Browsing by Author "Swati Suman"

Now showing 1 - 14 of 14

Application of geospatial technology in agricultural water management
(Elsevier, 2020) Ram Kumar Singh; Pavan Kumar; Semonti Mukherjee; Swati Suman; Varsha Pandey; Prashant K. Srivastava
The geospatial technology is an emerging technique to study real earth geographic information using Geographical Information System (GIS), Remote Sensing (RS) and other ground information from various devices and instruments. In this chapter, various geospatial process-based techniques segregated into two different categories, i.e., conventional and advanced, are provided for agricultural water management. The descriptions of several approaches are provided to understand the role of geospatial technology in agricultural water management. Most of the approaches are based on remote sensing and GIS in correspondence with statistical learning techniques that can be possibly used for agricultural water management. © 2021 Elsevier Inc. All rights reserved.
Appraisal of radiative transfer model 6SV for atmospheric correction of multispectral satellite image towards land surface temperature retrieval
(Elsevier, 2022) Prashant K. Srivastava; Nishita Jaiswal; Swati Suman; Smrutisikha Mohanty; Sharma Mona
Land surface temperature (LST) is very important parameter for broad range of applications related to weather and climate, hydrology, etc. For LST, satellite could be a very promising solution, but need to be corrected especially for atmospheric noises present in bands such as in red and near-infrared (NIR) windows. The atmospheric noises occurred due to ozone, water vapor, and aerosols cause reduction in reflectance values of red and the NIR bands which makes the normalized difference vegetation index (NDVI) values smaller than their true value. NDVI considered as an important variable for emissivity estimation and any loss in reflectance causes error in LST retrieval. In this study, for atmospheric correction of satellite image, radiative transfer model 6SV (second simulation of satellite signal in the solar spectrum-vector) was used and then the corrected image was utilized for the LST retrieval using Landsat 8 satellite thermal and visible bands. Changes in the values are found in the atmospherically corrected images of NDVI, plant fraction, emissivity, and LST as compared to the atmospherically uncorrected images. Hence, the reported approach could be a better choice for retrieval of LST due to its robust nature. © 2023 Elsevier Ltd. All rights reserved.
Appraisal of SMAP operational soil moisture product from a global perspective
(MDPI AG, 2020) Swati Suman; Prashant K. Srivastava; George P. Petropoulos; Dharmendra K. Pandey; Peggy E. O'Neill
Space-borne soil moisture (SM) satellite products such as those available from Soil Moisture Active Passive (SMAP) offer unique opportunities for global and frequent monitoring of SM and also to understand its spatiotemporal variability. The present study investigates the performance of the SMAP L4 SM product at selected experimental sites across four continents, namely North America, Europe, Asia and Australia. This product provides global scale SM estimates at 9 km x 9 km spatial resolution at daily intervals. For the product evaluation, co-orbital in situ SM measurements were used, acquired at 14 test sites in North America, Europe, and Australia belonging to the International Soil Moisture Network (ISMN) and local networks in India. The satellite SM estimates of up to 0-5 cm soil layer were compared against collocated ground measurements using a series of statistical scores. Overall, the best performance of the SMAP product was found in North America (RMSE = 0.05 m3/m3) followed by Australia (RMSE = 0.08 m3/m3), Asia (RMSE = 0.09 m3/m3) and Europe (RMSE = 0.14 m3/m3). Our findings provide important insights into the spatiotemporal variability of the specific operational SM product in different ecosystems and environments. This study also furnishes an independent verification of this global product, which is of international interest given its suitability for a wide range of practical and research applications. © 2020 by the authors.
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.
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.
Comparison of soil dielectric mixing models for soil moisture retrieval using SMAP brightness temperature over croplands in India
(Elsevier B.V., 2021) Swati Suman; Prashant K. Srivastava; Dharmendra K. Pandey; Rajendra Prasad; R.K. Mall; Peggy O'Neill
The accurate estimation of soil moisture (SM) using microwave remote sensing depends mostly on careful selection of retrieval parameters among which the soil dielectric mixing model is the important one. These models are often categorized into empirical, semi-empirical or volumetric based on their methodologies and input data requirements. To study in detail, the comparative performance of four dielectric mixing models – Wang & Schmugge model, Hallikainen model, Dobson model and Mironov model were used with Soil Moisture Active Passive (SMAP) L-band brightness temperature and Single Channel Algorithm for SM retrieval over agricultural landscapes in India. The highest performance statistics combination in terms of Root Mean Square Error (RMSE), correlation coefficient (R2) and percentage bias (PBIAS) against the concurrent in-situ SM measurements were calculated at the selected validation sites. The overall results indicate that the best performance was given by the Mironov model (RMSE = 0.07 m3/m3), followed by Wang & Schmugge model (RMSE = 0.08 m3/m3), Hallikainen model (RMSE = 0.09 m3/m3), Dobson model (RMSE = 0.10 m3/m3) and original SMAP radiometer SM (RMSE = 0.12 m3/m3). Findings of this study provides important insights into application and performance of dielectric mixing models in mapping surface SM variations. This study also underlines the pivotal role of local conditions for SM retrieval which should be carefully included in the algorithms. © 2021 Elsevier B.V.
