Leaf area index estimation using top-of-canopy airborne RGB images R Raj, JP Walker, R Pingale, R Nandan, B Naik, A Jagarlapudi International Journal of Applied Earth Observation and Geoinformation 96, 102282, 2021 | 55 | 2021 |
Precision agriculture and unmanned aerial Vehicles (UAVs) R Raj, S Kar, R Nandan, A Jagarlapudi Unmanned aerial vehicle: Applications in agriculture and environment, 7-23, 2020 | 47 | 2020 |
Geo-ICDTs: Principles and applications in agriculture S Suradhaniwar, S Kar, R Nandan, R Raj, A Jagarlapudi Geospatial technologies in land resources mapping, monitoring and management …, 2018 | 20 | 2018 |
Impact of irrigation scheduling methods on corn yield under climate change R Nandan, DK Woo, P Kumar, J Adinarayana Agricultural Water Management 255, 106990, 2021 | 15 | 2021 |
Drone-based sensing for leaf area index estimation of citrus canopy R Raj, S Suradhaniwar, R Nandan, A Jagarlapudi, J Walker Proceedings of UASG 2019: Unmanned Aerial System in Geomatics 1, 79-89, 2020 | 12 | 2020 |
Formosat-2 with Landsat-8 temporal-multispectral data for wheat crop identification using Hypertangent Kernel based Possibilistic classifier R Nandan, A Kamboj, A Kumar, AS Kumar, KV Reddy Journal of Geomatics 10 (1), 2016 | 8 | 2016 |
Evaluating optical remote sensing methods for estimating leaf area index for corn and soybean R Nandan, V Bandaru, J He, C Daughtry, P Gowda, AE Suyker Remote sensing 14 (21), 5301, 2022 | 7 | 2022 |
Precision agriculture and unmanned aerial vehicles (UAVs). Unmanned aerial vehicle: Applications in agriculture and environment R Raj, S Kar, R Nandan, A Jagarlapudi Springer: Berlin, 2020 | 6 | 2020 |
Wheat Monitoring by Using Kernel Based Possibilistic c-Means Classifier: Bi-sensor Temporal Multi-spectral Data RN Tripathi, R Kumar, A Kumar, A Senthil Kumar Journal of the Indian Society of Remote Sensing 45, 1005-1014, 2017 | 6 | 2017 |
Improving data management and decision-making in precision agriculture S Kar, R Nandan, R Raj, S Suradhaniwar, J Adinarayana Improving data management and decision support systems in agriculture 1, 1:22, 2020 | 2 | 2020 |
Nitrogen allocation modelling for ecohydrological application: Role of photosynthetic nitrogen in C4 crops under climate change R Nandan, P Kumar, D Woo, J Adinarayana | | 2024 |
Inter-annual Changes in Carbon Balance under Major US Cropping Systems V Bandaru, S Pote, R Nandan, CD Jones, M Cosh, M Kolli AGU23, 2023 | | 2023 |
Robust Spatial Parameterization Scheme to Improve Regional Scale Modeling of CO2 Fluxes Using USDA-Ltar and Satellite Remote Sensing Data V Bandaru, R Nandan, D Menefee, M Cosh, M Williams, P Wagle, ... ASA, CSSA, SSSA International Annual Meeting, 2023 | | 2023 |
Assessing the optimal water and nitrogen management methods for Maize yield intensification under climate change in Semi-Arid India R Nandan, P Kumar, D Woo, J Adinarayana, B Naik AGU Fall Meeting Abstracts 2022, H22H-06, 2022 | | 2022 |
Assimilating SMAP-based Soil Moisture Products into EPIC Crop Model for Improved Simulation of Surface and Subsurface Soil Moisture R Nandan, V Bandaru, M Cosh, S Naga, P Wagle, N Saliendra, MA Liebig, ... AGU Fall Meeting Abstracts 2022, H16E-08, 2022 | | 2022 |
CCMS v. 2.0: Improved Cropland Carbon Monitoring System V Bandaru, R Nandan, C Daughtry, P Beeson, CD Jones, M Cosh AGU Fall Meeting Abstracts 2022, INV25C-0522, 2022 | | 2022 |
Role of leaf-level nitrogen allocation in nitrogen-fertilizer management of maize crop under climate change R Nandan, P Kumar, D Woo, J Adinarayana AGU Fall Meeting Abstracts 2021, H55H-0833, 2021 | | 2021 |
Improving data management and decision support systems in agriculture. S Kar, R Nandan, R Raj, S Suradhaniwar, J Adinarayana | | 2020 |
Effects of Precipitation Variation and Irrigation Scheduling Methods on Maize under Future Climatic conditions R Nandan, D Woo, P Kumar, J Adinarayana AGUFM 2019, GC41H-1253, 2019 | | 2019 |
Modelling to guide maize yield intensification through water and nitrogen management under climate change R Nandan Mumbai, 0 | | |