Assimilation of remotely sensed soil moisture and vegetation with a crop simulation model for maize yield prediction-Reference-Cited by-同舟云学术

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Assimilation of remotely sensed soil moisture and vegetation with a crop simulation model for maize yield prediction

Published:2013-11 Issue: Volume:138 Page:149-164
ISSN:0034-4257
Container-title:Remote Sensing of Environment
language:en
Short-container-title:Remote Sensing of Environment

Author:

Ines Amor V.M.,Das Narendra N.,Hansen James W.,Njoku Eni G.

Funder

CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS)

European Union

United States Agency for International Development (USAID)

Canadian International Development Agency (CIDA)

New Zealand Ministry of Foreign Affairs and Trade

Danish International Development Agency (DANIDA)

UK Department for International Development (DfID)

Irish Aid

Instituto de Investigação Científica Tropical, Portugal (IICT)

Publisher

Elsevier BV

Subject

Computers in Earth Sciences,Geology,Soil Science

Reference41 articles.

1. Soil moisture mapping and AMSR-E validation using the PSR in SMEX02;Bindlish;Remote Sensing of Environment,2006

2. Improved prediction of quasi-global vegetation conditions using remotely-sensed surface soil moisture;Bolten;Geophysical Research Letters,2012

3. Linking physical remote sensing models with crop growth simulation models, applied for sugar beet;Bouman;International Journal of Remote Sensing,1992

4. Simulation and systems analysis tools for crop yield forecasting;Bouman,1997

5. Analysis scheme in the ensemble Kalman filter;Burgers;Monthly Weather Bulletin,1998

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