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Transcript of Jinyang Du, Lucas A. Jones, and John S. Kimball
Development of Consistent Long-term Global Land Parameter Data Record based on AMSR-
E, AMSR2 and MWRI observations
Jinyang Du, Lucas A. Jones, and John S. KimballNumerical Terradynamic Simulation Group and Flathead Lake Biological Station, Division of Biological
Sciences, The University of Montana
AMSR Joint Science Team Meeting23-24 September 2014Huntsville, AL
AMSR Joint Science Team Meeting 22-23 September 2014, Huntsville, AL
OVERVIEW
Consistent long-term land parameter records including retrievals of vegetation optical depth, surface temperature & moisture, landscape freeze/thaw dynamics, open water inundation & Atm. water vapor changes are desirable for Ecological studies & applications
In this study, satellite Tb inter-calibrations were carried out between similar sensors including AMSR-E (2002-2011), AMSR2 (from Jun-2012) and FY3B-MWRI (from Dec., 2010);
Further developments and re-calibration of UMT Global Land Parameter algorithms were also made for L1R AMSR2 swath data and then implemented for the reprocessed ( RSS V7) AMSR-E data;
AMSR-E algorithm has been applied to the calibrated Tb datasets to produce long-term land parameter data records from 2002 to 2014.
2002 AMSR-E
MWRI
AMSR2 NOW
Algorithm Calibration
• WMO Stations Temp.• AIRS Water Vapor• MODIS Land Cover• DEM
Subset Brightness Temperature and AIRS products for WMO Stations
Screening Datasets for RFI, Snow, Precipitation and High DEM variations
Adjust Algorithm Parameters based on WMO measurements and AIRS products
Calibrated brightness Temperature
AMSR-E / AMSR/MWRISwath Brightness Temperature
Gridded brightness Temperature
Data Gridding & Tb Inter-Calibration
Run Land Parameter Retrieval Algorithms
General Data Record Production Procedures
AMSR Joint Science Team Meeting 22-23 September 2014, Huntsville, AL
Towards Consistent Land Surface Parameter Records --Part I
Inter-calibration of AMSR-E, AMSR2 and MWRI Observations
AMSR Joint Science Team Meeting 22-23 September 2014, Huntsville, AL
Instruments Configurations
AMSR-2 AMSR-E MWRISatellite Platform GCOM-W1 AQUA FY3B
Altitude 700 km 705 km 836 km
Equator Crossing Time1:30 PM Ascending 1:30 PM Ascending 1:40 PM Ascending
Antenna Size 2 m 1.6 m 0.977 m x 0.897 m
Incident Angle 55 55 53
Spatial Resolution [km x km]Band[GHz] AMSR-2 AMSR-E MWRI
6.93 62 x 35 75 x 43 N/A7.3 62 x 35 N/A N/A
10.65 42 x 24 51 x 29 85x 5118.7 22 x 14 27 x 16 50x 3023.8 26 x 15 32 x 18 45x 2736.5 12 x 7 14 x 8 30x 18 89.0 5 x 3 6 x 4 15x 9
AMSR Joint Science Team Meeting 22-23 September 2014, Huntsville, AL
Sensor Inter-calibration – Estimating Observation Biases
Estimating Sensor Biases using Double Difference Method
_ 2 _
_ _
( 2, - ) ( 2, ) ( - , )
( 2, )
( - , )
b AMSR b MWRI
b AMSR E b MWRI
DD AMSR AMSR E SD AMSR MWRI SD AMSR E MWRI
SD AMSR MWRI T T
SD AMSR E MWRI T T
Example global distributions of estimated ascending orbit biases between uncalibrated AMSR2 and AMSR-E baseline observations for the H-Polarized (a) 23GHz, and (b) 18GHz channels, respectively (areas with correlation coefficient R<0.95 are marked in grey)
(a)23 GHz
(b)18 GHz
Source: Du et al. 2014. Remote Sensing 6
AMSR Joint Science Team Meeting 22-23 September 2014, Huntsville, AL
H-Polarized 18 GHz Tb comparisons from overlapping MWRI and AMSR ascending orbit observations for the selected Amazon tropical reference area
Sensor Inter-Calibration– Linear Calibration
_ 1 1 _b AMSR E b MWRIT a b T _ 2 2 2 _b AMSR b MWRIT a b T
_ _ 2b AMSR E b AMSRT a b T
Linear Calibration of AMSR2 and MWRI against AMSR-E observations
AMSR Joint Science Team Meeting 22-23 September 2014, Huntsville, AL
Towards Consistent Land Surface Parameter Records --Part II
Precipitable Water Vapor (PWV) & Surface Air Temperature Retrieval over Land from AMSR2
