SOILMOISTURE

IMPROVING PREDICTIONS OF VEGETATION CONDITION BY OPTIMALLY MERGING SATELLITE REMOTE SENSING-BASED SOIL MOISTURE PRODUCTS

(Marie Curie FP7 Career Integration Grant, SOILMOISTURE, Grant Number: 630110)

Research Objectives
•    Obtain individual error characteristics of each soil moisture datasets (Gruber et al., 2016) by also considering the error cross-correlation information;
•    Obtain improved soil moisture and uncertainty estimates over Europe and northern Africa;
•    Evaluate the predictability of vegetation conditions using merged soil moisture estimates and the predictive skill differences between least squares and data assimilation;
•    Form a basis for an operational system that merges soil moisture datasets:

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Average soil wetness values (%) of ASCAT (Wagner et al., 1999) between 2010 and 2015.

 

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Average soil moisture values (%) of LPRM (Parinussa et al., 2015) between 2012 and 2015.

 

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Average soil moisture values (%) of NOAH (obtained from GLDAS simulations distributed by NASA GES DISC; Rodell et al., 2004) between 2010 and 2015.

 

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Figure 4, Average soil moisture values (%) of SMOS (Kerr et al., 2001, 2012) between 2010 and 2015.

 

 

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Figure 5, Average MODIS MOD13C1 NDVI values between 2010 and 2015 [obtained from NASA’s Land Processes Distributed Active Archive Center (LP DAAC) located at the USGS Earth Resources Observation and Science (EROS) Center].

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Average weekly NDVI – Soil Moisture (ASCAT, LPRM, NOAH, and SMOS) correlations between 2010 and 2015.

 

FUNDING AGENCY

This project is currently being funded by EU FP7-PEOPLE-2013-CIG (Marie Curie Career Integration Grant), Project acronym SOILMOISTURE, Grant Number: 630110.

 

REFERENCES
Gruber, A., Su, C. H., Zwieback, S., Crow, W., Dorigo, W., & Wagner, W. (2016). Recent advances in (soil moisture) triple collocation analysis. International Journal of Applied Earth Observation and Geoinformation, 45, 200-211.
Kerr, Y. H., Waldteufel, P., Richaume, P., Wigneron, J. P., Ferrazzoli, P., Mahmoodi, A., … & Leroux, D. (2012). The SMOS soil moisture retrieval algorithm. IEEE Transactions on Geoscience and Remote Sensing, 50(5), 1384-1403.
Parinussa, Robert M., Thomas R. H. Holmes, Niko Wanders, Wouter A. Dorigo, and Richard A. M. de Jeu, 2015: A Preliminary Study toward Consistent Soil Moisture from AMSR2. J. Hydrometeor.,
16, 932–947, doi: 10.1175/JHM-D-13-0200.1.
Rodell, M., P. R. Houser, U. Jambor, J. Gottschalck, K. Mitchell, C-J. Meng, K. Arsenault, B. Cosgrove, J. Radakovich, M. Bosilovich, J. K. Entin*, J. P. Walker, D. Lohmann, and D. Toll (2004). The Global Land Data Assimilation System. Bull. Amer. Meteor. Soc., 85, 381–394, doi: 10.1175/BAMS-85-3-381.
Wagner, W., Lemoine, G., & Rott, H. (1999). A method for estimating soil moisture from ERS scatterometer and soil data. Remote sensing of environment, 70(2), 191-207.
Zwieback, S., Scipal, K., Dorigo, W., & Wagner, W. (2012). Structural and statistical properties of the collocation technique for error characterization. Nonlinear Processes in Geophysics, 19(1), 69-80.