Remote Sensing in Agriculture

Satellites like MODIS and Landsat give ecologists a continuous, synoptic view of Earth's vegetation—tracking how plant communities green up in spring, lose biomass under drought, and shift their ranges as climates change. By calculating indices such as NDVI from reflected light, researchers can monitor phenology and land cover across entire continents without setting foot on the ground, linking what sensors detect to the carbon cycles and biodiversity patterns that underpin ecosystem function. A central challenge is improving the accuracy of these observations: machine learning methods are increasingly used to classify complex land cover types and to disentangle human land-use change from climate-driven vegetation shifts. Open questions remain around how to reconcile the coarse temporal resolution of some sensors with the fine spatial detail needed to detect localized ecological responses, and whether long-term satellite archives can reliably reconstruct the trajectory of vegetation change across decades of accelerating global warming.

Works

131,977

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1,980,072

Keywords

Remote SensingVegetation MonitoringPhenologyMODISLandsatNDVI