PLMR Calibration Valerio Paruscio Dr Jeff Walker University of Melbourne.

Slides:



Advertisements
Similar presentations
Remote Sensing of Soil National and Regional Scales Alan Marks CSIRO Land and Water Monday 22 Sept., 2008 Water for a Healthy Country.
Advertisements

MoistureMap: Multi-Angle Retrieval Sandy Peischl PhD-candidate Jeffrey Walker, Dongryeol Ryu, Christoph Rüdiger, Damian Barrett, Robert Gurney, Jetse Kalma,
MoistureMap: Australian Arid Zone and Murrumbidgee Airborne Experiments Christoph Rüdiger, Jeffrey Walker Mahdi Allahmoradi, Damian Barrett, Yann Kerr,
MoistureMap: Multi-sensor Retrieval of Soil Moisture Mahdi Allahmoradi PhD Candidate Supervisor: Jeffrey Walker Contributors: Dongryeol Ryu, Chris Rudiger.
If water can pass through a surface, the surface is permeable.
2 nd NAFE Workshop 13–14 February 2006 g Rocco Panciera NAFE’05 HYDRA PROBE DATA Rocco Panciera and Jeffrey Walker University of Melbourne Jetse Kalma.
Second NAFE Workshop NAFE’06: A Proposed Campaign Strategy J Walker, O Merlin and R Panciera Dept Civil and Env Engg The University of Melbourne, Australia.
The ESA CoSMOS study for the validation of the SMOS L2 prototype K Saleh Contell, Y. Kerr, MJ Escorihuela, G. Boulet, P. Maisongrande, P. de Rosnay, JP.
Extension and application of an AMSR global land parameter data record for ecosystem studies Jinyang Du, John S. Kimball, Lucas A. Jones, Youngwook Kim,
GEOGRAPHIC INFORMATION SYSTEM (GIS) AND REMOTE SENSING Lecture 4 Zakaria Khamis.
Probes Calibration Dr. Jeffrey Walker THE UNIVERSITY OF MELBOURNE Dept. of Civil and Environmental Engineering Daniele Biasioni.
Calibration and Validation Studies for Aquarius Salinity Retrieval Shannon Brown and Sidharth Misra Jet Propulsion Laboratory, California Institute of.
1 Analysis of Airborne Microwave Polarimetric Radiometer Measurements in the Presence of Dynamic Platform Attitude Errors Jean Yves Kabore Central Florida.
Near Surface Soil Moisture Estimating using Satellite Data Researcher: Dleen Al- Shrafany Supervisors : Dr.Dawei Han Dr.Miguel Rico-Ramirez.
Thermal IR February 23, 2005 Emissivity Kirchoff’s Law Thermal Inertia, Thermal Capacity, and Thermal Conductivity Review for Midterm Reminder: Midterm.
Soil-climate impacts on water cycling at the patch level.
Infiltration Introduction Green Ampt method Ponding time
SMOS SAG Meeting, ESAC-Villafranca del Castillo, 2-3 Nov 2006 KAUZAR SALEH YANN H. KERR COSMOS PROPOSAL TEAM Presentation of the study.
CSIRO LAND and WATER Estimation of Spatial Actual Evapotranspiration to Close Water Balance in Irrigation Systems 1- Key Research Issues 2- Evapotranspiration.
Soil Temperature Retrieval Using Multi-Sensor Data Paul O'Neill, Remy Dehaan EH Graham Centre and Charles Sturt University Jeff Walker University of Melbourne.
6-1 EE/Ge 157b Week 6 EE/Ae 157 a Passive Microwave Sensing.
Intercalibration of AMSR-E and WindSat TB over Tropical Forest Scenes Thomas Meissner adapted by Marty Brewer for AMSR Science Team Meeting Huntsville,
Experiments with the microwave emissivity model concerning the brightness temperature observation error & SSM/I evaluation Henning Wilker, MIUB Matthias.
Jeffrey Walker et al. PLMR Data Jeffrey Walker and Valerio Paruscio Dept of Civil and Env Engg The University of Melbourne, Australia Ed Kim Hydrospheric.
Option G: Ecology and Conservation Chpt. 18: pages
7 th SMOS Workshop, Frascati, October /17 AMIRAS campaign Fernando Martin-Porqueras.
L-band Microwave Emission of the Biosphere (L-MEB)
