SMAP and Agricultural Productivity Applications

Slides:

Advertisements

Similar presentations

Environmental Application of Remote Sensing: CE 6900 Tennessee Technological University Department of Civil and Environmental Engineering Course Instructor:

Advertisements

Contribution of Remote Sensing Technology to Catchment Water Resources Management Toshio Koike 1 and Dara Entekhabi 2 1 University of Tokyo 2 Massachusetts.

SNPP VIIRS green vegetation fraction products and application in numerical weather prediction Zhangyan Jiang 1,2, Weizhong Zheng 3,4, Junchang Ju 1,2,

Enhancing vegetation productivity forecasting using remotely-sensed surface soil moisture retrievals Wade T. Crow USDA Hydrology and Remote Sensing Laboratory,

R. Reichle 1 * and C. Draper 1,2 With contributions from: G. De Lannoy, R. de Jeu, Q. Liu, V. Naeimi, R. Parinussa, W. Wagner AMSR-E Technical Interchange.

USDA Foreign Agricultural Service Operationally Applying NASA Soil Moisture Product For Improved Agricultural Forecasting John D. Bolten, Code 617, NASA.

U.S. Department of the Interior U.S. Geological Survey NASA/USDA Workshop on Evapotranspiration April 6, 2011 – Silver Spring, Maryland ET for famine early.

HAL and little more. DRAFT – Page 2 – May 19, 2015 Hydrometeorology and Arctic Lab Hydrometeorology –Instrumented Study area –MESH model –Some board participation.

Potential Soil Moisture Retrieval from Aquarius Instrument Mississippi State University Geosystems Research Institute.

Identifying Soil Types using Soil moisture data CVEN 689 BY Uday Sant April 26, 2004.

Catchment Monitoring for Scaling and Assimilation of Soil Moisture and Streamflow C. Rüdiger a, R.E. Davidson b, H.M. Hemakumara b, J.P. Walker a, J.D.

Introduction Global soil moisture is one of the critical land surface initial conditions for numerical weather, climate, and hydrological predictions.

The Soil Moisture Active/Passive (SMAP) Mission: Monitoring Soil Moisture and Freeze/Thaw State John Kimball, Global Vegetation Workshop, June

Flood Risk Modeling in Thailand … and more … Alexander Lotsch Commodity Risk Management Group Agriculture and Rural Development The World Bank Group.

Earth Observation, Navigation & Science Page 1 Capacity Final Presentation, , Estec, Noordwijk Report for WP 3300 WP 3300.

Role of Soil Moisture/Climate Networks in SMAP Validation T. J

Slide 1/32NOAA Soil Moisture/Soil Temperature Workshop, Oak Ridge, TN, 3-5 March, 2009 Value of Ground Network Observations in Development of Satellite.

The University of Mississippi Geoinformatics Center NASA MRC RPC: April 2008 Greg Easson, Ph.D.- (PI) Robert Holt, Ph.D.- (Co-PI) A. K. M. Azad Hossain.

Wade Crow USDA ARS Hydrology and Remote Sensing Laboratory John Bolten NASA Goddard Space Flight Center Thanks: Xiwu Zhan (NOAA NESDIS), Curt Reynolds.

Liliana Balbi Senior Economist, Team Leader GIEWS FAO Trade and Markets Division Agricultural Market Information Network in the Mediterranean Region Kick.

EVALUATION OF SMAP LEVEL 2 SOIL MOISTURE ALGORITHMS USING SMOS DATA

Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 Science Support for NASA-NOAA Research to Operations (R2O) and GPM Ralph.

Multi-mission synergistic activities: A new era of integrated missions Christa Peters- Lidard Deputy Director, Hydrospheric and Biospheric Sciences, Goddard.

SMAP Cal/Val T. J. Jackson USDA ARS Hydrology and Remote Sensing Lab December 11, 2012.

Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 Image: MODIS Land Group, NASA GSFC March 2000 Center for Satellite Applications.

National Institute of Food and Agriculture/USDA Multi-Model Simulations and Satellite Observations for Assessing Impacts of Climate Variability on the.

A Global Agriculture Information System Zhong Liu 1,4, W. Teng 2,4, S. Kempler 4, H. Rui 3,4, G. Leptoukh 3 and E. Ocampo 3,4 1 George Mason University,

Prospects for improving global agricultural drought monitoring using microwave remote sensing Wade Crow USDA Agricultural Research Services Hydrology and.

