Digital Earth Pacific Annual Surface Reflectance Composite (Landsat, GeoMAD, 30 m)

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[...]

DE Africa's Sentinel-1 backscatter product is developed to be compliant with the CEOS Analysis Ready Data for Land (CARD4L) specifications. The Sentinel-1 mission, composed of a constellation of two C-band Synthetic Aperture Radar (SAR) satellites, are operated by European Space Agency (ESA) as part of the Copernicus Programme. The mission currently collects data every 12 days over Africa at a spatial resolution of approximately 20 m. Radar backscatter measures the amount of microwave radiation reflected back to the sensor from the ground surface. This measurement is sensitive to surface roughness, moisture content and viewing geometry. DE Africa provides Sentinel-1 backscatter as Radiometrically Terrain Corrected (RTC) gamma-0 (0) where variation due to changing observation geometries has been mitigated. The dual polarisation backcastter time series can be used in applications for forests, agriculture, wetlands and land cover classification. SAR's ability to 'see through' clouds [...]

Digital Earth Africa’s Monthly NDVI Anomaly service provides estimate of vegetation condition, for each caldendar month, against the long-term baseline condition measured for the month from 1984 to 2020 in the NDVI Climatology. A standardised anomaly is calculated by subtracting the long-term mean from an observation of interest and then dividing the result by the long-term standard deviation. Positive NDVI anomaly values indicate vegetation is greener than average conditions, and are usually due to increased rainfall in a region. Negative values indicate additional plant stress relative to the long-term average. The NDVI anomaly service is therefore effective for understanding the extent, intensity and impact of a drought.Abrupt and significant negative anomalies may also be caused by fire disturbance.

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.