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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. Kimball

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Jinyang Du, Lucas A. Jones, and John S. Kimball

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  1. 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. Kimball Numerical Terradynamic Simulation Group and Flathead Lake Biological Station, Division of Biological Sciences, The University of Montana AMSR Joint Science Team Meeting 23-24 September 2014 Huntsville, AL

  2. 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

  3. General Data Record Production Procedures Algorithm Calibration • 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 Data Gridding & Tb Inter-Calibration • WMO Stations Temp. • AIRS Water Vapor • MODIS Land Cover • DEM AMSR-E / AMSR/MWRI Swath Brightness Temperature Run Land Parameter Retrieval Algorithms Gridded brightness Temperature Calibrated brightness Temperature

  4. Towards Consistent Land Surface Parameter Records --Part IInter-calibration of AMSR-E, AMSR2 and MWRI Observations

  5. Instruments Configurations

  6. Sensor Inter-calibration – Estimating Observation Biases • Estimating Sensor Biases using Double Difference Method 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

  7. Sensor Inter-Calibration– Linear Calibration • Linear Calibration of AMSR2 and MWRI against AMSR-E observations H-Polarized 18 GHz Tb comparisons from overlapping MWRI and AMSR ascending orbit observations for the selected Amazon tropical reference area

  8. Towards Consistent Land Surface Parameter Records --Part IIPrecipitable Water Vapor (PWV) & Surface Air Temperature Retrieval over Land from AMSR2

  9. Development of PWV Retrieval Algorithm for AMSR2 Descending Ascending Comparisons between AMSR2 & AIRS PWV retrievals over 200 global WMO sites Source: Du et al. 2014. TGARS (In-review)

  10. Comparisons of PWV Seasonal Distribution Patterns from Three Products Summer (JJA) Winter (DJF) AMSR2 AMSR2 MODIS MODIS NVAP-M NVAP-M Source: Du et al. 2014. TGARS (In-review)

  11. Updated AMSR2 Surface Air Temperature Retrieval Algorithm Winter (DJF) Summer (JJA) AMSR2 mean daily maximum air temperature over Winter (left) and Summer (Right)

  12. Towards Consistent Land Surface Parameter Records-- Part IIIGenerating Long-term Land Surface Parameters from 2002-2014 with Updated UMT Land Parameter Algorithm

  13. Evaluation of the Extended Daily Maximum/Minimum Temperature Records based on WMO site measurements AMSR2(2013) AMSR-E (2010) AMSR2 & MWRI (2012)

  14. Statistical Summary of the Validation Results Surface Air Temperature Validation Results Water Vapor Validation Results

  15. Recent Ecological Application Studies

  16. Spring Hydrology Determines Summer Net Carbon Uptake in Northern Ecosystems 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. • Major Findings: • Wetter springs promote summer net carbon uptakeindependent 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). Source: Yi et al. 2014. ERL 9, 064003

  17. Surface Water Inundation in the boreal-Arctic: Potential Impacts on Regional Methane Emissions 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. • 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. 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. Source: Watts et al. 2014. ERL 9, 075001 Sponsors: NASA Earth Science program 1ERL Video Abstract: http://bcove.me/qcjjracp

  18. 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.

  19. 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

  20. Thank You! 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 Funding for this study provided from NASA Terra and Aqua Science, and MEaSUREs programs.

  21. Backup Slides

  22. Algorithm Flowchart Tb 18.7, 23.8 V & H pol. Tb 6.9 or 10.7 V & H pol. Estimate emissivity Temperature Algorithm Estimate slope parameter: 60-day running smoother 60-day running smoother Invert for VOD (assume dry baseline soil conditions) Invert for SM

  23. 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)

  24. AMSR2 PWV Validation using GPS retrievals AMSR2 PWV vs GPS PWV Ascending Descending 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

  25. 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) AMSR2 (2013) MWRI(2012)

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