SMAP Early Adopters. Soil Moisture Active Passive Mission SMAP. Barry Weiss Jet Propulsion Laboratory California Institute of Technology

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1 National Aeronautics and Space Administration Soil Moisture Active Passive Mission SMAP SMAP Early Adopters Barry Weiss Jet Propulsion Laboratory California Institute of Technology

2 SMAP Data Products Data Product Short Name Description Beta Product Distribution Begins Validated Product Distribution Begins Average Latency to User Community after Acquisition L1A_Radar Parsed Radar Instrument Telemetry 8/1/ /1/ hours L1A_Radiometer Parsed Radiometer Instrument Telemetry 8/1/ /1/ hours L1B_S0_LoRes Low Resolution Radar σ o in Time Order 8/1/ /1/ hours L1C_S0_HiRes High Resolution Radar σ o on Swath Grid 8/1/ /1/ hours L1B_TB Radiometer T B in Time Order 8/1/ /1/ hours L1C_TB Radiometer T B 11/1/2015 5/1/ hours L2_SM_A Radar Soil Moisture 11/1/2015 5/1/ hours L2_SM_P Radiometer Soil Moisture 11/1/2015 5/1/ hours L2_SM_AP Active-Passive Soil Moisture 11/1/2015 5/1/ hours L3_FT_A Daily Global Composite Freeze/Thaw State 11/1/2015 5/1/ hours L3_SM_A Daily Global Composite Radar Soil Moisture 11/1/2015 5/1/ hours L3_SM_P Daily Global Composite Radiometer Soil Moisture 11/1/2015 5/1/ hours L3_SM_AP Daily Global Composite Active-Passive Soil Moisture 11/1/2015 5/1/ hours L4_SM Surface & Root Zone Soil Moisture 11/1/2015 5/1/ days L4_C Carbon Net Ecosystem Exchange 11/1/2015 5/1/ days July 16, 2015 BHW-1

3 SMAP Early Adopters SMAP was the first NASA Earth mission to sponsor an Early Adopter program Early Adopters foresee value in mission data for their design and implementation of applications Early Adopters gain access to product design and simulated data well before launch. Value is symbiotic: Early Adopters build or modify applications to employ mission data well before launch Early Adopters present plans to mission team and other Early Adopters Early Adopters provide feedback about mission product design. SMAP currently has 55 Early Adopters Topics are highly varied Scientific Social Scientific Economic July 16, 2015 BHW-2

4 BHW-3 Provisions for Early Adopters Early Adopter candidates propose an application that uses mission data Team from Science and Science Data System review the candidate application and accept or reject them Proposal must be viable and display an adequate understanding of product content Mission requires that approved applicants sign agreement that specifies: The data are simulated, have no value for scientific research The data may not be distributed beyond the Early Adopter team Based on additional criteria, some Early Adopters currently receive pre-beta data Early Adopter demonstrated effective use of simulated data Data collected during pre-beta period is relevant to their research

5 Numerical Weather Prediction Jonathan Case, Clay Blankenship and Bradley Zavodsky, NASA Short-term Prediction Research and Transition (SPoRT) Center; SMAP Contact: Molly Brown Data assimilation of SMAP observations, and impact on weather forecasts in a coupled simulation environment The NASA SPoRT Center is assimilating SMOS into the Weather Research and Forecasting numerical weather prediction NWP model through coupling with the Land Information System (LIS). PLE: NUMERICAL WEATHER PREDICTION By assimilating soil moisture observations, modelers can improve a land surface model s ability to simulate evapotranspiration and latent and sensible heating at the surface, important inputs to NWP models. ASA Short-Term Prediction Research and Transition Center (SPoRT) plementing the assimilation of soil moisture observations from SMOS he WRF numerical weather prediction (NWP) model through coupling the July Land 16, 2015 Information System to 1) investigate the impact of soil BHW-4

6 BHW-5 Agricultural Productivity Catherine Champagne, Agriculture and Agri-Food Canada (AAFC); SMAP Contact: Stephane Bélair Soil moisture monitoring in Canada Currently, weekly national maps of soil moisture condition are produced using sensors such as AMSR and SMOS AAFC will integrate the soil moisture information from SMAP into existing monitoring programs and achieve improvements from increased spatial resolution and data continuity that will enhance agricultural monitoring capacity.