William Crosson, Ashutosh Limaye, Charles Laymon National Space Science and Technology Center Huntsville, Alabama, USA Soil Moisture Retrievals Using C-
Trading Day Effects in Time Series. Example: single family construction Home construction activity at the state and local level is measured by permits.
Institute of Hydrology Slovak Academy of Sciences Katarína Stehlová 6 th ALPS-ADRIA SCIENTIFIC WORKSHOP 30 April - 5 May, 2007 Obervellach, Austria Assessment.
Variation of Surface Soil Moisture and its Implications Under Changing Climate Conditions 1.
Remote Sensing Microwave Image. 1. Penetration of Radar Signal ► ► Radar signals are able to penetrate some solid features, e.g. soil surface and vegetative.
Two modes: (1) stop and measure (SAM); (2) drive and measure (DAM). Can do: (1) 1-D transects. (2) 2-D maps. Mobile sensing of surface moisture: COSMOS.
(II) Displaying Information Information can be displayed using (a) Pictographs (b) Bar Graphs (c ) Line Graphs (d) Pie Charts.
Comparison of L and P band radar time series for the monitoring of Sahelian area P.-L. Frison, G. Mercier, E. Mougin, P. Hiernaux.
CCAR / University of Colorado 1 Airborne GPS Bistatic Radar in CLPX Dallas Masters University of Colorado, Boulder Valery Zavorotny NOAA ETL Stephen Katzberg.
Airborne Passive microwave response to soil moisture: A case study for the Rur catchment Sayeh Hasan (1), Carsten Montzka (1), Heye Bogena (1), Chris Rüdiger.
Use of AMSR-E Land Parameter Modeling and Retrievals for SMAP Algorithm Development Steven Chan Eni Njoku Joint AMSR Science Team Meeting Telluride, Colorado.
Whiteboardmaths.com © 2004 All rights reserved
Whiteboardmaths.com © 2004 All rights reserved
Overview of also works.
1 dimensional static Array Int[] a = new int[4]; A[0]A[1]A[2]A[3] Int[] b= new int[1]; B[0] Array is a list of variables having same name and same data.
Universitat Politècnica de Catalunya CORRECTION OF SPATIAL ERRORS IN SMOS BRIGHTNESS TEMPERATURE IMAGES L. Wu, I. Corbella, F. Torres, N. Duffo, M. Martín-Neira.
(Mon) There are 2 main types of errors in science experiments: accuracy and precision. What is the difference between accuracy and precision? (4 pts /
Microwave Emission Signature of Snow-Covered Lake Ice Martti Hallikainen (1), Pauli Sievinen (1), Jaakko Seppänen (1), Matti Vaaja (1), Annakaisa von Lerber.
5 Day Forecast Mon Tues Wed Thu Fri.
Simulation of stream flow using WetSpa Model
PowerPointmaths.com © 2004 all rights reserved
GANTT CHARTS Example Example Example Example text Tasks Example 1
Mon – 2Tue – 3Wed – 4Thu - 5Fri - 6Sat - 7Sun - 8 Mon – 9Tue – 10Wed – 11Thu - 12Fri – 13Sat – 14Sun -15 Mon – 16Tue – 17Wed – 18Thu - 19Fri – 20Sat –
DEPT OF CIVIL ENGINEERING, TEXAS A&M UNIVERSITY MAY 03, 2004
MON TUE WED THU
Whiteboardmaths.com © 2004 All rights reserved
Scaling Properties of L-band Passive Microwave Soil Moisture: From SMOS to Paddock Scale My work is focused on the scaling properties of L-band retrieval.
January MON TUE WED THU FRI SAT SUN
January MON TUE WED THU FRI SAT SUN
Physics! – Motion!.
January Sun Mon Tue Wed Thu Fri Sat
January MON TUE WED THU FRI SAT SUN
2008 Calendar.
Sun Mon Tue Wed Thu Fri Sat
Sun Mon Tue Wed Thu Fri Sat
Notes Over 2.4 Writing an Equation Given the Slope and y-intercept
Observation/Research Formulate a Hypothesis Experiment
1/○~1/○ weekly schedule MON TUE WED THU FRI SAT SUN MEMO
2016 | 10 OCT SUN MON TUE WED THU FRI SAT
Sun Mon Tue Wed Thu Fri Sat
Experimental design: Review
1 January 2018 Sun Mon Tue Wed Thu Fri Sat
2008 Calendar.
Presentation transcript:

