HIGH RESOLUTION TEMPORAL NORMALIZED DIFFERENCE VEGETATION INDICES FOR SPECIFIC CROP IDENTIFICATION

7 citations as recorded by crossref.

1. Comparative assessment of satellite‐ and drone‐based vegetation indices to predict arthropod biomass in shrub‐steppes J. Traba et al. https://doi.org/10.1002/eap.2707
2. Contextual and heterogeneity-aware transplanted paddy field monitoring using dual-sensor data A. Palavesam Sarathamani et al. https://doi.org/10.1080/01431161.2025.2487230
3. Optimizing Harvested Paddy Field Classification: Leveraging Combined Local Convolution and Individual Sample as Mean Training Approach A. Palavesam Sarathamani et al. https://doi.org/10.1007/s12524-025-02286-w
4. Classifying Pinus roxburghii Using an Innovative Training Approach of Fuzzy Models While Handling Heterogeneity Within Class in Western Himalayan Forests S. Mehrotra et al. https://doi.org/10.1007/s12524-024-01887-1
5. Synergistic contextual information and individual sample as mean training approach: paddy stubble burning mapping A. Palavesam Sarathamani & A. Kumar https://doi.org/10.1007/s10661-025-14052-z
6. Studying dual-sensor time-series remote sensing data for Dalbergia sissoo mapping in a Lesser Himalayan area S. Mehrotra et al. https://doi.org/10.1117/1.JRS.16.034521
7. Fuzzy machine learning model for class-based flood damage assessment from planetscope temporal data A. Sarathamani & A. Kumar https://doi.org/10.1117/1.JRS.18.014523