Satellite imagery is one of the most versatile functions that can be implemented in agricultural production in order to improve the decision-making process. Making decisions, based on a large quantity of data, can help the farmers, agronomists, or advisors to comprehend the situation and the processes that are occurring in the agricultural fields that are subject to monitoring.
What is satellite imaging?
Satellite imaging, also known as satellite remote sensing, is the process of using satellite imagery to gather information about the Earth’s surface, atmosphere, and oceans. This technology involves the use of satellites equipped with specialized sensors and cameras that can capture high-resolution images of the Earth from space.
The images captured by the satellites can be used for a variety of applications, including monitoring weather patterns, tracking changes in the environment, mapping land use and vegetation, and assessing the impact of natural disasters. They can also be used for military and intelligence purposes.
Satellite imaging with its benefits can present situations that can not be seen with regular observation. Regular observations can be very demanding in the sense of the quantity of implemented workforce, finance, and time.
Even with regular observations made during the growth period of the crops, there are several things that can not be seen with the naked eye, such as the early progression of disease and damage induced by the pests that ultimately result in chlorophyll degradation on a cell level.
With the integration and calculation of spectral bands invisible to the human eye, the growers and the advisors can easily visualize chlorophyll degradation before the visible disease or pest damage symptoms on the plants.
These advantages in crop monitoring are beneficial with nowadays technologies, allowing implementation of such information to be integrated into geographic information systems, resulting in creating a prompt and quality set of data ready to be utilized in the decision-making processes regarding agricultural production systems.
Besides making information-based decisions, satellite imagery can be used for reporting, record-keeping, and integrating with different sets of data (disease and pest models, yield maps, pest monitoring, fertilization maps and etc,) in order to create an even more functional system of crop monitoring and the way how the farmer or other professionals see the progress of the crops during their growing period.
How are satellite imagery used in farming?
The images can be used to model and calculate spectral indices which later are equated to be used for visualization in the form of color synthesis, either in the visible part of the spectrum or by including other wavelengths. Properly selected color synthesis can reveal crop growth, stress or soil erosion displayed with different colors.
Spectral indices are combinations of spectral reflection of two or more wavelengths to show features of interest. Crop indices are most commonly used in agriculture, but the indices are used for the identification of burned areas, other artificial characteristics, water, and other geological features.
Useful spectral indices in crop production include:
- NDVI (Normalized Difference Vegetation Index).
- EVI (Enhanced Vegetation Index).
- RENDVI or NDRE (Red Edge Normalized Difference Vegetation Index).
- GNDVI (The Green Normalized Difference Vegetation Index).
- MSI or NDWI (Moisture Stress Index).
- LAI (Leaf Area Index).
NDVI is one of the most used indices and is frequently used to review the initial state of a crop. Other indexes work with certain characteristics, so the results are specific to related to specific agricultural sites, it is important to work with the history of land vegetation.
NDVI (Normalized Difference Vegetation Index)
This index is a measure of green vegetation and is generally the most widely used index. The leaves reflect infrared light (NIR) and use only visible light for photosynthesis. This means that a healthy plant with a good photosynthesis rate can be analyzed by comparing NIR with visible red light.
Unhealthy vegetation will reflect more visible light and lower NIR. Healthy vegetation will reduce some of the visible light that falls on it. However, NDVI is sensitive to the effects of soil (light and color), cloud cover, and shade. Also, the NDVI values can be incorrect in a situation with dense vegetation.
EVI (Enhanced Vegetation Index)
This index is the standard for the moderate resolution spectra – an instrument used on the Terra and Aqua satellites. EVI presents an alternative to NDVI which refers to some of its deficiencies, e.g., soil and atmospheric constraints, by optimizing the reflectivity of leaf vegetation.
It uses the blue part of the visible spectrum for signal correction, lowering the effects of the abovementioned constraints including the scattering of electromagnetic radiation by aerosols.
RENDVI or NDRE (Red Edge Normalized Difference Vegetation Index)
This index is based on the standard NDVI index, but with modifications. RENDVI is very useful in precision agriculture, forest monitoring, and the detection of crop stress.
Its effectiveness is due to the inclusion of wavelengths that fall into the red end band, rather than wavelengths that correspond to the value of reflection and absorption. It is especially convenient for detecting small changes in the vegetation condition.
GNDVI (The Green Normalized Difference Vegetation Index)
This index is similar to the NDVI index and measures the reflection of the light in the green part of the spectrum in the wavelength range of 540 to 570 nanometers, instead of the red part of the spectrum. This makes the index more sensitive to the chlorophyll content in the green parts of the crops.
MSI (Moisture Stress Index)
This index is sensitive to the increase in water content in the leaves. The MSI index is used to detect water scarcity stress and is a good indicator of crop conditions often used for crop modeling, fire analysis, and ecosystem physiology. High values indicate water stress and lower water content.
