Browsing by Author "Dileep Kumar Gupta"

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A novel deep learning change detection approach for estimating spatiotemporal crop field variations from Sentinel-2 imagery
(Elsevier B.V., 2024) Neelam Dahiya; Gurwinder Singh; Dileep Kumar Gupta; Kleomenis Kalogeropoulos; Spyridon E. Detsikas; George P. Petropoulos; Sartajvir Singh; Vishakha Sood
The analysis of crop variation and the ability to quantify it is a critical and challenging task. Remote sensing (RS) has proven to be an effective tool for monitoring crops and detecting seasonal variations worldwide. This opens new opportunities for developing effective crop monitoring models, with deep learning models showing great promise. This study presents a deep learning-based U-Net v5 Change Detection (UCD) model capable of identifying and monitoring the spatio-temporal variations in crop fields. The application of the model is demonstrated using Sentinel-2 imagery over Patiala district in India to monitor the seasonal crop variation (rabi crop) during 2017–2018. The results have shown that the UCD model has achieved better results (95.6–98.4%) in accuracy for classified maps and more than (91.6%–96.6%) in accuracy for change maps. This study will be useful for crop monitoring, precision agriculture and crop yield prediction and can assist in decision and policy making towards a more sustainable environment. © 2024
A statistical significance of differences in classification accuracy of crop types using different classification algorithms
(Taylor and Francis Ltd., 2017) Pradeep Kumar; Rajendra Prasad; Arti Choudhary; Varun Narayan Mishra; Dileep Kumar Gupta; Prashant K. Srivastava
Crop classification is needed to understand the physiological and climatic requirement of different crops. Kernel-based support vector machines, maximum likelihood and normalised difference vegetation index classification schemes are attempted to evaluate their performances towards crop classification. The linear imaging self-scanning (LISS-IV) multi-spectral sensor data was evaluated for the classification of crop types such as barley, wheat, lentil, mustard, pigeon pea, linseed, corn, pea, sugarcane and other crops and non-crop such as water, sand, built up, fallow land, sparse vegetation and dense vegetation. To determine the spectral separability among crop types, the M-statistic and Jeffries–Matusita (J–M) distance methods have been utilised. The results were statistically analysed and compared using Z-test and χ2-test. Statistical analysis showed that the accuracy results using SVMs with polynomial of degrees 5 and 6 were not significantly different and found better than the other classification algorithms. © 2016 Taylor & Francis.
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.
Appraisal of Climate Response to Vegetation Indices over Tropical Climate Region in India
(MDPI, 2023) Nitesh Awasthi; Jayant Nath Tripathi; George P. Petropoulos; Dileep Kumar Gupta; Abhay Kumar Singh; Amar Kumar Kathwas; Prashant K. Srivastava
Extreme climate events are becoming increasingly frequent and intense due to the global climate change. The present investigation aims to ascertain the nature of the climatic variables association with the vegetation variables such as Leaf Area Index (LAI) and Normalized Difference Vegetation Index (NDVI). In this study, the impact of climate change with respect to vegetation dynamics has been investigated over the Indian state of Haryana based on the monthly and yearly time-scale during the time period of 2010 to 2020. A time-series analysis of the climatic variables was carried out using the MODIS-derived NDVI and LAI datasets. The spatial mean for all the climatic variables except rainfall (taken sum for rainfall data to compute the accumulated rainfall) and vegetation parameters has been analyzed over the study area on monthly and yearly basis. The liaison of NDVI and LAI with the climatic variables were assessed at multi-temporal scale on the basis of Pearson correlation coefficients. The results obtained from the present investigation reveals that NDVI and LAI has strong significant relationship with climatic variables during the cropping months over study area. In contrast, during the non-cropping months, the relationship weakens but remains significant at the 0.05 significance level. Furthermore, the rainfall and relative humidity depict strong positive relationship with NDVI and LAI. On the other, negative trends were observed in case of other climatic variables due to the limitations of NDVI viz. saturation of values and lower sensitivity at higher LAI. The influence of aerosol optical depth was observed to be much higher on LAI as compared to NDVI. The present findings confirmed that the satellite-derived vegetation indices are significantly useful towards the advancement of knowledge about the association between climate variables and vegetation dynamics. © 2023 by the authors.
