How does Satellite Imagery in Farming work?

Jun 29, 2022

Let’s start by understanding how satellite imagery in farming works before tackling the actual benefits of this technology. If you aren’t interested in the technical details and only want to read about the potential upsides, then skip ahead to the next big section.

Satellite technology has been around since 1957 and the first earth-observation satellite (Landsat 1) was launched in 1972. We now have more than four decades worth of images of the earth. Constantly evolving camera technology now produces better, high-resolution images for the public.

Precision agriculture uses satellites to capture multiple types of light spectrums beyond the usual visual light (VIS). Let’s first take a look at which spectrums are commonly used!

Different Types of Satellite Spectroscopy:

One valuable Aspect of Satellite Imagery is the use of channels beyond the standard Red, Green, Blue. This helps to Highlight relevant information and reduce the impact of disruptive factors (like clouds).

VIS – visible light

VIS is the most common type of satellite imagery. It’s exactly like your google maps satellite option and shows the location from a birdseye view. Using this type of spectrum will visualize the earth and vegetation in mostly the same way you’d see them from an airplane.

NIR- near-infrared

This spectrum captures the reflected infrared light and helps to Highlight Vegetation. The near-infrared Spectrum can be found at a Wavelength between the upper 0,7μm to around 1,2μm. Healthy Plants with ample Chlorophyll have a higher Reflectance Rate (Darker Red) compared to stressed plants (lighter Red). Furthermore, Soil comes in a variety of brown tones, and clouds/snow appear white. Clearwater appears in dark blue and dirty brown water in cyan colors.

SWIR – Short-wave infrared

Short-wave infrared includes light on a wavelength between 1,1μm – 3 μm. This spectrum is primarily used to measure the water concentration in soil and plants. According to Mohamed et al. and Nasa, water absorbs SWIR light at a wavelength around 1,4/1,9/2,4 μm. This layer builds the foundation of the NDWI!

NDVI – Normalized Difference Vegetation Index

The NDVI plays a vital role in agricultural satellite imagery and helps to showcase differences in plant health over a whole field. To calculate NDVI, we use this formula (NIR – RED)/(NIR + RED), where RED is the reflected red range of the spectrum.

NDVI builds the backbone of satellite-managed farming, as it’s used to show where external inputs (fertilizer, water) are needed and which parts of the field are thriving. This data can also be used to make long-term decisions about crop rotation, soil management, and more.

NDWI – Normalized Difference Water Index

The NDWI or NDMI (Normalized Difference Moisture Index) finds application in the management of water stress in crops and soil. Long-term exposure to excessive amounts or lack of water can lead to yield reduction and plant death – The Normalized Difference Water Index is calculated with the following Formula: (NIR-SWIR)/(NIR+SWIR), where SWIR is set to a point in which water absorbs most of the reflection.

TIR – Thermal Infrared

Thermal Infrared (TIR or LWIR) includes wavelengths between 8,0-15,0 μm and in the context of satellite imagery, TIR helps to visualize temperature distribution. This spectrum is often depicted in grayscale but can also be color-coded. LWIR is mostly emitted and not reflected, which makes it possible to measure Thermal Infrared without the presence of the sun. Furthermore, TIR can also indicate the amount of water a plant uses by analyzing the temperature difference in fields caused by evapotranspiration.

SAR – Synthetic Aperture Radar

One of the recent developments in Satellite Observation Technology is Synthetic Aperture Radar, which for example can help to measure crop yields. The Wavelength of Radar Systems ranges from 0.04 inches (1 millimeter) to more than 62 miles (100 kilometers) and uses the reflection of pulses to calculate an accurate image of the surface below. SAR is not affected by clouds, making it an excellent supplementation to VIS and NIR.