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Low to medium spatial resolution full-disk images show somewhat less bright polar regions as compared to the ultraviolet.
Crop type mapping using medium spatial resolution imagery data has been considered as one of the most important management tools.
Medium spatial resolution (≥250 m) satellite sensors with daily revisit capability are typically employed for this purpose.
In literature, a decrease of the signal-to-noise ratio (SNR) by roughly one order of magnitude was reported for medium (spatial) resolution holography [50].
To fill the gap, the paper proposes a new plastic greenhouse index (PGI) based on the spectral, sensitivity, and separability analysis of PGs using medium spatial resolution images.
This work identifies low temporal resolution that characterizes the commonly used medium spatial resolution thermal sensors as a major limitation to mapping urban surface temperature.
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Previous research has primarily focused on unmixing medium-spatial resolution multispectral imagery using spectral mixture analysis (SMA) in order to estimate the abundance of urban components.
The result illustrates that the object-based MESMA is promising for unmixing the medium-spatial resolution hyperspectral imagery to quantify the urban composition, and it is an attractive alternative to the traditional pixel-based mixture analysis for various applications.
It is hoped that the results of this study will enhance the application of medium-low spatial resolution images in inundation detection and mapping, and thereby support the ecological and environmental studies of river basins.
In recent missions, classification methods using medium or high spatial resolution data, which are multi-temporal with multiple frequencies, have become more attractive.
This research generally employs medium or coarser spatial resolution data and primarily focuses on the effects of a few classes of land-cover composition and pattern at the neighborhood or larger level using regression models.
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