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This Page describes the various GeoPhysical Products generated by NRSC
The basic index for measuring the 'greenness' of the earth's surface is the Normalized Difference Vegetation Index (NDVI), which is basically a calculation of the differences between red and NIR channels. A reasonable estimation of the density and coverage of green vegetation can be determined by measuring how green the earth's surface is. NDVI values range from -1 to 1 and are unit less. Values greater than 0.2 generally denote increasing degrees in the greenness and intensity of vegetation. Values between 0 and 0.2 are commonly characteristic of rocks and bare soil, and values less than 0 sometimes indicate clouds, rain, and snow.
Vegetation indices are used for global monitoring of vegetation conditions and are used in products displaying land cover and land cover changes. These data may be used as input for modeling global biogeochemical and hydrologic processes and global and regional climate. These data also may be used for characterizing land surface biophysical properties and processes, including primary production and land cover conversion.
Global OCM2 vegetation indices are realized from GAC L1B data to provide consistent spatial and temporal comparisons of vegetation conditions. The products are delivered at a spatial resolution of 9km in Geotiff format with geographic projection. These products are validated using MODIS MOD13 NDVI products and the correlation is found to be better than 80%. These products are made freely available and downloadable at Bhuvan
As NDVI time series are cyclical and smooth in nature sudden changes in the NDVI is not due to sudden changes in the target pixel and is assumed to be from atmosphere, clouds etc. So besides the operational NDVI/VF Products , a special processing based on modified FASIR ( Fourier adjusted + Spline fit) method to estimate the pixels contaminated by cloud in NDVI time series was carried out and these filtered products for all local/global NDVI/VF products will be made available in BHUVAN portal.
1.2.1 Vegetation Fraction (VF) & Normalized Difference Vegetation Index (NDVI) – OCM2
Vegetation fraction (VF) is defined as the percentage or fraction of occupation of vegetation canopy in a given ground area in vertical projection. It is popularly treated as a comprehensive quantitative index in forest management and vegetation communities to monitor respective land cover conditions. Field measurement approach has been the traditional method of estimating the vegetation fraction; however, the reliability of such measurements for the vegetation fractional coverage is questionable, besides the high cost. To overcome these, satellite based data are strongly pursued recently. This document describes briefly monthly Vegetation Fraction (VF) Products realized by using Oceansat-2 Ocean Color Monitor (OCM2) sensor. This is a value added product from OCM whose spectral bands are originally designed for ocean color retrieval applications. However, the two-day Repeativity with a wide swath of 1420 km and high radiometric resolution of 12 bits per pixel from the OCM sensor can provide useful information for agricultural applications.
To maximize the occurrence of clear sky pixels, NDVI products are generated for a 15 day period using maximum NDVI compositing technique. The products are delivered at a spatial resolution of 1080mts in Geotiff format with geographic projection. NDVI products are validated using MODIS MOD13 NDVI products and the correlation is found to be better than 95%. These products are made freely available and downloadable at Bhuvan
1.2.2 NDVI - AWIFS
Vegetation indices are realized from Resourcesat-2/AWIFS standard geo-referenced data to provide consistent spatial and temporal comparisons of vegetation conditions over Indian terrain. The products are delivered at a spatial resolution of 250mts in Geotiff format with Albers conical equal area projection. These products are validated using MODIS MOD13 NDVI products and the correlation is found to be better than 85%. These products will be shortly available on Bhuvan web portal.
1.2.3 Broadband & Visible Albedo Products- OCM2
Albedo is a key parameter that is widely used in land- surface energy balance studies, mid- to long-term weather prediction and global climate change investigation. Surface albedo is the ratio of upwelling radiant energy relative to the down-welling irradiance incident upon a surface. The most relevant albedo quantity for applications related to the energy budget refers to the total short-wave broad-band interval comprising the visible and near infrared wavelength ranges where the solar down-welling radiation dominates. Satellite remote sensing represents the best way to compile such consistent albedo characterizations. OCM2 Level-1C imagery has been used to generate fortnightly composites of snow free land surface albedo products. Presently two kinds of albedo products are generated namely Broadband and visible.
Broadband albedo was computed for the region 0.3-3 µm and visible albedo is computed for the region 0.3-0.7 µm. The regions between satellite bands were arbitrarily divided between band edges to accommodate the entire broadband. Narrow to broad band conversion coefficients are simulated using radiative transfer codes.To arrive at clear sky pixels in the 15-day composite image, second minimum albedo compositing criteria was opted for the temporal scenes with the assumption that cloud shadow pixel exists at most once during a compositing period (15 days). As OCM bands saturate for snow, permanent snow regions are masked in the product. Broadband albedo products are validated using MODIS MCD43 16-day albedo products and the correlation is found to be better than 90%. The products are delivered at a spatial resolution of 1080mts in Geotiff format with geographic projection. These products are made freely available and downloadable at Bhuvan
1.2.4 Broadband Snow Albedo – AWIFS
Broad band Snow albedo is an important geophysical parameter for studies related to weather, climate, and hydrometeorology. Snow has the highest albedo in nature and hence has a significant influence on surface energy budget and on Earth’s radiative balance. The albedo of snow is defined as the ratio of reflected to incident solar energy and is a function of sun angle, atmospheric parameters and cloudiness, and the size, shape, density and impurity contaminations of the snow crystals. Freshly fallen snow has a very high reflectance in the visible wavelength. As it ages, the reflectivity of snow decreases in the visible and especially in the longer (near-infrared) wavelengths. This can be due to the impurities that can get deposited over time or melting and refreezing process within the snow which lead to increased grain size.
