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FEWS NET Software Tools

EWX

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.


DSM

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.


GeoWRSI

The GeoWRSI is a geo-spatial, stand-alone implementation of the Water Requirements Satisfaction Index (GeoWRSI), as it is implemented by the USGS for the FEWSNET Activity. The program runs a crop-specific water balance model for a selected region in the world, using raster data inputs. The program produces a range of outputs which can either be used qualitatively to help assess and monitor crop conditions during the crop growing season, or can be regressed with yields to produce yield estimation models and yield estimates. Other tools are available to post-process the GeoWRSI outputs so that they can be used in yield estimation models.


Afghanistan MODIS 8-day Snow Cover Extent

The snow cover product from the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra 8-day L3 Global 500m Grid (MOD10A2) data set contains fields for maximum snow cover extent over an eight-day period and a chronology of snow occurrence observations. The MODIS snow cover data are based on a snow mapping algorithm that employs a Normalized Difference Snow Index (NDSI) and other criteria tests for determining snow classification. The algorithm identifies snow by its unique reflectance, absorption, and emittance properties.


Afghanistan Snow Water Equivalent (Basin)

Snow modeling for FEWS NET was developed and originally implemented by the NOAA National Operational Hydrologic Remote Sensing Center (NOHRSC), using a spatially distributed land surface model (LSM) operating within the Land Information System (LIS) software framework. LIS was developed to provide a means for high-performance land surface modeling utilizing multiple potential forcings (Kumar et al, 2006). SWE data are currently processed for FEWS NET at the NASA Goddard Space Flight Center (GSFC) using the NOAH version 3.2 LSM. This model simulates important biogeophysical, hydrological, and energy balance processes that occur at the surface, offering a physically-based approach to snow modeling.


Afghanistan Snow Water Equivalent (Dam Location)

Snow modeling for FEWS NET was developed and originally implemented by the NOAA National Operational Hydrologic Remote Sensing Center (NOHRSC), using a spatially distributed land surface model (LSM) operating within the Land Information System (LIS) software framework. LIS was developed to provide a means for high-performance land surface modeling utilizing multiple potential forcings (Kumar et al, 2006). SWE data are currently processed for FEWS NET at the NASA Goddard Space Flight Center (GSFC) using the NOAH version 3.2 LSM. This model simulates important biogeophysical, hydrological, and energy balance processes that occur at the surface, offering a physically-based approach to snow modeling.


Pakistan MODIS 8-Day Snow cover Extent

The snow cover product from the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra 8-day L3 Global 500m Grid (MOD10A2) data set contains fields for maximum snow cover extent over an eight-day period and a chronology of snow occurrence observations. The MODIS snow cover data are based on a snow mapping algorithm that employs a Normalized Difference Snow Index (NDSI) and other criteria tests for determining snow classification. The algorithm identifies snow by its unique reflectance, absorption, and emittance properties.


Pakistan Snow Water Equivalent

Snow modeling for FEWS NET was developed and originally implemented by the NOAA National Operational Hydrologic Remote Sensing Center (NOHRSC), using a spatially distributed land surface model (LSM) operating within the Land Information System (LIS) software framework. LIS was developed to provide a means for high-performance land surface modeling utilizing multiple potential forcings (Kumar et al, 2006). SWE data are currently processed for FEWS NET at the NASA Goddard Space Flight Center (GSFC) using the NOAH version 3.2 LSM. This model simulates important biogeophysical, hydrological, and energy balance processes that occur at the surface, offering a physically-based approach to snow modeling.


Tajikistan MODIS 8-Day Snow cover Extent

The snow cover product from the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra 8-day L3 Global 500m Grid (MOD10A2) data set contains fields for maximum snow cover extent over an eight-day period and a chronology of snow occurrence observations. The MODIS snow cover data are based on a snow mapping algorithm that employs a Normalized Difference Snow Index (NDSI) and other criteria tests for determining snow classification. The algorithm identifies snow by its unique reflectance, absorption, and emittance properties.


