Browsing by Author "Varsha Pandey"

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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.
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.
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.
Development of android application for visualisation of soil water demand
(Elsevier, 2020) Prashant K. Srivastava; Prachi Singh; Varsha Pandey; Manika Gupta
A real-time and accurate estimation of soil moisture content is a key factor for irrigation water management. For conventional and precision irrigation system, an irrigation demand tool is required that is economical, easy-to-use, has large-scale coverage, provides the users useful information on irrigation requirement and can be accessible on smartphone or wireless sensor platform. Owing to this, the current study aims to develop a user-friendly mobile app for monitoring and visualising irrigation water demand in terms of soil moisture deficit (or SMD). Irrigation Scheduler App is designed by using the Android Studio 3.1.4 software and Java RE 1.8.0 version. The database file of Irrigation Scheduler App contains ground measured soil moisture content and other soil physical properties such as field capacity and texture. Any android phone having code Kitkat, Lolipop, Marshmallow and Nougat support this mobile application. Future efforts will focus on expansion of this study area and updation of the application. © 2021 Elsevier Inc. All rights reserved.
Drought hazard zonation using GIS based multi-critaria evaluation approach with remotely sensed datasets
(Asian Association on Remote Sensing, 2017) Varsha Pandey; Prashant K Srivastava
Drought is an intricate phenomenon governed by several atmospheric factors such as precipitation, temperature, evapotranspiration, soil moisture, vegetation cover, stream flow etc. To monitor drought hazard, several criteria and factors will need to be evaluated. The main objective of this study was to evolve a drought hazard map with the selected five main parameters viz., standardized precipitation index, land surface temperature, soil moisture, evapotranspiration and normalized difference vegetation index during the monsoon period from 2002 to 2014 employing GIS aided multi-criteria evaluation (MCE) technique. To standardized the input data layers and deciding the factors weight for the MCE, analytical hierarchy process (AHP) approach was applied. Bundelkhand region of Uttar Pradesh was selected for this study, as drought is very frequent and dominant here. The results depicted that about 55.9% and 7.5% of the total area is classified under high and extreme drought hazard zone respectively however, about 36.59% of the total area found to be the least vulnerable (moderate to low) to drought hazard. Central parts of the region namely Jhansi, Mahoba, Jalaun and Hamirpur districts are highly affected by drought condition. Based on finding of this study we recommend the use of MCE techniques for effective and precise drought hazard zonation. © 2017 ACRS. All rights reserved.
Drought identification and trend analysis using long-term chirps satellite precipitation product in bundelkhand, india
(MDPI AG, 2021) Varsha Pandey; Prashant K. Srivastava; Sudhir K. Singh; George P. Petropoulos; Rajesh Kumar Mall
Drought hazard mapping and its trend analysis has become indispensable due to the aggravated impact of drought in the era of climate change. Sparse observational networks with minimal maintenance limit the spatio-temporal coverage of precipitation data, which has been a major constraint in the effective drought monitoring. In this study, high-resolution satellite-derived Climate Hazards Group Infrared Precipitation with Station (CHIRPS) data has been used for computation of Standardized Precipitation Index (SPI). The study was carried out in Bundelkhand region of Uttar Pradesh, India, known for its substantial drought occurrences with poor drought management plans and lack of effective preparedness. Very limited studies have been carried out in assessing the spatio-temporal drought in this region. This study aims to identify district-wide drought and its trend characterization from 1981 to 2018. The run theory was applied for quantitative drought assessment; whereas, the Mann-Kendall (MK) test was performed for trend analysis at seasonal and annual time steps. Results indicated an average of nine severe drought events in all the districts in the last 38 years, and the most intense drought was recorded for the Jalaun district (1983–1985). A significant decreasing trend is observed for the SPI1 (at 95% confidence level) during the post-monsoon season, with the magnitude varying from −0.16 to −0.33 mm/month. This indicates the increasing severity of meteorological drought in the area. Moreover, a non-significant falling trend for short-term drought (SPI1 and SPI3) annually and short-and medium-term drought (SPI1, SPI3, and SPI6) in winter months have been also observed for all the districts. The output of the current study would be utilized in better understanding of the drought condition through elaborate trend analysis of the SPI pattern and thus helps the policy makers to devise a drought management plan to handle the water crisis, food security, and in turn the betterment of the inhabitants. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Evaluation of Satellite Precipitation Data for Drought Monitoring in Bundelkhand Region, India
