The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Download
Publications Copernicus
Download
Citation
Articles | Volume XLIII-B3-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 1045–1052, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-1045-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 1045–1052, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-1045-2022
 
30 May 2022
30 May 2022

INTRA-FIELD CROP YIELD VARIABILITY BY ASSIMILATING CUBESAT LAI IN THE APSIM CROP MODEL

M. G. Ziliani1, M. U. Altaf1, B. Aragon2, R. Houborg2, T. E. Franz3, Y. Lu4, J. Sheffield4, I. Hoteit5, and M. F. McCabe1 M. G. Ziliani et al.
  • 1Biological and Environmental Science and Engineering (BESE), King Abdullah University of Science and Technology, Kingdom of Saudi Arabia
  • 2Planet, San Francisco, California, USA
  • 3School of Natural Resources, University of Nebraska-Lincoln, USA
  • 4School of Geography and Environmental Sciences, University of Southampton, UK
  • 5Physical Science and Engineering (PSE), King Abdullah University of Science and Technology, Kingdom of Saudi Arabia

Keywords: Crop yield prediction, Crop modeling, CubeSat, LAI, APSIM, Particle filter

Abstract. Predicting within-field crop yield early in the season can help address crop production challenges to improve farmers’ economic return. While yield prediction with remote sensing has been a research aim for years, it is only recently that observations with the suited spatial and temporal resolutions have become accessible to improve crop yield predictions.

Here we developed a yield prediction framework that integrates daily high-resolution (3 m) CubeSat imagery into the APSIM crop model. The approach trains a regression model that correlates simulated yield to simulated leaf area index (LAI) from APSIM. That relationship is then employed to determine the optimum date at which the regression best predicts yield from the LAI. Additionally, our approach can forecast crop yield by utilizing a particle filter to assimilate CubeSat-based LAI in the model APSIM to generate yield maps at 3 m several weeks before the optimum regression date. Our method was evaluated for a rainfed site located in the US Corn belt, using a collection of spatially varying yield data. The proposed approach does not need in situ data to rain the regression, with outcomes reporting that even with a single assimilation step, accurate yield predictions were provided up to 21 days before the optimum regression date. The spatial variability of crop yield was reproduced fairly well, with a good correlation against in situ measurements (R2 = 0.73 and RMSE = 1.69), demonstrating that high-resolution yield predictions early in the season have great potential to meet and improve upon digital agricultural goals.