Tajikistan Snow Water Equivalent

Snow modeling for FEWS NET was developed and originally implemented by the NOAA National Operational Hydrologic Remote Sensing Center (NOHRSC), using a spatially distributed land surface model (LSM) operating within the Land Information System (LIS) software framework. LIS was developed to provide a means for high-performance land surface modeling utilizing multiple potential forcings (Kumar et al, 2006). SWE data are currently processed for FEWS NET at the NASA Goddard Space Flight Center (GSFC) using the NOAH version 3.2 LSM. This model simulates important biogeophysical, hydrological, and energy balance processes that occur at the surface, offering a physically-based approach to snow modeling.


Iraq Tigris-Euphrates MODIS 8-Day Snow Cover Extent

The snow cover product from the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra 8-day L3 Global 500m Grid (MOD10A2) data set contains fields for maximum snow cover extent over an eight-day period and a chronology of snow occurrence observations. The MODIS snow cover data are based on a snow mapping algorithm that employs a Normalized Difference Snow Index (NDSI) and other criteria tests for determining snow classification. The algorithm identifies snow by its unique reflectance, absorption, and emittance properties.


Iraq Tigris-Euphrates Snow Water Equivalent

Snow modeling for FEWS NET was developed and originally implemented by the NOAA National Operational Hydrologic Remote Sensing Center (NOHRSC), using a spatially distributed land surface model (LSM) operating within the Land Information System (LIS) software framework. LIS was developed to provide a means for high-performance land surface modeling utilizing multiple potential forcings (Kumar et al, 2006). SWE data are currently processed for FEWS NET at the NASA Goddard Space Flight Center (GSFC) using the NOAH version 3.2 LSM. This model simulates important biogeophysical, hydrological, and energy balance processes that occur at the surface, offering a physically-based approach to snow modeling.


GeoCLIM

The GeoCLIM is a spatial analysis tool designed for climatological analysis of historical rainfall and temperature data. The GeoCLIM provides non-scientists with an array of accessible analysis tools for climate-smart agricultural development. These user friendly tools can be used to obtain and analyze climate data, blend station data with satellite data to create more accurate datasets, analyze seasonal trends and/or historical climate data, create visual representations of climate data, create scripts (batch files) to quickly and efficiently analyze similar “batches” of climate data, view and/or edit shapefiles and raster files, and extract statistics from raster datasets to create time series.


Water Point Viewer

The water point map viewer, which monitors 234 water points from Mali to Somalia, will help a range of government and non-government actors understand the current availability of water for livestock and human consumption. This will inform food security analysis, humanitarian assistance planning, and a range of other activities.


Afghanistan eMODIS NDVI irrigated areas (Basin)

eMODIS NDVI irrigated area - Summarized by Basin. Irrigated area maps identify the irrigated cropland across country annually. To map the irrigated areas consistently over time, a geospatial model was developed and driven by remotely sensed Normalized Difference Vegetation Index (NDVI) data. The near-real time smoothed 10-day NDVI composites with intervals every 5 days from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, known as eMODIS, at 250m spatial resolution are used to create seasonal NDVI time series with peak NDVI identified. Seasonal peak values and time series were used in a threshold-dependent decision tree algorithm specific to dry and wet years and applied on pre-classified non-agricultural masked out lands to map irrigated areas. The maps were created as a means of distinguishing the spatial distribution and interannual variability in irrigated areas. Knowledge of such variability and its relation to production can be an important tool in monitoring food security condition.


Afghanistan eMODIS NDVI irrigated areas (Province)

eMODIS NDVI irrigated area - Summarized by Province. Irrigated area maps identify the irrigated cropland across country annually. To map the irrigated areas consistently over time, a geospatial model was developed and driven by remotely sensed Normalized Difference Vegetation Index (NDVI) data. The near-real time smoothed 10-day NDVI composites with intervals every 5 days from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, known as eMODIS, at 250m spatial resolution are used to create seasonal NDVI time series with peak NDVI identified. Seasonal peak values and time series were used in a threshold-dependent decision tree algorithm specific to dry and wet years and applied on pre-classified non-agricultural masked out lands to map irrigated areas. The maps were created as a means of distinguishing the spatial distribution and interannual variability in irrigated areas. Knowledge of such variability and its relation to production can be an important tool in monitoring food security condition.