Remote Sensing in Agriculture
This cluster of papers focuses on the use of remote sensing technology, particularly MODIS and Landsat data, for monitoring vegetation dynamics, phenology, and biomass estimation in response to global change and climate variability. The papers also explore the application of machine learning techniques for land cover classification and the assessment of ecological responses to environmental change.
Papers listed on taxonomy pages are the top few works per node from the OpenAlex snapshot. That list is not exhaustive and is not an endorsement. The topic map and the journal registry remain separate: there is still no authoritative topic-to-venue or topic-to-organization edge. Search is a lexical lookup, not a claim that a venue publishes a topic.
Most cited
- Google Earth Engine: Planetary-scale geospatial analysis for everyone
- Red and photographic infrared linear combinations for monitoring vegetation
- Overview of the radiometric and biophysical performance of the MODIS vegetation indices
- A soil-adjusted vegetation index (SAVI)
- NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space
- MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets
Most recent
- Using a planted tree biodiversity experiment to evaluate imaging spectroscopy for species classification
- Full-season wheat phenotyping using UAV-based multi-source remote sensing: dynamic time-series modeling and high-throughput trait extraction
- Geospatial Foundation Models Improve Atoll Island Ecosystem Mapping: A Case Study Using AlphaEarth Embeddings
- Landscape Greening Following Unseasonal Precipitation Along a Desert–Alpine Gradient
- A Dual-Factor-Driven Temporal Network for Pixel-Level NDVI Prediction in the Hulunbuir Grassland
- Mapping and Monitoring Seasonal Wetland Dynamics of Koonthankulam Ramsar Site Using Sentinel-2 Imagery and Random Forest Classification on Google Earth Engine