NDVI stands for Normalized Difference Vegetation Index. It is a comparison of the near-infrared light that is reflected by a plant and the red light that is absorbed. Healthy plants reflect near-infrared light and adsorb more red light. The comparison is given a value and that value is presented as a color on the map.

By satellite, specifically the Sentinel-2 Satellites. Imagery is publicly available, however, Semios performs post-capture processing to attribute the imagery to field shapes and to provide the best colors for viewing (ease of viewing for color-blindness) as well as removing imagery that is poor quality, such as that obscured by cloud cover.

Resolution is determined by the instruments onboard the Sentinel-2 satellites. The instruments provide a resolution of 10m x 10m. 10m x 10m pixels are also referred to as satellite pixels.

For NDVI, the more green a part of the image is, the healthier the plants are. The closer to red, the less healthy they are.

Average NDVI is determined by averaging the NDVI of each satellite pixel in the image.

In addition to NDVI, there are options to see change in NDVI. Change in NDVI is change in the level of greenness. Yellow means there has been little to no change. Red is a decrease in greenness. And blue is an increase in greenness.

Uniformity is a measurement of the uniformity of the NDVI values across the block in question. Uniformity is calculated as the CV (Coefficient of Variation).

Only imagery that is of high enough quality is shown. In order to be shown, 90% of the pixels in a block have to reach the quality standards to be shown. The Sentinel-2 satellites are not always overhead, but their orbit is designed to pass over the equator once every 5 days. So, as long as the imagery was high enough quality the most recent image shouldn’t be older than 5 days. The exact date the image was taken can be seen by hovering over the image and looking for the date on the tooltip. Example below.

Short term change compares the two most recent NDVI images that were of high-enough quality. The tooltip will show the exact dates of the images.

Depending on the timing of quality imagery, Long Term Change compares images from the most current image and an image captured from 30 to 45 days before. The tooltip will show the exact dates of the images.

While NDVI can’t tell you exactly what is wrong from the imagery alone, it can give you a place to start your investigation. Areas with low greenness or decrease in greenness are good places to scout for inadequacies or problems with irrigation, nutrition, or pests

Clouds, bare soil, cropping practices, satellite problems. Clouds and their shadows can obscure the ground and make it so it is impossible to gather imagery data. Bare soil causes extremely low NDVI values and nulls the usability of the data. Cropping practices, such as shade cloth and white or shiny fabrics laid between the tree rows can change reflectance and adsorption values and render false NDVI images. Finally, satellites, like all machines can have equipment issues that make it so they don’t work properly. Semios uses systems to identify and remove problematic data.

1: Check that NDVI Imagery is turned on. The toggle to turn it on is listed as “Vegetation: and is located mid-way down the map controls box. (See image)