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. 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. MWRI AMSR2 NOW

3. 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/MWRI Swath Brightness Temperature Gridded brightness Temperature Data Gridding & Tb Inter-Calibration Run Land Parameter Retrieval Algorithms General Data Record Production Procedures

4. 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

5. AMSR Joint Science Team Meeting 22-23 September 2014, Huntsville, AL Instruments Configurations AMSR-2AMSR-EMWRI Satellite PlatformGCOM-W1AQUAFY3B Altitude700 km705 km836 km Equator Crossing Time1:30 PM Ascending 1:40 PM Ascending Antenna Size2 m1.6 m0.977 m x 0.897 m Incident Angle55 53 Spatial Resolution [km x km] Band [GHz] AMSR-2AMSR-EMWRI 6.9362 x 3575 x 43N/A 7.362 x 35N/A 10.6542 x 2451 x 29 85x 51 18.722 x 1427 x 16 50x 30 23.826 x 1532 x 1845x 27 36.512 x 714 x 830x 18 89.0 5 x 3 6 x 4 15x 9

6. AMSR Joint Science Team Meeting 22-23 September 2014, Huntsville, AL 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. 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 Linear Calibration of AMSR2 and MWRI against AMSR-E observations

8. 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

9. AMSR Joint Science Team Meeting 22-23 September 2014, Huntsville, AL Development of PWV Retrieval Algorithm for AMSR2 Ascending Descending Comparisons between AMSR2 & AIRS PWV retrievals over 200 global WMO sites Source: Du et al. 2014. TGARS (In-review)

10. 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)

11. 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) RMSE Tmn (Kelvin) RMSE Tmx (Kelvin) 0.813.430.883.40

12. 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

13. 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)

14. AMSR Joint Science Team Meeting 22-23 September 2014, Huntsville, AL Statistical Summary of the Validation Results Retrieved Air Temperature vs WMO measurements TminTmax RMSE (Celcius) R^2RMSE (Celcius) R^2 AMSR-E (year 2010) 3.50.793.40.86 AMSR2&MWRI (year 2012) 3.50.783.50.86 AMSR2 (year 2013) 3.50.773.40.87 Retrieved PWV vs AIRS products AscendingDescending RMSE (mm) R^2RMSE (mm) R^2 AMSR-E (year 2010) 4.60.835.90.74 AMSR2 &MWRI (year 2012) 5.10.766.90.63 AMSR2 (year 2013) 4.70.806.20.72 Surface Air Temperature Validation Results Water Vapor Validation Results

15. AMSR Joint Science Team Meeting 22-23 September 2014, Huntsville, AL Recent Ecological Application Studies

16. 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 ( 1 AMSR-E); positive values denote wetter-than-normal conditions from the mean (2002-2011). A synthesis of atmospheric CO 2 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 CO 2 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

17. 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 CH 4 emission estimates. Sponsors: NASA Earth Science program Above: AMSR-E Fw retrievals indicate that ~5% (8.4 x 10 5 km 2 ) of northern tundra & peatland landscapes are inundated during non-frozen summer months. 1 ERL Video Abstract: http://bcove.me/qcjjracphttp://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 CH 4 emissions reveals a strong regulating influence by contrasting regional wetting and drying patterns. Source: Watts et al. 2014. ERL 9, 075001

18. 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.

19. AMSR Joint Science Team Meeting 22-23 September 2014, Huntsville, AL Summary Detectable biases found between AMSR-E, AMSR2 and MWRI 1 observations. Inter- calibrations based on swath T b data records significantly decreased sensor biases and improved T b 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 T b 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. 1 AMSR-E V7 reprocessed T b record provided by Remote Sensing Systems; AMSR2 L1R data are from JAXA; MWRI data are from China National Satellite Meteorological Centre

20. 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-0451http://freezethaw.ntsg.umt.edu

21. AMSR Joint Science Team Meeting 22-23 September 2014, Huntsville, AL Backup Slides

22. 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 Estimate slope parameter: Estimate emissivity 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. 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

25. 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)