Using Remotely-Sensed Estimates of Soil Moisture to Infer Soil Texture and Hydraulic Properties Christa D. Peters-Lidard, Joseph A. Santanello, Jr., and.

Slides:



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

Future Directions and Initiatives in the Use of Remote Sensing for Water Quality.
How will SWOT observations inform hydrology models?
Comparison of Satellite-Derived and In-Situ Observations of Ice and Snow Surface Temperatures over Greenland Dorothy K. Hall, Jason E. Box, Kimberly A.
Robbie Hood NOAA UAS Program Director 20 June 2013.
USDA Foreign Agricultural Service Operationally Applying NASA Soil Moisture Product For Improved Agricultural Forecasting John D. Bolten, Code 617, NASA.
TRMM Tropical Rainfall Measurement (Mission). Why TRMM? n Tropical Rainfall Measuring Mission (TRMM) is a joint US-Japan study initiated in 1997 to study.
Sea water dielectric constant, temperature and remote sensing of Sea Surface Salinity E. P. Dinnat 1,2, D. M. Le Vine 1, J. Boutin 3, X. Yin 3, 1 Cryospheric.
Near Surface Soil Moisture Estimating using Satellite Data Researcher: Dleen Al- Shrafany Supervisors : Dr.Dawei Han Dr.Miguel Rico-Ramirez.
Goddard’s new airborne SMAP simulator-- the Scanning L-band Active Passive (SLAP)--conducts first test flights in preparation for SMAP cal/val Alicia Joseph,
Sea Ice Thickness from Satellite, Aircraft, and Model Data Xuanji Wang 1 and Jeffrey R. Key 1 Cooperative.
ATS 351 Lecture 8 Satellites
SMOS – The Science Perspective Matthias Drusch Hamburg, Germany 30/10/2009.
Princeton University Global Evaluation of a MODIS based Evapotranspiration Product Eric Wood Hongbo Su Matthew McCabe.
STAR-Light: Enabling a New Vision for Land Surface Hydrology in the Arctic A. W. England and Roger De Roo Atmospheric, Oceanic, and Space Sciences Electrical.
The first three rows in equation control the estimates of soil moisture from the regression equation assuring that the estimated soil moisture content.
Can we use microwave satellite data to monitor inundation at high spatial resolution? Competing issues Passive (high repeat) data have poor (50km) spatial.
A Multi-Sensor, Multi-Parameter Approach to Studying Sea Ice: A Case-Study with EOS Data Walt Meier 2 March 2005IGOS Cryosphere Theme Workshop.
Profiling Clouds with Satellite Imager Data and Potential Applications William L. Smith Jr. 1, Douglas A. Spangenberg 2, Cecilia Fleeger 2, Patrick Minnis.
Fig. 2: Radiometric angular response from deciduous Paulownia trees is plotted. The red, blue, black, and green curves trace the simulated values of four.
The University of Mississippi Geoinformatics Center NASA RPC – March, Evaluation for the Integration of a Virtual Evapotranspiration Sensor Based.
Wade Crow USDA ARS Hydrology and Remote Sensing Laboratory John Bolten NASA Goddard Space Flight Center Thanks: Xiwu Zhan (NOAA NESDIS), Curt Reynolds.
Multi-mission synergistic activities: A new era of integrated missions Christa Peters- Lidard Deputy Director, Hydrospheric and Biospheric Sciences, Goddard.
Recent advances in remote sensing in hydrology
Prospects for improving global agricultural drought monitoring using microwave remote sensing Wade Crow USDA Agricultural Research Services Hydrology and.
SMOS+ STORM Evolution Kick-off Meeting, 2 April 2014 SOLab work description Zabolotskikh E., Kudryavtsev V.
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,
Water Cycle Breakout Session Attendees: June Wang, Julie Haggerty, Tammy Weckwerth, Steve Nesbitt, Carlos Welsh, Vivek, Kathy Sharpe, Brad Small Two objectives:
Enhancing the Value of GRACE for Hydrology
NW NCNE SCSESW Rootzone: TOTAL PERCENTILEANOMALY Noah VEGETATION TYPE 2-meter Column Soil Moisture GR2/OSU LIS/Noah 01 May Climatology.
Passive Microwave Remote Sensing
Landslide Hazard Assessment in Central America Dalia Kirschbaum, Code 617, NASA GSFC Figure 2: Landslide hazard assessment and forecasting system that.
The University of Mississippi Geoinformatics Center NASA MRC RPC – 11 July 2007 Greg Easson, Ph.D. Robert Holt, Ph.D. A. K. M. Azad Hossain University.
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.
January 29, 2009 “Sensors with Wings” Unmanned Aircraft Systems (UAS) for Earth Science Geoff Bland NASA GSFC WFF (614.6) – AeroScienceLab.
William Crosson, Ashutosh Limaye, Charles Laymon National Space Science and Technology Center Huntsville, Alabama, USA Soil Moisture Retrievals Using C-
SeaWiFS Highlights February 2002 SeaWiFS Views Iceland’s Peaks Gene Feldman/SeaWiFS Project Office, Laboratory for Hydrospheric Processes, NASA Goddard.