AMSR Joint Science Team Meeting 22-23 September 2014, Huntsville, AL
Development of PWV Retrieval Algorithm for AMSR2
PWV=𝑎0+𝑎1𝑇 𝑠+𝐴𝑣
𝑎𝑣 23−𝑎𝑣 18
(𝑎2+𝑎3 ex p(−𝐻))+𝑎4 log(∆𝑇 𝑏 (89.0 )∆𝑇 𝑏 (36.0 ) )
𝐴𝑣=−[ 12
log(MAWVI𝛽 )cos (𝜃 )+𝑎𝑜23−𝑎𝑜18 ]MAWVI=𝛽 ∙𝛤 (23.8 )𝛤 (18.7 )
Ascending Descending
Comparisons between AMSR2 & AIRS PWV retrievals over 200 global WMO sites
Source: Du et al. 2014. TGARS (In-review)
AMSR Joint Science Team Meeting 22-23 September 2014, Huntsville, AL
Comparisons of PWV Seasonal Distribution Patterns from Three Products
AMSR2
MODIS
NVAP-M
AMSR2
MODIS
NVAP-M
Winter (DJF) Summer (JJA)
Source: Du et al. 2014. TGARS (In-review)
AMSR Joint Science Team Meeting 22-23 September 2014, Huntsville, AL
Updated AMSR2 Surface Air Temperature Retrieval Algorithm
Winter (DJF) Summer (JJA)
AMSR2 mean daily maximum air temperature over Winter (left) and Summer (Right)
Tmn RMSE Tmn(Kelvin)
Tmx RMSE Tmx(Kelvin)
0.81 3.43 0.88 3.40
𝑇𝑚𝑥=3.90+0.90 (𝑇 𝑠 )−16.23𝑇 𝑐+18.54𝑇 𝑐2−0.14 (|(𝐿𝑎𝑡 )|)+3.42𝛶 cos (𝑡)
𝑇𝑚𝑛=1.12+0.89 (𝑇 𝑠 )−2.02𝑇𝑐+4.87𝑇 𝑐2−0.12 (|(𝐿𝑎𝑡)|)+3.61𝛶 cos(𝑡 )
AMSR Joint Science Team Meeting 22-23 September 2014, Huntsville, AL
Towards Consistent Land Surface Parameter Records-- Part III
Generating Long-term Land Surface Parameters from 2002-2014 with Updated
UMT Land Parameter Algorithm
AMSR Joint Science Team Meeting 22-23 September 2014, Huntsville, AL
Evaluation of the Extended Daily Maximum/Minimum Temperature Records based on WMO site measurements
AMSR-E (2010)
AMSR2 & MWRI (2012)
AMSR2(2013)
AMSR Joint Science Team Meeting 22-23 September 2014, Huntsville, AL
Statistical Summary of the Validation Results
Retrieved Air Temperature vs WMO measurements
Tmin Tmax
RMSE(Celcius)
R^2 RMSE(Celcius)
R^2
AMSR-E (year 2010) 3.5 0.79 3.4 0.86AMSR2&MWRI (year 2012) 3.5 0.78 3.5 0.86
AMSR2 (year 2013) 3.5 0.77 3.4 0.87
Retrieved PWV vs AIRS products
Ascending Descending
RMSE (mm)
R^2 RMSE (mm)
R^2
AMSR-E (year 2010) 4.6 0.83 5.9 0.74AMSR2 &MWRI (year 2012) 5.1 0.76 6.9 0.63
AMSR2 (year 2013) 4.7 0.80 6.2 0.72
Surface Air Temperature Validation Results
Water Vapor Validation Results
AMSR Joint Science Team Meeting 22-23 September 2014, Huntsville, AL
Recent Ecological Application Studies
AMSR Joint Science Team Meeting 22-23 September 2014, Huntsville, AL
Spring Hydrology Determines Summer Net Carbon Uptake in Northern Ecosystems
Major Findings:• Wetter springs promote summer net carbon
uptake independent of temperature effects;• Warming still promotes widespread greening
(as observed by NDVI), but with less net carbon uptake in warmer, drier years;
• Stronger coupling of northern carbon & water cycles with continued climate warming.
Surface soil moisture anomaly for June, 2009 from satellite observations (1AMSR-E); positive values denote wetter-than-normal conditions from the mean (2002-2011).
A synthesis of atmospheric CO2 and satellite and regional climate data reveals the major role of spring hydrology in determining summer net carbon uptake (NCU) for northern (≥50°N) ecosystems. Spring wetting inhibits fire emissions and promotes NCU, independent of temperature effects.
Summer (JJA) Net Ecosystem CO2 Exchange (NEE) anomaly for 2009 from CarbonTracker; NEE +/- sign denotes ecosystem carbon gain/loss, where NCU ≈ NEE + fire emissions.
Source: Yi et al. 2014. ERL 9, 064003
AMSR Joint Science Team Meeting 22-23 September 2014, Huntsville, AL
Surface Water Inundation in the boreal-Arctic: Potential Impacts on Regional Methane Emissions
Key Findings:• Strong temporal variability in boreal-Arctic Fw, with
sensitivity to regional temp. & precip. patterns; • Longer-term (2003-2011) drying across boreal
ecosystems, with substantial Fw wetting in Arctic tundra & continuous permafrost landscapes (Above);
• Accounting for dynamic changes in high-latitude wetland extent (e.g. Fw inputs) can significantly reduce regional CH4 emission estimates.
