Terrestrial Sciences
Applications
Net Eco System productivity –MODIS
The Carnegie-Ames-Stanford Approach (CASA) terrestrial ecosystem model is implemented for simulating long-term monthly Net Primary Productivity (NPP), Net Ecosystem Productivity (NEP), Soil Respiration, Soil Organic Content and associated CO2exchange parameters between India terrestrial ecosystem and atmosphere at 5 km grids during 2001-2014. This model is driven by time varying normalized difference vegetation index (NDVI), climate parameters (air-temperature, precipitation, solar radiation) and land cover and soil attribute maps.
Net primary productivity –GIMMS
The Carnegie-Ames-Stanford Approach (CASA) terrestrial ecosystem model is implemented for simulating long-term monthly Net Primary Productivity (NPP), Net Ecosystem Productivity (NEP), Soil Respiration, Soil Organic Content and associated CO2 exchange parameters between India terrestrial ecosystem and atmosphere at 5 km grids during 1981-2006. .
Net primary productivity – MODIS
The Carnegie-Ames-Stanford Approach (CASA) terrestrial ecosystem model is implemented for simulating long-term monthly Net Primary Productivity (NPP), Net Ecosystem Productivity (NEP), Soil Respiration, Soil Organic Content and associated CO2 exchange parameters between India terrestrial ecosystem and atmosphere at 5 km grids during 1981-2006.
Monitoring dust storms in Thar Desert region
Dust storms are common in arid and semi-arid areas of Thar Desert region. Minute dust particles eroded from Indian Thar desert region especially during the summer months create a blanket of dust haze over western India and the Indo-Gangetic Plains. Study has been carried out over Thar Desert region using INSAT 3Dand MODIS L1B satellite data. Several algorithms such as IR Split window technique, Normalized Difference Dust Index (NDDI), Sand Dust Index, Band Rationing, Modified Normalized Vegetation Index and various band compositions have been applied to discriminate dust pixels from other features. Infrared Difference Dust Index (IDDI), spatial coherence technique have been applied to discriminate dust pixels using INSAT-3D data. The validation of the output shows reasonable agreement with Aerosol Optical Depth (AOD), PM10, wind speed and visibility data.