It is with great pleasure that we introduce the inaugural issue of the International Journal of Space Applications, published by Pioneer Research Publications (P) Limited. This issue features six articles, comprising both original research papers and review articles, that advance current understanding in the domains of LiDAR technology, Google Earth Engine (GEE), machine learning-based crop yield estimation, drone-based large-scale mapping, hybrid polarimetric decomposition, and radar applications for monitoring power lines.

This edition highlights the increasing relevance and deep penetration of drone-based large-scale mapping and the use of LiDAR technology for various operational applications. With the growing popularity and utility of Google Earth Engine (GEE) for various geospatial applications, it is the need of the hour for all the students and researchers working in the field of Remote Sensing & Geospatial applications to explore and exploit the GEE for modeling, prediction, and many other applications using large time-series remote sensing data.  However, for many researchers, effective use of GEE may be a difficult task. In order to provide novice users of GEE with information on the platform’s features, functionalities, and applications, this issue includes an article that introduces readers to the basics of Google Earth Engine and delves into practical examples illustrating its real-world applications. This issue also presents a study that demonstrates a machine learning based comprehensive methodology to estimate wheat production using Remote Sensing time series data in the top wheat-producing states of India. Next, to provide a deeper insight into hybrid polarimetric SAR data, various decomposition methods such as m-δ, m-α, and m-χ are compared with a focus on their capability to discriminate various scattering mechanisms relevant to land cover features. Finally, the issue presents an interesting study for the fault detection of high-tension power lines using radar systems. This study presents an improved RCS calculation method that combines the Characteristic Mode (CM) theory with the Sherman-Morrison-Woodbury Algorithm (SMWA). The proposed method efficiently computes both RCS and theoretical backscattering coefficients (σ°) for power lines operating from P-band (0.3 GHz) to Ka-band (30 GHz) & voltage range from 220 Volts to 33,000 Volts.

The editorial team hopes that the interesting articles presented in this issue will provide insight to the readers in different disciplines learning or practicing remote sensing techniques and geospatial analyses, and will help the students, academicians, researchers, and policymakers working in the field of AI/ML, GEE, LiDAR, drone-based large-scale mapping, crop yield estimation, hybrid polarimetric SAR, and infrastructure projects monitoring.