Sponsors: NASA Earth Science program
Above: AMSR-E Fw retrievals indicate that ~5% (8.4 x 105 km2) of northern tundra & peatland landscapes are inundated during non-frozen summer months.
1ERL Video Abstract: http://bcove.me/qcjjracp
A satellite data-driven model study of surface temperature and AMSR-E daily fractional open water (Fw) inundation controls on high-latitude wetland CH4 emissions reveals a strong regulating influence by contrasting regional wetting and drying patterns.
Source: Watts et al. 2014. ERL 9, 075001
AMSR Joint Science Team Meeting 22-23 September 2014, Huntsville, AL
Recent Publications
Journal papers:
Du, J.; Kimball, J.S.; Shi, J.; Jones, L.A.; Wu, S.; Sun, R.; Yang, H. Inter-Calibration of Satellite Passive Microwave Land Observations from AMSR-E and AMSR2 Using Overlapping FY3B-MWRI Sensor Measurements. Remote Sens. 2014, 6, 8594-8616.
Du, J., J.S. Kimball, and L.A. Jones, 2014. Satellite microwave retrieval of total precipitable water vapor and surface air temperature over land from AMSR2. TGARS (In-review).
Jang, K., S. Kang, J.S. Kimball, et al. 2014. Retrievals of all-weather daily air temperature using MODIS and AMSR-E data. Remote sensing, 6, 9, 8387-8404.
Watts, J.D., J.S. Kimball, A. Bartsch, and K.C. McDonald, 2014. Surface water inundation in the boreal-Arctic: potential impacts on regional methane emissions. ERL, 9, 075001.
Yi, Y., J.S. Kimball, and R.H. Reichle, 2014. Spring hydrology determines summer net carbon uptake in northern ecosystems. ERL 9, 064003.
AMSR Joint Science Team Meeting 22-23 September 2014, Huntsville, AL
Summary
• Detectable biases found between AMSR-E, AMSR2 and MWRI1 observations. Inter-calibrations based on swath Tb data records significantly decreased sensor biases and improved Tb correlations.
• UMT Global Land Parameter algorithms successfully adapted to AMSR2, with favorable accuracy in surface air temperature and water vapor retrievals. The updated algorithms applied to calibrated AMSR2, MWRI & AMSR-E (V7) Tb data.
• Based on AMSR-E and calibrated AMSR2/MWRI Tb observations and recent algorithm updates, long-term UMT land surface parameter records have been produced. In general, similar retrieval accuracy has been found for the AMSR-E and post AMSR-E periods, except that MWRI water vapor retrieval is slightly lower than the other sensor products.
• Continuing calibration & extension of UMT record planned in support of several global ecosystem studies.
1AMSR-E V7 reprocessed Tb record provided by Remote Sensing Systems; AMSR2 L1R data are from JAXA; MWRI data are from China National Satellite Meteorological Centre
Thank You!
Funding for this study provided from NASA Terra and Aqua Science, and MEaSUREs programs.
NTSG Project Team: John Kimball, Jinyang Du, Lucas Jones, Youngwook Kim, Joe Glassy, Matt Jones, Jennifer Watts,
Yonghong Yi
Project Data Archives (Updates coming soon!):
http://nsidc.org/data/nsidc-0451
http://freezethaw.ntsg.umt.edu
AMSR Joint Science Team Meeting 22-23 September 2014, Huntsville, AL
Backup Slides
AMSR Joint Science Team Meeting 22-23 September 2014, Huntsville, AL
Algorithm Flowchart
Temperature Algorithm
Tb 6.9 or 10.7 V & H pol. Tb 18.7, 23.8 V & H pol.
60-day running smoother
60-day running smoother
Estimate slope parameter:
Estimate emissivity
Invert for VOD(assume dry baseline soil conditions)
Invert for SM
Selection of the WMO stations & GPS sites for PWV & Air Temperature Retrieval Study
PWV/Temperature Algorithm Development: WMO Training Sites (black Square) and Validation Sites (black triangle) over the MODIS IGBP
global land cover map; PWV Algorithm Validation: SuomiNet North American GPS stations
(white circles with black outlines)
AMSR Joint Science Team Meeting 22-23 September 2014, Huntsville, AL
AMSR2 PWV Validation using GPS retrievals
Ascending Descending
AMSR2 PWV vs GPS PWV
Histogram of the absolute AMSR2 PWV estimation errors for the ascending (left) and descending (right) orbit retrievals relative to independent PWV
observations from 350 SuomiNet North American GPS sites
AMSR Joint Science Team Meeting 22-23 September 2014, Huntsville, AL
Extended Land Parameter Records – Global Vegetation Optical Thickness Distributions
X-band Vegetation Optical Thickness (May 30, 2010,2011,2013 / May 31,2012)
AMSR-E (2010)
AMSR-E (2011)
MWRI(2012) AMSR2 (2013)