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.
Exploring Economic Perspectives in Ornamental Fish Farming in India: Present Status, Opportunities, and Threats
(Springer Science+Business Media, 2025) Avdhesh Sharma; Ankit Yadav; Swati Suman; Debasmita Baruah; Debashish Kumar; Shubhi Patel; Chetan Kumar Garg
The emerging sector of ornamental fish farming in India is a viable route for both economic growth and biodiversity preservation. This chapter examines the current situation, challenges, and future prospects in this industry. India has a large biodiversity and a climate that is ideal for fish farming, but it exports very little ornamental fish to the international market. Barriers to the sector’s potential growth include a lack of infrastructure, regulatory uncertainties, and disorganized value chain. Nonetheless, there are other opportunities for expansion, including support from regulators, market diversification, infrastructure spending, capacity creation, and adoption of new technologies. The study highlights the ways in which ornamental fish farming aids in the advancement of science, economic growth, establishment of livelihoods, preservation of biodiversity, and tourism. It concludes with actionable suggestions on how government organizations, academic institutions, and business troupes may work together to realize the sector’s full potential and promote long-term, sustainable growth. © 2025 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.
Modelling key parameters characterising land surface using the SimSphere SVAT model
(Elsevier, 2020) Swati Suman; Matthew R. North; George P. Petropoulos; Prashant K. Srivastava; Dionissios T. Hristopulos; Daniela Silva Fuzzo; Toby N. Carlson
The present study investigates the ability of SimSphere, a soil vegetation atmosphere transfer model, to predict key parameters in characterising land surface interactions. In particular, the model’s performance in predicting Net Radiation (Rnet), Latent Heat (LE) and Sensible Heat (H) was examined. For this purpose, concurrent in-situ measurements of the corresponding parameters for a total of 70days of the year 2011 from seven CarboEurope network sites were acquired, incorporating a variety of environmental biomes and climatic conditions in the model evaluation. In overall, SimSphere was largely able to accurately predict the variables against which it was evaluated for most of the experimental sites. Statistical analysis showed highest agreement of H fluxes to the measured in-situ values for all ecosystems, with an average root mean square difference of 55.36Wm-2. Predicted latent fluxes and Rnet also agreed well with the corresponding in-situ data with RSMDs of 62.75 and 64.65Wm-2, respectively. Our findings contribute towards a better understanding of the model structure, functioning and its correspondence to the real-world system. They also further establish its capability as a useful teaching and research tool in modelling Earth’s land surface interactions. This is important given its increasing use, including its synergies with Earth observation data. © 2021 Elsevier Inc. All rights reserved.
Monitoring changes in urban cover using landsat satellite images and demographical information
(IGI Global, 2016) Prashant K. Srivastava; Swati Suman; Smita Pandey
The monitoring of urban cover is very important for the planner, management, governmental and non-governmental organizations for optimizing the use of urban resources and minimizing the environmental losses. The study here aims at analyzing the changes that occurred in urban green cover over a time span of 1991-2001 using multi-date Landsat satellite images data over the Varanasi district, India and its relation to demographical changes. The Support Vector Machines (SVMs) classifier has been used for image classification. The urbanization indicators such as Land Consumption Ratio (LCR) and Land Absorption Coefficient (LAC) were also used in order to understand the changes in urban cover and population dynamics. All the analysis indicates significant changes in the urban cover values with increasing population at both spatial and temporal scale.
Monitoring changes in urban cover using landsat satellite images and demographical information
(IGI Global, 2018) Prashant K. Srivastava; Swati Suman; Smita Pandey
The monitoring of urban cover is very important for the planner, management, governmental and nongovernmental organizations for optimizing the use of urban resources and minimizing the environmental losses. The study here aims at analyzing the changes that occurred in urban green cover over a time span of 1991-2001 using multi-date Landsat satellite images data over the Varanasi district, India and its relation to demographical changes. The Support Vector Machines (SVMs) classifier has been used for image classification. The urbanization indicators such as Land Consumption Ratio (LCR) and Land Absorption Coefficient (LAC) were also used in order to understand the changes in urban cover and population dynamics. All the analysis indicates significant changes in the urban cover values with increasing population at both spatial and temporal scale. © 2019 by IGI Global.
