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Soil Moisture Variability From 8 Years of AMSR-E Data Steven Chan and Eni Njoku

Soil Moisture Variability From 8 Years of AMSR-E Data Steven Chan and Eni Njoku Jet Propulsion Laboratory California Institute of Technology Pasadena, CA 91109 USA. AMSR Science Team Meeting ■ Huntsville, Alabama ■ June 2-3, 2010. Outline. Motivation

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Soil Moisture Variability From 8 Years of AMSR-E Data Steven Chan and Eni Njoku

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  1. Soil Moisture Variability From 8 Years of AMSR-E Data Steven Chan and Eni Njoku Jet Propulsion Laboratory California Institute of Technology Pasadena, CA 91109 USA AMSR Science Team Meeting ■ Huntsville, Alabama ■ June 2-3, 2010

  2. Outline • Motivation • Creation of AMSR-E Monthly Soil Moisture Time Series • Time Series Modeling Based on Autoregression • Soil Moisture Variability: • Seasonal Magnitude • Seasonal Phase • Linear Trend • Site Validation • Potential of Longer Data Records • Conclusion AMSR Science Team Meeting ■ Huntsville, Alabama ■ June 2-3, 2010

  3. Motivation Soil moisture variability plays a key role in global water and energy cycles. In particular, regional drying and wetting soil moisture trends have profound impacts on climate evolution, agricultural sustainability, and water resources management. In this study, we used 8 years of AMSR-E monthly soil moisture time series to examine soil moisture variability on a global basis. AMSR Science Team Meeting ■ Huntsville, Alabama ■ June 2-3, 2010

  4. Creation of AMSR-E Monthly Soil Moisture Time Series Obtaining the data: We used the NASA’s Warehouse Inventory Search Tool (WIST)’s to download the entire archive of descending soil moisture data field from the AE_Land3 product. There are 2,856 granules as of May 13, 2010. Processing the data: For each month between June 2002 and May 2010, we computed the monthly median soil moisture at each 25-km Global EASE-Grid pixel. Time series that lose too much data to AE_Land3’s internal land cover masking were discarded. AMSR Science Team Meeting ■ Huntsville, Alabama ■ June 2-3, 2010

  5. Creation of AMSR-E Monthly Soil Moisture Time Series C T S V We modeled AMSR-E monthly soil moisture time series as a sum of 4 components: constant (C), trend (T), seasonality (S), and variability (V). We then determined their relative magnitudes by multiple regression. AMSR Science Team Meeting ■ Huntsville, Alabama ■ June 2-3, 2010

  6. Time Series Modeling Based on Autoregression Formally, the time series can be written as (Weatherhead, 1998): constant trend seasonality variability For t = 1, 2, …, T. The coefficient describes the linear trend of soil moisture and has the unit of cm3/cm3 per year. and describe the magnitude and phase of the annual cycle. Least squares were used to estimate the regression coefficients, whose 95% confidence intervals depend on the variance of the variability term . AMSR Science Team Meeting ■ Huntsville, Alabama ■ June 2-3, 2010

  7. Time Series Modeling Based on Autoregression Like most climate signals, manifests serial correlation. To model it properly, we used the first-order autoregressive model: where = Corr( , ) and are independent zero-mean normal random variables. Tiaoet al. showed that the estimated number of years of data needed to detect a real trend of magnitude is: In general, the number of years for trend detection increases with serial correlation and variability magnitude. AMSR Science Team Meeting ■ Huntsville, Alabama ■ June 2-3, 2010

  8. Time Series Modeling Based on Autoregression How well does AR(1) describe AMSR-E monthly soil moisture? Over 85% of global land area has correlation better than 0.60. Correlation coefficient AMSR Science Team Meeting ■ Huntsville, Alabama ■ June 2-3, 2010

  9. Soil Moisture Variability: Seasonal Magnitude describes the “strength” of soil moisture annual cycle Seasonal magnitude (cm3/cm3) AMSR Science Team Meeting ■ Huntsville, Alabama ■ June 2-3, 2010

  10. Soil Moisture Variability: Seasonal Phase describes the starting phase of soil moisture annual cycle Seasonal phase (deg) AMSR Science Team Meeting ■ Huntsville, Alabama ■ June 2-3, 2010

  11. Soil Moisture Variability: Linear Trend Coefficient describes the linear trend of soil moisture 95% significant linear trend (cm3/cm3) AMSR Science Team Meeting ■ Huntsville, Alabama ■ June 2-3, 2010

