satellite images search
  • Remote sensing

Tips For Improving Satellite Imagery Search

Are you running a satellite images search to look at a city district? Or an agricultural field whose geographic coordinates you’ve captured with a smartphone? Or do you need an image of an uncharted iron mine that can’t be found on a map?

The good news is you can find any of that using sophisticated web tools like EOSDA LandViewer, which allows running quick search across a single satellite image dataset or several at once. To decide which data source is the right kind for you, read our guide to choosing the satellite image spatial resolution.

The possible inputs for a imagery search include: a location name, a set of coordinates, a drawn or uploaded polygon (Area of Interest), a pinpointed area on a map. Sometimes though you would need to blind-search for features and areas where a certain event took place; that’s where extra tools and remote sensing techniques may come in handy. Let’s look at every search scenario you may have and things you should do to avoid the pitfalls.

Satellite Imagery Search By AOI, Name, Address Or Geographic Coordinate

  • Search by Area of Interest (AOI).

    Setting an AOI is the most recommended type of imagery search. You can draw it manually or upload the already existing AOI file in Shapefile, KML or GeoJSON format.

    IMPORTANT: Save the AOI that you’ve drawn or uploaded and next time it’ll only take you to select the saved area from the list to run the search. A great time-saving feature!

    Want to save even more time? Then Subscribe to the saved AOI (one or several) with the preferred satellite images search parameters, and EOSDA LandViewer will send you emails every time a fresh scene of your area is added. It works perfectly for regular monitoring of agricultural fields, forests, or any kind of Earth observations you may need.

    Tip: EOSDA LandViewer also allows bulk uploading of AOIs, which basically means you can get all your areas of interest in a single upload and proceed to search.

    how to draw aarea of interest (AOI) manually in EOSDA LandViewer

    In high-resolution satellite imagery search, AOI fulfills one more function: it serves as the basis for price calculation. Instead of paying for a full image, you can draw an exact Area of Interest, instantly find out the cost of AOI in every matching scene and select the one, saving hundreds (if not thousands) of dollars.

  • Search by name or address.

    In EOSDA LandViewer, any urban features (buildings, streets, roads, landmarks), natural features (mountains, lakes, islands) or any mapped places can be found by their names and addresses. For example, to run a satellite images address search for a well-known Fifth Avenue, just type it in the search box and select it from the dropdown list.

    Tip: add the name of a city, area or country after a comma to filter out all the other places that may have the same name. There’s about a dozen of cities named “Paris” in at least three different countries, so be specific. Alternatively, you can find out the coordinates using satellite maps with GPS coordinates (Google Maps, Bing Maps, etc.) and use them to search for data.

    search AOI by name or address

  • Search by GPS coordinates. A more precise satellite images search can be achieved with the exact latitude and longitude coordinates of your area. WGS84 is the standard reference coordinate system for GPS, and also the one commonly used for georeferencing. All you need to do is enter GPS coordinates in either DMS (53° 32′ 8″ N, 8° 33′ 56″ E) or decimal format (53.535556, 8.565556) into EOSDA LandViewer search box; it will find the area and load all available images

    If you spot anything on an image and want to check it from the ground, you should first get the exact satellite coordinates for address or place. Click on that place on a map and EOSDA LandViewer will reveal its latitude and longitude at the bottom of the screen. Now you know how to find coordinates of an image! If you’re also interested to find out the altitude, first click the “Identify facility” icon in the right menu and then the location to get full AOI by GPS coordinates
  • Search by UTM coordinates. UTM coordinates represent another kind of a reference coordinate system. Unfortunately, they can’t be used to search for images or find out satellite images coordinates of a location in EOSDA LandViewer. Luckily, you can convert your UTM coordinates into latitude and longitude using online tools like this one and proceed to regular image search.

