Dataset Metadata User Interface Developers API

ESI

The Evaporative Stress Index (ESI) describes temporal anomalies in evapotranspiration (ET), highlighting areas with anomalously high or low rates of water use across the land surface. Here, ET is retrieved via energy balance using remotely sensed land-surface temperature (LST) time-change signals. LST is a fast-response variable, providing proxy information regarding rapidly evolving surface soil moisture and crop stress conditions at relatively high spatial resolution. ESI values quantify stand...

CHIRPS

Scientists at Famine and Early Warning System (FEWS NET) who are members of the SERVIR Applied Sciences Team used 30+ years' (1981-present) worth of multiple satellite data sources and ground observations to produce an unprecedented, global, spatially and temporally consistent and continuous 30-year record of satellite-derived rainfall data. Spanning 50°S-50°N (and all longitudes), CHIRPS incorporates 0.05° resolution satellite imagery with in-situ station data to create gridded rainfall time se...

NDVI

Normalized Difference Vegetation Index products were computed from cloud-free composites of satellite images from Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 Operational Land Imager (OLI) for 2000 and 2014, covering the Lower Mekong region. The products were produced by NASA Stennis Applied Science Program and computed for use by SERVIR-Mekong implementers, partners, and beneficiaries....

IMERG

This metadata entry represents a set of SERVIR-hosted web mapping services of precipitation data from the Integrated Multi-satellitE Retrievals (IMERG) for Global Precipitation Mission (GPM). The IMERG algorithm intercalibrates, merges and interpolates “all” satellite passive microwave precipitation estimates, together with microwave-calibrated infrared (IR) satellite estimates, monthly precipitation gauge analyses, and potentially other precipitation estimators at fine time and space scales for...

Seasonal Forecast

Forecasts of future precipitation are also critical to decision-makers. The NMME dataset, a compliation by National Oceanic and Atmospheric Administration (NOAA), reflects cutting edge work on seasonal forecasting. A SERVIR AST project has taken the NMME data and performed bias correction and spatial disaggregation using standard, well-accepted techniques to generate daily, 180-day temperature and precipitation forecasts for the entire globe. These seasonal forecasts, along with the CHIRPS histo...

USDA SMAP

The NASA-USDA Enhanced SMAP Global soil moisture data provides soil moisture information across the globe at 10-km spatial resolution. This data set includes: surface and subsurface soil moisture (mm), soil moisture profile (%), surface and subsurface soil moisture anomalies (-).The data set is generated by integrating satellite-derived Soil Moisture Active Passive (SMAP) soil moisture observations into the modified two-layer Palmer model using a 1- D Ensemble Kalman Filter (EnKF) data assi...