Accurate early season predictions of crop yield at the within-field scale can be used to address a range of crop production, management, and precision agricultural challenges. While the remote sensing of within-field insights has been a research goal for many years, it is only recently that observations with the required spatio-temporal resolutions, together with efficient assimilation methods to integrate these into modeling frameworks, have become available to advance yield prediction efforts. Here we explore a yield prediction approach that combines daily high-resolution CubeSat imagery with the APSIM crop model. The approach employs APSIM to train a linear regression that relates simulated yield to simulated leaf area index (LAI). That relationship is then used to identify the optimal regression date at which the LAI provides the best prediction of yield: in this case, approximately 14 weeks prior to harvest. Instead of applying the regression on satellite imagery that is coincident, or closest to, the regression date, our method implements a particle filter that integrates CubeSat-based LAI into APSIM to provide end-of-season high-resolution (3 m) yield maps weeks before the optimal regression date. The approach is demonstrated on a rainfed maize field located in Nebraska, USA, where suitable collections of both imagery and in-situ data were available for assessment. The procedure does not require in-field data to calibrate the regression model, with results showing that even with a single assimilation step, it is possible to provide yield estimates with good accuracy up to 21 days before the optimal regression date. Yield spatial variability was reproduced reasonably well, with a strong correlation to independently collected measurements (R2 = 0.73 and rRMSE = 12%). When the field averaged yield was compared, our approach reduced yield prediction error from 1 Mg/ha (control case based on a calibrated APSIM model), to 0.5 Mg/ha (using satellite imagery alone), and then to 0.2 Mg/ha (results with assimilation up to three weeks prior to the optimal regression date). Such a capacity to provide spatially explicit yield predictions early in the season has considerable potential to enhance digital agricultural goals and improve end-of-season yield predictions.
|Original language||English (US)|
|Journal||Agricultural and Forest Meteorology|
|State||Published - Nov 29 2021|
Bibliographical noteKAUST Repository Item: Exported on 2021-12-14
Acknowledged KAUST grant number(s): OSR-2017-CRG6
Acknowledgements: Research was supported by the King Abdullah University of Science and Technology (Grant number OSR-2017-CRG6) and in collaboration with Planet Labs corporation and the Eastern Nebraska Research and Extension Center at the University of Nebraska-Lincoln (Lincoln, United States), who provided datasets for model calibration and validation. Funding for the AmeriFlux core site was provided by the U.S. Department of Energy's Office of Science. Field data used within this research was a contribution from the Long-Term Agroecosystem Research (LTAR) network. LTAR is supported by the United States Department of Agriculture. T.E.F. acknowledges the financial support of the USDA National Institute of Food and Agriculture, Hatch project #1009760, #1020768 and project #2019–67021–29312. The author would like to thank Prof. Scott Chapman (University of Queensland) and Dr. BangYou Zhang (CSRIO) for their advice with the APSIM crop model simulations.
ASJC Scopus subject areas
- Global and Planetary Change
- Agronomy and Crop Science
- Atmospheric Science