While searching for satellite images, it’s highly important to filter out the unnecessary data at the very beginning. We came up with the most frequent requests from users and, most importantly, with how special search filters and buttons can help in every case:

How do I find 100% cloudless images? Easy! Just set the Cloudiness filter in the Scene Search menu to zero and wait until EOSDA LandViewer finds the best scenes. If nothing is found, then set the filter to 5-10% at least: after all, there’s a chance that clouds cover that bit of an image which you’re not interested in.

How do I find an image for a particular date or period? Whenever you need a scene for a certain date, season or period of time, use the filter for satellite images search by date. In the Scene Search menu click the Date and mark the date(s) you need in the calendar. Pay attention that all the days when the satellite(s) made a pass over your area will be marked in blue.

How do I find an image for certain hour(s) of the day? As of now, there are no satellite images search tools in public access that would allow you to search images by exact time of the day or night (for night-time acquisition satellites). So there are a few things you can do, depending on what you already know:

  • If you know exactly that a satellite acquired an image at a required time of the day, just run your image search by date.
  • If you are not sure if the required image was actually taken, use the Sun Elevation filter along with the Date filter. Find out the exact angle values with the help of a Solar elevation calculator. To check the exact time when an image was acquired, download its metadata file for full information (more details are given below).
  • There are resources that can help you find out the whereabouts of a satellite in a moment in the past or in the near future. USGS maintains records of all the past passes of Landsat 7 and Landsat 8 as well as the Landsat acquisition calendar online. Same can be found for Sentinel-2A and Sentinel-2B: both their archived passes and their upcoming passes. You can also watch the near real-time image acquisitions from Landsats and MODIS sensors with FarEarth Observer.
  • Need a satellite to be in your area at a certain time of the day? Wait just a little bit: soon you’ll be able to use the Tasking option in EOSDA LandViewer and get the high-resolution sensor to take the image you need.

How do I find an image that covers my area in full? Use the special Full AOI coverage toggle button to make your searching for satellite images more effective. But it may be tricky: if your area is too big or falls exactly at the intersection of satellite rows/paths, you won’t find a single image. In this case, we advise you to turn off the Full AOI coverage or change AOI.

How do I search for only Sentinel-2 images (only Landsat/MODIS, etc.)? To narrow down your search to only one image dataset, untick all the other sensors in the Scene Search. Alternatively, you may want to run a satellite imagery search across two, three or more datasets: just tick the boxes next to them.

The All Sensors toggle button will include back all the data sources for you to explore all the available imagery.

How do I find and download metadata file? GIS experts may often need to download the detailed information about an image such as acquisition time, level of processing, cloud coverage statistics, etc. contained in the metadata file. You need to go to the Scene Downloading tab after selecting the image, then switch to the Analytic tab, scroll down to the bottom of the list and tick the box(es) next to metadata file(s).

EOSDA Crop Monitoring

Fields analytics tool with access to high-resolution satellite images for remote problem areas identification!

Satellite Imagery Search By Feature

By feature, we mean a natural or artificial object on Earth’s surface that you are interested to look at from space. Environmental scientists would be looking for images of forests, water bodies, islands or glaciers, while man-made constructions such as roads, buildings, and bridges may be the focus of engineers’ or real estate developers’ search.

Your image search should start from finding the location by name, AOI or satellite coordinates on map as described above. Then the most complicated part follows: choosing the sensor for observing the feature of your interest.

Here is a short guide to choosing the satellite sensor and remote sensing band combination that will help you reveal various features:

Vegetation. Passive satellite sensors of medium resolution such as Sentinel-2, Landsat-8 or CBERS-4 will be most useful for vegetation observations as they contain the spectral bands, which can deliver a variety of information about plants.

To highlight vegetation against other features that may look similar in a Natural Color image, use the Color Infrared band from the Band Combinations tab. For a more in-depth study of plants or trees, consider applying NDVI or other vegetation indexes.

Water bodies. Whenever you need to look at a lake, river, or sea from space, use either passive and active sensors. The active sensors, like lidar and radar, work best for mapping the floods caused by extreme weather conditions as they provide cloudless imagery, unlike the passive ones. But if you decide to go with the passive sensors, consider applying the Land/Water band to better distinguish between water and something that may only look like water.

