Jianqiang Ren

TL;DR: MODIS-NDVI data, with a 250 m resolution, was used to estimate the winter wheat yield in one of the main winter-wheat-growing regions and the method suggested was good for predicting regional winter wheat production and yield estimation.

TL;DR: The significant improvements in the yield estimation suggest that PF-based crop model data assimilation is feasible and a configuration using a particle size of 50, LAI maps with a moderate spatial resolution, and an assimilation interval of 20 d results in a reasonable tradeoff between accuracy and effectiveness in regional applications.

TL;DR: The results show that the LUT inversion approach was more suitable for LAI and CWC estimation than the spectral index-based empirical model or the NN algorithm and able to reach higher accuracies when red edge bands were used.

TL;DR: Joint assimilation of LAI and SM from Sentinel-1 and Sentinel-2 at a 20 m resolution into the WOFOST provides a robust method to improve crop yield estimations, and suggests that LAI was the first-choice variable for crop data assimilation over SM.

TL;DR: A new four-dimensional variational algorithm (POD4DVar) merging the Monte Carlo and proper orthogonal decomposition techniques was introduced to develop a data assimilation strategy using the Crop Environment Resource Synthesis-Wheat model, which was more accurate and efficient than the EnKF-based strategy.