{"id":122,"date":"2016-11-01T11:47:40","date_gmt":"2016-11-01T08:47:40","guid":{"rendered":"http:\/\/blog.metu.edu.tr\/tuyilmaz\/?page_id=122"},"modified":"2020-03-09T17:27:22","modified_gmt":"2020-03-09T14:27:22","slug":"soilmoisture","status":"publish","type":"page","link":"https:\/\/blog.metu.edu.tr\/tuyilmaz\/projects\/soilmoisture\/","title":{"rendered":"SOILMOISTURE"},"content":{"rendered":"

IMPROVING PREDICTIONS OF VEGETATION CONDITION BY OPTIMALLY MERGING SATELLITE REMOTE SENSING-BASED SOIL MOISTURE PRODUCTS<\/strong><\/p>\n

(Marie Curie FP7 Career Integration Grant, SOILMOISTURE, Grant Number: 630110)<\/p>\n

Research Objectives<\/strong>
\n\u2022\u00a0\u00a0 \u00a0Obtain individual error characteristics of each soil moisture datasets (Gruber et al., 2016) by also considering the error cross-correlation information;<\/em>
\n\u2022\u00a0\u00a0 \u00a0Obtain improved soil moisture and uncertainty estimates over Europe and northern Africa;<\/em>
\n\u2022\u00a0\u00a0 \u00a0Evaluate the predictability of vegetation conditions using merged soil moisture estimates and the predictive skill differences between least squares and data assimilation;
\n\u2022\u00a0\u00a0 \u00a0Form a basis for an operational system that merges soil moisture datasets:<\/em><\/p>\n

\"figure-13\"<\/a><\/p>\n

Average soil wetness values (%) of ASCAT (Wagner et al., 1999) between 2010 and 2015.<\/p>\n

 <\/p>\n

\"figure-14\"<\/a><\/p>\n

Average soil moisture values (%) of LPRM (Parinussa et al., 2015) between 2012 and 2015.<\/p>\n

 <\/p>\n

\"figure-15\"<\/a><\/p>\n

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

 <\/p>\n

\"figure-16\"<\/a><\/p>\n

Figure 4, Average soil moisture values (%) of SMOS (Kerr et al., 2001, 2012) between 2010 and 2015.<\/p>\n

 <\/p>\n

 <\/p>\n

\"figure-17\"<\/a><\/p>\n

Figure 5, Average MODIS\u00a0MOD13C1 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].<\/p>\n

\"figure-33\"<\/a><\/p>\n

Average weekly NDVI – Soil Moisture (ASCAT, LPRM, NOAH, and SMOS) correlations between 2010 and 2015.<\/p>\n

 <\/p>\n

FUNDING AGENCY
\n<\/strong><\/p>\n

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

 <\/p>\n

REFERENCES<\/strong><\/div>\n
<\/div>\n
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<\/i>, 45<\/i>, 200-211.<\/div>\n
\n
<\/div>\n
\n
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<\/i>, 50<\/i>(5), 1384-1403.<\/div>\n
<\/div>\n<\/div>\n<\/div>\n
Parinussa, Robert M., Thomas R. H. Holmes, Niko Wanders, Wouter A. Dorigo, and Richard A. M. de Jeu<\/span>, 2015<\/span>: A Preliminary Study toward Consistent Soil Moisture from AMSR2.<\/span> J. Hydrometeor.,<\/span><\/i><\/div>\n
16<\/span><\/b>, 932\u2013947, <\/span> doi: 10.1175\/JHM-D-13-0200.1.<\/span><\/div>\n
<\/div>\n
\n
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 (2<\/span>004). <\/span>The Global Land Data Assimilation System.<\/span> Bull. Amer. Meteor. Soc.,<\/span><\/i> 85<\/span><\/b>, 381\u2013394, <\/span> doi: 10.1175\/BAMS-85-3-381.<\/span><\/div>\n
<\/div>\n
\n
Wagner, W., Lemoine, G., & Rott, H. (1999). A method for estimating soil moisture from ERS scatterometer and soil data. Remote sensing of environment<\/i>, 70<\/i>(2), 191-207.<\/div>\n<\/div>\n<\/div>\n
<\/div>\n
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Zwieback, S., Scipal, K., Dorigo, W., & Wagner, W. (2012). Structural and statistical properties of the collocation technique for error characterization. Nonlinear Processes in Geophysics<\/i>, 19<\/i>(1), 69-80.<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

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 \u2022\u00a0\u00a0 \u00a0Obtain individual error characteristics of each soil moisture datasets (Gruber et al., 2016) by also considering the error cross-correlation information; \u2022\u00a0\u00a0 \u00a0Obtain improved soil moisture and uncertainty estimates […]<\/p>\n","protected":false},"author":1739,"featured_media":0,"parent":180,"menu_order":1,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":"","_links_to":"","_links_to_target":""},"class_list":["post-122","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/blog.metu.edu.tr\/tuyilmaz\/wp-json\/wp\/v2\/pages\/122","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blog.metu.edu.tr\/tuyilmaz\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/blog.metu.edu.tr\/tuyilmaz\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/blog.metu.edu.tr\/tuyilmaz\/wp-json\/wp\/v2\/users\/1739"}],"replies":[{"embeddable":true,"href":"https:\/\/blog.metu.edu.tr\/tuyilmaz\/wp-json\/wp\/v2\/comments?post=122"}],"version-history":[{"count":0,"href":"https:\/\/blog.metu.edu.tr\/tuyilmaz\/wp-json\/wp\/v2\/pages\/122\/revisions"}],"up":[{"embeddable":true,"href":"https:\/\/blog.metu.edu.tr\/tuyilmaz\/wp-json\/wp\/v2\/pages\/180"}],"wp:attachment":[{"href":"https:\/\/blog.metu.edu.tr\/tuyilmaz\/wp-json\/wp\/v2\/media?parent=122"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}