Soil Moisture Variability From 8 Years of AMSR-E Data

Steven Chan and Eni Njoku

Jet Propulsion LaboratoryCalifornia Institute of Technology

Pasadena, CA 91109USA

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

1. Motivation2. Creation of AMSR-E Monthly Soil Moisture Time Series3. Time Series Modeling Based on Autoregression4. Soil Moisture Variability:

Seasonal MagnitudeSeasonal PhaseLinear Trend

5. Site Validation6. Potential of Longer Data Records7. Conclusion

Outline

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

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.

Motivation

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

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.

Creation of AMSR-E Monthly Soil Moisture Time Series

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

Creation of AMSR-E Monthly Soil Moisture Time Series

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

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.

Time Series Modeling Based on Autoregression

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

Formally, the time series can be written as (Weatherhead, 1998):

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 .

constant trend seasonality variability

Time Series Modeling Based on Autoregression

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

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. Tiao et 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

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

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

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

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

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/

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.

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

Potential of Longer Data Records

Cumulative/incremental gain in global land area showing significant trends

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

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.

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

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.

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

Backup

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

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

Helpful links:

http://en.wikipedia.org/wiki/Autocorrelationhttp://en.wikipedia.org/wiki/Unbiased_estimation_of_standard_deviation