Ankur Srivastava

About

Ankur Srivastava received the B.Tech. Agricultural Engineering Degree in 2014 from Acharya N. G. Ranga Agricultural University, Hyderabad, India, and M.Tech. degree in 2016 in Land and Water Resources Engineering from the Indian Institute of Technology, Kharagpur, India.

He completed his PhD (Civil Engineering) in 2021 from the University of Newcastle, Australia where his topic of research was “Climate – Soil – Vegetation Interactions: Eco-hydro-geomorphic Inferences from Landscape Evolution Model”. His main research interests are on Agricultural Engineering, Digital Agriculture, Agricultural Water Management, Ecohydrology, Remote sensing.

He completed his Postdoctoral Research studies at Ecosystem Dynamics Health and Resilience Lab at University of Technology, Sydney, Australia. He worked on developing best-practice Himawari data products for enhanced sub-daily monitoring of Australia’s ecosystems” (Terrestrial Ecosystem Research Network (TERN) project) focusing on the development of fine resolution vegetation phenology products.

Employment

• University of Technology Sydney Postdoctoral Research Fellow
  2021 - 2024
• The University of Newcastle Casual Academic - Remote Sensing and GIS
  2021 - 2021
• University of Newcastle Australia Postdoctoral Research Fellow
  2021 - 2021
• The University of Newcastle Casual Academic - Engineering Risk and Uncertainty
  2021 - 2021
• The University of Newcastle Casual Academic - Contaminant Hydrogeology
  2020 - 2020
• The University of Newcastle Casual Academic (Water Engineering)
  2017 - 2020
• The University of Newcastle Casual Academic (Hydrology)
  2016 - 2021
• Indian Institute of Technology Kharagpur Research Fellow
  2015 - 2016

Education

Education history is unavailable.

Projects & Funding

Projects & funding information is unavailable.

Publications (58)

• Development and evaluation of a mobile tire testing device for rolling resistance under varying speed, load, and cone index
  Journal of Terramechanics 2026 DOI: 10.1016/j.jterra.2026.101118
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• Approaches for Assessment of Soil Moisture with Conventional Methods, Remote Sensing, UAV, and Machine Learning Methods—A Review
  Water 2025 DOI: 10.3390/w17162388
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• Development, optimization and modelling of performance parameters for remote-controlled mechatronic precision planter using RSM and Hybrid PSO-ANN model
  Smart Agricultural Technology 2025 DOI: 10.1016/j.atech.2025.100993
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• Remote Sensing-Based Phenology of Dryland Vegetation: Contributions and Perspectives in the Southern Hemisphere
  Remote Sensing 2025 DOI: 10.3390/rs17142503
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• RUSLE model insights for soil conservation and sustainable land use in semiarid environments
  Euro-Mediterranean Journal for Environmental Integration 2025 DOI: 10.1007/s41207-024-00664-3
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• Evaluating the Relationship Between Vegetation Status and Soil Moisture in Semi-Arid Woodlands, Central Australia, Using Daily Thermal, Vegetation Index, and Reflectance Data
  Remote Sensing 2025 DOI: 10.3390/rs17040635
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• Application of Various Hydrological Modeling Techniques and Methods in River Basin Management
  Water 2025 DOI: 10.3390/w17010083
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• Generalization Ability of Bagging and Boosting Type Deep Learning Models in Evapotranspiration Estimation
  Water 2024 DOI: 10.3390/w16162233
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• Spatiotemporal Variations in Near-Surface Soil Water Content across Agroecological Regions of Mainland India: 1979–2022 (44 Years)
  Remote Sensing 2024 DOI: 10.3390/rs16163108
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• Predictive Modelling of Reference Evapotranspiration Using Machine Learning Models Coupled with Grey Wolf Optimizer
  Water 2023 DOI: 10.3390/w15050856
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