Integration of Optical and Synthetic Aperture Radar Data with Different Synthetic Aperture Radar Image Processing Techniques and Development Stages to Improve Soybean Yield Prediction

Isabella A. Cunha, Gustavo M. M. Baptista, Victor Hugo R. Prudente, Derlei D. Melo and Lucas R. Amaral ()
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Isabella A. Cunha: School of Agricultural Engineering, University of Campinas—UNICAMP, Campinas 13083-875, SP, Brazil
Gustavo M. M. Baptista: Institute of Geoscience, University of Brasília, Brasília 70910-900, DF, Brazil
Victor Hugo R. Prudente: School for Environment and Sustainability, University of Michigan, Ann Arbor, MI 48109, USA
Derlei D. Melo: School of Agricultural Engineering, University of Campinas—UNICAMP, Campinas 13083-875, SP, Brazil
Lucas R. Amaral: School of Agricultural Engineering, University of Campinas—UNICAMP, Campinas 13083-875, SP, Brazil

Agriculture, 2024, vol. 14, issue 11, 1-21

Abstract: Predicting crop yield throughout its development cycle is crucial for planning storage, processing, and distribution. Optical remote sensing has been used for yield prediction but has limitations, such as cloud interference and only capturing canopy-level data. Synthetic Aperture Radar (SAR) complements optical data by capturing information even in cloudy conditions and providing additional plant insights. This study aimed to explore the correlation of SAR variables with soybean yield at different crop stages, testing if SAR data enhances predictions compared to optical data alone. Data from three growing seasons were collected from an area of 106 hectares, using eight SAR variables (Alpha, Entropy, DPSVI, RFDI, Pol, RVI, VH , and VV ) and four speckle noise filters. The Random Forest algorithm was applied, combining SAR variables with the EVI optical index. Although none of the SAR variables showed strong correlations with yield (r < |0.35|), predictions improved when SAR data were included. The best performance was achieved using DPSVI with the Boxcar filter, combined with EVI during the maturation stage (with EVI:RMSE = 0.43, 0.49, and 0.60, respectively, for each season; while EVI + DPSVI:RMSE = 0.39, 0.49, and 0.42). Despite improving predictions, the computational demands of SAR processing must be considered, especially when optical data are limited due to cloud cover.

Keywords: precision agriculture; SAR vegetation index; backscatter coefficient; polarimetric decomposition; EVI; machine learning (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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