  12. Site Validation The observed soil moisture trends appear to be consistent with in-situ monthly rainfall anomaly*. Some trends were felt throughout the year; some over only a few dominating months. Net gain: 58.65 mm Net loss: 59.45 mm * Data available at http://www.bom.gov.au/climate/averages/ AMSR Science Team Meeting ■ Huntsville, Alabama ■ June 2-3, 2010

  13. Potential of Longer Data Records According to , weaker trends (smaller ) take longer data records to become detectable. As AMSR-E data records grow longer, we expect to identify real trends at more places. Beyond a certain data length, however, further gains become smaller and smaller. Cumulative/incremental gain in global land area showing significant trends AMSR Science Team Meeting ■ Huntsville, Alabama ■ June 2-3, 2010

  14. Conclusion • AMSR-E monthly soil moisture time series provide useful information on land hydrological cycles, trends, and their spatial distribution. • Certain trends appear to be consistent with local rainfall patterns. • The same AR(1) model can apply to other environmental parameters. • Longer data records are needed to detect subtle trends. AMSR Science Team Meeting ■ Huntsville, Alabama ■ June 2-3, 2010

  15. Future Work (JPL & USDA) • Acquire and maintain critical in situ soil moisture data and resources for validation, and refine and test validation procedures and metrics for satellite and in situ soil moisture data comparisons • Inter-compare the AMSR-E soil moisture standard algorithm with other research algorithms, and understand their differences and relative merits under varying conditions • Implement refinements to the standard algorithm and support reprocessing of the standard AMSR-E soil moisture product • Provide algorithm maintenance, quality control, metadata updates and archival documentation for reprocessed data. Provide product support to research and applications users, and for collaborative studies using the soil moisture product • Evaluate the transition of the AMSR-E standard soil moisture algorithm to other near-term instruments and missions

  16. Publications Using/Evaluating AMSR-E Soil Moisture • Njoku, E.G. and T. K. Chan (2006): Vegetation and surface roughness effects on AMSR-E land observations, Rem. Sens. Environ., 100, 190–199. • Bindlish, R., T. J. Jackson, et al. (2006): Soil moisture mapping and AMSR-E validation using the PSR in SMEX02, Rem. Sens. of Environ., 103, 127-139. • Reichle, R. H., R. D. Koster, P. Liu, S. P. Mahanama, E. G. Njoku and M. Owe (2007): Comparison and assimilation of global soil moisture retrievals from AMSR-E and SMMR, J. Geophys. Res., 112, D09108, doi:10.1029/2006JD008033. • Crow, W. T. (2007): A novel method for quantifying value in spaceborne soil moisture retrievals, J. of Hydrometeorology, 8, 56-67. • Crow, W. T. and X. Zhan (2007): Continental-scale evaluation of remotely sensed soil moisture products, IEEE Geosci. and Rem. Sens. Letters, 4, 451-455. • Jones, L. A., J. S. Kimball, K. C. McDonald, S. K. Chan, E. G. Njoku and W. C. Oechel (2007): Satellite microwave remote sensing of boreal and Arctic soil temperatures from AMSR-E, IEEE Trans. Geosci. Rem. Sens., 45, 2004–2018. • Gruhier, C., P. de Rosnay, S. Hasenauer, et al. (2010): Soil moisture active and passive microwave products: intercomparison and evaluation over a Sahelian site, Hydrology and Earth System Sciences, 14, 141-156. • Jones, L., C. Ferguson, J. Kimball, K. Zhang, S. Chan, K. McDonald, E. Njoku, and E. Wood (2010): Satellite microwave remote sensing of daily land surface air temperature minima and maxima from AMSR-E, IEEE J. of Selected Topics in Appl. Earth Obs. and Rem. Sens., 3, 111-123. • Li, L., P. Gaiser, B. Gao, R. Bevilacqua, T. Jackson, E. Njoku, C. Rudiger, J.-C. Calvet, and R. Bindlish (2010): WindSat global soil moisture retrieval and validation, IEEE Trans. on Geosci. Rem. Sens., 48, 2224-2241.

  17. Backup AMSR Science Team Meeting ■ Huntsville, Alabama ■ June 2-3, 2010

  18. The conditions for detecting a 5 units/decade trend in 5, 10, 15, 20, 30, and 40 years. In general, weaker trends take longer time to be detected. AMSR Science Team Meeting ■ Huntsville, Alabama ■ June 2-3, 2010

  19. Helpful links: http://en.wikipedia.org/wiki/Autocorrelation http://en.wikipedia.org/wiki/Unbiased_estimation_of_standard_deviation AMSR Science Team Meeting ■ Huntsville, Alabama ■ June 2-3, 2010

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