Earth and Climatic Studies

Annual Crop Land Information

The annual cropland products were the integrated outcome of the Land use land cover (LULC) output generated at 56m resolution under National Level LULC mapping on 1: 2,50,000 scale using multi-temporal AWiFS project. The study involved use of multi temporal AWiFS data covering Kharif (Aug –Nov), Rabi (Jan- Mar), Zaid (April- May) seasons to address spatial and temporal variability in cropping pattern and other land cover classes.

Seasonal information at annual scale representing Kharif, Rabi, Net sown area and fallow are during 2005-06 to 2015-16 are hosted on Bhuvan-Annual Crop Land.

Select NICES Project – Terrestrial Sciences – Annual Crop land data set, List and view the data of the selected time period. Technical Document(hyper link :Annual Crop land)

Broadband Snow Albedo

Broad band Snow albedo is an important geophysical parameter for studies related to weather, climate, and hydrometeorology and so on. Snow has the highest albedo in nature and hence has a significant influence on surface energy budget and on Earth's radiative balance Seasonal information at Broad Band Albedo during 2014-2018 are hosted on Bhuvan-Broadband Snow Albedo.

Select NICES Project – Terrestrial Sciences – Broad band Snow Albedo, List and view the data of the selected time period. Technical Document(hyper link : Broad Band Snow Albedo)

Water Bodies Fraction

Water Bodies consists of all surface water bodies viz. reservoirs, irrigation tanks, lakes, ponds, and rivers / streams. The area under water bodies is dynamic in natures and hence multi-temporal satellite data was used for monitoring. An automated extraction algorithm developed is used for quick processing of satellite data acquired from Resourcesat -1, Resourcesat-2 AWiFS sensors (56m spatial resolution) which enables the generation of water body layer.

Fortnightly Water bodies Fraction information at during 2014-2018 are hosted on Bhuvan-Water bodies Fraction.

Select NICES Project – Terrestrial Sciences –Water Bodies Fraction, List and view the data of the selected time period. Technical Document (hyper link :Water Bodies Fraction)

Forest Fire Regime

Fire regime over India was analysed using the MODIS fire record from 2003 to 2017.The analysis used AQUA MODIS daytime fires for the 2003 - 2017 time period. Only detections with a detection confidence of over 10% and flagged as forest by using the NRSC forest fraction layer were used in the analysis.

Forest Fire Regime consisting of Average Fire Density, Fire Period and are hosted on Bhuvan-Forest Fire Regime.

Select NICES Project – Terrestrial Sciences – Forest Fire Regime, List and view the data of the selected parameter. Technical Document (hyper link : Water Bodies Fraction)

Forest Fraction Cover

The status of global forest cover has large uncertainty for several continents on earth owing to the paucity of comprehensive studies related to long term forest cover change. The aim of the present work is to prepare a nation-wide multi-date forest cover database which describes and quantifies historical changes in forests of India.

Forest Fraction cover during 1930, 1975, 2013 are hosted on Bhuvan-Forest Fraction Cover.

Select NICES Project – Terrestrial Sciences – Forest Fraction Cover. List and view the data of the selected time period. Technical Document(hyper link : Forest Fraction Cover)

Forest Type Grid

Vegetation classification is a prerequisite for understanding carbon stocks, biodiversity, sustainable use of natural resources and global change. This study has classified forest types of India based on multi-season Resourcesat-2 Advanced Wide Field Sensor (AWiFS) data of 2013.

Forest type Grid (5Km) information is hosted on Bhuvan-Forest Type Grid

Select NICES Project – Terrestrial Sciences – Forest Type Grid –Select the Forest Type and view the data. Technical Document(hyper link : Forest Type Grid)

Hydrological products

Hydrological modeling (Variable Infiltration Capacity Model) framework has been setup at 9min (~16.5km & 13709 grids) grid level for the entire country. Model specific input parameters (Soil, Vegetation, Routing) are prepared for the entire country at 9min grid level . Historical meteorological data has been organized (1951-2013) from various sources and VIC model specific meteorological inputs are prepared. The model performance has been optimized through calibration of model estimated runoff with measured stream discharge (CWC) using historic gridded meteorological products from IMD (1976-2005) for selected river basins.

Long-term (1951-2013) hydrological fluxes (Surface runoff, Evapotranspiration and Soil moisture) have been generated at 9min grid level for the entire country. Since Jun 2014, near-real-time meteorological data (Rainfall, Temperature, etc.) are collected and processed on daily basis and model computations are being carried out at daily time-step. National scale, 9min grid-wise surface runoff, evapotranspiration, soil moisture are being estimated with two-day time lag

Geo-spatial products of the daily water balance components are being hosted on Bhuvan-Hydrological Products

Select NICES Project – Terrestrial Sciences – Forest Type Grid –Select the Forest Type and view the data. Technical Document(hyper link : Hydrological products)

Indian Soil Datasets

Soil properties (soil depth, soil texture, soil carbon) are important for their key role in supporting ecosystem services, plant growth, water availability and maintaining carbon stocks. Soil depth is defined as the depth (in cm) to a lithic or paralithic contact (USDA Soil Survey Manual). Soil texture in dicates the relative content of particles of various sizes, such as sand, silt and clay in the soil. Texture influences the ease with which soil can be worked, the amount of water and air it holds, and the rate at which water can enter and move through soil.

Inventories of soil carbon stocks are needed to assess the possible impacts of global climate change on soil organic, inorganic carbon content in relation to potential green house gas emissions.