Application of remote sensed precipitation for landslide hazard assessment Dalia Kirschbaum, NASA GSFC, Code The increasing availability of remotely.
Calibration/Validation Efforts at Calibration/Validation Efforts at UPRM Hamed Parsiani, Electrical & Computer Engineering Department University of Puerto.
Modern Era Retrospective-analysis for Research and Applications: Introduction to NASA’s Modern Era Retrospective-analysis for Research and Applications:
5. Accumulation Rate Over Antarctica The combination of the space-borne passive microwave brightness temperature dataset and the AVHRR surface temperature.
An Overview of the Hurricane Imaging Radiometer (HIRAD) Robbie Hood, Ruba Amarin, Robert Atlas, M.C. Bailey, Peter Black, Courtney Buckley, Shuyi Chen,
Cooling and Enhanced Sea Ice Production in the Ross Sea Josefino C. Comiso, NASA/GSFC, Code The Antarctic sea cover has been increasing at 2.0% per.
Pg. 1 Using the NASA Land Information System for Improved Water Management and an Enhanced Famine Early Warning System Christa Peters-Lidard Chief, Hydrological.
Diane E. Wickland NPP Program Scientist NPP Science: HQ Perspective on VIIRS May 18, 2011.
NASA Snow and Ice Products NASA Remote Sensing Training Geo Latin America and Caribbean Water Cycle capacity Building Workshop Colombia, November 28-December.
Educator Resources Lauren Ritter, NASA Education Pathways Intern Hurricane and Severe Storm Sentinel (HS3) Global Precipitation Measurement (GPM) Soil.
Design Features of a Boresighted GPM Core Radiometer Christopher S. Ruf Dept. of Atmospheric, Oceanic & Space Sciences University of Michigan, Ann Arbor,
Fog- and cloud-induced aerosol modification observed by the Aerosol Robotic Network (AERONET) Thomas F. Eck (Code 618 NASA GSFC) and Brent N. Holben (Code.
NASA’s Land Information System Supports Alaska Snow Analysis for NOAA’s Operational Hydrologic Remote Sensing Center (NOHRSC) Christa D. Peters-Lidard,
Autonomous Polar Atmospheric Observations John J. Cassano University of Colorado.
National Aeronautics and Space Administration Jet Propulsion Laboratory California Institute of Technology Pasadena, California Soil Moisture Active and.
Land-Atmosphere Coupling in Modern Reanalysis Products Joseph A. Santanello, Jr. 1, Josh Roundy 1, and Paul Dirmeyer 2 1 Hydrological Sciences Laboratory,
Use of AMSR-E Land Parameter Modeling and Retrievals for SMAP Algorithm Development Steven Chan Eni Njoku Joint AMSR Science Team Meeting Telluride, Colorado.
Matt Rodell NASA GSFC Multi-Sensor Snow Data Assimilation Matt Rodell 1, Zhong-Liang Yang 2, Ben Zaitchik 3, Ed Kim 1, and Rolf Reichle 1 1 NASA Goddard.
Hydrologic Data Assimilation with a Representer-Based Variational Algorithm Dennis McLaughlin, Parsons Lab., Civil & Environmental Engineering, MIT Dara.
Satellites Storm “Since the early 1960s, virtually all areas of the atmospheric sciences have been revolutionized by the development and application of.
SeaWiFS Views Equatorial Pacific Waves Gene Feldman NASA Goddard Space Flight Center, Lab. For Hydrospheric Processes, This.
A Wide Swath Imaging Microwave Radiometer for Hurricane Observations Central Florida Remote Sensing Lab University of Central Florida Presented at Marshall.
Determining Satellite Era Accumulation Patterns over WAIS Divide: The SEAT Traverse Lora Koenig, Code 614.1, NASA GSFC Figure 1: Image of the near surface.
TS 15 The Great Salt Lake System ASLO 2005 Aquatic Sciences Meeting Climatology and Variability of Satellite-derived Temperature of the Great Salt Lake.
SCM x330 Ocean Discovery through Technology Area F GE.
NOAA, May 2014 Coordination Group for Meteorological Satellites - CGMS NOAA Activities toward Transitioning Mature R&D Missions to an Operational Status.
Airborne Science Technology Institute (ASTI) The Airborne Science and Technology Institute (ASTI) concept development project is underway with the University.
New Projects: Collaborators Sought NSF OPP Instrumentation Project: STAR-Light – a 1.4 GHz aperture synthesis radiometer for use on light aircraft in arctic.
“CMORPH” is a method that creates spatially & temporally complete information using existing precipitation products that are derived from passive microwave.
IMAGE PIXELS OF RFI<0.2 ONLY
Alexander Loew1, Mike Schwank2
UAV Vision Landing Motivation Data Gathered Research Plan Background
Jili Qu Department of Environmental and Architectural College
Presentation transcript:

