Remote Sensing in Agriculture

Remote sensing uses satellite-borne instruments like MODIS and Landsat to track how vegetation across Earth's surface grows, greens, and responds to shifting climate conditions—all without a single boot on the ground. By measuring spectral signals such as NDVI, researchers can reconstruct seasonal cycles of plant growth (phenology), estimate biomass, and detect land-cover change at continental scales over decades. As global temperatures and precipitation patterns continue to shift, a pressing question is how accurately these satellite records capture fine-grained ecological responses, particularly in heterogeneous or data-sparse landscapes. Machine learning is increasingly being applied to improve land-cover classification and to disentangle human land use from climate-driven vegetation change, though linking remote observations to underlying ecological mechanisms remains an active challenge.

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Keywords

Remote SensingVegetation MonitoringPhenologyMODISLandsatNDVI