LAI (Leaf Area Index)
This index is used to estimate leaf area and to predict plant growth and yield.
Types of crop satellite imagery
|Satellites and technology providers||Type of satellite imagery||Repeat cycle||Spatial resolution|
|Landsat4-9||RGB, MULTISPECTRAL, PANCHROMATIC||8 days||15, 30, 100 m|
|Sentinel-2,||RGB, MULTISPECTRAL||5 days||10 m|
|Planet Scope||RGB, MULTISPECTRAL||Daily||3-4 m|
|Planet SkySat||PANCHROMATIC, MULTISPECTRAL||Daily||0.5, 0.71-0.82 m|
|Airbus Pleiades||MULTISPECTRAL||Daily||0.5 m|
|Pleades NEO||PANCHROMATIC, MULTISPECTRAL||40 minutes after request||0.3 m|
|ICEEYE||SYNTHETIC APERTURE RADAR||Daily||0.25 m|
Satellite imaging use-cases
Here are some important use of satellite images in crop monitoring:
The heat emitted by ground-level objects can be seen in thermal photography taken from the air, which also reveals temperature variations that correspond to crop stress. The removal of unnecessary elements, such as pivot equipment, from the image through calibration and image correction prevents data skew.
Cooler regions show purple and warmer regions appear yellow in the final imagery. Thermal imaging is helpful for finding leaks, jams, and other irrigation problems since water cools vegetation. Thermal imaging aids growers in identifying pre-symptomatic disease and pest pressures and responding with more timely and focused interventions by revealing tiny changes in leaf surface temperature.
Agriculture must take into account soil moisture. For precision farming applications (at the scale of individual fields) and with the anticipation of improving crop yield modeling, the availability of high-resolution soil moisture maps is especially crucial.
Due to the poor spatial resolution and shallow depth of the observations, soil moisture products generated from satellites have so far been employed sparingly in farm- or field-scale agricultural decision support. However, if it can deliver pertinent data on acceptable temporal and spatial dimensions, satellite-derived soil moisture is anticipated to have much potential.
High-resolution satellite imaging from low-orbit satellites has recently become more developed and accessible, providing another potential for phenotyping applications. This paper illustrates how satellite photography is used in crop phenotyping and agricultural production, and it identifies plant features that can be assessed using high-resolution satellite data.
The paper covers the benefits of using satellite-based phenotyping in crop breeding programs as well as the drawbacks, such as cloud blockage. It also explores potential uses for high-resolution satellite imaging as a phenotyping tool in the future.
In order to help plant breeders choose high-yielding, stress-tolerant varieties that can help meet global food demand while coping with climate change, high-resolution satellite imagery can be used as a phenotyping tool for the evaluation of crop varieties.
Applications of satellite imagery
GeoPard capabilities and satellite imaging applications offer the users to visualize, normalize, analyze and derive insights directly from the agricultural plots in order to improve crop production. This data utilization can be done with the help of ready-to-use GeoPard algorithms or by creating your own algorithms to make them useful for agronomy.
These algorithms allow evaluation of crop growth, stress, etc. (see photo) or even create prescription maps: for example Nitrogen VR application maps, Crop Protection spraying application maps.
Frequently Asked Questions
1. How to get satellite imagery for farm?
To get it for your farm, research providers, sign up, and access their database or portal. Specify your farm’s location and desired parameters to retrieve relevant images for agricultural monitoring.
2. Why is satellite imagery helpful to understanding food webs?
It is helpful in understanding food webs due to its ability to provide a broad and comprehensive view of ecosystems. By capturing large-scale images of land and water bodies, it allows scientists to observe and analyze the spatial distribution of various habitats and resources.
This, in turn, aids in studying the interactions between different species, identifying key feeding relationships, and comprehending the flow of energy through food webs.
It helps unravel complex ecological dynamics, contributing to a deeper understanding of ecosystem functioning and conservation efforts.
3. How expensive is satellite imagery?
Its cost varies depending on several factors. These include the provider, resolution, frequency of acquisition, and extent of coverage needed. Prices can range from affordable options for low-resolution imagery to more expensive options for high-resolution and real-time monitoring.
Additionally, specialized services or customized data requests may incur additional costs. It is advisable to explore different providers and their pricing models to find a satellite imaging solution that aligns with your specific requirements and budget.
4. What is infrared satellite imagery? How to read it?
It captures the infrared radiation emitted by objects and surfaces on the Earth’s surface. It provides valuable insights into temperature variations and thermal patterns.
To read infrared satellite imaging, one must understand that warmer objects appear brighter in the image, while cooler objects appear darker. By analyzing these temperature variations, one can assess cloud formations, identify land and water temperature disparities, detect wildfires, and even monitor ocean currents.
Understanding the color scale and interpreting the brightness levels on the imagery helps in extracting meaningful information from infrared satellite images.Satellite Imagery