(Institute of Electrical and Electronics Engineers Inc., 2019) Varsha Pandey; Prashant K Srivastava
Drought is a recurrent phenomenon in the semiarid regions of India that significantly affects the regional social, economic, and environmental conditions. Drought monitoring and assessment are challenging especially for regions that have sparse or very limited rain gauge observation networks. In this study, a comparative analysis was performed between satellite precipitation products including Tropical Rainfall Measuring Mission (TRMM-3B42), Climate Hazards Group InfraRed Precipitation (CHIRP) and ground-measured Indian Meteorological Department (IMD) precipitation data over the Bundelkhand region of Uttar Pradesh, India to assess the meteorological drought in this region. The district wise study was done to determine the regional differences among these satellite precipitation products and to statistically verify their performance in estimating the degree and spatial pattern over the study area. The Standardized Precipitation Index (SPI) was computed using the open-source DRINC software. The IMD derived SPI was found more correlated with TRMM compared to CHIRP for SPI-1, 3 and 12 with r values 0.74, 0.81 and 0.65 respectively. However, SPI-6 shows low positive and negative and correlation for both TRMM and CHIRP data. The current case study highlights the outperformance of TRMM data that enables real-time drought assessment owing to better accuracy and higher spatiotemporal resolution. © 2019 IEEE.
GIS and remote sensing aided information for soil moisture estimation: A comparative study of interpolation techniques
(MDPI AG, 2019) Prashant K. Srivastava; Prem C. Pandey; George P. Petropoulos; Nektarios N. Kourgialas; Varsha Pandey; Ujjwal Singh
Soil moisture represents a vital component of the ecosystem, sustaining life-supporting activities at micro and mega scales. It is a highly required parameter that may vary significantly both spatially and temporally. Due to this fact, its estimation is challenging and often hard to obtain especially over large, heterogeneous surfaces. This study aimed at comparing the performance of four widely used interpolation methods in estimating soil moisture using GPS-aided information and remote sensing. The DistanceWeighting (IDW), Spline, Ordinary Kriging models and Kriging with External Drift (KED) interpolation techniques were employed to estimate soil moisture using 82 soil moisture field-measured values. Of those measurements, data from 54 soil moisture locations were used for calibration and the remaining data for validation purposes. The study area selected was Varanasi City, India covering an area of 1535 km2. The soil moisture distribution results demonstrate the lowest RMSE (root mean square error, 8.69%) for KED, in comparison to the other approaches. For KED, the soil organic carbon information was incorporated as a secondary variable. The study results contribute towards efforts to overcome the issue of scarcity of soil moisture information at local and regional scales. It also provides an understandable method to generate and produce reliable spatial continuous datasets of this parameter, demonstrating the added value of geospatial analysis techniques for this purpose. © 2019 by the authors.
Hydrological modelling for post-monsoon agricultural drought assessment and implications for the agro-economy
(Taylor and Francis Ltd., 2024) Varsha Pandey; Prashant Kumar Srivastava; Pulakesh Das; Mukunda Dev Behera; Eswar Rajasekaran
Reliable drought monitoring is a prerequisite for minimizing potential agricultural losses. Soil moisture is a key variable for monitoring agricultural drought assessment. This study conducted in the Bundelkhand region of Uttar Pradesh uses a macroscale variable infiltration capacity (VIC) hydrological model to simulate soil moisture and calculate soil moisture deficit index (SMDI) for agricultural drought assessment for the Rabi crop growing season, 1998–2016. Crop yield was linked with SMDI and other covariates using a random forest machine learning-based regression technique. The results show that the VIC model effectively simulated root zone soil moisture when compared with the reference data. Major droughts were identified in the years 2000–01, 2007–08, and 2015–16 in the study region. The RF-based crop yield prediction accuracy improved when irrigational factors were added. The study provides a noteworthy reference for drought assessment and prevention, water resource management, and ensuring food security. © 2024 IAHS.