7 BHW-6 Use of AMSR, SMOS and SMAP soil moisture for Agro-climate risk monitoring

8 BHW-7 National Soil Moisture Modeling Zhengwei Yang and Rick Mueller, USDA National Agricultural Statistical Service (NASS); SMAP Contact: Wade Crow US National cropland soil moisture monitoring using SMAP The USDA National Agricultural Statistical Service (NASS) has launched a webbased U.S. crop vegetation condition assessment and monitoring application: VegScape ( This web-based application has been designed to be a platform for accessing, visualization, assessing and disseminating crop soil moisture condition derivative data products produced using SMAP data.

9 BHW-8 Agricultural Drought Curt Reynolds, USDA Foreign Agricultural Service (FAS); SMAP Contact: Wade Crow Enhancing USDA s global crop production monitoring system using SMAP soil moisture products The figure describes EXAMPLE: AGRICULTURAL areas of the DROUGHT world where the assimilation of AMSR-E surface soil moisture retrievals significantly impacts the sampled crosscorrelation between soil moisture anomalies and NDVI anomalies. Red areas correspond to regions where the availability of satellite-based surface soil moisture retrievals significantly improves our ability to forecast agricultural drought relative to the water balance modeling system currently employed by USDA FAS. Areas of world where assimilation of AMSR-E surface soil moisture retrievals significantly impacts the sampled cross-correlation between soil moisture anomalies for month i and NDVI anomalies for month i-1. As a result, red areas correspond to regions where the availability of satellite-based surface soil moisture retrievals significantly improves our ability to forecast agricultural drought using off-line water balance modeling. Assimilation model is the standard USDA Foreign Agricultural Service, 2-Layer Palmer water balance model and plotted values are sigma-levels of statistical significance for changes in cross-correlation relative to a model-only baseline (Bolton et al., 2013).

10 BHW-9 Effect of the soil moisture on dust emission Hosni Ghedira, Masdar Institute, UAE; SMAP Contact: Dara Entekhabi Estimating and mapping the extent of Saharan dust emissions using SMAP-derived soil moisture data. AOT April to mid June Mid June to July SMOS SM (m 3 /m 3 ) a) The AERONET aerosol optical depth (AOT 870 ) was correlated to the SMOS soil moisture data collected from 2010 to 2011 in south Sahel. The results show that as the SMOS soil moisture increase, the AERONET AOT decrease up to a threshold moisture content above which no dust emission takes place.

11 BHW-10 Vehicle Mobility Gary McWilliams, Army Research Laboratory (ARL); George Mason, U.S. Army Engineer Research and Development Center (ERDC) Geotechnical and Structures Laboratory (GSL); Li Li, Naval Research Laboratory (NRL); and Andrew Jones, Colorado State University (CSU); SMAP Contact: Susan Moran Exploitation of SMAP data for Army and Marine Corps mobility assessment Soil moisture can be used to estimate soil strength, which is a key factor in terrain trafficability. This figure shows a sample of the difference in the estimated time to target for a military ground vehicle using satellite soil moisture data (left) versus not using satellite soil moisture data (right).

12 BHW-11 Military Maneuver Planning John Eylander and Susan Frankenstein, U.S. Army Engineer Research and Development Center (ERDC) Cold Regions Research and Engineering Laboratory (CRREL); SMAP Contact: Susan Moran U. S. Army ERDC SMAP adoption for USACE civil and military tactical support The soil strength quantified using the Rating Cone Index (RCI) as an indicator of soil shear strength with climatological data (left) and SMAP simulated data (right). These images are used to map vehicle speeds for the Army maneuvers.

13 BHW-12 Cryosphere Processes Lars Kaleschke, Institute of Oceanography, University of Hamburg; SMAP Contact: Simon Yueh SMOS to SMAP migration for cryosphere and climate application Coincident L-band radiometer (EMIRAD-2) and ice thickness measurements, CryoSat2 satellite underflights Conducted successful SMOS Ice validation campaign in the Barents Sea east of Svalbard (Left) The advantage of the high-resolution active SMAP radar data (L1C_S0_HiRes) is to develop methods to account for the subpixelscale heterogeniety for the retrieval of geophysical snow and ice parameters from the low-resolution brightness temperature measurements (L1B_TB or L1C_TB).

