Raghavendra Jana

Raghu received his Ph.D. in Biological and Agricultural Engineering (focus: Soil Hydrology) from Texas A&M University in College Station, Texas, USA in 2010. Much of his doctoral research was funded by a NASA Earth System Sciences Fellowship competitive grant. His current research goals relate to improvement of agricultural productivity and resource utilization by leveraging traditional modeling of environmental processes, remote sensing, data analysis, and integration of Artificial Intelligence and Machine Learning techniques.

Raghu joined Skoltech in 2018 as part of the Centre for Computational and Data Intensive Science and Engineering, and moved to the Digital Agriculture Lab/Center for AgroTechnologies in 2021. Previously, Raghu worked at the Indian Institute of Science (India), King Abdullah University of Science and Technology (Saudi Arabia), University of Stuttgart (Germany), and Texas A&M University (USA). He has been involved in projects funded by NASA, NSF (USA), USDA, and the World Bank, among others. Raghu has authored/co-authored 20+ peer reviewed publications/ book chapters, and has presented his research at over 40 international conferences/seminars. He has been a mentor to over a dozen graduate students.

The primary focus of Raghu’s current research is on observation and modeling of environmental processes, and their application in enhancing agricultural productivity. He is utilizing data science and artificial intelligence techniques in conjunction with process-based modeling to help improve the efficiency of water and nutrient use in agriculture, especially in the context of climate change. This involves various inter-disciplinary components such as soil water balance modelling; numerical, statistical, and physical modeling of environmental processes; remote sensing; application of AI/ML for agro-hydrological modeling and prediction; and, better integration of environmental effects in plant breeding, among others. With agriculture being the major consumer, and significant source of contamination, of fresh water resources, his research outcomes are expected to help move this sector towards more sustainable practices while not sacrificing but, instead, improving the crop yield.

Envirotyping and Enviromics
Numerical, statistical, and physical modeling of environmental processes
Hydrology and soil physics
Remote sensing
Application of AI/ML for agro/hydrological processes