Integration of microwave and optical/infrared derived datasets for a drought hazard inventory in a sub-tropical region of India
(MDPI AG, 2019) Varsha Pandey; Prashant K. Srivastava
Drought is an intricate phenomenon assessed by analyzing several hydro-meteorological factors such as rainfall, soil moisture, temperature, evapotranspiration, vegetation cover, etc. For effective drought hazard management and preparedness, the monitoring of drought requires the evaluation of influencing factors via the Drought Hazard Inventory (DHI). The main objective of this study is to compare spatial occurrences of drought hazard with the help of microwave and Optical/Infrared datasets obtained from multiple satellites. The long-term climatology of the Tropical Rainfall Measuring Mission (TRMM) Rainfall, Climate Change Initiative soil moisture (CCI-SM) and Moderate Resolution Imaging Spectroradiometer (MODIS) derived Land Surface Temperature (LST), Evapotranspiration (ET) and Normalized Difference Vegetation Index (NDVI) were used in this study for drought hazard assessment. This study was carried out in the Bundelkhand region of Uttar Pradesh, considered as one of the most frequent and dominant drought-prone areas of India. The current study includes the Analytical Hierarchy Process (AHP) technique based on Multi-Criteria Decision Making Analysis (MCDM) for weighting assignment and decision making, while the geospatial platform was used for data layer standardization, integration, and drought assessment. The results indicate that a large percentage of area (38.05% and 27.54%, respectively) lying in the central part of Bundelkhand region is under high to extreme drought conditions, where precautionary measures are needed. To demonstrate the robustness of our results, we compare them with the long-term in-situ ground water depletion as a proxy. Finally, based on the findings of this study, we recommend the methodology for drought assessment at a larger scale, as well as in the remote areas where ground based measurements are limited. © 2019 by the authors.
Irrigation water demand estimation in Bundelkhand region using the variable infiltration capacity model
(Elsevier, 2020) Varsha Pandey; Prashant K. Srivastava; Pulakesh Das; Mukunda Dev Behera
The soil moisture level effectively estimates the extremity of irrigation demand, and thus acts as a significant indicator of irrigation water management. In recent time due to increase in population, demand for freshwater in all competing sectors is being a constraint for irrigation that raises the need to optimise utilisation of irrigation water with its high efficiency. Therefore, an accurate and precise assessment of soil moisture content (SMC) is required for optimal allocation and management of water in agriculture. Owing to this, the current study aims to simulate SMC from water balance-based macro-scale Variable Infiltration Capacity (VIC) hydrological model for various applications in irrigation water management. The current study was carried out in the Bundelkhand region of Uttar Pradesh, India, for a period of 52 value ranging from 0.73 to 0.90 and low RMSE value ranging from 0.03 to 0.05 m3m-3. The dataset of simulated and CCI-SM content values was close to the 1:1 scale line for approximately all the periods. Additionally, the Soil Moisture Deficit Index (SMDI), a dryness index, was computed for estimating irrigation demand from VIC-derived SMC for Kharif season (July-October) from 2010 to 2014; this shows more irrigation water demand in 2014 and sowing period of 2010 and 2012. © 2021 Elsevier Inc. All rights reserved.
