1. NDVI is an index that quantifies the photosynthetic capacity of vegetation. It is derived from visible and near-infrared reflectance measurements made by Advanced Very High Resolution Radiometer (AVHRR) sensors onboard NOAA's polar orbiting satellites (in this case NOAA-17). Taken as time series measurements, NDVI indicates the response of vegetation to seasonal and interannual variations in climate.
2. SST data are a blend of direct observations from ships, buoys, satellite imagery also from National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) instruments, and SSTs simulated by sea-ice cover. The monthly optimum interpolated fields were derived by a linear interpolation of the weekly fields to daily fields, and then averaging daily values over a month.
All anomaly fields (as shown here) are derived by subtracting the monthly values from the respective long-term monthly means.", "items": [], "extra_data": {} }, { "id": 349499, "url": "https://svs.gsfc.nasa.gov/3905/#media_group_349499", "widget": "Single image", "title": "", "caption": "", "description": "This map shows combined anomalies in Sea-Surface Temperatures(SST)(oceans) and Normalized Difference Vegetation Index (NDVI) (land), observed globally for January 2007. The unusually high Sea Surface Temperatures (red) sustained in the western equatorial Indian Ocean and the central and eastern equatorial Pacific Ocean since the fall of 2006 caused an anomalous growth in vegetation (green-purple), in Eastern Africa due to persistent and heavy rainfall. 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"NASA/GSFC" } ] }, { "role": "Scientist", "people": [ { "name": "Assaf Anyamba", "employer": "USRA" }, { "name": "Jennifer Small", "employer": "SSAI" } ] } ], "missions": [], "series": [], "tapes": [], "papers": [ "PLOS Neglected Tropical Disease (PLOS NTDs) Article: Climate Teleconnections and Recent Patterns of Human and Animal Disease Outbreaks", "PLOS Neglected Tropical Disease (PLOS NTDs) Article: Climate Teleconnections and Recent Patterns of Human and Animal Disease Outbreaks" ], "datasets": [ { "name": "NDVI", "common_name": "", "platform": "NOAA", "sensor": "AVHRR", "type": "Other", "organizations": [], "description": "", "credit": "", "url": "", "date_range": "January 2007 Collection: 2003-2008" }, { "name": "Sea Surface Temperature", "common_name": "", "platform": "NOAA", "sensor": "AVHRR", "type": "Other", "organizations": [], "description": "", "credit": "", "url": "", "date_range": "January 2007 Collection: 1982-2008" } ], "nasa_science_categories": [ "Earth" ], "keywords": [ "Africa", "Diseases/Epidemics", "Earth Science", "East Africa", "Environmental science", "Human Dimensions", "Human geography", "Human Health", "Location" ], "recommended_pages": [], "related": [ { "id": 3807, "url": "https://svs.gsfc.nasa.gov/3807/", "page_type": "Visualization", "title": "Predicting Disease Outbreaks from Space", "description": "These visualizations were created for the May 18, 2012 Library of Congress Talk Predictiding Disease Outbreaks from Space. In this talk NASA scientist Assaf Anyamba, will present how using remote-sensing data we can see links among weather, diseases and famine.An early warning system more than a decade in development successfully predicted the 2006-2007 outbreak of the deadly Rift Valley Fever (RVF) in East Africa and subsequent outbreaks in Sudan (2007) and South Africa (2008-2011). RVF is a deadly hemorrhagic disease transmitted by mosquitoes that infects livestock and human populations episodically. An international team of research scientists, public-health professionals, agricultural specialists and military personnel had worked for a decade to successfully predict when and where an outbreak of RVF would occur. || ", "release_date": "2011-08-31T00:00:00-04:00", "update_date": "2025-01-05T22:03:27.946904-05:00", "main_image": { "id": 486091, "url": "https://svs.gsfc.nasa.gov/vis/a000000/a003800/a003807/DiseaseRainfallSST60fps_3397.jpg", "filename": "DiseaseRainfallSST60fps_3397.jpg", "media_type": "Image", "alt_text": "This visualization shows the relationships between Sea Surface Temperature (SST) Anomaly, Rainfall Anomaly and Rift Valley Fever (RVF) outbreaks in Africa during 2005-2010.", "width": 1920, "height": 1080, "pixels": 2073600 } } ], "sources": [], "products": [], "newer_versions": [], "older_versions": [], "alternate_versions": [] }