Sudhanshu Panda
Cited by
Cited by
Application of vegetation indices for agricultural crop yield prediction using neural network techniques
SS Panda, DP Ames, S Panigrahi
Remote sensing 2 (3), 673-696, 2010
Remote sensing and geospatial technological applications for site-specific management of fruit and nut crops: A review
SS Panda, G Hoogenboom, JO Paz
Remote Sensing 2 (8), 1973-1997, 2010
Assessment of storm direct runoff and peak flow rates using improved SCS-CN models for selected forested watersheds in the Southeastern United States
A Walega, DM Amatya, P Caldwell, D Marion, S Panda
Journal of Hydrology: Regional Studies 27, 100645, 2020
Artificial neural networks application in lake water quality estimation using satellite imagery
SS Panda, V Garg, I Chaubey
Journal of Environmental Informatics 4 (2), 65-74, 2004
Distinguishing blueberry bushes from mixed vegetation land use using high resolution satellite imagery and geospatial techniques
SS Panda, G Hoogenboom, J Paz
Computers and Electronics in Agriculture 67 (1-2), 51-58, 2009
Application of LiDAR data for hydrologic assessments of low-gradient coastal watershed drainage characteristics
D Amatya, C Trettin, S Panda, H Ssegane
Scientific Research Publishing, 2013
Site-specific management of common olive: Remote sensing, geospatial, and advanced image processing applications
O Noori, SS Panda
Computers and Electronics in Agriculture 127, 680-689, 2016
2016 billion-ton report: advancing domestic resources for a thriving bioeconomy, volume 2: environmental sustainability effects of select scenarios from volume 1
RA Efroymson, MH Langholtz, K Johnson, B Stokes, CC Brandt, MR Davis, ...
Oak Ridge National Lab.(ORNL), Oak Ridge, TN (United States), 2017
Automating arc hydro for watershed delineation
C Kraemer, SS Panda
Proceedings of the 2009 Georgia water resources conference, held at the …, 2009
Application of vegetation indices for agricultural crop yield prediction using neural network techniques. Remote Sens., 2, 673–696
SS Panda, DP Ames, S Panigrahi
Crop yield forecasting from remotely sensed aerial images with self-organizing maps
SS Panda, S Panigrahi, DP Ames
Transactions of the ASABE 53 (2), 323-338, 2010
Forests, land use change, and water
DM Amatya, G Sun, CG Rossi, HS Ssegane, JE Nettles, S Panda
Impact of Climate Change on Water Resources in Agriculture, 2015
Assessment of spatial and temporal variation of potential evapotranspiration estimated by four methods for South Carolina
DM Amatya, A Muwamba, S Panda, T Callahan, S Harder, CA Pellett
Journal of South Carolina Water Resources 5 (1), 5, 2018
Variation of acoustical parameters of dextran in 2 (M) glycine with temperature and concentrations
S Panda, AP Mahapatra
International Journal of Chemical and Physical Sciences 5 (5), 15-22, 2016
Remote Sensing Systems—Platforms and Sensors: Aerial, Satellite, UAV, Optical, Radar, and LiDAR
SS Panda, MN Rao
Remotely Sensed Data Characterization, Classification, and Accuracies, 37-92, 2015
Blueberry crop growth analysis using climatologic factors and multi-temporal remotely sensed imageries
SS Panda, J Martin, G Hoogenboom
Georgia Institute of Technology, 2011
Assessment of miscanthus yield potential from strip-mined lands (SML) and its impacts on stream water quality
K Sahoo, AM Milewski, S Mani, N Hoghooghi, SS Panda
Water 11 (3), 546, 2019
Stomatal Conductance, Canopy Temperature, and Leaf Area Index Estimation Using Remote Sensing and OBIA techniques.
S Panda, DM Amatya, G Hoogenboom
Journal of Spatial Hydrology 12 (1), 2014
Estimation of evapotranspiration and its parameters for pine, switchgrass, and intercropping with remotely-sensed images based geospatial modeling
SS Panda, DM Amatya, A Muwamba, G Chescheir
Environmental Modelling & Software 121, 104487, 2019
Analysis of remotely sensed aerial images for precision farming.
SS Panda, P Suranjan
Analysis of remotely sensed aerial images for precision farming., 1-24, 2000
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