satellite imagery of Earth
  • Remote sensing

Spatial Resolution In Remote Sensing: Which Is Enough?

The spatial resolution of the images is one of the essential aspects of remote sensing. An image’s level of detail depends on the spatial resolution of satellite used. There’s a common misconception that more detail is better, but the reality is that the amount of detail needed varies a lot depending on the given issue. We’ll research the spatial resolutions of satellite images thoroughly so you can make an informed decision on which one is best for your business.

What Is Spatial Resolution In Remote Sensing?

To put it simply, spatial resolution in remote sensing is the size of one pixel — the smallest spot visible to the sensor. However, a person unfamiliar with the topic may be confused by this simplification. In reality, a remote sensing satellite sensor perceives an image through its elliptical instantaneous field of view (IFOV) , which is then processed into a square pixel. To illustrate, if you look at a photo with a spatial resolution of 30m, you won’t be able to recognize any object smaller than 30m, and you’ll need to look at something much larger than 30m to make out any details at all.

Based on the distance to the object and the equipment’s capabilities, remote sensing can be performed at low, medium, and high spatial resolutions. For example, drones flying close to the ground can capture images with exceptionally high spatial resolution. Satellites, which are much further away from Earth, may nonetheless take high-resolution remote sensing images of the planet’s surface.

Keeping in mind that remote sensing technology is constantly developing, categorization into low, medium, and high spatial resolutions is merely a reference point. In the 1980s, a resolution of 60 meters per pixel on NASA’s Landsat satellite was regarded as relatively high, yet today, it is considered exceedingly low.

What is the finest spatial resolution of a satellite sensor?

30cm is the best spatial resolution option available today for remote sensing using high-resolution commercial satellites.

Let’s explore some real-world examples of spatial resolution in remote sensing. Look at these three remote sensing images of the Jangokh — the neighborhood in Tashkent, Uzbekistan, to notice the spatial resolution difference for yourself. The high-resolution 0.4m/px image from Kompsat-3A lets you clearly see buildings, roads, and even cars, but in most cases you have to pay for that level of detail. The view from low-resolution (30m/px) and medium-resolution (10m/px) photos is much blurrier, but this remote sensing data is free.

comparison of low, medium and high spatial resolution images

So we are already familiar with the glaring contrast between low- and high-resolution remote sensing satellite photographs. While higher spatial resolution gives finer details, they are not always necessary for accurate spatial analysis. In some cases, a medium or even low spatial resolution will do. Let’s get into more depth about the various spatial resolutions of different types of satellites, their practical benefits, and limitations.

Medium And Low Spatial Resolution Remote Sensing

Nowadays, there is a plethora of low- and medium-resolution remote sensing imagery available, primarily from the Sentinel and Landsat satellites. This data, which spans 50 years and includes diverse spectral bands, is publicly available for free and can be used in various contexts.

So what are the drawbacks of low- and medium-resolution remote sensing images, then? They have only one significant flaw, and that is a lack of detail. Further consideration is needed to weigh these advantages and disadvantages.

Massive Free Imagery Collection

It’s easy to get your hands on low- and medium-resolution remote sensing images thanks to the abundance of online resources. The EOSDA LandViewer satellite data web service, by itself, provides access to eight free Earth observation datasets. These datasets come from Sentinel 2, Landsat 8 OLI and TIRS, Landsat ETM+, and MODIS. Through the use of different sources of remote sensing data, users can browse, analyze, and download freshly updated imagery with the following characteristics:

  • spatial resolutions from 10 to 500 m/pixel;
  • revisit period (temporal resolution) from 2 to 16 days;
  • spectral resolution from 4 to 12 bands and the option to create your custom setup of band combinations.

Data From Many Spectral Bands Available For Analysis

The abundance of information that can be gained from the many spectral bands and combinations thereof makes low- and medium-resolution remote sensing photos incredibly helpful despite their apparent lack of detail. This remote sensing data includes insights into a wide range of objects and their properties that would be otherwise inaccessible.

Historical Overview

Our current wealth of medium and low spatial resolution remote sensing imagery is a direct result of the Landsat project, which began over half a century ago. By viewing and analyzing remote sensing satellite images as far back as 1982 in the EOSDA LandViewer, you can gain valuable insight into how your study objects have evolved over time.

