His research integrates satellite remote sensing, eco-hydrological modeling, and machine learning to estimate key Biophysical Parameters such as leaf area index, gross primary productivity, and fraction of absorbed photosynthetically active radiation. He has contributed to the development of high-resolution vegetation products by combining multisource satellite data with deep learning approaches. His work advances methodological frameworks for accurate environmental monitoring in mountainous regions. He has authored a scholarly book on mountain remote sensing and published extensively in leading international journals. His research supports improved understanding of mountain ecosystem functioning under environmental and climatic variability. In addition, his studies address data fusion, scaling, and uncertainty analysis in complex terrain. He has made methodological contributions to time-series analysis of satellite observations. His research provides valuable scientific support for ecosystem modeling and climate-related environmental assessments.