Publications

Peer Reviewed Journal 

1.  Mane, S., Das, N.N, Singh, G., Cosh, M., Dong, Y*. (2024). Advancements in dielectric constant-based soil moisture sensor calibration: Methods and Techniques. Computer & Electronic in Agriculture.  218, 108686.

2. Banda, E., Rafiei, V., Kpodo, J., Nejadhashemi, A.P*., Singh, G., Das, N.N., Rabin, K., Diallo, A. (2024). Millet yield estimations in Senegal: Unveiling the power of regional water-stress analysis and advanced predictive modelling. Agricultural Water Management, 291, 108618.

3. Singh, G., Das, N.N*., Colliander, A., Entekhabi, D., Yueh, S. (2023). Impact of SAR-based vegetation attributes on the SMAP high-resolution soil moisture product. Remote Sensing of Environment, 298, 113826.

4.  Lal, P., Singh, G., Das, N.N*., Entekhabi, D., Rowena Lohman, Colliander, A., Pandey, D.K., Setia, R.K. (2023). Multi-Scale algorithm for the NISAR mission high-resolution soil moisture product. Remote Sensing of Environment, 113667. 

5.  Keerthana, A., Nair, A*., Singh, G. (2023). An improved hybrid-coupled model for delineation of groundwater potential zones using surface and climatological factors. Theoretical and Applied Climatology, 151: 2001–2022.                                     

6.  Lal, P., Singh, G., Das, N.N*., Colliander, A., Entekhabi, D. (2022). Assessment of ERA5-Land Volumetric Soil Water Layer Product Using In Situ and SMAP Soil Moisture Observations. IEEE Geoscience and Remote Sensing Letters, 19: 2508305.

7.  Singh, G., Das, N.N*. (2022). A data-driven approach using the remotely sensed soil moisture product to identify water-demand in agricultural regions. Science of The Total Environment, 837: 155893. 

8.  Singh, G., Das, N.N*., Panda, R. K., Mohanty, B.P., Entekhabi, D., Bhattacharya, B.K., (2021). Soil Moisture Retrieval using SMAP L-band Radiometer and RISAT-1 C-band SAR Data in the Paddy Dominated Tropical Region of India. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14: 10644-10664. 

9.  Singh, G*., Panda, R. K., Bisht, D.S. (2021). Improved Generalized Calibration of an Impedance Probe for Soil Moisture Measurement at Regional Scale using Bayesian Neural Network and Soil Physical Properties. Journal of Hydrologic Engineering, 26(3): 04020068.                        

10.  Singh, G*., Panda, R.K., Nair, A. (2020). Regional Scale Trend and Variability of Rainfall Pattern over Agro-climatic Zones I n the mid-Mahanadi River Basin of Eastern India. Journal of Hydro-environment Research, 29: 5-19.                                         

11.  Singh, G., Panda, R. K., Mohanty, B. P*. (2019). Spatiotemporal Analysis of Soil Moisture and Optimal Sampling Design for Regional Scale Soil Moisture Estimation in a Tropical Watershed of India. Water Resources Research, 55(3): 2057-2078.        

12.  Singh, G*., Das, N.N., Panda, R. K., Colliander, A., Jackson, T., Mohanty, B.P., Entekhabi, D., and Yueh, S. (2019). Validation of SMAP Soil Moisture Products using Ground-based Observations for the Paddy Dominated Tropical Region of India. IEEE Transactions on Geoscience and Remote Sensing, 57(11): 8479-8491.

13.  Samantaray, A. K., Singh, G., Ramadas, M*, and Panda, R. K. (2018). Drought hotspot analysis and risk assessment using probabilistic drought monitoring and severity–duration–frequency analysis. Hydrological Processes, 33(3): 432–449.    

14.  Singh, G*., Panda, R. K. (2017). Grid-cell based Assessment of Soil Erosion Potential for Identification of Critical Erosion Prone Areas using USLE, GIS and Remote Sensing: A Case Study in the Kapgari Watershed, India. International Soil and Water Conservation Research, 5(3): 202-211.  

15.  Nair, A., Singh, G*., and Mohanty, U.C. (2017). Prediction of Monthly Summer Monsoon Rainfall using Global Climate Models through Artificial Neural Network Technique. Pure and Applied Geophysics, 175(1): 403–419.      

16.  Singh, G., Panda, R.K.* and Lamers, M. (2015). Modeling of Daily Runoff from a Small Agricultural Watershed using Artificial Neural Network with Resampling Techniques. Journal of Hydroinformatics, 17(1): 56-74.                                                     

17.  Singh, G*. and Panda, R. K. (2015). Bootstrap based Artificial Neural Network Analysis for Prediction of Daily Sediment Yield from a Small Agricultural Watershed. International Journal of Hydrology Science and Technology, 5(4): 333-348.                                  

