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    Satellite Imagery

    Definition

    Satellite imagery in agriculture is Earth-observation data collected by orbital sensors and used to monitor crop health, soil moisture, field boundaries, and land use. Public satellites (Sentinel-2, Landsat 8/9, MODIS) provide free imagery with 10–500 m resolution; commercial satellites (Planet, Maxar) offer sub-meter imagery at daily revisit. Globally, over 200 Earth-observation satellites operated in 2023 (ESA).

    How Satellite Imagery Works

    Satellite imagery comes in several flavors. Optical multispectral imagery captures reflected sunlight in visible (red, green, blue) plus near-infrared (NIR) and shortwave-infrared (SWIR) bands, enabling computation of vegetation indices such as NDVI, NDRE, and EVI. Thermal imagery measures land-surface temperature (useful for detecting water stress before visible wilting). Radar imagery (Sentinel-1 C-band, ALOS L-band) penetrates clouds and measures soil moisture and crop structure — essential for cloudy regions where optical sensors fail for weeks.

    The free-satellite revolution transformed agricultural monitoring. Sentinel-2 (European Space Agency) delivers 10-meter imagery with 5-day revisit globally, completely free. Landsat (USGS/NASA) has provided 30-meter imagery continuously since 1972, also free. MODIS delivers 250-meter imagery daily. Combined, these datasets enable field-level NDVI time series, multi-year trend analysis, and early-warning crop stress detection on any farm anywhere on Earth. Commercial constellations (Planet Labs with ~200 SuperDoves delivering daily 3-meter imagery) fill the resolution gap when 10-meter is too coarse for small fields.

    Operational workflows include in-season crop monitoring (weekly NDVI flagging stress zones), yield forecasting (peak-canopy NDVI correlates with final yield at r² = 0.7–0.9 for most grains), disease detection (anomalous NDVI patterns + thermal signatures), boundary mapping (AI-segmented fields from imagery), and insurance claim validation. Challenges include cloud cover (typical 30–50% of acquisitions in tropical and temperate regions are cloud-contaminated), variable sun angle, and atmospheric correction complexity. WiseYield integrates Sentinel-2 imagery automatically for farms with GPS boundaries, calculating field-level NDVI time series and flagging zones where values drop significantly below the farm mean.

    Sources

    1. European Space Agency (2023). Sentinel-2 user handbook for agricultural applications.
    2. USGS (2023). Landsat mission — 50 years of Earth observation.

    Related Terms

    Remote Sensing
    Technology
    NDVI
    Technology
    Precision Agriculture
    Technology
    GIS
    Technology
    Back to all glossary terms

    Apply Satellite Imagery on Your Farm

    WiseYield puts these concepts to work — AI-powered crop predictions, satellite imagery, irrigation scheduling, and financial tools in one platform.

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