Guimarães, N.; Fraga, H.; Fonseca, A.; Pacheco, F.; Fernandes, L.F.; Moura, J.P.; Carlos, C.; Pereira, L.; Jurado, J.M.; Negri, S.;
et al. High-Resolution Soil Surface Moisture Projections for European Perennial Crops: A Machine Learning Framework Integrating Sentinel-1 and CMIP6 Climate Scenarios. Remote Sens. 2026, 18, 1902.
https://doi.org/10.3390/rs18121902
AMA Style
Guimarães N, Fraga H, Fonseca A, Pacheco F, Fernandes LF, Moura JP, Carlos C, Pereira L, Jurado JM, Negri S,
et al. High-Resolution Soil Surface Moisture Projections for European Perennial Crops: A Machine Learning Framework Integrating Sentinel-1 and CMIP6 Climate Scenarios. Remote Sensing. 2026; 18(12):1902.
https://doi.org/10.3390/rs18121902
Chicago/Turabian Style
Guimarães, Nathalie, Helder Fraga, André Fonseca, Fernando Pacheco, LuÃs Filipe Fernandes, João Paulo Moura, Cristina Carlos, Leonor Pereira, Juan M. Jurado, Sara Negri,
and et al. 2026. "High-Resolution Soil Surface Moisture Projections for European Perennial Crops: A Machine Learning Framework Integrating Sentinel-1 and CMIP6 Climate Scenarios" Remote Sensing 18, no. 12: 1902.
https://doi.org/10.3390/rs18121902
APA Style
Guimarães, N., Fraga, H., Fonseca, A., Pacheco, F., Fernandes, L. F., Moura, J. P., Carlos, C., Pereira, L., Jurado, J. M., Negri, S., Jonczak, J., & Santos, J. A.
(2026). High-Resolution Soil Surface Moisture Projections for European Perennial Crops: A Machine Learning Framework Integrating Sentinel-1 and CMIP6 Climate Scenarios. Remote Sensing, 18(12), 1902.
https://doi.org/10.3390/rs18121902