Peer Reviewed Journal Articles
  1. Busari, I., D. Sahoo, and R. B. Jana (2023), Prediction of Harmful Algal Blooms using Deep Learning with Bayesian Approximation for Uncertainty Assessment, Journal of Hydrology, accepted. (Journal Ranking: Q1; IF: 6.400)
  2. Fomenko, S. I., R.  B. Jana, and M. V. Golub (2023), Numerical modeling of elastic wave propagation in porous soils with vertically inhomogeneous fluid contents due to infiltration, Mathematics, 11(19), pp 4131, doi: 10.3390/math11194131. (Journal Ranking: Q2; IF: 3.400)
  3.  Poornima, S., M. Pushpalatha, R. B. Jana, and L. A. Patti (2023), Rainfall Forecast and Drought Analysis for Recent and Forthcoming Years in India, Water, 15(3), pp 592, doi: 10.3390/w15030592. (Journal Ranking: Q1; IF: 3.530)
  4. Djouider, S. I., L. Gentzbittel, R. B. Jana, M. Rickauer, C. Ben, and M. Lazalli (2022), Contribution to improving chickpea (Cicer arietinum L.) efficiency in low-phosphorus farming systems: Assessment of the relationships between P and N nutrition, nodulation capacity and productivity performance in P-deficient field conditions, Agronomy, 12, pp 3150, doi: 10.3390/agronomy12123150. (Journal Ranking: Q1; IF: 3.949)
  5. Petrovskaia, A., R. B. Jana, and I. V. Oseledets (2022), A single image deep learning approach to restoration of corrupted remote sensing products, Sensors, 22, pp 9273, doi: 10.3390/s22239273. (Journal Ranking: Q1; IF: 3.847)
  6. Matvienko, I., M. Gasanov, A. Petrovskaia, M. Kuznetsov, R. B. Jana, M. Pukalchik, and I. V. Oseledets, (2022), Bayesian aggregation improves traditional single image crop classification approaches, Sensors, 22, pp 8600, doi: 10.3390/s22228600. (Journal Ranking: Q1; IF: 3.847)
  7. Pourshamsaei, H., A. Nobakhti, and R. B. Jana (2021), Adaptive Proper Orthogonal Decomposition for large scale reliable soil moisture estimation, Measurement Science and Technology, 32, pp 10, doi: 10.1088/1361-6501/ac16af. (Journal Ranking: Q2; IF: 2.398)
  8. Shadrin, D., A. Nikitin, P. Tregubova, V. Terekhova, R. B. Jana, S. Matveev, and M. Pukalchik (2021), An automated approach to groundwater quality monitoring – Geospatial mapping based on combined application of Gaussian Process Regression and Bayesian Information Criterion, Water, 13(4), pp 400, doi: 10.3390/w13040400. (Journal Ranking: Q1; IF: 3.530)
  9. Spiridonov, D., M. Vasilyeva, E. T. Chung, Y. Efendiev, and R. B. Jana (2020), Multiscale model reduction of unsaturated flow problem in heterogeneous porous media with rough surface topography, Mathematics, 8(6), pp 904, doi: 10.3390/math8060904. (Journal Ranking: Q2; IF: 2.592)
  10. Arora, B., D. Dwivedi, B. A. Faybishenko, R. B. Jana, and H. M. Wainwright (2019), Understanding and predicting vadose zone processes, Reviews of Mineralogy and Geochemistry, 85 (1), pp 303, doi: 10.2138/rmg.2019.85.10. (Journal Ranking: Q1; IF: 5.630)
  11. Jana, R. B., A. Ershadi, and M. F. McCabe (2016), Examining the relationship between intermediate scale soil moisture and terrestrial evaporation within a semi-arid grassland, Hydrology and Earth System Sciences, 20(10), pp 3987, doi: 10.5194/hess-20-3987-2016. (Journal Ranking: Q1; IF: 6.617)
  12. Jana, R. B., and B. P. Mohanty (2012), On topographic controls of soil hydraulic parameter scaling at hill-slope scales, Water Resources Research, 48(2), doi: 10.1029/2011WR011204. (Journal Ranking: Q1; IF: 6.159)
  13. Jana, R. B., and B. P. Mohanty (2012), A topography-based scaling algorithm for soil hydraulic parameters at hill-slope scales: Field testing, Water Resources Research, 48(2), doi: 10.1029/2011WR011205. (Journal Ranking: Q1; IF: 6.159)
  14. Jana, R. B., and B. P. Mohanty (2012), A comparative study of multiple approaches to soil hydraulic parameter scaling applied at the hill-slope scale, Water Resources Research, 48(2), doi: 10.1029/2011WR010185. (Journal Ranking: Q1; IF: 6.159)
  15. Jana, R. B., B. P. Mohanty, and Z. Sheng (2012), Upscaling soil hydraulic parameters in the Picacho Mountain region using Bayesian Neural Networks, Transactions of the ASABE, 55(2), pp 463, doi: 10.13031/2013.41396. (Journal Ranking: Q2; IF: 1.238)
  16. Jana, R. B., and B. P. Mohanty (2011), Enhancing PTFs with remotely sensed data for multi-scale soil water retention estimation, Journal of Hydrology, doi: 10.1016/j.jhydrol.2010.12.043. (Journal Ranking: Q1; IF: 6.708)
  17. Jana, R. B., B. P. Mohanty, and E. P. Springer (2008), Multiscale Bayesian neural networks for soil water content estimation, Water Resources Research, 44(8), W08408, doi: 10.1029/2008WR006879. (Journal Ranking: Q1; IF: 6.159)
  18. Jana, R. B., B. P. Mohanty, and E. P. Springer (2007), Multiscale pedotransfer functions for soil water retention, Vadose Zone Journal, 6(4), 868-878, doi: 10.2136/vzj2007.0055. (Journal Ranking: Q1; IF: 2.945)
Book Chapters
  1. Mohanty, B. P., A. V. M. Ines, Y. Shin, N. Gaur, N. N. Das, and R. B. Jana (2016), A Framework for Assessing Soil Moisture Deficit, and Crop Water Stress at Multiple Space and Time Scales Under Climate Change Scenarios Using Model Platform, Satellite Remote Sensing, and Decision Support System. In: Remote Sensing of Hydrological Extremes, Lakshmi, V. (Ed.), Springer Remote Sensing/Photogrammetry, Springer, doi: 10.1007/978-3-319-43744-6_9.
Other peer Reviewed Publications
  1. Petrovskaia, A., R. B. Jana, and I. V. Oseledets (2020), A single image deep learning approach to restoration of corrupted remote sensing products, Spotlight paper in Computer Vision for Agriculture (CV4A), workshop at International Conference on Learning Representations (ICLR) 2020, (April 26 – 30, 2020), accepted.
  2. Matvienko, I., I. V. Oseledets, M. Gasanov, A. Petrovskaia, M. Pukalchik, and R. B. Jana (2020), Bayesian aggregation improves traditional single image crop classification approaches, Computer Vision for Agriculture (CV4A), workshop at International Conference on Learning Representations (ICLR) 2020, (April 26 – 30, 2020), accepted.
  3. Altaf, M. U., R. B. Jana, I. Hoteit, and M. F. McCabe (2016), Continuous data assimilation for downscaling large-footprint soil moisture retrievals, Proc. SPIE 9998, Remote Sensing for Agriculture, Ecosystems, and Hydrology XVIII, 99981O (October 25, 2016), doi:10.1117/12.2241042.
  4. Singh, G., R. K. Panda, B. P. Mohanty, and R. B. Jana (2016), Soil moisture variability across different scales in an Indian watershed for satellite soil moisture product validation, Proc. SPIE 9877, Land Surface and Cryosphere Remote Sensing III, 98772B (May 5, 2016), doi:10.1117/12.2222743.
  5. Jana, R. B., A. Ershadi, and M. F. McCabe (2015), Hydrological links between cosmic-ray soil moisture retrievals and remotely sensed evaporation across a semi-arid pasture site, in Weber, T., McPhee, M.J. and Anderssen, R.S. (eds) MODSIM2015, 21st International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand, December 2015, pp. 1372–1378, ISBN: 978-0-9872143-5-5.
  6. Qu, Z., Li, X., Tian, D., Jana, R. B., & Mohanty, B. P. (2011), Development of regional-scale pedotransfer functions based on Bayesian Neural Networks in the Hetao Irrigation District of China, Proc. 7th International Conference on Natural Computation, ICNC 2011 (Vol. 2), IEEE, doi:10.1109/ICNC.2011.6022191.
Research Reports
  1. Jana, R. B., and B. P. Mohanty (2009), Use of Satellite Data for Soil Parameter Estimation in Rio Grande Basin, Report submitted to NASA Earth System Science Graduate Student Fellowship Program.
  2. Jana, R. B., B. P. Mohanty, and E. P. Springer (2005), Soil Hydrologic Properties for Simulation of Semi-Arid River Basin Water Balance: A Report, Joint Texas A&M University and Los Alamos National Laboratory Report.