Pang-Wei Liu 1, Roger De Roo 2, Anthony England 2,3, Jasmeet Judge 1 1. Center for Remote Sensing, Agri. and Bio. Engineering, U. of Florida 2. Atmosphere,

1 SPSRB Decision Brief on Declaring a Product Operational Instructions / Guidance This template will be used by NESDIS personnel to recommend to the SPSRB.

Enhancing the Value of GRACE for Hydrology

1 Soil Moisture Assimilation in NCEP Global Forecast System Weizhong Zheng 1, Jerry Zhan 2, Jiarui Dong 1, Michael Ek 1 1 Environmental Modeling Center,

Michael A. Palecki USCRN Science Project Manager National Climatic Data Center DOC/NOAA/NESDIS USCRN PROGRAM STATUS MARCH 3, United States Climate.

APPLICATIONS OF THE INTEGRAL EQUATION MODEL IN MICROWAVE REMOTE SENSING OF LAND SURFACE PARAMETERS In Honor of Prof. Adrian K. Fung Kun-Shan Chen National.

William Crosson, Ashutosh Limaye, Charles Laymon National Space Science and Technology Center Huntsville, Alabama, USA Soil Moisture Retrievals Using C-

Application of remote sensed precipitation for landslide hazard assessment Dalia Kirschbaum, NASA GSFC, Code The increasing availability of remotely.

2 SMAP Applications in NOAA - Numerical Weather & Seasonal Climate Forecasting 24-Hours Ahead Atmospheric Model Forecasts Observed Rainfall 0000Z to 0400Z.

Preparations for Assimilating Land Surface Observations from GOES-R, NPP/VIIRS and AMSR2 in NCEP NWP Models from GOES-R, NPP/VIIRS and AMSR2 in NCEP NWP.

THE ARS WATERSHED RESEARCH PROGRAM Watershed Processes National Program Component AGRICULTURAL WATERSHED RESEARCH History Facilities.

Global Observing System Simulation Experiments (Global OSSEs) How It Works Nature Run 13-month uninterrupted forecast produces alternative atmosphere.

Donald W. Cline National Operational Hydrologic Remote Sensing Center National Weather Service, NOAA CLPM Planning Workshop June Fort Collins, Colorado.

Heat Exchange and Ice Production in the Arctic Ocean as Derived from ICESat Nathan Kurtz, Thorsten Markus, Code 614.1, NASA GSFC The rate at which heat.

National Polar-orbiting Operational Satellite System (NPOESS) Microwave Imager/Sounder (MIS) Capabilities Pacific METSAT Working Group Apr 09 Rebecca Hamilton,

Pg. 1 Using the NASA Land Information System for Improved Water Management and an Enhanced Famine Early Warning System Christa Peters-Lidard Chief, Hydrological.

NASA Applied Sciences Program Update John A. Haynes Program Manager, Weather National Aeronautics and Space Administration Applied Sciences Program Earth.

SeaWiFS Views the Agulhas Retroflection Gene Feldman NASA GSFC, Laboratory for Hydrospheric Processes, SeaWiFS Project Office

Application of the Tor Vergata scattering model to L-band backscattering over corn Alicia T. Joseph, Code 617, NASA GSFC Figure 2: Scattering contributions.

   Alys Thomas 1, J.T. Reager 1,2, Jay Famiglietti 1,2,3, Matt Rodell 4 1 Dept. of Earth System Science, 2 UC Center for Hydrologic Modeling, 3 Dept.

National Aeronautics and Space Administration Jet Propulsion Laboratory California Institute of Technology Pasadena, California Soil Moisture Active and.

A Remote Sensing Approach for Estimating Regional Scale Surface Moisture Luke J. Marzen Associate Professor of Geography Auburn University Co-Director.

National Aeronautics and Space Administration Jet Propulsion Laboratory California Institute of Technology Pasadena, California Evaluation of the SMAP.

Recent SeaWiFS view of the forest fires over Alaska Gene Feldman, NASA GSFC, Laboratory for Hydrospheric Processes, Office for Global Carbon Studies

Latin American and Caribbean Flood and Drought Monitor Colby Fisher, Nathaniel Chaney, Justin Sheffield, Eric F. Wood Princeton University … with support.

Global vs mesoscale ATOVS assimilation at the Met Office Global Large obs error (4 K) NESDIS 1B radiances NOAA-15 & 16 HIRS and AMSU thinned to 154 km.

NOAA, May 2014 Coordination Group for Meteorological Satellites - CGMS NOAA Activities toward Transitioning Mature R&D Missions to an Operational Status.