Man-made constructions. Any high-resolution satellite image dataset is undoubtedly the most accurate source for urban area observations due to a high level of detail, which is essential. However, passive sensors of medium resolution can also be used for monitoring the construction progress on a large scale. With False Color (Urban) band combination applied, you will easily outline all urban features. Like we did with a Sentinel-2 image to highlight a mine in Melita, Greece.

Sentinel-2 satellite image of a mine in Melita, Greece

Snow cover and ice. As with water, snow can also be studied with both passive and active satellite sensors. While active sensors like Sentinel-1 SAR are most often used for ice and glacier monitoring, the passive sensors like Sentinel-2 and Lansats are applied in snow cover observations.

In the latter case, the Normalized Difference Snow Index (NDSI) is recommended for use as Natural Color images can misguide you about the snow amount: clouds are often misinterpreted for snow. To distinguish between the two, you can also use the Snow/Clouds band combination. We tried it on a Sentinel-2 scene of the snow-covered mountains near Sweden’s Kiruna.

natural cover image of snow-covered mountains near Sweden’s KirunaSnow/Clouds band combination image of mountains near Sweden’s Kiruna

You can see on the images above that what looks like snow on a Natural Color image (in the center and bottom to the right) is actually a cloud cover.

If you don’t know the exact location of a feature, activate the Satellite layer (Google Maps) from the Layers list in the left menu. It may not be the most updated but it will provide a detailed mosaic of the globe for the initial search. Let’s imagine you need to find deforested areas in the Amazon basin, which embraces huge territories. With the Google Maps Satellite layer, you’ll be able to identify the largest “fishbone” deforestation areas and proceed to imagery search within those areas.

Satellite layer

Satellite Imagery Search By Event

Satellite imagery has become a go-to source of information whenever a natural or man-made disaster occurs. It is also instrumental for any multitemporal observations of changes and natural phenomena.

If this is your case, you need to start the image search by finding the location and setting the date(s) in the Scene Search. But there’s also a less obvious tip: use the Time-Lapse Animation tool.

Even if you don’t want to make a GIF out of your research, the Time-Lapse Animation will be a shortcut to selecting the right satellite images ‒ especially if years of data are in question. When we decided to look at the shrinking Salton Sea in California, there was no immediately noticeable difference in the shoreline from year to year. It became evident once we looked at the same data with the Time-Lapse tool.

shrinking Salton Sea in California the Time-Lapse tool

You can preview up to 300 satellite scenes from one dataset or several, and tick/untick any of them leaving only the good ones. Once satisfied by what’s left, memorize the image dates, then go back from the Time-Lapse Animation tool to Scene Search and render each image separately.

For short-term phenomena such as wildfires, floods, hurricanes that last for months at their most, you may use the Comparison Slider as an alternative help-tool in your search. It will be extremely helpful for spotting and mapping the smallest changes between Before and After imagery.

Now that you’ve mastered the art of finding satellite data by coordinates, location name, AOI, as well as features and events, it’s time to start your search. Good luck!

About the author:

Nataliia Ivanchuk Scientist at EOS Data Analytics

Natalia Ivanchuk holds a Master’s degree in Applied Mathematics (2013) from the National University of Water and Environmental Engineering. She is an author of 60+ scientific publications, monographs, and other scientific works.

In 2019, she successfully defended a thesis, “Mathematical modeling of filtration processes in soil environments taking into account the influence of elements of engineering structures” and earned the Candidate of Technical Sciences degree.

Currently, Natalia is an Associate Professor at the Department of Computer Sciences and Applied Mathematics at the National University of Water and Environmental Engineering in Rivne, Ukraine. She teaches different programming languages (C++, C#, JS, among others) at the said university.

Natalia is actively engaged in scientific research related to the practical application of new programming algorithms. Programming is also something she likes to do in her spare time.

Her expertise and constant desire to learn and perfect her programming skills, especially in working with Python, has been most beneficial for EOS Data Analytics.

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