Estimation of Winter Wheat Grain Protein Content Based on Multisource Data Assimilation

Data assimilation is a robust method to predict crop biophysical and biochemical parameters. However, no previous study has attempted to predict grain protein content (GPC) at a regional scale using this method. This study explored the feasibility of designing an assimilation model for wheat GPC est...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2020-10, Vol.12 (19), p.3201
Main Author: Chen, Pengfei
Format: Article
Language:English
Subjects:
Accuracy
Agricultural production
Algorithms
Calibration
CERES-wheat model
Corn
Cost function
Crop growth
Crops
Data assimilation
Data collection
Feasibility studies
Grain
grain protein content
Growth models
Landsat
Landsat satellites
Leaf area
Leaf area index
Methods
Phenology
Predictions
Proteins
Radiation
Rain
Regional analysis
Remote observing
Remote sensing
Sampling
satellite image
Satellite imagery
Simulation
Variables
Wheat
Winter
Winter wheat
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Summary:Data assimilation is a robust method to predict crop biophysical and biochemical parameters. However, no previous study has attempted to predict grain protein content (GPC) at a regional scale using this method. This study explored the feasibility of designing an assimilation model for wheat GPC estimation using remote sensing, a crop growth model, and a priori knowledge. The data included a field experiment and regional sampling data, and Landsat Operational Land Imager images were employed, with the CERES (Crop Environment REsource Synthesis)-Wheat model used as simulation model. To select an optimal method for data assimilation in GPC prediction, different state variable scenarios and cost function solving algorithm scenarios were compared. Additionally, to determine whether a priori information could improve GPC prediction, the collected leaf area index (LAI) and leaf N content sampling data and the range of GPC in the study region were used to constrain the data assimilation process. Furthermore, the data assimilation method was compared to the use of only the CERES-Wheat model. The results showed that GPC could be predicted by remote sensing observation, a crop growth model, and a priori knowledge at regional scale, where the use of data assimilation improved the GPC prediction compared to using only the CERES-Wheat model.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs12193201