Low Level Of Detail

Due to the lack of detail in low- and medium-resolution remote sensing photos, you can distinguish only extensive features like bridges, canals, or street patterns. Even the Colosseum, which is significantly larger than your home, will look like a dot.

Taking these pros and cons into account, it becomes clear that medium and low spatial resolutions will be enough for particular tasks that don’t require high-precision remote sensing imagery. Now, let’s talk about the benefits and drawbacks of high spatial resolution remote sensing.

High Spatial Resolution Remote Sensing

The biggest benefit of high spatial resolution vs. low spatial resolution is the precise level of detail it shows. Additionally, high-resolution remote sensing provides coverage whenever and wherever it is needed. However, higher-quality images are typically more expensive and less available to customers. Another drawback of high resolution remote sensing images is their small territory coverage. Let’s look closer at the pros and cons of high-resolution imagery.

High Level Of Detail

High-resolution remote sensing photography has the apparent benefit of revealing finer details such as individual trees, vehicles, buildings, and more. In EOSDA LandViewer, you may choose from eight data sets from satellites with the highest spatial resolutions, ranging from 5 meters (SPOT 5) to 40 centimeters (Kompsat-3A).

On-Demand Coverage Of Any Location At Any Time

With the help of modern commercial satellites, remote sensing at a specific location at a particular time is now possible. High spatial resolution satellites can be tasked if the remote sensing data you need is not readily available in the database of government satellites following allowed paths.

The EOS SAT, a satellite constellation whose first satellite is scheduled for launch very soon, can provide a view of the client’s site on tasking every other day. It will allow studying natural and anthropogenic factors that are difficult to assess from the ground and enable the quickest possible response to shifts and mitigating disasters.

High Cost

Data from high-spatial-resolution satellites comes with a hefty price tag because of the sophisticated sensors  required to capture usable remote sensing imagery. High-resolution remote sensing photos can be obtained at a reduced cost through reselling systems like EOSDA LandViewer, which only charges customers for the portion of the image that falls within their area of interest (AOI). It is a good bargain compared to the cost of an entire image.

Small Area Coverage

Less ground area is typically captured in a remote sensing image with a higher spatial resolution. Consequently, high-resolution satellite spatial data is ideal for targeted observation and investigation. It would take four images from high spatial resolution satellites like Pleiades-1, Kompsat-3, or SuperView-1 to cover an area the size of London, whereas a single low-resolution Landsat 8 image would capture a zone the size of twenty-five Londons.

Lower Availability

Clouds can make it difficult for satellites to acquire data. But in high-resolution remote sensing, when satellites often move away from a predetermined trajectory, this becomes crucial. As a result, there will be a lot fewer high-resolution images available than there are lower-resolution ones. Furthermore, due to the short history of high-resolution (by today’s standards) remote sensing — since 2010, such images are not optimal for a thorough investigation of the dynamics of diverse phenomena and processes.

So, there are still limitations of high spatial resolution images in remote sensing, even though they offer far more information about the visible objects on the Earth’s surface. Before settling on a spatial resolution, you should carefully weigh the pros and cons of each remote sensing option.

What Is The Ideal Spatial Resolution For You?

Unfortunately, there is no “one size fits all” choice for spatial resolution in remote sensing. To help you decide whether or not to pull out your wallet, consider the following:

  • What resources are required for your project?
  • How precise do visual representations of remote sensing data have to be?
  • In what spectral bands do you need the images to be delivered?

Although they both provide imagery of the same domains, low and high spatial resolutions of satellites serve distinct purposes, meaning that different industries can benefit from low-res and high-res remote sensing in different ways.

The role of spatial resolution in land cover mapping is significant since it is crucial to acquiring data from remote sensing satellite imagery. Biodiversity research, climate change analysis, wildfire prevention, environmental modeling, and the creation and evaluation of land use policies rely heavily on understanding land cover  spatial patterns.

