Land Pulse: Real-Time Agriculture Parameter Forecasting

Growing crops and trees together is known as agroforestry, and it is often regarded as an integrated strategy for sustainable land management. The proposed project explores the possibility of generating geographically precise estimates of crop output in an agroforestry system using Sentinel-2 time series data. Sentinel-2 is a European Space Agency (ESA) satellite mission that provides high-resolution multispectral imagery for land and water monitoring. Since agroforestry systems (AFSs) are thought to be better able to capture and utilize growth resources (light, nutrients, and water) than single-species crop or pasture systems, they are thought to have a higher capacity to store carbon (C). Sentinel-2's high-resolution multispectral imaging allows for precise land cover mapping in agroforestry landscapes. Sentinel-2 data aids in determining the size and distribution of agroforestry areas by differentiating between various land cover types, such as forests, crops, and grasslands. Additionally, Sentinel-2's spectral reflectance data makes it possible to monitor vegetation and measure the health and growth of plants. The Normalized Difference in Vegetation Index (NDVI), one of the vegetation indices obtained from Sentinel-2 data, offers insights into the vigor and productivity of both trees and crops within agroforestry systems. Understanding light interception, water usage effectiveness, and general ecosystem functioning in agroforestry depends on evaluating tree canopy properties. The structure and density of tree canopies can be learned from Sentinel-2 data by estimating metrics like the Leaf Area Index (LAI) and Fractional Vegetation Cover (FVC). Sentinel-2 data also makes it possible to monitor and assess crop growth in agroforestry systems. Farmers can maximize yields by monitoring crop phenology changes and identifying stress factors.