Artificial neural network for the estimation of soil moisture using earth observation datasets
(Elsevier, 2020) Sumit Kumar Chaudhary; Jyoti Sharma; Dileep Kumar Gupta; Prashant K. Srivastava; Rajendra Prasad; Dharmendra Kumar Pandey
Surface Soil Moisture (SSM) is an important variable in agricultural water management, required for irrigation water demand, scheduling, etc. In this chapter, the estimation of SSM is carried out using Artificial Neural Network (ANN) model trained by MODIS land surface temperature (LST) and normalised difference vegetation index (NDVI) feature spaces and validated using the in-situ data. The ANN model is trained, validated and tested using the three different combinations of input-output datasets. The first combination of datasets is considered as MODIS LST (input) and in-situ SSM (output) datasets for ANN-I model. The second combination of datasets is considered as MODIS NDVI (input) and in-situ SSM (output) datasets for ANN-II model. The third combination of datasets is considered as MODIS LST and NDVI (input) and in-situ SSM (output) datasets for ANN-III model. The performance of ANN-I, ANN-II and ANN-III models are evaluated in terms of correlation coefficient (r), bias and root mean squared error. In overall, the performance of ANN-II model was found good for SSM estimation. © 2021 Elsevier Inc. All rights reserved.
Artificial neural network with different learning parameters for crop classification using multispectral datasets
(Institute of Electrical and Electronics Engineers Inc., 2016) Pradeep Kumar; Rajendra Prasad; Varun Narayan Mishra; Dileep Kumar Gupta; Arti Choudhary; Prashant K. Srivastava
Present study evaluated the performance of artificial neural network (ANN) algorithm using different learning parameters for various crop classification in Varanasi, India. Satellite images such as Linear Imaging Self Scanning (LISS) IV and Landsat 8 Operational Land Imager (OLI) were used for crop classification and comparative analysis study. The following crop such as barley, wheat, lentil, mustard, pigeon pea, linseed, corn, pea, sugarcane and other crops and non-crop such as water, sand, built up, fallow land, sparse vegetation and dense vegetation were identified in the area and classified. Results indicated a better classification accuracy of ANN algorithm for crop classification study when used with LISS-IV data in the comparison to Landsat 8-OLI multispectral satellite data. The larger values of the learning rates resulted high fluctuations and less classification accuracy using LISS-IV data, while less but nearly uniform results were found using the Landsat 8-OLI data. © 2015 IEEE.
Assessment of climatic impact on growth and production of rice (Kharif) and wheat (Rabi) using geospatial technology over Haryana
(India Meteorological Department, 2023) Nitesh Awasthi; Jayant Nath Tripathi; K.K. Dakhore; Dileep Kumar Gupta; Y.E. Kadam
Global climate change could have a substantial negative influence on Indian agriculture and becoming more common and intense growing as a result of food security. Indeed, the examination of weather variability on agricultural growth and production is always complex. The weather variability impact on agricultural growth and production has been evaluated by Pearson correlation analysis among various weather variables (minimum temperature, maximum temperature, relative humidity, wind speed and rainfall), vegetation indices (NDVI and LAI) and crop yield (wheat and rice) on yearly and monthly basis for the time period from the year 1991 to 2020 in the present study. Initially, the temporal behavior of weather variables and vegetation indices have been explored on the monthly and yearly time scale for the long term (1991-2020) along with crop yield over Indian state of Haryana. After that a Pearson correlation analysis have been carried out among the weather variables, vegetation indices and crop yield on monthly and yearly time scale, individually to understand the relationship of NDVI-weather and LAI-weather along with the long-term weather impact on agricultural production. A significant correlation is found between NDVI-weather and LAI-weather on monthly and yearly basis. The positive impact of the temperature, relative humidity and rainfall is found on the rice crop production, while the wind speed showed the negative impact on the rice crop production during the Kharif season in Haryana state of India during the years 1998-2018. In case of wheat crop (Rabi season), the minimum temperature, rainfall and relative humidity supports the wheat crop production, while the maximum temperature and wind speed showed the negative impact on the wheat yield in Haryana during the years 1998-2018. Overall, this study has found the annual increase in wheat crop yield approximately 0.044 tons per hectare and rice crop yield 0.029 tons per hectare. © 2023, India Meteorological Department. All rights reserved.
