Crop models, like many representations of environmental processes, tend to be over-parameterised. A redesign of the SUCROS family of crop models, largely driven by sensitivity analysis, is presented here. In particular, two new versions of WOFOST, the most widespread model from this family, were developed. The first (WOFOST-GT) reduces model complexity through the definition of functions driven...

In this study, a regional winter wheat yield prediction method was developed by integration of time series of Moderate-Resolution Imaging Spectroradiometer MODIS LAI products (MOD15A2) with WOrld FOod STudies (WOFOST) model through Ensemble Kalman Filter (EnKF) algorithm at the regional scale in the Hengshui District, Hebei province in China. WOFOST model was selected as the crop growth dynamic...

The WOFOST simulation model is a tool for analysing the growth and production of field crops under a wide range of weather and soil conditions. Such an analysis is important first to assess to what extent crop production is limited by the factors of light, moisture and macro-nutrients, and second to estimate what improvements are possible. The theoretical concept of a production situation, as m...

The approach of using multispectral remote sensing (RS) to estimate soil available nutrients (SANs) has been recently developed and shows promising results. This method overcomes the limitations of commonly used methods by building a statistical model that connects RS-based crop growth and nutrient content. However, the stability and accuracy of this model require improvement. In this article, ...

Heavy metal contamination in crops is a worldwide problem that requires accurate and timely monitoring. This study is aimed at improving the accuracy of monitoring heavy metal stress levels in rice utilizing remote sensing data. An assimilation framework based on remote sensing and improved crop growth model was developed to continuously monitor heavy metal stress levels over the entire period ...

Field crop yield prediction is crucial to grain storage, agricultural field management, and national agricultural decision-making. Currently, crop models are widely used for crop yield prediction. However, they are hampered by the uncertainty or similarity of input parameters when extrapolated to field scale. Data assimilation methods that combine crop models and remote sensing are the most eff...

Rapidly and accurately estimating yields at field scales are very significant. Each type of currently yield estimation model has been well studied, yet all them have certain limitations. Based on a coupled Carnegie-Ames-Stanford approach (CASA)-World Food Studies (WOFOST) time series Sentinel-2 imagery, we achieved daily crop simulations estimations two adjacent farms in China. The results indi...

Regional crop yield prediction is a vital component of national food security assessment. Data assimilation method which combines crop growth model and remotely sensed data has been proven the most potential method in regional crop production estimation. This paper takes Hengshui district as study area, WOFOST as crop model, MODIS-LAI as observation data to test and verify the efficiency of EnK...

THIS PAPER AIMS (i) to identify at national scale areas where crop yield formation is currently most prone to climate-induced stresses, (ii) to evaluate how the severity of these stresses is likely to develop in time and space, and (iii) to appraise and quantify the performance of two strategies for adapting crop cultivation to a wide range of (uncertain) climate change projections. To this end...