Performance assessment of phased array type L-band Synthetic Aperture Radar and Landsat-8 used in image classification
(Elsevier, 2022) Swati Suman; Prashant K. Srivastava; George P. Petropoulos; Ram Avtar; Rajendra Prasad; Sudhir Kumar Singh; S.K. Mustak; Ioannis N. Faraslis; Dileep Kumar Gupta
Owing to its large spatial and periodic temporal coverage, satellite remote sensing has emerged for formulating and implementing strategies for natural resources management. This study focuses on an appraisal of satellite sensors and artificial intelligence techniques such as kernels-based support vector machines (SVMs) and artificial neural networks (ANNs). These methods are used for land cover classification on multispectral and microwave satellite images acquired from Landsat-8 and Advanced Land Observing Satellite (ALOS-2) Phased Array type L-band Synthetic Aperture Radar (PALSAR) over Varanasi, India. The analysis shows comparable the performance of the microwave-classified image compared with the multispectral Landsat-8 image. ANNs and SVMs performed best with an overall accuracy of 97.3% and kappa coefficient of 0.97 for the Landsat-8 image, whereas SVM radial basis function was the best classifier for the ALOS PALSAR image with 94% overall accuracy. Other statistical indices such as kappa total disagreement and allocation disagreement scores revealed similar performances. The analysis demonstrated the ability of microwave data in land cover classification studies with satisfactory performance. These data can be used in nearly all weather and environmental conditions for broad image classification purposes when optical and infrared imagery such as Landsat are unavailable. © 2022 Elsevier Inc. All rights reserved.
Quantifying land use/land cover spatio-temporal landscape pattern dynamics from Hyperion using SVMs classifier and FRAGSTATS®
(Taylor and Francis Ltd., 2018) Salim Lamine; George P. Petropoulos; Sudhir Kumar Singh; Szilárd Szabó; Nour El Islam Bachari; Prashant K. Srivastava; Swati Suman
This study aims to quantify the landscape spatio-temporal dynamics including Land Use/Land Cover (LULC) changes occurred in a typical Mediterranean ecosystem of high ecological and cultural significance in central Greece covering a period of 9 years (2001–2009). Herein, we examined the synergistic operation among Hyperion hyperspectral satellite imagery with Support Vector Machines, the FRAGSTATS® landscape spatial analysis programme and Principal Component Analysis (PCA) for this purpose. The change analysis showed that notable changes reported in the experimental region during the studied period, particularly for certain LULC classes. The analysis of accuracy indices suggested that all the three classification techniques are performing satisfactorily with overall accuracy of 86.62, 91.67 and 89.26% in years 2001, 2004 and 2009, respectively. Results evidenced the requirement for taking measures to conserve this forest-dominated natural ecosystem from human-induced pressures and/or natural hazards occurred in the area. To our knowledge, this is the first study of its kind, demonstrating the Hyperion capability in quantifying LULC changes with landscape metrics using FRAGSTATS® programme and PCA for understanding the land surface fragmentation characteristics and their changes. The suggested approach is robust and flexible enough to be expanded further to other regions. Findings of this research can be of special importance in the context of the launch of spaceborne hyperspectral sensors that are already planned to be placed in orbit as the NASA’s HyspIRI sensor and EnMAP. © 2017 Informa UK Limited, trading as Taylor & Francis Group.
Techniques and tools for monitoring agriculture drought: A review
(Elsevier, 2024) Varsha Pandey; Prashant K. Srivastava; Anjali Kumari Singh; Swati Suman; Swati Maurya
Drought is a global phenomenon that silently spreads and creates an insidious hazard by destabilizing the hydrological cycle over a large region. Due to increased frequency, severity, and negative impact on climate conditions, droughts have drawn worldwide attention. Real-time drought monitoring and quantification is a prerequisite to ensure the well-being of inhabitants and appropriate management of water, food, and social resources. This chapter provides a comprehensive overview of the agricultural drought and its association with other drought types and monitoring using satellite remote sensing images and various hydrological model-simulated datasets. Furthermore, a few widely used and essential indices for agricultural drought were discussed along with their importance and limitations. Usage of an advanced drought assessment platform and related case studies were also discussed. The chapter concludes with the challenges in agriculture drought monitoring and provides the roadmap for future research. © 2024 Elsevier Inc. All rights reserved.