Assessment of equivalent black carbon variations and its source apportionment over Varanasi, Indo-Gangetic Basin
(Elsevier B.V., 2024) Prashant Kumar Chauhan; Shani Tiwari; Dileep Kumar Gupta; Akhilesh Kumar; Vineet Pratap; Abhay Kumar Singh
In this study, the temporal variation of Equivalent Black Carbon (eBC) and its source apportionment is studied using a yearlong (Dec. 2020–Nov. 2021) multiwavelength Aethalometer (AE-33 model) measurements over Varanasi, located in the central Indo-Gangetic Basin (IGB). Results suggest that mean mass concentrations of eBC vary in the range between 0.46 ± 0.13 to 11.22 ± 5.09 μg m−3 with an annual mean value of ∼3.57 ± 2.39 μg m−3 during the study period. A strong temporal variation in eBC and its components i.e., eBCff (eBC from fossil fuel), and eBCbb (eBC from biomass burning) are found which shows a large variation on different temporal scales with an average value during winter (6.21 ± 3.56 μg m−3), summer (5.09 ± 3.61 μg m−3), monsoon season (1.52 ± 1.03 μg m−3), and post-monsoon (3.75 ± 2.68 μg m−3). The diurnal variation of eBC shows two different maxima between 07:00–08:00 a.m. and 08:00–10:00 p.m. An inverse relationship between eBC concentration and all meteorological parameters (temperature, wind speed, and boundary layer height) is found except relative humidity. The concentration of eBC increases with respect to RH (up to 70 %) suggesting hygroscopic growth while for higher RH (>70 %) value, eBC concentration decreases and indicates the possible wet scavenging processes in the atmosphere. Source apportionment of eBC using the “Aethalometer Model” reveals that eBCff is dominant over eBCbb in total eBC loading during the study period. Cluster analysis of HYSPLIT (Hybrid Single Particle Lagrangian Integrated Trajectory) model computed five days airmass back-trajectory suggests that airmass reached at Varanasi passes through a highly dense fire count region over the northwestern IGB and surrounding which could be the most responsible for the black carbon loading over the study region. © 2024 Turkish National Committee for Air Pollution Research and Control
Atmospheric Aerosol and weather vulnerability on Maize production in India
(Institute of Electrical and Electronics Engineers Inc., 2022) Dileep Kumar Gupta; Subhajit Pramanick; Abhay Kumar Singh
The gradual increment of anthropogenic aerosol pollutants, changes the properties of atmosphere and causes a significantly negative impacts on agriculture. The present study evaluated the long term aerosols and weather impact on the maize yield during the time period from the years 1998 to 2019 in India. The multiple linear regression analysis is carried out between the dependent variable (maize yield) and independent variables (AOD and weather). The performance of multiple linear regression analysis is found excellent between the dependent and independent variables in terms of the coefficient of determination (R2=0.945), root mean squared error (RMSE = 0.515 tons/hectare) and bias (0.0 tons/hectare). The atmospheric aerosols impact on the weather parameters is also evaluated because the changes in weather parameters due to aerosol is also affected the crop yield. The linear regression analysis is carried out to evaluate the aerosols impact on weather parameters and it is observed that the yearly increases in AOD impacts more on temperature variations than the fraction of absorbed photosynthetically active radiation (FAPAR) variation. The overall loss in maize yield is found approximately 8.8% per year due to variations in the weather variables with the increment of anthropogenic aerosol pollutants during 1998-2019 over India. In future, the climate warming and increment of aerosols may have differing impacts on crop yield and should be jointly considered in any assessment of Indian food security. © 2022 International Radio Science Union (URSI).