CGMS-43-NOAA-WP-25 Coordination Group for Meteorological Satellites - CGMS NOAA Use of Soil Moisture Products Presented to CGMS-43 Working Group 2 session,

39 th Conference of Directors of EU Paying Agencies ESTEC, 25 May 2016 M. Drusch, Principal Scientist Earth Observation Programmes Directorate Science,

Quick overview of existing Early Adopter paradigms in NASA Earth Science Doreen Neil July 12, 2016.

JPL Technical Activities

NASA Drought Project Meeting

NASA Drought Project Meeting

NASA Drought Project Meeting

VegDRI Products Additional VegDRI products for rangeland decision makers and other users will be available at the VegDRI page within the Monitoring section.

Overview of the Soil Merge (SMERGE) root-zone soil moisture product

Global Agricultural Monitoring

Rolf Reichle1, Gabrielle DeLannoy1,2, and Wade Crow3

SMOS Soil Moisture Validation With U.S. In Situ Networks

Soil Moisture Active Passive (SMAP) Satellite

JDS international seminar

Ben Zaitchik, Matt Rodell, Rolf Reichle, Rasmus Houborg, Bailing Li,

Presentation transcript:

SMAP and Agricultural Productivity Applications Wade T. Crow USDA Hydrology and Remote Sensing Laboratory, Beltsville, MD Thanks: John Bolten (NASA GSFC), Xiwu Zhan (NOAA NESDIS) and Curt Reynolds (USDA FAS IPAD) ISDWG SMAP Applications Workshop March 3, 2011 Funded by the NASA Applied Sciences Program (joint with NASA HSB, NESDIS STAR, USDA ARS, and USDA FAS, PI: Wade Crow, Program Manager: Brad Doorn)

SMAP Mission Concept L-band unfocused SAR and radiometer system with offset- fed 6-m light-weight deployable mesh reflector rotating about nadir axis (14.6 rpm) Single feed (dual-pol radar and polarimetric radiometer) Conical scan, fixed incidence angle across swath Contiguous 1000 km swath Radar resolution: 1-3 km (degrades over center 30%) Radiometer resolution: 40 km Sun-synchronous dawn/dusk orbit Mission Ops duration 3 years 2

The USDA Foreign Agricultural Service (FAS) International Production Assessment Division (IPAD): Monthly global production estimates for commodity crops. Vital for economic competitiveness, national security and food security applications. Utilizes a wide-range of satellite data sources, input databases, climate data, crop models, and data extraction routines to arrive at yield and area estimates. Analyst-based decision support system. Characterizing the extent and impact of agricultural drought (i.e. root-zone soil moisture limitations) is critical for monitoring variations in agricultural productivity.

Past USDA FAS/IPAD Treatment of Soil Moisture Time/space attributes: 12-km resolution. Global coverage within agriculturally relevant areas. Monthly reporting cycle (< weekly latency). 2-Layer Soil Moisture Model Analysts Global Rain and Met Forcing Data Crop Stress (Alarm) Models Crop Models

Current USDA IPAD Treatment of Soil Moisture Funded by the NASA Applied Sciences Program (joint with NASA HSB, NESDIS STAR, USDA ARS, and USDA FAS, W.T. Crow PI, Brad Doorn, program manager) Remotely-Sensed Soil Moisture Data Assimilation 2-Layer Soil Moisture Model Analysts Global Rain and Met Forcing Data Crop Stress (Alarm) Models Crop Models

What is the added value of integrating remotely-sensed soil moisture information? Data Assimilation 2-Layer Soil Moisture Model Analysts Global Rain and Met Forcing Data Crop Stress (Alarm) Models Crop Models

Data Denial Methodology

Example Time Series Results Can the poor-precipitation open loop case be corrected to resemble the good-precipitation benchmark case through the assimilation of remotely-sensed soil moisture (EnKF case)?

Improvement in Root-Zone RMSE Fit to Benchmark Bolten, J.D., W.T. Crow, T.J. Jackson, X. Zhan and C.A. Reynolds, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 3, 57-66, 10.1109/JSTARS.2009.2037163, 2010.

Improvement in Lagged Correlation with EVI Preliminary 2002 to 2008 results in United States Southern Great Plains

Summary: X-band AMSR-E soil moisture retrievals can add value to both global-scale root-zone soil moisture monitoring and vegetation status forecasting activities. Added skill will be enhanced by improved L-band SMAP retrieval accuracy (versus existing X-band AMSR-E). Evaluate added skill all the way to agricultural yield and production statistics (the central goal of USDA FAS IPAD). Thank you….questions?