Extraction of crop information through the spatiotemporal fusion of OLI and MODIS images

Abstract

Spatiotemporal data fusion algorithms have been developed to fuse satellite imagery from sensors with different spatial and temporal resolutions and generate predicted imagery. In this study, we compare the predictions of three spatiotemporal data fusion algorithms in blending Landsat-8/OLI and Terra-Aqua/MODIS images for mapping soybean and corn under five classification scenarios. The Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), and Flexible Spatiotemporal Data Fusion (FSDAF) algorithms were compared to generate images for the 2016/2017 summer crop-year. Classifications including phenological metrics extracted from FSDAF- and STARFM-predicted EVI time series had overalls accuracies higher than the other scenarios, 93.11% and 91.33%, respectively. The results show that phenological metrics extracted from predicted images are an interesting alternative to overcome cloud cover frequency limitations for soybean and corn mapping in tropical areas.

Keywords:

• Time series
• phenology
• STARFM
• ESTARM
• FSDAF

Acknowledgements

The authors would like to acknowledge Dr. Xiaolin Zhu for making available the ESTARFM and FSDAF packages and Dr. Feng Gao for making available the STARFM package. Also, they would like to thank the anonymous reviewers for their very helpful comments.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) - Finance Code 001, CAPES-PRINT/Satellite Applications for Sustainable Development, and the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq). E. Mercante acknowledge the CNPq for his Research Productivity Fellowship (PQ - 303953/2019-5).