Vegetation and forests cover more than 65％ of the terrestrial land cover.Life of human is not only related to vegetation just for food, but also vegetation and especially forests keep balance in ecosystems by separating carbon from the atmosphere and use them in producing energy necessary to keep them alive but also to keep the cycle of ecosystem running for every member of that ecosystem.Thus exact monitoring of croplands and forests have always been the primitive tasks of remote sensing technologies in the context of ecosystem processes and climate change issues.Thanks to the advancements in the field of remote sensing, it has become possible to assess the amount, quality and type of vegetation through satellites both for the local farmers and government agencies for proper planning.Remote Sensing is also helpful to assess the health of trees in forests and provides the long term time series analysis dates back to 1972 when Landsat Multispectral Scanner was launched.These information from remote sensing is interpreted by using several vegetation indices (VIs).This thesis covers extensively two indices—Universal Normalized Vegetation Index (UNVI), and a Tasseled Cap Transformation (TCT) index.It was found that contemporary remote sensing literature did not use these two indices extensively for studying ecosystem exchange processes, primary production, phenology and landuse-landcover change at global scale in contrast to Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI).In this thesis UNVI is introduced as a new index as a modification to oblique orthogonal index vegetation index based on universal pattern decomposition method (VIUPD).While TCT, which is an orthogonal set of indices, is derived for Landsat 8 Operational Land Imager (OLI) both for at-sensor reflectance as well as surface reflectance.In this way data continuity for vegetation analysis is made possible based on TCT spanning over four decades for Landsat series of satellites.This thesis provides new mechanisms and standards for deriving the TCT for future sensors in strict accordance with the traditional concept of TCT as proposed by its propounders.A complete code in IDL is also provided with this thesis for calculating UNVI for Moderate Resolution Imaging Spectroradiometer (MODIS), Landsat Enhanced Thematic Mapper Plus (ETM+), and Landsat 8 Operational Land Imager (OLI).It is expected that this thesis will be helpful for future researchers to use TCT and UNVI for global scale monitoring of ecosystems processes.To check the usefulness of UNVI and TCT for estimating GPP, Greenness and Radiation (GR)model was used for six sites of US belonging to three different plant functional types (PFTs)—deciduous forest, cropland and grassland.GR model was employed based on NDVI, EVI, TCT and UNVI.If one index gave good approximation for one site, it gave low values of correlation for other sites.Although none of the indices suit for all PFTs, but EVI and TCT gave overall higher values of correlation for most of the sites with TCT Greenness gives the overall highest correlation.