Abstract
A long standing debate has existed on the utility of Mid Wave Infrared (MWIR) vs Long Wave Infrared (LWIR) remote-sensing imagery for Precision Agriculture applications. We have conducted a combined qualitative and quantitative, multi-field, aircraft remote-sensing study to show that LWIR measurements often result in more actionable for day-to-day farm management applications. LWIR imagery when processed with SaraniaSat's nonlinear, weak-signal detection algorithms is an effective predictor of crops stress and ultimate harvest yield. It is also shown that both processed LWIR and MWIR imagery contain actionable, crop-stress information that is absent in the conventional Normalized Difference Vegetation Index (NDVI) that is currently, widely used for Precision Agriculture. Finally, at a simulated 10m ground resolution (satellite remote sensing) it is shown, once again, that LWIR is superior and more actionable.
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