Long-term trend analysis of precipitation and extreme events over kosi river basin in india
(MDPI AG, 2021) Prashant K. Srivastava; Rajani Kumar Pradhan; George P. Petropoulos; Varsha Pandey; Manika Gupta; Aradhana Yaduvanshi; Wan Zurina Wan Jaafar; Rajesh Kumar Mall; Atul Kumar Sahai
Analysis of spatial and temporal changes of long-term precipitation and extreme precipitation distribution at a local scale is very important for the prevention and mitigation of water-related disasters. In the present study, we have analyzed the long-term trend of 116 years (1901-2016) of precipitation and distribution of extreme precipitation index over the Kosi River Basin (KRB), which is one of the frequent flooding rivers of India, using the 0.25° × 0.25° resolution gridded precipitation datasets obtained from the Indian Meteorological Department (IMD), India. The non-parametric Mann-Kendall trend test together with Sen’s slope estimator was employed to determine the trend and the magnitude of the trend of the precipitation time series. The annual and monsoon seasons revealed decreasing trends with Sen’s slope values of −1.88 and −0.408, respectively. For the extreme indices viz. R10 and R20 days, a decreasing trend from the northeastern to the southwest part of the basin can be observed, whereas, in the case of highest one-day precipitation (RX1 day), no clear trend was found. The information provided through this study can be useful for policymakers and may play an important role in flood management, runoff, and understanding related to the hydrological process of the basin. This will contribute to a better understanding of the potential risk of changing rainfall patterns, especially the extreme rainfall events due to climatic variations. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Multi-satellite precipitation products for meteorological drought assessment and forecasting in Central India
(Taylor and Francis Ltd., 2022) Varsha Pandey; Prashant K. Srivastava; R.K. Mall; Francisco Munoz-Arriola; Dawei Han
In this study, a comparative analysis of three satellite precipitation products including the Tropical Rainfall Measuring Mission (TRMM-3B43 V7), the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), and the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS V2) with ground-measured Indian Meteorological Department (IMD) precipitation data were performed to estimate the meteorological drought in the Bundelkhand region of Central India. The high-resolution CHIRPS data showed the closest agreement with the IMD precipitation and well captured the drought characteristics. The Standardized Precipitation Index (SPI) identified seven major droughts events during the period of 1981 to 2016. Appropriate calibration and validation were performed for drought forecasting using the Auto-Regressive Integrated Moving Average (ARIMA) model. The forecasting result showed a reasonably good agreement with the observed datasets with the one-month lead time. The outcomes of this study have policy level implications for drought monitoring and preparedness in this region. © 2020 Informa UK Limited, trading as Taylor & Francis Group.
Retrieval of soil moisture deficit through climate change initiative (CCI) soil moisture data and probability distributed model
(Asian Association on Remote Sensing, 2017) Prashant K Srivastava; Swati Maurya; Varsha Pandey; Dharmendra Pandey
Soil moisture deficit (or SMD) is very important variable for many applications such as for flood and drought modelling, which is now possible to estimate using the remote sensing data. This study is an attempt to evaluate the climate change initiative (CCI) soil moisture data for SMD estimation at a catchment scale. The SMD (Soil Moisture Deficit) estimated from the Probability Distribution Model, by using the in situ station data was used as a benchmark for all the comparisons. Approaches based on generalized linear model and relevance vector machine are provided for the estimation of SMD. The overall analysis reveals that CCI soil moisture is of reasonable quality in estimating the soil moisture deficit at a catchment level. Therefore, this study provides first time comprehensive evaluation of CCI soil moisture in Indian condition and the result provides supportive evidence of the potential value of this product for meso-scale studies and hydrological applications. © 2017 Institute of Mechanics and Mechatronics, Faculty of Mechanical, TU Wien. All rights reserved.
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.
The Contribution of Earth Observation in Disaster Prediction, Management, and Mitigation: A Holistic View
(wiley, 2020) Varsha Pandey; Prashant K. Srivastava; George P. Petropoulos
In the era of climate change, disasters are among the events of concern in the research field due to their devastating effects worldwide. Satellite-based Earth observation (EO) is an established technology for mapping and moni- toring spatial information about disasters at frequent intervals in all weathers at real time. Further, the information from past and present conditions gathered and stored by EO enables identifying the changes occurring in Earth’s cover, and helps in framing management and mitigation strategies. Chapter 3 already introduced various types of disasters and their management briefly, and in this chapter we review some commonly used sensors on board EO satellites and spatial analysis approaches with their application in disaster impact assessment and modeling. It provides a holistic view of EO techniques in disaster prediction, management, and mitigation. Â© 2020 John Wiley & Sons, Inc.