regional recovery assessments. We analyzed a multi-year (2003-2010) satellite VOD record from the NASA AMSR-E (Advanced Microwave Scanning Radiometer for EOS) sensor to estimate forest recovery trajectories for 14 large boreal fires from 2004 in Alaska and Canada. The VOD record indicated initial post-fire canopy biomass recovery within 3-7 years, lagging NDVI recovery by 1-5 years. The VOD lag was attributed to slower non-photosynthetic (woody) and photosynthetic (foliar) canopy biomass recovery, relative to the faster canopy greenness response indicated from the NDVI. The duration of VOD recovery to pre-burn conditions was also directly proportional (P < 0.01) to satellite (moderate resolution imaging spectroradiometer) estimated tree cover loss used as a metric of fire severity. Our results indicate that vegetation biomass recovery from boreal fire disturbance is generally slower than reported from previous assessments based solely on satellite optical-infrared remote sensing, while the VOD parameter enables more comprehensive assessments of boreal forest recovery.

Global Satellite Retrievals of the Near-Surface Atmospheric Vapor Pressure Deficit from AMSR-E and AMSR2.

Near-surface atmospheric Vapor Pressure Deficit (VPD) is a key environmental variable affecting vegetation water stress, evapotranspiration, and atmospheric moisture demand. Although VPD is readily derived from in situ standard weather station measurements, more spatially continuous global observations for regional monitoring of VPD are lacking. Here, we document a new method to estimate daily (both a.m. and p.m.) global land surface VPD at a 25-km resolution using a satellite passive microwave remotely sensed Land Parameter Data Record (LPDR) derived from the Advanced Microwave Scanning Radiometer (AMSR) sensors. The AMSR-derived VPD record shows strong correspondence (correlation coefficient ≥ 0.80, p-value < 0.001) and overall good performance (0.48 kPa ≤ Root Mean Square Error ≤ 0.69 kPa) against independent VPD observations from the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) data. The estimated AMSR VPD retrieval uncertainties vary with land cover type, satellite observation time, and underlying LPDR data quality. These results provide new satellite capabilities for global mapping and monitoring of land surface VPD dynamics from ongoing AMSR2 operations. Overall good accuracy and similar observations from both AMSR2 and AMSR-E allow for the development of climate data records documenting recent (from 2002) VPD trends and potential impacts on vegetation, land surface evaporation, and energy budgets.

Global Assessment of the SMAP Level-4 Surface and Root-Zone Soil Moisture Product Using Assimilation Diagnostics.

The Soil Moisture Active Passive (SMAP) mission Level-4 Soil Moisture (L4_SM) product provides 3-hourly, 9-km resolution, global estimates of surface (0-5 cm) and root-zone (0-100 cm) soil moisture and related land surface variables from 31 March 2015 to present with ~2.5day latency. The ensemble-based L4_SM algorithm assimilates SMAP brightness temperature (Tb) observations into the Catchment land surface model. This study describes the spatially distributed L4_SM analysis and assesses the observation-minus-forecast (O-F) Tb residuals and the soil moisture and temperature analysis increments. Owing to the climatological rescaling of the Tb observations prior to assimilation, the analysis is essentially unbiased, with global mean values of ~0.37 K for the O-F Tb residuals and practically zero for the soil moisture and temperature increments. There are, however, modest regional (absolute) biases in the O-F residuals (under ~3 K), the soil moisture increments (under ~0.01 m3 m-3), and the surface soil temperature increments (under ~1 K). Typical instantaneous values are ~6 K for O-F residuals, ~0.01 (~0.003) m3 m-3 for surface (root-zone) soil moisture increments, and ~0.6 K for surface soil temperature increments. The O-F diagnostics indicate that the actual errors in the system are overestimated in deserts and densely vegetated regions and underestimated in agricultural regions and transition zones between dry and wet climates. The O-F auto-correlations suggest that the SMAP observations are used efficiently in western North America, the Sahel, and Australia, but not in many forested regions and the high northern latitudes. A case study in Australia demonstrates that assimilating SMAP observations successfully corrects short-term errors in the L4_SM rainfall forcing.