Bistatic scatterometer for the retrieval of soil moisture
(Elsevier, 2020) Dileep Kumar Gupta; Rajendra Prasad; Prashant K. Srivastava
An outdoor test beds with different soil surface roughness and soil moisture contents were prepared beside the Department of Physics, Indian Institute of Technology (BHU), Varanasi, India. In the present experiment, the bistatic scatterometer measurements were carried out for bare soil surface in the incident angle range of 20° to 70° for HH- and VV-polarisation at X-band. The bistatic scattering coefficient was found to increase with the soil moisture content and decrease with increase in soil surface roughness. The dynamic range of bistatic scattering coefficient was found more at VV-polarisation than HH-polarisation. The linear regression analysis was performed between bistatic scattering coefficients and soil moisture content at different soil surface roughness conditions for selecting the suitable incidence angle to generate the datasets for the calibration and validation of the artificial neural network model. The bistatic scattering coefficients at lower incidence angles were found more suitable to generate the datasets for the calibration and validation of ANN models. The observed values of soil moisture, RMS height and correlation length were found close with the values retrieved by ANN for both HH- and VV-polarisations. The retrieval of soil surface parameters by scatterometer data using ANN model was found more accurate at VV-polarisation than HH-polarisation. © 2021 Elsevier Inc. All rights reserved.
Challenges in Radar remote sensing
(Elsevier, 2022) Prashant K. Srivastava; Rajendra Prasad; Sumit Chaudhary Kumar; Suraj A. Yadav; Jyoti Sharma; Swati Suman; Varsha Pandey; Rishabh Singh; Dileep Kumar Gupta
This chapter provides different challenges that are generally faced by the radar remote sensing community. The different types of challenges of radar remote sensing in biochemical and biophysical parameter retrieval, flood detection and monitoring, soil moisture, snow, droughts, sensor development, and instrumentation are briefly provided. © 2022 Elsevier Inc. All rights reserved.
Characterization and impact of airborne particulate matter over Varanasi: A year-long study on concentration, morphology, and elemental composition
(Elsevier Ltd, 2024) Prashant Kumar Chauhan; Dileep Kumar Gupta; Abhay Kumar Singh
Air pollution is an important worldwide issue, especially pronounced in metropolitan and suburban regions, significantly affecting both public health and surroundings. This study investigates the particles' morphology and elemental analysis in Varanasi, a highly inhabited metropolis in the Indo-Gangetic Plain. The research was conducted over a year, from April 2019 to March 2020, utilizing Scanning Electron Microscopy with Energy Dispersive X-ray Spectroscopy, Ion Chromatography, and Atomic Absorption Spectroscopy to analyse particulate matter. Results indicated that mean values of PM2.5 and PM10 were 106.5 ± 67.2μg/m³ and 180.8 ± 71.4 μg/m³, respectively. Often, these amounts exceeded the National Ambient Air Quality Standards. SEM-EDX analysis revealed diverse particle morphologies, with significant contributions from both manmade sources including industrial activities and vehicle emissions, and natural sources, like soil dust. Elemental analysis identified major components, including Carbon, Oxygen, Fluorine, Aluminium, and Silicon. IC analysis highlighted dominant ionic species, such as Ca++, SO4−-, NO3−, and Cl−, with monthly variations reflecting different emission sources. Heavy metals concentrations such as Ni, Cd, Cr, Mn, Cu, Pb, Zn, and Fe were quantified, with concentrations varying significantly across months. The findings underscore the complex nature of aerosols in Varanasi and highlight the immediate need for targeted control over air quality measures to minimize the particulate matter's detrimental effects on the local population and ecosystem. © 2024
Cloud computing platforms-based remote sensing big data applications
(Elsevier, 2025) Swati Suman; Swati Maurya; Varsha K. Pandey; Prashant Kumar Srivastava; Dileep Kumar Gupta
Google Earth Engine (GEE) stands as the leading cloud-based geospatial remote sensing data processing platform. GEE repositories contain a range of satellite imageries, which can be used for various environmental applications, thanks to its easy and user-friendly application programming interface (API). One of the most compelling features of GEE includes enabling its users to explore, analyze, and visualize big geospatial data easily, all without requiring access to supercomputers or specialized coding expertise. Remarkably, even a decade after GEE's launch, its impact on remote sensing and geospatial science remains largely unnoticed. In this review, we provide a state-of-the-art report on the usage of cloud computing platforms such as GEE for processing various remote sensing data sources. We further explore the application of GEE for assessing vegetation health, agricultural monitoring, disaster management, image processing, and numerous other environmental applications using GEE. © 2025 Elsevier Ltd. All rights reserved.
