To improve the prediction of crop yields at an aggregate scale, we developed a data assimilation-crop modeling framework that incorporates remotely sensed soil moisture and leaf area index (LAI) into a crop model using sequential data assimilation. The core of the framework is an Ensemble Kalman ...
Enlace original:
https://cgspace.cgiar.org/handle/10568/33838
Ines, Amor V. M.
,
Das, NN
,
Hansen, James
,
Njoku, EG
,
[Assimilation of remotely sensed soil moisture and vegetation with a crop simulation model for maize yield prediction]
,
Assimilation of remotely sensed soil moisture and vegetation with a crop simulation model for maize yield prediction
