Browsing by Author "Damanti Murmu"

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Application of GNSS in Earth System Sciences
(CRC Press, 2025) Bhawana Sharma; Leesh Ray; Damanti Murmu; Ayushi Gupta; Dileep Kumar Gupta
The global navigation satellite system (GNSS) is extensively used to study numerous geodynamic processes consisting of tectonic plate movements, seismic activities, deformations in plate boundaries, deformations by surface loads, deformations due to volcanic events, and glacial isostatic adjustments. GNSS applications also extend to study forest dynamics as it is very cost-effective, more efficient, and accurate than many other approaches. Nowadays, multi-GNSS technology accelerates the real-time applications of GNSS in multiple Earth Science disciplines to accommodate highly reliable and accurate predictions. The applicability of GNSS to various dynamics of Earth System Sciences—such as meteorological, hydrological, ecological, hazards specific, crustal displacement, oceanography, glacial deformation, and Space weather phenomena—are discussed in this chapter. © 2025 selection and editorial matter, Dileep Kumar Gupta and Abhay Kumar Singh; individual chapters, the contributors.
GNSS-enabled Precision Agriculture
(CRC Press, 2025) Ayushi Gupta; Damanti Murmu; Dileep Kumar Gupta; Prasad S. Thenkabail
Global Navigation Satellite Systems (GNSS) have revolutionized precision agriculture, enhancing productivity and sustainability. This chapter delves into the integration of GNSS in modern farming practices, highlighting its pivotal role in precision agriculture (PA). GNSS technology provides precise location data, enabling accurate soil sampling, variable rate technology (VRT), precision planting, and yield mapping. These applications optimize resource use, minimize environmental impact, and increase crop yields. The chapter also explores the vulnerabilities of GNSS, including signal interference and economic barriers, and discusses potential solutions. Furthermore, it examines the integration of GNSS with emerging technologies such as Geographic Information Systems (GIS), unmanned aerial vehicles (UAVs), the Internet of Things (IoT), and advanced sensor technologies. Case studies demonstrate the effectiveness of GNSS in diverse agricultural contexts from automated irrigation systems to robotic harvesting. Despite challenges, the future of GNSS-enabled precision agriculture looks promising, with advancements in signal accuracy, satellite constellations, and receiver sensitivity. This integration is set to transform agriculture, ensuring efficient and sustainable food production. By leveraging GNSS, farmers can make data-driven decisions, enhancing productivity and reducing waste. This chapter underscores the critical role of GNSS in achieving global food security and sustainable agricultural practices. © 2025 selection and editorial matter, Dileep Kumar Gupta and Abhay Kumar Singh; individual chapters, the contributors.
Scatterometers for leaf area index estimation: A review
(Elsevier, 2025) Damanti Murmu; Bhawana Sharma; Srishti Gwal; Ayushi Gupta; Prashant Kumar Srivastava
Scatterometers are active remote sensing instruments that emit and receive radio detection and ranging (RADAR) pulses backscattered from targeted features on the Earth's surface. Scatterometer-based leaf area index (LAI) estimation is demonstrated as a potential approach to understand ecosystem productivity, crop yields, vegetation health, etc., as LAI serves as an important variable in empirical, semi-empirical, and process-based models used in studying vegetation dynamics. Scatterometers are anticipated to facilitate large-scale, uninterrupted LAI monitoring as they penetrate dense vegetation canopies under varying weather conditions. LAI computation using a scatterometer demands backscattering signal strength measurement, which primarily depends upon the dynamics of vegetation structure, surface texture, and soil moisture. The water-cloud model estimates soil moisture of areas possessing vegetation using backscattering data. The model provides a precise estimation of LAI as it can explicitly distinguish between the soil and vegetation backscatter. Further enhancement of these estimations can be carried out by data fusion methods that incorporate the scatterometer data with optical or synthetic aperture radar data. With the aid of case studies, this chapter demonstrates versatile applications of scatterometer across a wide range of environmental aspects, including estimation of above-ground biomass, carbon sequestration, soil moisture, yield prediction, vegetation structure analysis, etc., for forest, agriculture, and grassland ecosystems. Scatterometer data in agricultural areas provides accurate production calculation and periodic crop growth monitoring. Because scatterometers provide a stable and scalable method of measuring LAI, they may be used to monitor vegetation dynamics, agricultural productivity, and environmental changes globally. © 2026 Elsevier Ltd. All rights reserved..