Indian soil datasets representing soil texture, soil depth and mean soil carbon density are hosted on Bhuvan-Indian Soil datasets

Select NICES Project – Terrestrial Sciences – Indian Soil Data sets –Select the parameter and view the data. Technical Document(hyper link :Indian Soil datasets)

Land Degradation

The information on land degradation is needed for a variety of purposes like planning reclamation programs, rational land use planning, for bringing additional areas into cultivation and also to improve productivity levels in degraded lands. Synoptic coverage in narrow and discrete spectral bands provided by space borne sensors at regular interval enabled inventorying degraded land and monitoring their temporal behavior at operational level.

Land Degradation categories representing salt affected, water logging, wind erosion, water erosion regions are hosted on Bhuvan-Land Degradation.

Select NICES Project – Terrestrial Sciences – Land Degradation –Select the parameter and view the data. Technical Document(hyper link : Land Degradation Dataset)

Meso scale Compatible Inputs for MM-5

IRS-P6 AWiFS derived gridded land use/land cover (LU/LC) data over Indian region as input to mesoscale models (MM5 and WRF). So far 25 land use/land cover categories of USGS derived global coverage with different resolutions data are used to run mesoscale models. IRSP6 AWiFS derived LU/LC data with 56m basic resolution has been scaled to 5, 2 minute & 30 second resolutions. Indian region of USGS data is replaced with AWiFS derived data and made compatible to MM5 & WRF model.

The resultant product is a global USGS LU/LC data with the Indian region replaced by the 56m resolution AWiFS based LU/LC adopted for mesoscale models, which is more accurate and updated at annual basis for the time period 2004-2005 to 2012-2013 (9 Cycles). Meso scale compatible LULC inputs for MM 5 model for the years 2005-06 to 2015-16 in the spatial resolutions of 30 second, 2 min and 5 min are hosted on Bhuvan-LULC-MM5.

Select NICES Project – Terrestrial Sciences –Mesoscale compatible inputs for MM 5 , List and view the data of the selected time period. Technical Document(hyper link :Mesosacle-MM5 )

Meso scale Compatible Inputs for WRF

IRS-P6 AWiFS derived gridded land use/land cover (LU/LC) data over Indian region as input to mesoscale models (MM5 and WRF). So far 25 land use/land cover categories of USGS derived global coverage with different resolutions data are used to run mesoscale models. IRSP6 AWiFS derived LU/LC data with 56m basic resolution has been scaled to 5, 2 minute & 30 second resolutions. Indian region of USGS data is replaced with AWiFS derived data and made compatible to MM5 & WRF model.

The resultant product is a global USGS LU/LC data with the Indian region replaced by the 56m resolution AWiFS based LU/LC adopted for mesoscale models, which is more accurate and updated at annual basis for the time period 2004-2005 to 2012-2013 (9 Cycles). Meso scale compatible LULC inputs for WRF model for the years 2005-06 to 2015-16 in the spatial resolutions of 30 second, 2 min and 5 min are hosted on Bhuvan-LULC-WRF.

Select NICES Project – Terrestrial Sciences –Mesoscale compatible inputs for WRF, List and view the data of the selected time period. Technical Document (hyper link: Mesosacle-WRF)

Net Eco System 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.

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. Besides that a number biome-specific parameters (such as Light Use Efficiency, C/N ratio) in the model are tuned to the values for accounting regional heterogeneity of the ecosystem response accurately. NPP and NEP products were analyzed for understanding seasonal, inter-annual and climatic variability of these parameters over India and published in the following references.

Net Ecosystem Productivity derived from GIMMS datasets is hosted on Bhuvan-NEP-GIMMS

Select NICES Project – Terrestrial Sciences – Net Primary productivity – GIMMS. Technical Document(hyper link :Net Productivity)

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. Besides that a number biome-specific parameters (such as Light Use Efficiency, C/N ratio) in the model are tuned to the values for accounting regional heterogeneity of the ecosystem response accurately. NPP and NEP products were analyzed for understanding seasonal, inter-annual and climatic variability of these parameters over India and published. This is the second edition product of NPP &NEP. It is simulated from 2001-2014 and the NDVI data is derived from MODIS in place of the GIMMS NDVI in the first edition.

Net Ecosystem Productivity derived from MODIS datasets is hosted on Bhuvan-NEP-MODIS

Select NICES Project – Terrestrial Sciences – Net Ecosystem productivity MODIS.Technical Document (hyper link: Net Productivity)

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.

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. Besides that a number biome-specific parameters (such as Light Use Efficiency, C/N ratio) in the model are tuned to the values for accounting regional heterogeneity of the ecosystem response accurately. NPP and NEP products were analyzed for understanding seasonal, inter-annual and climatic variability of these parameters over India and published in the following references.

Net Primary Productivity derived from GIMMS datasets is hosted on Bhuvan-NPP-GIMMS

Select NICES Project – Terrestrial Sciences – Net Primary productivity – GIMMS. Technical Document(hyper link :Net Productivity)

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 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. Besides that a number biome-specific parameters (such as Light Use Efficiency, C/N ratio) in the model are tuned to the values for accounting regional heterogeneity of the ecosystem response accurately. NPP and NEP products were analyzed for understanding seasonal, inter-annual and climatic variability of these parameters over India and published. This is the second edition product of NPP &NEP. It is simulated from 2001-2014 and the NDVI data is derived from MODIS in place of the GIMMS NDVI in the first edition.

Net Primary Productivity derived from MODIS datasets is hosted on Bhuvan-NPP-MODIS

Select NICES Project – Terrestrial Sciences – Net Primary productivity – GIMMS. Technical Document(hyper link :Net Productivity)