Remote Sensing

Agricultural remote sensing uses several sensor types. Optical/multispectral sensors (Sentinel-2, Landsat, PlanetScope) capture reflected light in visible + near-infrared bands and drive indices like NDVI, NDRE, and EVI. Thermal sensors measure surface temperature, revealing water stress before visible wilting. Radar (Sentinel-1, RADARSAT) penetrates clouds and measures soil moisture and crop structure. LiDAR maps canopy height and biomass. Drones (sub-10 cm resolution) fill the gap when satellite imagery is too coarse.

Free public satellite data has transformed agriculture. Sentinel-2 delivers 10-meter optical imagery every 3–5 days globally, freely downloadable; Landsat 8/9 delivers 30-meter imagery every 8–16 days; MODIS delivers daily 250-meter imagery. Commercial platforms (Planet, Maxar) sell sub-meter daily imagery for precision applications.

Typical agricultural workflows: weekly NDVI monitoring to spot stress zones, end-of-season yield-potential mapping from peak-canopy imagery, in-season disease detection from thermal and multispectral data, and historical change detection (e.g., how a field's productivity zones have shifted over 5 years). The main limitations are cloud cover (optical sensors cannot see through clouds — radar workaround available but with coarser resolution) and revisit interval (a 5-day gap during rapid crop change can miss critical windows). WiseYield integrates Sentinel-2 optical imagery with weather and soil data to deliver field-level NDVI and actionable zone recommendations automatically.