Browsing by Author "Singh S.N."

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    Digital hemispherical photographs and Sentinel-2 multi-spectral imagery for mapping leaf area index at regional scale over a tropical deciduous forest
    (Springer, 2024) Behera M.D.; Krishna J.S.R.; Paramanik S.; Kumar S.; Behera S.K.; Anto S.; Singh S.N.; Verma A.K.; Barik S.K.; Mohanta M.R.; Sahu S.C.; Jeganathan C.; Srivastava P.K.; Pradhan B.
    The leaf area index (LAI) provides valuable input for modeling climate and ecosystem processes. However, ground-based observations are necessitated across various phenophases from dense tropical forests for a better understanding in terms of their contribution to carbon fixation. In this study, Digital Hemispherical Photography (DHP) was used for LAI observation from Similipal Biosphere Reserve, and to predict high-resolution LAI using Random Forest Machine Learning approach. Observations were taken from ninety-three Elementary sampling units (ESUs) corresponding to the beginning and end of leaf fall seasons across moist deciduous, dry deciduous, and semi-evergreen forests. LAI demonstrated high values for dry deciduous, followed by semi-evergreen and moist deciduous forests for the start of the leaf fall season, whereas moist deciduous forests demonstrated high values during the end of the leaf fall season. Satellite-based spectral reflectance bands of Sentinel-2 and vegetation indices (VIs) were used as predictor variables, wherein the band-7, band-8, band-12, enhanced vegetation index (EVI), and Red-edge based EVI were evaluated as the most dominant responsive variables for LAI estimation. Random Forest (RF) model provided good accuracy (R2 = 0.64, RMSE = 0.62) with observed DHP-based LAI. However, a comparison of RF model-based predicted LAI with global LAI products (MOD15A2H and VNP15A2H) provided a moderate correlation. Such studies demonstrate the potential of site or region-specific case studies to evaluate coarser-resolution global LAI products for possible improvement. � International Society for Tropical Ecology 2024.
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    Nexus between nanotechnology and agricultural production systems: challenges and future prospects
    (Springer Nature, 2024) Rana L.; Kumar M.; Rajput J.; Kumar N.; Sow S.; Kumar S.; Kumar A.; Singh S.N.; Jha C.K.; Singh A.K.; Ranjan S.; Sahoo R.; Samanta D.; Nath D.; Panday R.; Raigar B.L.
    Sustainable agriculture is crucial for meeting the growing global food demand. With the pressure of climate change, resource depletion, and the need for increased agricultural productivity, innovative approaches are essential. Nanotechnology is an emerging technology in achieving sustainable development goals (SDGs). Despite its promising benefits, the safe implementation of nanotechnology in agriculture requires careful consideration of potential health and environmental risks. However, there is a lack of comprehensive documentation on the application, potential and limitations of nanotechnology in the field of agriculture. To address this gap, a desk research approach was used by utilizing peer-reviewed electronic databases like PubMed, Scopus, Google Scholar, Web of Science, and Science Direct for relevant articles. Out of 157 initially identified articles, 85 were deemed pertinent, focusing primarily on potential nanotechnology in smart agricultural systems. Taking into account research findings worldwide, we found significant improvements with nanotechnology over traditional methods which underscores the practical benefits of nanotechnology, including increased crop yields, efficient resource use, and reduced environmental footprint. The objective of this systematic review is to explore the nexus between nanotechnology and agricultural systems, highlighting its potential to enhance productivity, sustainability, and resilience and to inform researchers, practitioners, and policymakers about the transformative impact of nanotechnology on sustainable agriculture and underscores the need for further research to address safety concerns and maximize its potential for agricultural advancement. � The Author(s) 2024.
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    Variation of aerosol parameters (AI, AOD) and SO2 over Indo-Gangetic basin during COVID-19 outbreaks
    (Springer, 2024) Kumar A.; Pratap V.; Singh S.N.; Singh A.K.
    To stop the spread of COVID-19 infections, idea of complete and partial lockdowns was implemented in several countries. In the present work, variation of aerosol index (AI), aerosol optical depth (AOD) and SO2 concentration over Indo-Gangetic Basin regions (over highly polluted cities: Varanasi, Kanpur and Delhi) were analyzed to see the impact of lockdown periods during 2020. AI data were taken from satellite based ozone monitoring instrument while AOD and SO2 data were taken from moderate resolution spectroradiometer (MODIS). Lockdown periods of March, April, May and June months of 2020 were compared with the same months of 2017, 2018 and 2019. Significantly large difference in AI was observed associated with decreased value of AI during lockdown periods followed by AOD values and SO2 concentrations. All these cities of Northern India (Varanasi, Kanpur and Delhi) show significant decline in aerosol index which indicate less emissions of black carbon and other absorbing aerosol particles into the atmosphere. Trend of AI and SO2 concentration were found to be negative due to the sudden decrease in their values. Decline in the aerosol parameters (AI and AOD) and air pollutant (SO2) suggest improved air quality of the highly polluted cities of India. � 2023, Indian Association for the Cultivation of Science.