Jianxi Huang

TL;DR: Li et al. as discussed by the authors used the shuffled complex evolution-University of Arizona algorithm to minimize the 4DVar cost function between the remotely sensed and modeled LAI and to optimize two important WOFOST parameters.

TL;DR: A critique of both the advantages and disadvantages of both EO data and crop growth models is provided, and a solid and robust framework for DA is introduced, where different DA methods are shown to be derived from taking different assumptions in solving for the a posteriori probability density function using Bayes’ rule.

TL;DR: In this paper, a two-step data-assimilation approach was implemented to overcome the scale mismatch between remote sensing observations and state variables simulated by crop growth models. And the results showed that the EnKF-assimilated LAI series produced more accurate estimates of regional winter wheat yield (R 2 ǫ= 0.43; root-mean-square error (RMSE) = 4.5% for pixels with wheat fractions of at least 50%.

TL;DR: The new method generally outperformed the thresholding methods, although the degree of improvement varied among WIs, and the advantages and limitations of the proposed method are discussed.

TL;DR: In this article, the authors used Growing Degree Day (GDD) information extracted from NCEP/NCAR reanalysis data in order to improve the winter wheat production forecast by increasing the timeliness of the forecasts while conserving the accuracy of the original model.