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)
• 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:
Average soil wetness values (%) of ASCAT (Wagner et al., 1999) between 2010 and 2015.
Average soil moisture values (%) of LPRM (Parinussa et al., 2015) between 2012 and 2015.
Average soil moisture values (%) of NOAH (obtained from GLDAS simulations distributed by NASA GES DISC; Rodell et al., 2004) between 2010 and 2015.
Figure 4, Average soil moisture values (%) of SMOS (Kerr et al., 2001, 2012) between 2010 and 2015.
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].
Average weekly NDVI – Soil Moisture (ASCAT, LPRM, NOAH, and SMOS) correlations between 2010 and 2015.
This project is currently being funded by EU FP7-PEOPLE-2013-CIG (Marie Curie Career Integration Grant), Project acronym SOILMOISTURE, Grant Number: 630110.