14 BHW-13 Global Flash Flood Guidance Konstantine Georgakakos, Hydrologic Research Center; SMAP Contact: Narendra Das Development of a strategy for the evaluation of the utility of SMAP products for the Global Flash Flood Guidance Program of the Hydrologic Research Center A pre-launch prototype of a Flash Flood Guidance System to ingest and assimilate L3_SM_A/P data An example of FFG estimated top-soil saturation fraction base map from operational Southern Africa Flash Flood Guidance system.

15 BHW-14 Flood Forecasting Kashif Rashid and Emily Niebuhr, UN World Food Programme; SMAP Contact: Eni Njoku Application of a SMAP-based index for flood forecasting in data-poor regions We have run about 10 years of daily ECMWF re-analysis rainfall data thru VIC to compute soil moisture as a surrogate for SMAP data Strong correlations of precipitation total and top layer soil moisture and the downstream floodplain inundation volume (m 3 ) were derived ( using rainfall only gives , resp.), clearly indicating that both rainfall and soil moisture can be used as predictors for flood inundation in our region

16 Flood Forecasting Luca Brocca, Research Institute for Geo-Hydrological Protection, Italian Dept. of Civil Protection; SMAP contact: Dara Entekhabi Use of SMAP soil moisture products for operational flood forecasting: data assimilation and rainfall correction With existing satellite SM data 1) Improving flood forecasting through the assimilation of ASCAT and AMSR-E soil moisture products 2) Rainfall estimation from ASCAT, AMSR-E, and SMOS SM data With SMAP simulated SM data Estimation of rainfall through SM2RAIN algorithm over Europe July 16, 2015 BHW-15

17 Soil moisture (m 3 m -3 ) July 16, 2015 BHW-16 Water Resources Assessment Luigi Renzullo, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia; SMAP Contact: Jeff Walker Preparing the Australian Water Resources Assessment (AWRA) system for the assimilation of SMAP data AWRA top soil layer (~8 cm) relative wetness. AWRA landscape water balance model runs operationally to generate stores and fluxes of water across Australia for legislated National Water Accounting and Assessment reporting Baldry Land surface data assimilation (DA) component of the AWRA system focuses on AMSR-E (passive) and ASCAT (active) soil moisture products currently under testing and further development Evaluating AWRA shallow root-zone SM against cosmic-ray moisture probes.

18 BHW-17 Sea Ice Mapping Georg Heygster, Institute of Environmental Physics, University of Bremen, Germany; SMAP Contact: Simon Yueh SMAP-Ice: Use of SMAP observations for sea ice remote sensing Retrieval algorithm for thickness of thin sea ice from SMOS observations incidence angle running operationally. See examples for Oct 2013 and Nov 2013 (Left). Current status: Reading and projecting SMAP simulated data. SMAP 1.4 GHz TBh (Left)

19 BHW-18 A water requirement satisfaction index (WRSI) based on SMAP-like soil moisture to improve crop yield estimates Map of WRSI anomalies from different SM products in In Niger, there are strong difference between the rainfall-derived WRSI (bucket and Noah) and the ECV* microwave WRSI. *European Space Agency (ESA) Essential Climate Variable (ECV) McNally, A., G.J. Huask, M. Brown, M. Carroll, C. Funk, S. Yatheendradas, K. Aresenault, C. Peters-Lidard, and J.P. Verdin, Calculating crop water requirement satisfaction in the West Africa Sahel with remotely sensed soil moisture. Journal of Hydrometeorology (this issue)

20 BHW-19 Seamless use of AMSR, SMOS and SMAP soil moisture for agro-climate risk monitoring Weeks 24 and 25 in 2011 SMOS soil moisture difference from baseline Land too wet to seed Champagne, C., A.M. Davison, P. Cherneski, J. L Heureux, and T. Hadwen, Monitoring Agricultural Risk in Canada Using L-Band Passive Microwave Soil Moisture from SMOS. Journal of Hydrometeorology (this issue)

21 Cumulative Irrigation Amount (mm) BHW-20 Simulated SMAP data were used in agricultural models to show the usefulness of soil moisture for crop yield estimation Incorporating the SMAP data into the agricultural model reduced the uncertainty of modeled crop yields when the weather input data to the crop model are subject to large uncertainty Ctl SMAP- Ctl SMAPconsistent consistent El Sharif, H.A., J. Wang, and A. Georgakakos, Modeling regional crop yield and irrigation demand using SMAP type of soil moisture data. Journal of Hydrometeorology (this issue)