For more than 40 years agronomists and scientists have used Normalized Difference Vegetation Index (NDVI) to assess the health or crops and plants.
But what is NDVI? And how do you know you're getting the real deal?
HOW DOES NDVI RELATE TO CROP HEALTH?
NDVI was created when scientists learned that a plant's unique reflection of a combination of visible red light and near-infrared (NIR) light gave a good indication of plant health - for superior to what the reflections of visible light can reveal. Simply put, NDVI data provides growers with an accurate measurement f crop vigor and allows them to zero in on problems areas that may need further attention.
SO, HOW IS IT CAPTURED?
The short answer is that an NVDI sensor processes wavelengths of light outside of those captured by regular RGB cameras, like you have on your cell phone. NDVI wavelengths are slightly longer than those of visible light and are located in the near-infrared (NIR) band - and that's why a "regular" sensor can't capture NDVI. When you hear of "synthetic NDVI" this means th data is being captured by a RGB sensor and the wavelengths are being synthetically processed to simulate NIR and produce a "best guess" NDVI value. That's like taking a black and white photo and guessing the color of someone's shirt. It's a guess! Don't bet your operation on it.
A BRIEF HISTORY OF NDVI COLLECTION
Some of the first sensors that could generate NDVI measurements were integrated into ERTS/LANDSAT satellites. Although the orbital altitude of a satellite means that the pixel resolution of satellite-based imagery is relatively low, the satellite-based sensors are usually extremely accurate and highly calibrated. LANDSAT sensors carried equipment that could sense in both the visual and NIR band, and scientists analyzing LANDSAT data created NDVI as an effective, easy-to-use measure of vegetative vigor.
NDVI TODAY
In the last 40 years, advances in technology have allowed for integration of high-quality NDVI images onto manned aircraft and, now Sentera offers TrueNDVI sensors that integrate onto several of the most popular UAV platforms. Even the smallest consumer-grade drones with accurate NDVI sensors can rival an expensive remote-sensing platform. And when combined with sophisticated NDVI analytics, and comparative data from previous flights, significant progress can be made in crop planning, optimizing inputs and recognizing crp health issues before they become major problems.
This is huge, not only for growers and agronomists, who can dramatically increase the efficiency of their operations, but for the world as a whole as we seek to address the growing burden of hunger caused by our burgeoning global population.