Resourcesat-2/AWIFS standard geo-referenced products are used to generate broadband snow albedo products. The products were generated after topographic correction to overcome the problems of differential illumination in rugged terrains like that of Himalayas. The narrow to broadband conversion coefficients which required for the estimation of albedos were simulated using 6S RT code. Comparison of the snow albedo estimated by the proposed method with the Landsat ETM snow albedo products, showed good correlation (better than 90%) in different stages of snow metamorphism.
With five day repeat cycle of AWiFS sensor, four cycles of snow albedo products per month are planned and will be delivered shortly at a spatial resolution of 250m in Geotiff format with Albers conical equal area projection.
1.2.5 Surface Water Layer – OCM2
Surface water layer products are realized from the data acquired by Ocean Color Monitor (OCM2) sensor. The spectral absorption characteristic of water in the visible and NIR bands together with the normalized indices such as NDVI, NDWI (NIR and Green bands) are used for the extraction of water features from OCM imagery. Knowledge map in the form LULC maps are further used to assist the classification of water features. Illumination angle information obtained from DEM is used to avoid misclassification of terrain shadows as water bodies.
With two days repeat cycle of OCM, water layer map for the entire country can be generated every alternate day. Surface water layer map for the entire Indian Subcontinent are being disseminated at spatial resolution of 360m in Geotiff format with geographic projection. Surface water products classify pure and mixed water pixels. OCM water maps are validated using the AWiFs water maps of the same period. Accuracy of water body detection was better than 90% for large water bodies. These products are made freely available and downloadable at Bhuvan
1.2.6 Water Fraction - AWIFS
As water pixels are mostly surrounded by vegetation or soil pixels, the spectral signatures of water pixels are affected by contribution from these two major ground cover types. Level2 geo-referenced product from AWiFS sensor onboard Resourcesat -2 has been used to generate water fraction products. The generation of water fraction products involves the following steps: 1) precision correction of images(less than a pixel) 2) Generation of Top of the atmosphere reflectance 3) Cloud and Cloud shadow masking 4) spectral reflectance characteristics of water in all the four bands are used for delineation of surface water bodies 5) Illumination angle information obtained from DEM is used to avoid misclassification of terrain shadows as water bodies 6) Further fraction of water contained in each pixel detected as water is also computed using the spectral indices thresholds. Water fraction products are generated on a monthly basis at 250m resolution in Geotiff format with Albers conical equal area projection and will be released shortly.
2. Ocean Products
2.1. Tropical Cyclone Heat Potential, Ocean Mean Temperature & Ocean Heat Content Products
Ocean heat content (OHC) and Ocean Mean Temperature (OMT) are important climatic parameters required for atmospheric and oceanic studies like cyclone and monsoon predictions and ocean heat transport estimations. The data used to estimate these parameters are (a) sea surface height anomaly (SSHA) from the available altimeters, (b) sea surface temperature (SST) from Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and the climatological values of OHC and OMT of various depths (50, 100, 150, 200, 300, 500, 700m and TCHP as an integral of OHC from surface to 26oc isotherm and it's mean temperature). These parameters are estimated on a daily basis from 1998 to present with a delay of 3 days using artificial neural network techniques. The estimated OHC and OMT values are validated with independent data set and are found to be significantly correlated with the observed values. These products are made freely available and downloadable at Bhuvan
3. Cryosphere Products
3.1. Sea Ice Motion Products
Polar sea ice plays an important role in the climate system which is, however, not well elucidated due to difficulties in obtaining regular information about the state of the sea ice cover, in particular in the Antarctic. Of all quantities describing the state of an oceanic ice cover, the vector of sea ice motion is of special importance, since it couples the vertical momentum fluxes in the lower atmosphere and in the upper ocean, causes opening and closing of the ice cover, which affects heat exchange, and transports the ice from the areas of freezing to those of melting and, thus, influences the thermohaline structure of the ocean as well as the convection by changing the density of water. Ice covered parts of the ocean with their high albedo change the surface heat balance of these areas due to the high amount of reflected radiation
Sea Ice motion has been systematically observed for about a century. Sea ice kinematics have been studied continuously over large areas, also taking advantage of the increasing availability of satellite data on scales of 25km to 60km. Synthetic aperture radar data of satellites allow the investigation of sea ice motion at scales of individual floes.
Since the knowledge of small scale behavior is important, e. g. for understanding deformation processes and for improving coupled sea-ice-ocean models, RISAT-1 SAR CRS data at 36m spatial resolution over Antarctica was used to generate Sea Ice Displacement products. These products will be generated once the required polar area is acquired twice/thrice during a time span of 24/48 hours. Each image pair product contains the displacement vectors calculated by tracking locations between 2 images separated by between 1 to 2 days. These products are validated using Multi Sensor OSI-SAF daily Southern Hemisphere products and the correlation is found to be better than 92%. These products will be made available shortly with 2- 5km resolution on a polar stereographic grid.