Concepts and methodologies for agricultural water management
(Elsevier, 2020) Prashant K. Srivastava; Swati Suman; Varsha Pandey; Manika Gupta; Ayushi Gupta; Dileep Kumar Gupta; Sumit Kumar Chaudhary; Ujjwal Singh
Water resource management is of paramount importance for sustainable agricultural and socioeconomic development. Agriculture is also one of the prominent factors responsible for the deterioration in the water quality mostly due to poor water management practices and lack of proper knowledge about soil-plant-atmosphere relationship. As such, optimally designed techniques and careful selection of irrigation system can ensure high efficiency and uniform distribution of applied water. Advanced planning and proper management of water could lead us towards sustainable agricultural development with optimal crop production even under physical, environmental, financial and technological restrictions. Therefore, to discuss some of the irrigation-through-computer approaches as a tool for better agricultural water management in this report, we present a detailed description of some of these advanced techniques including decision support systems such as Hydra, Hydrus, DSSAT, CropSyst and MOPECO and irrigation practices such as drip, sprinkler and mulching systems. © 2021 Elsevier Inc. All rights reserved.
Dual-polarimetric C-band SAR data for land use/land cover classification by incorporating textural information
(Springer Verlag, 2017) Varun Narayan Mishra; Rajendra Prasad; Pradeep Kumar; Dileep Kumar Gupta; Prashant K. Srivastava
The work presented here showed a comprehensive evaluation of dual-polarimetric RISAT-1 data for land use/land cover (LULC) classification. The textural images were extracted with the help of gray-level co-occurrence matrix approach. Analysis of inter-class separability using transformed divergence method was performed to recognize the potential textural images. The best combination of textural images was also identified on the basis of standard deviation of preferred textural images and correlation coefficients. The maximum likelihood classifier-based classification results for different scenarios were compared. Furthermore, various classification algorithms, maximum likelihood classifier (MLC), artificial neural network (ANN), random forest (RF) and support vector machine (SVM), were performed on the best identified scenario in order to observe the most suitable algorithm for LULC classification. The combination of radiometric and their related textural images was found improving the overall classification accuracy than individual datasets. The highest overall classification accuracy was found using SVM (88.97%) followed by RF (88.45%), ANN (83.65%) and MLC (78.18%). © 2016, Springer-Verlag Berlin Heidelberg.
Evaluation of Radar/Optical Based Vegetation Descriptors in Water Cloud Model for Soil Moisture Retrieval
(Institute of Electrical and Electronics Engineers Inc., 2021) Sumit Kumar Chaudhary; Dileep Kumar Gupta; Prashant K. Srivastava; Dharmendra Kumar Pandey; Anup Kumar Das; Rajendra Prasad
The accurate consideration of vegetation descriptors in water cloud model (WCM) is necessary for precise SM retrieval. Most of the vegetation descriptors are sourced from optical remote sensors. The acquisitions from optical sensors are largely hampered by bad weather conditions. For all-weather monitoring, Synthetic Aperture Radar (SAR) based vegetation descriptors are needed to identify and evaluate their performance for SM retrieval. The present study evaluates the various sources/combinations of SAR based vegetation descriptors in WCM to identify the better alternatives of optical-based vegetation descriptors. The performance of three radar-based vegetation descriptors, namely VH polarized backscattering coefficients, depolarization ratio and radar vegetation index (RVI) along with the one optical-based vegetation descriptor, namely leaf area index (LAI) from MODIS were utilized in WCM. The WCM for each vegetation descriptor has been performed using Sentinel-1 VV polarized backscattering coefficients and in-situ SM. The in-situ SM measurements were carried out in the fields around Varanasi District in India during the winter season sown with the wheat crop. The correlations coefficient (r), root mean square error (RMSE) and bias were used to evaluate the performances of vegetation descriptors in WCM for SM retrieval. The study showed that the depolarization ratio is the best for SM retrieval with accuracy of 0.096 m3m-3 followed by RVI, cross-polarized and LAI with 0.100 m3m-3 , 0.124 m3m-3 and 0.124 m3m-3 , respectively. Thus, the depolarization ratio can be used for the retrieval of SM using Sentinel-1 VV polarized backscattering coefficients over the wheat crop. © 2001-2012 IEEE.