High Resolution Uses

High-resolution remote sensing images, with a spatial resolution 1-5 meters per pixel, or even less than 1 meter per pixel, which stands for very high spatial resolution, come in handy in areas where you need maximum detail for relatively small areas, namely:

  • detection of crop diseases or pests in precision agriculture;
  • identification of erosive soil processes;
  • detection of field borders and field mapping;
  • livestock observation and management;
  • deforestation detection and forestry management;
  • detection and mitigation of local anomalies;
  • 3D city modeling.
high spatial resolution satellite image of forested area
High-resolution (0.5m/pixel) image from Kompsat 3 where you can distinguish each individual tree.

Medium Resolution Uses

Medium-resolution photos (5-30 meters per pixel) can be used for tasks that don’t require extreme precision but need extensive coverage remote sensing. The following are some examples of what these might be:

  • crop health and growth monitoring;
  • moisture and nutrient content monitoring;
  • vegetation density monitoring;
  • pest and disease detection;
  • estimation of biodiversity loss in the forest lands;
  • identification of natural anomalies on a large scale;
  • monitoring water bodies;
  • urban expansion analysis.
medium spatial resolution satellite image of agricultural lands
Medium-resolution (10m/pixel) image from Sentinel-2 L2A, allowing you to make out the field boundaries without field details.

Low Resolution Uses

Despite its lack of precision, low spatial resolution remote sensing (30-250 meters per pixel) catches a wide area and adds information by sampling spectral levels missed by higher-resolution methods. Its application areas include:

  • crop growth modeling;
  • predicting yields;
  • trend mapping;
  • large-scale anomaly detection;
  • monitoring infrastructure changes on a large scale.
low spatial resolution satellite image of the Earth's terrains
Low-resolution (30m/pixel) image from Landsat 8 OLI and TIRS displaying the general landscape features of a vast area.

As a result, by selecting the most appropriate spatial resolution for each task, you may get it done more efficiently and affordably.

Spatial Resolution Of EOS SAT Satellite Constellation

Soon, EOSDA will launch into sun-synchronous orbit the first satellite of its EOS SAT remote sensing constellation. These satellites will capture high-resolution imagery and give actionable insights within 24 hours, completing a full-service cycle for the agriculture sector.

EOS SAT remote sensing technology will provide imagery with the following spatial resolutions:

  • Panchromatic — 1.4m/px. Panchromatic images combine the Red, Green, and Blue channels into a single band for higher spatial resolution of satellite images. Although the generated images lack wavelength (color) information, they represent the subject matter in great detail.
  • Multispectral — 2.8m/px. The multispectral remote sensing image in EOS SAT includes 13 spectral bands, allowing you to quantify and analyze spatial patterns to gain a deeper understanding of many agricultural factors. In particular, you can assess vegetation indices, soil moisture levels, weather risks, etc., when using RGB and NIR bands with high spatial resolution satellite images. The SWIR band of multispectral remote sensing sensors is helpful for many different purposes, including but not limited to analyzing land usage and vegetation cover, mapping field borders, identifying crops and monitoring their health, and predicting yields.

EOS SAT perfectly blends high spatial resolution remote sensing and affordable prices for farmers. The unique set of bands will allow us to address agricultural issues that no other remote sensing satellite can. Therefore, farmers will have better access to timely, high-quality data, improving their decision-making and, in the long run, promoting the development of more sustainable agriculture strategies.

About the author:

Natalia Borotkanych Project coordinator

Natalia Borotkanych has been working in the space sector for more than 15 years now. Her experience includes working in business, science, education, and government projects.

Natalia has a PhD in space history, Master’s Degree in Foreign Policy from the Diplomatic Academy of Ukraine, as well as Master’s Degree in Public Management and Administration from National Academy for Public Administration under the President of Ukraine.

Building upon her experience of working in the State Space Agency of Ukraine, Natalia now specializes in helping the state bodies and NGOs to implement the satellite monitoring technologies for solving real-world problems and for smart decision-making.

Natalia is an active science communicator. She is a scientific editor at The Universe. Space. Tech magazine. She also teaches a “Space diplomacy” course at the National Aviation University.

Natalia's experience in project coordination and scientific expertise in the space sector are much appreciated at EOS Data Analytics.

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