Latest satellite image - Makó

You can read about the meaing of each indices and how to interpret them by clicking here!

Change detection

Move the interactive slider sideways to compare the last two shots.
You will find all the differences marked in red on the images, below the comparison panels.

RGB satellite image

NDVI - Normalized Difference Vegetation Index

NDWI - Normalized Difference Water Index

NDMI - Normalized Difference Moisture Index

Archived data

Browse previously recorded and processed data from the years below!

Types of data published on Satmapper

During the development of the site, the primary consideration was to provide data that could be useful to agricultural and forestry professionals, municipalities or a particular community, therefore the following types of images were made available:

Piros
(Red)

Zöld
(Green)

Kék
(Blue)

rgb_web

RGB satellite image

The RGB color composite images available on the site are a composition of the RED- GREEN- BLUE band images taken by the Sentinel-2 satellite, as is the case with most digital images.
The final image undergoes brightness and gamma correction, therefore the displayed colors may differ slightly from reality.

Useage of the RGB satellite image:
These kind of images provide a spectacular snapshot of the examined area, making a lot of detail become visible.

ndvi_example

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NDVI

NDVI - Normalized difference vegetation index

NDVI is one of the most commonly used indices in precision agriculture. Its value is calculated from the ratio of the RED and NIR (near-infrared) light reflected by the vegetation on the surface. If the studied area is covered with healthy vegetation, the reflected red light decreases compared to near-infrared, while in the case of withered plants, the two lights are reflected in almost equal proportions.
NDVI map values can range from -1.0 to + 1.0 and can be calculated as follows:

NDVI = (NIR-RED) / (NIR+RED)

By assigning the color scale shown below the images to the NDVI values calculated from the satellite images, the areas with different vegetation can also be visually separated.

  • The greener an area, the higher its vegetation index, which indicates that the area is covered with healthy green vegetation, and photosynthetic activity is adequate.
  • Pale green or yellowish areas indicate reduced or no photosynthetic activity. In reality, these areas are typically fields without vegetation (e.g., roads, buildings, fallow land) or where vegetation is under stress (e.g., suffering from nutrient or water deficiencies, possibly affected by pests or diseases). Vegetation still in the growing phase as well as the ones that are just drying up appear in the same color.
  • Areas marked in red and orange may indicate water surfaces, lakes, rivers, or snow.

Usage of NDVI satellite image:
It helps to monitor the development of vegetation over time (e.g. monitoring of the condition of crop areas, detection of illegal logging), shows whether there are stressed plants within an area, thus helping with targeted irrigation, spraying or fertilization.

ndwi_example

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NDWI

NDWI - Normalized difference water index

With the help of NDWI, open water surfaces such as lakes, rivers and inland waterbodies can be visualized.
Its value is calculated from the ratio of the reflected GREEN and near infrared NIR (near-infrared) light.
NDWI map values can range from -1.0 to + 1.0, but are often displayed between -0.8 and +0.8.
NDWI values are calculated as follows:

NDWI = (GREEN-NIR) / (GREEN+NIR)

Where a significant amount of water is present, the NIR value decreases since water absorbs the near-infrared spectrum excellently, resulting an NDWI value around +1.0.
The color scale below the images is designed so that values below 0 are not visible, only those above, aiming for a better visualistaion experience. By assigning NDWI values calculated from satellite images to the color scale, the water-covered areas become visible.

  • The darker the color of a marked area, the greater the amount of water is indicated by the NDWI map

Usage of NDWI satellite image:
It helps identify larger waterbodies like lakes, rivers and inland waters. 
By monitoring the NDWI over time, we can get an idea of the continuous changes in the bed of our lakes and rivers.

ndmi_example

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NDMI

NDMI - Normalized difference moisture index

With the help of the NDMI index, we can obtain information on wet and dry areas, which can help in the economical irrigation of plants.
NDMI values can be calculated in several ways, but for Sentinel-2 satellites, it is advisable to use Narrow NIR and SWIR (short-wavelength infrared) images for the calculation.
NDMI map values can range from -1.0 to + 1.0, but are often displayed between -0.8 and +0.8.
NDMI values are calculated as follows:

NDMI = (Narrow NIR-SWIR) / (Narrow NIR+SWIR)

The magnitude of the Narrow NIR reflection coming from the surface depends on the condition of the leaves of the vegetation, but does not depend on their water content, while the reflection value of the SWIR is greatly influenced by the water content in the leaves.
By assigning NDMI values calculated from satellite images to the color scale below the images, wet and dry areas become visible.

  • The darker the color of the examined area, the higher the moisture content.
  • Green areas refer to urban environments or artificial roads.
  • Areas sloping from yellow to red indicate a dry surface.

Usage of NDMI satellite image:
It helps to monitor the moisture content of the surface. Using NDMI, the areas to be irrigated as well as the parts affected by drought become visible.

The data published on the Satmapper.hu website is for information purposes only!

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