Timely monitoring of crop lands is important in order to make agricultural activities more
sustainable, as well as ensuring food security. The use of Earth Observation (EO) data …

Bayesian inference has found widespread application and use in science and engineering
to reconcile Earth system models with data, including prediction in space (interpolation) …

The frequent occurrence of global climate change and natural disasters highlights the
importance of precision agricultural monitoring, yield forecasting, and early warning …

Crop growth models are powerful tools for predicting crop growth and yield. Gross primary
production (GPP) is a major photosynthetic flux that is directly linked to crop grain yield. To …

The synthetic aperture radar (SAR) interferometric coherence can complement optical data
for the estimation of crop growth parameters, but it has not been yet investigated for …

While process-based crop models are increasingly applied in multiple research areas, the
role of model calibration and its data requirement have been rarely addressed. We used …

Calibration of crop model is standard practice, and it involves estimation of crop parameters
based upon observed field data. It is the process of estimation of unknown parameters using …

A crop can be viewed as a complex system with outputs (eg yield) that are affected by inputs
of genetic, physiology, pedo-climatic and management information. Application of numerical …

Crop models are used to estimate crop productivity under future climate projections, and
modellers manage uncertainty by considering different scenarios and GCMs, using a range …

The performance of crop models in simulating various aspects of the cropping system is
sensitive to parameter calibration. Parameter estimation is challenging, especially for time …