Using Remotely-Sensed Estimates of Soil Moisture to Infer Soil Texture and Hydraulic Properties Christa D. Peters-Lidard, Joseph A. Santanello, Jr., and David M. Mocko, NASA/GSFC Code Near-surface soil moisture is a critical component of land-surface energy and water balance models, and controls water and energy cycles, as well as weather, climate, hydrological, and agricultural prediction. Accurate soil moisture prediction requires soil texture and hydraulic property information, which is poorly characterized over most of the globe. Comparisons of simulated and measured soil texture demonstrate new capabilities in NASA’s Land Information System model. The optimized modeling capabilities represent a significant improvement over previous approaches, and improve NASA support of agency needs for accurate soil moisture measurements Optimized vs. Measured Soil Textures Christa Peters-Lidard NASA/GSFC Multiple agencies are responding to this need for accurate soil moisture measurements a) Default Soil Texture b) Optimized Soil Texture SIMULATED ERROR PBMR

Name: Christa Peters-Lidard, NASA/GSFC Phone: References: Santanello, J.A., Jr., C. D. Peters-Lidard, M. Garcia, D. Mocko, M. Tischler, MS. Moran, and D.P. Thoma, Using Remotely-Sensed Estimates of Soil Moisture to Infer Soil Texture and Hydraulic Properties across a Semi-arid Watershed, In Press, Remote Sensing of the Environment. Peters-Lidard, Christa D., David M. Mocko, Joseph A. Santanello, Jr., Michael A. Tischler, M. Susan Moran, Matthew Garcia, and Y. Wu, The role of precipitation uncertainty for soil property estimation using soil moisture retrievals in a semi-arid environment. Submitted to Water Resources Research. Garcia, M., C.D. Peters-Lidard, and D.C. Goodrich, Spatial interpolation of precipitation in a dense gauge network for monsoon storm events in the southwestern US. Submitted to Water Resources Research. Data Sources: This is a joint effort composed of multiple agencies including the USDA-Agricultural Research Service (watershed, remote-sensing data), NASA-GSFC (land-surface modeling within the Land Information System (LIS; coupled with Parameter Estimation (PEST)) and US Army Corps of Engineers-Engineering Research and Development Center (financial support, operational testing, user interface development), Near-surface (0-5cm) soil moisture observations (Figure 1) derived from successive aircraft flights using NASA’s L-Band Push-Broom Microwave Radiometer (PBMR; a precursor to the Hydros/SMAP mission) were acquired during the Monsoon ‘90 experiment in SE Arizona, and used to calibrate soil hydraulic properties in the Noah land-surface model executed within LIS at a very high horizontal spatial resolution of 40 meters. Technical Description of Image: Figure 1: Simulated (top), PBMR-observed (middle), and difference (bottom) 0-5 cm soil moisture using a) default (USDA SSURGO) soils and b) soil properties calibrated using LIS/Noah+PEST on DOY 214. The soil moisture bias and RMS error are greatly reduced after the soil property calibration. Limited remote microwave retrievals of near-surface soil moisture can be used to calibrate the soil texture and hydraulic properties using this combined observation, modeling and parameter estimation approach. (Fig. 1). [Santanello et al., 2007] Figure 2: Percentages of sand, silt, and clay estimated using this approach at the eight sites compared with in-situ soil measurements from Schmugge et al. (1994). The LIS/Noah+PEST system correctly estimates highly-sandy soils in this semi-arid watershed. Soil texture estimated using this approach corresponds well with in situ observations of sand, silt, and clay at various sites across the watershed. By estimating within a continuous range of soil properties such as sand, silt, and clay percentages rather than applying disconitnuous soil texture classes, the physical accuracy and consistency of the estimated soil properties is assured and can be more easily assessed against in situ measurements. Scientific significance: Soil texture and hydraulic properties are required for accurate prediction of soil moisture, and are poorly known over many areas of the globe. This work is the first to demonstrate how remotely sensed soil moisture combined with a physical model may be combined to infer these critical parameters. Relevance for future science and relationship to Decadal Survey: Soil moisture is a critical control on water and energy cycles, as well as weather, climate, hydrological and agricultural prediction. Soil texture and hydraulic properties are required for accurate prediction of soil moisture. Soil moisture observations from the SMAP mission, to be launched in the timeframe, combined with precipitation observations from the GPM mission, to be launched in 2013, will provide the necessary data products to infer soil textures world wide using the techniques presented in this work. Christa Peters-Lidard NASA/GSFC

