| Full text | |
| Author(s): |
Cunha, Isabella A.
;
Baptista, Gustavo M. M.
;
Prudente, Victor Hugo R.
;
Melo, Derlei D.
;
Amaral, Lucas R.
Total Authors: 5
|
| Document type: | Journal article |
| Source: | AGRICULTURE-BASEL; v. 14, n. 11, p. 21-pg., 2024-11-01. |
| 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. (AU) | |
| FAPESP's process: | 22/03160-8 - Soil spatial variability mapping and optimized sampling supported by sensing techniques: bases for a more efficient and sustainable precision agriculture |
| Grantee: | Lucas Rios do Amaral |
| Support Opportunities: | Research Grants - Initial Project |