Abstract
Remote sensing is providing an increasing number of crucial data about Earth. Systematic revisitation time allows the analysis of long time series as well as imagery utilization in the most interesting moments. Nevertheless, the current huge amount of data makes essential the usage of automatic methods to select the best captures, as many of them are not useful because of clouds, shadows, etc. Because of that, one of the characteristics of the more recent missions is the distribution, along with the spectral data, of a large amount of quality ancillary datasets. These datasets can act synergistically in the aim of selecting the best quality images, but the criteria they provide are not always enough. Indeed, these datasets are often used on a per pixel basis and the spatial pattern of the different spectral bands is forgotten, so ignoring the key information they can provide for our goals. With this aim, our work takes one of the most successful instruments in remote sensing, MODIS, and demonstrates, through geostatistical techniques, that the role of the spatial patterns of the spectral bands can effectively improve image selection in a complex (for climate, relief, and vegetation and crop phenology) region of 63,700 km2. The results show that band 01 (red) is the preferred one, as it achieves a 13% higher success than when only using quality bands criteria: a 94% global accuracy (66 true classifications, and only four omissions and one commission error). A second, important finding, is that the geostatistical selection improves results when using any band, except for band 02 (NIR1), which makes our proposal potentially useful for most remote sensing missions. Finally, the method can be executed in a reasonable computing time due to previously developed high-performance computing techniques.
Highlights
The increasing interest shown by the scientific community in using sensors from the Earth Observation System (EOS) satellites, and MODIS (MODerate Resolution Imaging Spectroradiometer), mainly arises from the need to better understand global changes by monitoring environmental and ecological dynamics at a global scale and at high temporal resolution
This paper explores the influence of the spatial pattern of different MODIS spectral bands and the first component of the principal component analysis (PCA)[25] of two subsets of spectral bands on the variability of the variogram
That means that a simple filter based on the usage of MODIS QA-SDS quality masks disagreed in 12 images with regard to the expert manual classification
Summary
The increasing interest shown by the scientific community in using sensors from the Earth Observation System (EOS) satellites, and MODIS (MODerate Resolution Imaging Spectroradiometer), mainly arises from the need to better understand global changes by monitoring environmental and ecological dynamics at a global scale and at high temporal resolution. In this framework, the free and accessible images provided by MODIS data constitute a large set of ancillary data, essential for many remote sensing (RS) studies. This large swath causes a panoramic distortion whereas partially overlapping scans produce data repetition.[5,6] This feature, known as the bow-tie effect, is exacerbated by Earth’s curvature (bands with a spatial resolution of 250 m at nadir have 1207 m at the image edges, while 1 km resolution bands have 4816 m at the image edges[7])
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