PLMR Calibration Valerio Paruscio Dr Jeff Walker University of Melbourne

Outline Admissible error in brightness temperature measurement Available data for calibration Calibration options and recommendation

Model Sensitivity Assessment How accurate has the Brightness Temperature measurement to be in order to have a Moisture content error less than 4%? Hypothesis: Roughness = 0.2 SS Albedo = 0.05 Soil Type = Silt clay loam

Variables: Moisture Content ( ) %v/%v Vegetation Water Content (0 3)kg/m 2 Soil Temperature ( )K Incidence Angle (0 40) Model Sensitivity Assessment How accurate has the Brightness Temperature measurement to be in order to have a Moisture content error less than 4%?

Graphs Tb sensitivity

H polarization error (K) V polarization error (K)

Critical scenarios High value of Vegetation Water Content High Incidence Angle Dry Soil Max Tb error permitted = Most common scenarios Low value of Vegetation Water Content Max Tb error permitted = Commonly Tb error up to 8K can be tolerated 4.7K (Hpol) 2.7K (Vpol) 2.2K (Hpol) 1.3K (Vpol)

Available data for calibration Box pre and post flight (~300K) actual measured

Available data for calibration Box pre and post flight (~300K) Sky pre and post flight (~5K) actual measured

Box and Sky Brightness Temperature Pre flight Box Pre flight Sky Post flight Sky Post flight Box

Available data for calibration Box pre and post flight (~300K) Sky pre and post flight (~5K) Water in flight (~110K) actual measured

Temperature and Conductivity spatial series

Temperature and Conductivity time series

Transect–Buoy data comparision

Week 1 Week 2 Week 3 Week 4 Thermal Infrared data over the lake Mon Tue Wed Thu Fri

Available data for calibration Box pre and post flight (~300K) Sky pre and post flight (~5K) Water in flight (~110K) Sky in flight (~5K) actual measured

Available data for calibration 1.Box pre flight (~300K) 2.Sky pre flight (~5K) 3.Water in flight (~110K) 4.Sky in flight (~5K) 5.Box post flight (~300K) 6.Sky post flight (~5K) actual measured

Calibration options 6 points (all) 4 points/2 points (box & sky from the ground) 3 points (box and water)

Calibration lines (6 beams, 2 polarizations)

Check the 4 pt calibration with water ponit H polV pol

Check the 3 pt calibration with in flight sky ponit V polH pol

Variation in Tb measurement passing from the 4 points cal to the 3 points cal

Time series of slope & offset (4 pts) From 4 pt

Variation in Tb measurement passing from the 4 points cal to the 2 points cal

Error using a unique 4 pt calibration instead of a daily one

Conclusion Recommendation this campaign V pol 4 pt daily H pol 4 pt daily (or unique) next campaign V pol 4 pt daily, 3 pt(?) H pol 4 pt/2 pt daily (or unique)