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ARS Home » Southeast Area » Stoneville, Mississippi » Crop Production Systems Research » Research » Publications at this Location » Publication #317935
Title: Assimilating a synthetic Kalman filter leaf area index series into the WOFOST model to improve regional winter wheat yield estimation

Author
item HUANG, JIANXI - Chinese Agricultural University
item SEDANO, FERNANDO - University Of Maryland
item Huang, Yanbo
item MA, HONGYUAN - Chinese Agricultural University
item LI, XINLU - Chinese Agricultural University
item LIANG, SHUNLIN - University Of Maryland
item TIAN, LIYAN - Texas A&M University
item WU, WENBIN - Chinese Academy Of Agricultural Sciences

Submitted to: Agricultural and Forest Meteorology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/22/2015
Publication Date: 11/22/2015
Citation: Huang, J., Sedano, F., Huang, Y., Ma, H., Li, X., Liang, S., Tian, L., Wu, W. 2015. Assimilating a synthetic Kalman filter leaf area index series into the WOFOST model to improve regional winter wheat yield estimation. Agricultural and Forest Meteorology. 216:188-202.

Interpretive Summary: In order to estimate crop yield precisely, schemes are developed to incorporate remotely sensed data into a crop growth model. Scientists from China Agricultural University, USDA-ARS, Crop Production Research Unit, Stoneville, Mississippi, University of Maryland, and Chinese Academy of Agricultural Science developed a new framework for winter wheat yield prediction with a synthetic Kalman filter leaf area index time series for a more realistic characterization of plant leaf phonological dynamics. The results illustrated that the new method produced more accurate estimates of regional winter wheat yield and indicated that the method provides a reliable and promising approach to improvement of estimation of winter wheat yield at regional scale.

Technical Abstract: The scale mismatch between remotely sensed observations and crop growth models simulated state variables decreases the reliability of crop yield estimates. To overcome this problem, we used a two-step data assimilation phases: first we generated a complete leaf area index (LAI) time series by combining Moderate Resolution Imaging Spectroradiometer (MODIS) data and three Landsat TM images with a Kalman filter algorithm (the synthetic KF LAI series), and then to assimilate this series into the WOFOST crop growth model to generate an ensemble Kalman filter LAI time series (the EnKF-assimilated LAI series). It was found that the synthetic KF time series of LAI estimates resulted in a more realistic characterization of LAI phenological dynamics. The synthetic EnKF LAI series were used to drive the WOFOST model to simulate winter wheat yields at a 1-km resolution for pixels with wheat fractions of at least 50%, and aggregated to the county level to allow a comparison with official regional statistical yield. The results indicated that assimilating the synthetic KF LAI series produced more accurate estimates of regional winter wheat yield (determination coefficient (R2) = 0.43; root-mean-square error (RMSE) = 439 kg ha-1) than three other approaches: WOFOST without assimilation (R2 = 0.14; RMSE = 647 kg ha-1), assimilation of Landsat TM LAI (R2 = 0.37; RMSE = 472 kg ha-1), and assimilation of S-G filtered MODIS LAI (R2 = 0.49; RMSE = 1355 kg ha-1). Thus, assimilating the synthetic KF LAI series into the WOFOST model with the EnKF strategy provides a reliable and promising method for improving regional estimates of winter wheat yield.