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East Africa

Temporally Smoothed NDVI
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Mean Anomaly
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Previous Year Difference
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Percent of Average
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Pentadal (Yearly) Period

Available Tools


DSM (Drought Status Monitor) is an experimental decision support tool based on weather and crop conditions. It incorporates drought monitoring rules that distinguish severity and provide a generalized drought indicator at national and sub-national scales.


The Early Warning eXplorer (EWX) software is an interactive web-based mapping tool that allows users to visualize continental-scale rainfall estimate (RFE), land surface temperature (LST) and normalized difference vegetation index (NDVI) data and anomalies at varied time steps and review time series analyses.

Map Viewer

Interactive map viewers allow users to visualize administrative and crop zone time series of normalized difference vegetation index, dekadal (10-day) rainfall, and seasonal cumulative rainfall, including options for data download.

Product Documentation

eMODIS TERRA Normalized Difference Vegetation Index (NDVI)
(Document updated 07/02/2013)


The U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center distributes a collection of satellite-derived vegetation products generated from the Moderate Resolution Imaging Spectroradiometer (MODIS). These products, known as "eMODIS," respond to operational land monitoring applications requiring near-real time Normalized Difference Vegetation Index (NDVI) data for comparison against historical records. Real-time and historical NDVI products are composited in 10-day intervals every 5 days on a Geographic mapping grid.


eMODIS 10-day maximum-value composite NDVI images at 250m spatial resolution are used to monitor vegetation condition. NDVI is a measure of the density of chlorophyll contained in vegetative cover and is defined as (NIR - RED) / (NIR + RED), where NIR is the near-infrared reflectance and RED is the visible-red reflectance. This vegetation product is calculated from MODIS L1B Terra surface reflectances, corrected for molecular scattering, ozone absorption, and aerosols using MODIS Science Team algorithms.

The NDVI and Anomaly maps are the product of a temporally smoothed 250m NDVI data set. A time series smoothing technique developed by Swets et al. (1999) was used to smooth NDVI composites for the years 2001 to 2010. The technique uses a weighted least squares linear regression approach to "correct" observations that are of poor quality due to clouds or other atmospheric contamination (Figure 1). This smoothed time series was used to derive a 10-year mean NDVI on a pixel-by-pixel basis for each of 72 composite periods per year. As current-year composites become available, they are added to the time series and smoothed, resulting in a smoothed composite comparable to the historical mean for a given 10-day period.

While temporal smoothing can be effective at improving time series data, it can be problematic to implement in real time for areas of persistent cloud cover. Therefore, we've implemented a process that steps back in the time series and replaces data after six composite periods, allowing cloud-free observations to have been obtained. Prior to the final corrected data being made available, interim graphics for all products are masked with cloud flags from the original input data.

eMODIS Readme Support Graphic
Figure 1. This time series NDVI plot, for a single 250m pixel, shows unsmoothed data in blue and temporally smoothed NDVI in red. Dramatic reductions in unsmoothed NDVI data represent clouds and/or other atmospheric contamination. The smoothing algorithm effectively corrects these erroneous NDVI values based on characteristics of the valid NDVI curve.

Image Products

  1. Temporally Smoothed NDVI - Smoothed 10-day NDVI composite.

  2. Mean Anomaly - Anomalies represent a subtraction of the mean NDVI values (2001-2010) for a 10-day period from current-year values for the same period, rendering an image where negative values portray less vigorous vegetation than average, and positive values represent areas that are more vigorous in the current year.

  3. Previous Year Difference - The difference image is a subtraction of the current year NDVI values from those of the previous year.

  4. Percent of Normal - The percent of normal uses the 2001-2010 mean to compare the current composite relative to average conditions.

Spatial Parameters for eMODIS Data

eMODIS NDVI data are stretched (mapped) linearly (to byte values) as follows:
[-1.0, 1.0] -> [0, 200] - Invalid Values: 201 - 255
NDVI = (value - 100) / 100; example: [ (150 - 100) / 100 = 0.5 NDVI ]

Anomaly / Difference Classification:
The absolute difference and anomaly images are stretched from - 0.3 to 0.3 NDVI. The area of relatively no difference is approximately -0.05 - 0.05.

Percent of Normal:
The percent of normal data are expressed as a percent, where values between 95 and 105 indicate average conditions. Values below 95 represent below average vegetation conditions, while those above 105 represent above average conditions.

All data are provided in GeoTIFF format with embedded color tables.

Units: DD (decimal degrees)
Spheroid: WGS84

Pixel size:
x-direction: 0.002 (deg)
y-direction: 0.002 (deg)


Swets, D.L., Reed, B.C., Rowland, J.D. & Marko, S.E. (1999). A weighted least-squares approach to temporal NDVI smoothing. In: 1999 ASPRS Annual Conference: From Image to Information, Portland, Oregon, May 17-21. Proceedings: Bethesda, Maryland, American Society for Photogrammetry and Remote Sensing.