Development of Estimation Techniques for Solar Radiation, NDVI and Net Primary Productivity

Net Primary Productivity (NPP) is a fundamental ecological metric that underpins the functioning of ecosystems. NPP estimation using the Carnegie–Ames–Stanford Approach (CASA) biosphere model is carried out for the Roorkee and Hyderabad study sites. The CASA model estimates the NPP at regional and global scales by using data of environmental factors like temperature, precipitation, total solar radiation, and the normalized difference vegetation index. For the study sites, the NPP is estimated using the CASA model, with high agreement between modeled and observed values. The findings show the usefulness of the CASA model for calculating the NPP. For the Roorkee site, the NPP estimates are significantly higher than those for the Hyderabad site. To determine the solar radiation for the Roorkee and Hyderabad stations, this study examines the monthly average values of cloud cover fraction, the duration of sunny hours and global solar radiation on a horizontal surface. It is found out that the relationship between cloud cover fraction and the maximum number of hours that can be spent in the sun is nonlinear. This results in a nonlinear relationship between cloud cover fraction and the solar radiation. The moderate resolution imaging spectroradiometer (MODIS) composite and the Landsat imagery are combined to generate the synthetic normalized difference vegetation index (NDVI) images which are further used as remote sensing data for the estimation of NPP. This study employs a hybrid methodology that combines smoothing, filtering, and regression analysis techniques. The combined mode filter combines the forward and backward Kalman filters.