Fuzzy logic for the retrieval of kidney bean crop growth variables using ground-based scatterometer measurements
(Elsevier, 2022) Dileep Kumar Gupta; Rajendra Prasad; Pradeep Kumar; Prashant K. Srivastava
This study is designed to explore the potential of σ° and fuzzy logic algorithms for the accurate retrieval of kidney bean crop variables at innumerable growth stages using ground-based multiangular, multitemporal, and dual-polarized bistatic scatterometer data. The bistatic radar system may be a better choice for retrieving crop growth variables than a monostatic radar system because of its great advantages The transmitter and receiver are set opposite each other or in different azimuthal configurations in the case of a bistatic scatterometer system. For this purpose, an outdoor kidney bean crop bed of 4×4m2 was specially prepared near the Department of Physics, Indian Institute of Technology (BHU), Varanasi, India. Bistatic scatterometer measurements are accomplished at innumerable growth stages of the kidney bean crop for incidence angle of 20 to 70 degrees at horizontal transmit–horizontal receive (HH) and vertical transmit–vertical receive (VV) polarization in the specular direction at an azimuthal angle (ϕ=0). Kidney bean crop variables such as the vegetation water content, leaf area index, and plant height measurements are also taken at the time of each bistatic scatterometer measurement. The sensitivity of bistatic scattering coefficients at HH and VV polarization with the kidney bean crop variable is evaluated using Pearson correlation analysis to retrieve kidney bean crop variables. The higher sensitivities of σ° with the kidney bean crop growth variables are at 50 and 40 degrees incidence angles for HH and VV polarizations, respectively. The σ° does not exhibit severe saturation effects when the crops are mature and has better sensitivity to crop growth variables. The retrieved values of crop growth variables by fuzzy inference system are close to the in situ values of crop growth variables. Outcomes of this study are beneficial for the scientific community for making policy decisions regarding food security and environmental issues. © 2022 Elsevier Inc. All rights reserved.
GNSS Applications in Earth and Space Observations: Challenges and Prospective Approaches
(CRC Press, 2025) Dileep Kumar Gupta; Abhay Kumar Singh
Global Navigation Satellite Systems (GNSSs) have become an essential technology used in navigation, positioning, and timing applications in meteorology, environmental monitoring, disaster management, and space exploration. This comprehensive book explores the various applications of GNSS technology in different fields of Earth and Space observations and provides researchers, professionals, and students valuable insights into these emerging trends. It discusses the challenges that impact the performance of GNSS technology and offers solutions through several case studies on Space weather and climate disasters, opening a different dimension of approaches in various paradigms of GNSS technology. Features: • Covers the most up-to-date GNSS applications in three major areas related to Earth and Space observations: climate studies, disaster management, and Space weather monitoring. • Includes case studies of best practices in climate studies and disaster management. • Explains the impacts of Space weather events on the near-Earth environment. • Describes limitations and future possibilities of better use of GNSS in Earth and Space observation and monitoring. • Highlights an integrated and interdisciplinary approach valuable to a wide range of readers studying Earth and Space interactions. This book is a valuable resource for professionals, researchers, academics, and students in Remote Sensing and GIS, Earth Science, Physics and Electronics, Climate Studies, Disaster Management, Geophysics, and Space Science. © 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.
Introduction to RADAR remote sensing
(Elsevier, 2022) Dileep Kumar Gupta; Shivendu Prashar; Sartajvir Singh; Prashant K. Srivastava; Rajendra Prasad
For several decades, remote sensing has had a crucial role in various fields. With the increase in the quality of earth-observing sensors, the ability of observers has been revolutionized. There are two main types of remote sensing: optical-based and microwave. Optical-based remote sensing measures reflected solar illumination from the object but cannot penetrate clouds and has applicability only in the daytime, whereas microwave-based remote sensing can be imaged the earth's surface during both daytime and nighttime and in almost all weather conditions. This study focused on microwave remote sensing and its fundamentals. Microwave remote sensing can be categorized as (1) active remote sensing that provides its source of microwave radiation to illuminate the object on the land surface and detect the backscattered cross-section of a particular land type, and (2) passive remote sensing that detects naturally emitted microwave energy within its field of view. In this chapter, we provide a brief overview of microwave remote sensing and review its applications. This study helps researchers understand the basics of microwave remote sensing and various advances in the applicability of microwave remote sensing. © 2022 Elsevier Inc. All rights reserved.