Digital Earth Pacific Annual Radar Composite (Sentinel-1, GeoMAD, 10 m)

This dataset provides annual cloud-free composite imagery derived from Sentinel-2 observations across the Pacific region using geometric median and absolute deviation (GeoMAD) methods. The geometric median uses a more robust method of determining the median observation than a simple median, and represents stable surface conditions. Along with the median observation, the GeoMAD produces three measures of variance, or absolute deviations (emad, smad and bcmad), which help to understand how the data change over the time period. For example, some areas, such as bare rock, will change very little, whereas crop land will change more. The dataset is available annually from 2017 and supports monitoring of vegetation, land cover, and coastal environments.

This dataset provides annual cloud-free composite imagery derived from Landsat surface reflectance observations across the Pacific region using geometric median and absolute deviation (GeoMAD) methods. The data are masked for cloud, shadows and other image artefacts using the associated pixel quality product to help provide as clear a set of observations as possible from which to calculate the medians. Along with the geometric median surface reflectance bands, the GeoMAD includes three measures of variability (Euclidean, spectral and Bray- Curtis median absolute deviations: emad, smad and bcmad) and a count of clear observations. The dataset is available annually from 2000 and supports monitoring of vegetation, land cover, and coastal environments.

GeoMAD is the Digital Earth Africa (DE Africa) surface reflectance geomedian and triple Median Absolute Deviation data service. It is a cloud-free composite of satellite data compiled over specific timeframes. The geomedian component combines measurements collected over the specified timeframe to produce one representative, multispectral measurement for every pixel unit of the African continent. The end result is a comprehensive dataset that can be used to generate true-colour images for visual inspection of anthropogenic or natural landmarks. The full spectral dataset can be used to develop more complex algorithms. For each pixel, invalid data is discarded, and remaining observations are mathematically summarised using the geomedian statistic. Flyover coverage provided by collecting data over a period of time also helps scope intermittently cloudy areas. Variations between the geomedian and the individual measurements are captured by the three Median Absolute Deviation (MAD) laye[...]

Synthetic Aperture Radar (SAR) sensor have the advantage of operating at wavelengths not impeded by cloud cover and can acquire data over a site during the day or night. The Sentinel-1 mission, part of the Copernicus joint initiative by the European Commission (EC) and the European Space Agency (ESA), provides reliable and repeated wide-area monitoring using its SAR instrument.Sentinel-1 Monthly Mosaics are analysis-ready product of individual Sentinel-1 acquisitions. Sentinel-1 monthly mosaics are generated from Radiometric Terrain Corrected (RTC) backscatter data, with variations from changing observation geometries mitigated. RTC images acquired within a calendar month are combined using a multitemporal compositing algorithm. This algorithm calculates a weighted average of valid pixels, assigning higher weights to pixels with higher local resolution (e.g., slopes facing away from the sensor). This local resolution weighting approach minimizes noise and improves spatial homogene[...]