Abstract
More than 1,000 satellites are launched into space, and they differ in their functions, rotation orbits, resolution, and other properties. Scientists divide the satellites into low-resolution, medium-resolution, high-resolution, and very high-resolution satellites by their properties. Now, the biggest challenge facing scientists is to use some of these different resolution images in their field. To get the expected result, it is very important to analyze the image that needs an which gives more accurate results. Therefore, the main attention of this article is aimed to find the answer to these problems. In this article 3 satellite images which have different resolution are analyzed. The possibility of middle-resolution images of MODIS, high-resolution images of Landsat, and very high-resolution images of WorldView-2 (WV-2) satellites using GIS are analyzed. A research area was the Syrdarya region, and downloaded different images of satellites of this area and compared with using e Cognition. According to the results, a more accurate satellite image for irrigation sets information is WorldView-2 images. In comparison analysis, it shows more accurate properties than other satellite images. As irrigation sets are small objects for the analysis, very high spatial resolution satellite images are important. Water discharge and surface change happen very fast; thus, it requires daily monitoring of the condition. And in this case, the temporal resolution of the MODIS and Landsat is 16 day, and it is a too long period.
Highlights
During the last 20 years, satellites were launched into the sky
Remote sensing consists of four resolutions, and satellite images differ according to these resolutions [17]: spatial, spectral, radiometric, temporal
The spatial resolution depends on the orbit where it is flying
Summary
During the last 20 years, satellites were launched into the sky. Those satellites were sending information about the Earth with different possibilities and properties. The main problem of the branches now is to find satellite information that gives more accurate results in analysis and financially accessible. The advantages of WV2 bands for land classification and especially for water object classifications (to analyse their width, length, blur, depth, and determining the amount of sediment in it) are given in detail on the website digitalglobe.com and in the articles of Tarantino [26], Pueto et al [22], Elshaarawy et al [11]. New bands of WV2 satellite (especially Coastal and NIR2) are widely used in water objects extraction
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