Ph. D., Biological and Agricultural Engineering, 2010
Texas A&M University, College Station, TX
Focus: Soil Hydrology
Title: Scaling Characteristics of Soil Hydraulic Parameters at Varying    Spatial Resolutions
Advisor: Dr. Binayak P. Mohanty

M. Eng., Civil Engineering, 2004
Texas A&M University, College Station, TX
Focus: Pavement Materials
Thesis Title: Surface Energy Characteristics of Bitumen Additives
Advisor: Dr. Dallas N. Little

B. Eng., Civil Engineering, 2001
M. S. Ramaiah Institute of Technology, Bangalore University, India

  • Analysis of remote sensing data related to hydrological, meteorological, and agricultural phenomena for improved understanding of related processes.
  • Application of improved understanding of optimizing water use, improving agricultural yield, and environmental sustainability.
  • Utilizing AI/ML and traditional process-based modeling to predict hydrological/agricultural conditions.
  • Conducting field experiments to measure data pertaining to current water application, soil conditions, weather, crop phenology, management practices, etc.
  • Conducting experiments to understand the water cycle partitioning for different vegetation and climate conditions.
  • Analysis of data generated from the above experiments to understand the process involved.
  • Incorporating the strategy into a software that is user friendly for the farmer and can be integrated with the farming machinery.

Robert E. Stewart Graduate Excellence Award [2009] Biological and Agricultural Engineering Department, Texas A&M University

Bill A., and Rita L. Stout International Graduate Student Achievement Award [2007] Biological and Agricultural Engineering Department, Texas A&M University

NASA Earth Systems Science Graduate Fellowship [2006 – 2009] National Aeronautics and Space Administration, Washington, DC

Joseph A. Orr Graduate Fellowship [2002] Civil Engineering Department, Texas A&M University