Publications

Huang, JX; Sedano, F; Huang, YB; Ma, HY; Li, XL; Liang, SL; Tian, LY; Zhang, XD; Fan, JL; Wu, WB (2016). 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.

Abstract
The scale mismatch between remote sensing observations and state variables simulated by crop growth models decreases the reliability of crop yield estimates. To overcome this problem, we implemented a two-step data-assimilation approach: first, we generated a time series of 30-m-resolution leaf area index (LAI) by combining Moderate Resolution Imaging Spectroradiometer (MODIS) data and three Landsat TM images with a Kalman filter algorithm (the synthetic KF LAI series); second, the time series were assimilated into the WOFOST crop growth model to generate an ensemble Kalman filter LAI time series (the EnKF-assimilated LAI series). The synthetic EnKF LAI series then drove the WOFOST model to simulate winter wheat yields at 1-km resolution for pixels with wheat fractions of at least 50%. The county-level aggregated yield estimates were compared with official statistical yields. The synthetic KF LAI time series produced a more realistic characterization of LAI phenological dynamics. Assimilation of the synthetic KF LAI series produced more accurate estimates of regional winter wheat yield (R-2 = 0.43; root-mean-square error (RMSE) = 439 kg ha(-1)) than three other approaches: WOFOST without assimilation (determination coefficient R-2 = 0.14; RMSE= 647 kg ha(-1)), assimilation of Landsat TM LAI (R-2 = 0.37; RMSE = 472 kg ha(-1)), and assimilation of S-G filtered MODIS LAI (R-2 = 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. (C) 2015 Elsevier B.V. All rights reserved.

DOI:
10.1016/j.agrformet.2015.10.013

ISSN:
0168-1923