“Sensors With Wings”: Aerotenna Ocean Marsh A prototype L-band radiometer on a UAV aircraft provides new capabilities for detailed studies of hydrological processes, including measurements of salinity, soil moisture and snow thickness (w/Hilliard/555 et al) The Aerotenna is an experimental system designed to investigate the use of a miniature L-band passive microwave instrument on a small aerial platform and will improve our understanding of local hydrological processes. A first flight was successfully conducted over Wallops Island on June 18, A measurement resolution of <100m was demonstrated.

Name: Geoff Bland, NASA/GSFC Phone number: References: Bland, G., Coronado, P., Miles, T., Bretthauer, P., The AEROS Project – Experiments with Small Electric Powered UAVs for Earth Science, AIAA 2005 Hilliard, L., Hildebrand, P., Markus, T., Johnson, C., Lawrence, R., Bland, G., Microwave Instrumentation for UAV Platforms Enabling Thin Ice Measurement, AIAA 2005 Lawrence, R., Hilliard, L., Bland, G., Markus, T., Autonomous Aerial Observation System Concepts for Microwave Remote Sensing, AIAA 2005 Hilliard, L., Phelps, N., Riley, J., Markus, T., Bland, G., Ruf, C., Lawrence, R., Reising, S., Pichel, T., Prototype Cryospheric Experimental Synthetic Array Radiometer (CESAR), International Geoscience and Remote Sensing Symposium (IGARSS), 2005 Data Sources: Experimental L-Band (~1.4 GHz) Radiometer uses Innovative Antennas and Electronics. Unmanned Aerial Vehicle (UAV) System uses Electric Propulsion and Weighs 12 Pounds. Technical Description of Images: Figures 1 and 2 show the Aerotenna UAV. The first flight was conducted June 18, 2007 over Wallops Island, and the results indicate good sensitivity and performance. Flown at an altitude of approximately 100 meters, surface feature resolution is better than the original goal of 100 meters. This radiometer operates at L-Band (~1.4 GHz) and two lightweight antenna arrays were mounted internally, one in each wing. The airframe, electric propulsion, power and data systems were tailored for this instrument, and GPS and a real-time video camera were included in the payload. The integrated instrument/vehicle system is performing well, and the team is encouraged that the “Sensors with Wings” approach will provide new capabilities for detailed studies of hydrological processes. Figure 3 shows the modulation of the sensor signals (left and right wing antennas) as it flies a racetrack pattern over the marsh/land and ocean areas. The marsh and land have higher brightness temperatures than the ocean, and the modulation of the marsh/land signal is due to the highly varied ground and water features, versus the relatively uniform ocean signal. Scientific Significance: The Aerotenna Unmanned Aerial Vehicle (UAV) is an experimental system intended to investigate the use of a miniaturized passive microwave instrument on a small aerial platform. High spatial resolution measurements of sea surface salinity, snow depth, and soil moisture are the objective, and the small UAV is well suited for local scale observations. The prototype antenna and electronics represent a significant step in the miniaturization of instruments suitable for small UAVs. Relevance for future science and relationship to Decadal Survey: Near term experiments include evaluating this system for the mapping of variations in salinity, particularly in coastal areas. A thermal infrared imager may be added to the instrument suite, and flights over agricultural areas are also planned. Tests to characterize the performance of this system in the arctic environment will be conducted as well. This effort is aimed to provide high resolution measurements that will be helpful in refining models based on satellite observations, as well as improve our understanding of local processes. These Aerotenna tests directly support the Decadal Survey recommendation to include UAV technology in the strategic planning of Earth science research.