18.  Singh, G*. and Panda, R.K. (2011). Daily Sediment Yield Modeling with Artificial Neural Network using 10-fold Cross Validation Method: A small agricultural watershed, Kapgari, India. International Journal of Earth Sciences and Engineering. 4(6): 443-450.  

19.  Kumar, K. S*., Singh, G., Rao, G.V., and Mouli, S.C. (2011). Spatial Distribution and Multiple Linear Regression Modeling of Ground Water Quality with Geo-statistics.  International Journal of Applied Engineering Research, 6(24): 2719-2730.  

                                                                                                                                                                                            (* = Corresponding author)          

Conference Proceedings  

• Shukla, C., Tiwari, K. N., Singh, G. (2021). Measurement of Soil Properties and Surface Hydrology Parameters to Assess the Variation Induced by Different Plantations. 2021 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor), pp. 215-220, doi: 10.1109/MetroAgriFor52389.2021.9628825. 

• Singh, G., Panda, R. K., Mohanty, B. P, Jana, R.B. (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; doi:10.1117/12.2222743.

• Panda, R. K., Singh, G. (2016). Analysis of Trend and Variability of Rainfall in the Mid-Mahanadi River Basin of Eastern India. Proc. World Academy of Science, Engg. and Tech., 10 (6): 609-613.

• Singh, G., Panda, R. K (2015). Modelling and Assimilation of Root-zone Soil moisture using Surface Observations from Soil Moisture Ocean Salinity Satellite. ASABE 1st Climate Change Symposium: Adaptation and Mitigation Conference Proceedings 152143960. doi:10.13031/cc.20152143960.

Book Chapter

• Singh, G., Panda R.K. (2023). An optimal sampling design to capture the watershed-scale soil moisture dynamic in a tropical agricultural watershed of eastern India. In: Dutta and Chembolu (Eds.) Recent Development in River Corridor Management, RCRM 2022. Lecture Notes in Civil Engineering, vol 376. Springer Nature Singapore. https://doi.org/10.1007/978-981-99-4423-1_22

• Singh, G., Bisht, D.S. (2023). Soil Moisture-Vegetation Stress-based agricultural drought index integrating remote sensing derived soil moisture and vegetation Indices. In: Vijay P. Singh, Deepak Jhajharia, Rasoul Mirabbasi, Rohitashw Kumar (Eds.), Integrated Drought Management (Volume 2), Taylor & Francis Group - 1st Edition. https://doi.org/10.1201/9781003276548

• Samantaray. A. K., Singh, G., and Ramadas, M. (2018). Application of the Relevance Vector Machine to Drought Monitoring. In: Bansal J., Das K., Nagar A., Deep K., Ojha A. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, Vol 816, pp. 891-898, Springer Nature Singapore. doi.org/10.1007/978-981-13-1592-3_71

International Conference

• Singh,G., and Das, N.N. (2021). Assessment of Water-demand Index using SMAP-Sentinel High-Resolution Soil Moisture Product, IEEE Geoscience and Remote Sensing Society- 2021 Paper Number: 3990.

• Singh, G., and Das, N.N. (2020). A Data Driven Approach using the SMAP-Sentinel High-Resolution Soil Moisture Product for Agricultural Application, AGU Fall Meeting-2020, H046-01-746621.

• Singh, G., Panda, R. K., and Mohanty, B. P. (2019). Spatiotemporal Analysis of Soil Moisture and Estimation of Soil Hydraulic Properties using Spatially Distributed Soil Moisture Measurements in a Tropical Watershed of India, AGU Fall Meeting-2019, Abstract No.: H51H-1588.

• Singh, G., Ramadas, M., and Panda, R. K. (2018). Development of Soil Moisture-Vegetation Stress-based Agricultural Drought Index (SVADI) through Integration of Remotely Sensed Soil Moisture and Vegetation Indices. AGU Fall Meeting-2018, Abstract No.: H43G-2529.

• Singh, G., Das, N.N, Panda, R. K., Mohanty, B. P., Entekhabi D., and Bhattacharya, B. (2016). High Resolution Soil Moisture Retrieval using SMAP-L Band Radiometer and RISAT-C band Radar Data for the Indian Subcontinent. AGU Fall Meeting-2016, Abstract No.: H31G-1478.

• Singh, G., Panda, R. K., and Mohanty, B. P. (2015) Prediction of Root Zone Soil Moisture using Remote Sensing Products and In-Situ Observation under Climate Change Scenario. AGU Fall Meeting-2015, Abstract No.: H43H-1638.