L-band and S-band frequencies assisted NISAR: A state of the art technology for sustainable agrarian monitoring

Shashank Mohan; Brajesh Kumar; AP Nejadhashemi

doi:10.69968/ijisem.2024v3si2309-314

Authors

Shashank Mohan Department of Biosystems and Agricultural Engineering, Michigan State University, USA.
Brajesh Kumar Department of Computer Science and IT, MJP Rohilkhand University, Bareilly, Uttar Pradesh 243006, India
AP Nejadhashemi Department of Biosystems and Agricultural Engineering, Michigan State University, USA.

DOI:

https://doi.org/10.69968/ijisem.2024v3si2309-314

Keywords:

NISAR, L-band, S-band, Agrarian monitoring Synthetic Aperture Radar (SAR), dual-frequency, Artificial Intelligence

Abstract

Agrarian monitoring plays a vital role in promoting sustainable development by providing detailed insights into the distribution, health, and types of agricultural practices within a region. This information is crucial for managing natural resources, planning land use, and preserving biodiversity. Current agrarian monitoring techniques, such as multi-spectral imaging, LiDAR, Normalized Difference Vegetation Index (NDVI), thermal infrared imaging, UAVs (Unmanned Aerial Vehicles), photogrammetry, vegetation indices, and machine learning, offer significant advantages in ease of monitoring. However, they also have limitations, particularly in their ability to provide all-weather, day-and-night imaging, wide-area coverage, large data volumes, and high-resolution imagery. NISAR (NASA-ISRO Synthetic Aperture Radar) has emerged as a cutting-edge technology for sustainable agrarian monitoring. It provides valuable and precise datasets for applications such as land subsidence monitoring, cryosphere studies, deforestation tracking, flood prediction, forest canopy analysis, biomass estimation, and crop growth and health assessment. NISAR's ability to operate in all weather conditions and at any time of day makes it especially valuable for monitoring in cloudy or densely vegetated regions. By integrating AI techniques with NISAR's advanced capabilities, we can enhance the analysis of the vast datasets it generates, enabling more accurate predictions and better decision-making in agricultural management. These features collectively position NISAR as a critical tool for advancing our understanding of Earth's dynamic systems and supporting the sustainable management of natural resources.

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