Remote Sensing and Earth Observation Research Laboratory

Welcome to the research group dedicated to advancing the field of remote sensing and polarimetric decomposition techniques for environmental monitoring. Our lab focuses on cutting-edge research and practical applications in Synthetic Aperture Radar (SAR), optical, and hyperspectral remote sensing. We offer exciting opportunities for future students to contribute to developing innovative solutions for understanding our planet's dynamic ecosystems.

Research Focus:

• SAR Remote Sensing: Our lab explores the use of SAR technology to observe and analyze the Earth's surface. We investigate the unique capabilities of SAR systems, such as interferometry, polarimetry, and high-resolution imaging, to study various environmental phenomena, including land cover classification, terrain mapping, and subsidence monitoring. By leveraging SAR data, we aim to enhance our understanding of natural processes and human-induced changes.

• Optical Remote Sensing: We employ optical sensors to capture detailed images of the Earth's surface. Our research in optical remote sensing involves the analysis of multispectral and hyperspectral data to extract valuable information about vegetation health, land use, urban growth, and water quality. We investigate advanced algorithms and machine learning techniques to unlock the potential of optical imagery for environmental monitoring and management.

• Hyperspectral Remote Sensing: Our lab focuses on the emerging field of hyperspectral remote sensing, which enables us to capture high-resolution spectral information across a wide range of electromagnetic wavelengths. By analyzing hyperspectral data, we aim to improve the characterization and monitoring of land surface properties, including vegetation species discrimination, mineral mapping, and pollution detection. We explore advanced data preprocessing, feature extraction, and classification techniques to unlock the wealth of information contained within hyperspectral imagery.

• Polarimetric Decomposition Techniques: We specialize in applying polarimetric decomposition techniques for remote sensing data analysis. Polarimetric decomposition allows us to extract valuable information about surface scattering mechanisms, target decomposition, and terrain characteristics. By studying and advancing polarimetric decomposition methods, we aim to enhance the interpretation and understanding of remote sensing data, particularly in complex environments such as vegetation, forests, urban areas, and coastal zones.

Datasets: 

Our research lab delves into the vast realm of satellite data to gain valuable insights into the Earth's surface and environment. We unlock critical information about our planet's ecosystems by leveraging Synthetic Aperture Radar (SAR) and optical satellite data, particularly from missions such as our ISRO's RISAT-1A(EOS-04), ESA's Sentinel-1 and Sentinel-2, NASA's, MODIS, and PROBA-V. Join us as we journey to understand and address environmental challenges, paving the way for sustainable resource management and conservation.

SAR Satellite Data: Synthetic Aperture Radar (SAR) offers a unique perspective, allowing us to capture images of the Earth's surface regardless of weather conditions or daylight limitations. We utilize SAR data for a variety of applications, including:

1. Land Cover Classification: SAR sensors, such as those on the Sentinel-1 satellite, provide high-resolution radar images that enable us to distinguish between different land cover types. This information aids in land management, ecosystem monitoring, and land-use planning.

2. Terrain Mapping: SAR data allows us to map and monitor changes in topography, detect land subsidence, and analyze the effects of natural disasters such as earthquakes or landslides. This information is crucial for urban planning, infrastructure development, and disaster mitigation.

Optical Satellite Data: Optical satellite sensors capture imagery using visible, near-infrared, and thermal infrared wavelengths, providing valuable information about the Earth's surface. We harness optical data for various environmental applications, including:

1. Vegetation Health Assessment: Optical sensors on satellites like Sentinel-2 and MODIS provide multispectral imagery, enabling us to monitor vegetation health, detect stress conditions, and assess crop productivity. This information supports precision agriculture, forestry management, and biodiversity studies.

2. Land Cover Mapping: By analyzing the spectral characteristics of different land surface features, optical satellite data allows us to classify and map land cover types. This information aids urban planning, natural resource management, and conservation efforts.

Examples of SAR and Optical Satellites:

1. SAR Satellites: Sentinel-1 (European Space Agency), RadarSat-2 (Canadian Space Agency), EOS-04 (ISRO).

2. Optical Satellites: Sentinel-2 (European Space Agency), MODIS (NASA), Landsat 8 (NASA).

Application

Remote sensing has significant applications in many areas, including the agricultural and defense sectors, providing valuable insights and enhancing operational capabilities.

• Agricultural Monitoring: Remote sensing has significant applications in optimizing crop management and improving productivity in agriculture. It enables farmers to monitor crop health, detect nutrient deficiencies, identify irrigation needs, and assess vegetation vigor. This information helps implement targeted interventions, increasing crop yields and reducing resource wastage. Remote sensing also aids in the early detection of pests, diseases, and invasive species, minimizing crop losses and supporting sustainable agricultural practices.

• Defense and Security: Remote sensing technologies provide valuable intelligence and surveillance capabilities for defense and security purposes. Satellite imagery and remote sensing data support identifying and monitoring potential threats, including troop movements, infrastructure changes, and illegal activities. Remote sensing also aids in mapping and monitoring border areas, facilitating border control and surveillance. Additionally, remote sensing supports terrain analysis, vegetation coverage assessment, and situational awareness, contributing to strategic planning and decision-making in defense operations.

• Environmental Monitoring: Remote sensing plays a crucial role in monitoring and assessing environmental changes. It helps in detecting and monitoring deforestation, land degradation, and habitat loss. Remote sensing data can track changes in vegetation cover, monitor water quality and availability, detect pollution sources, and assess the impact of natural disasters. This information aids in effective environmental management, conservation planning, and the protection of natural resources.

• Disaster Management: Remote sensing is instrumental in disaster management and response. It provides timely and accurate information for assessing the extent of damage caused by natural disasters such as earthquakes, hurricanes, and tsunamis. Satellite imagery helps identify affected areas, assess infrastructure damage, and support search and rescue operations. Remote sensing also enables monitoring of post-disaster recovery and reconstruction efforts, aiding in mitigating the impact of disasters and facilitating efficient disaster response.

• Water Resource Management: Remote sensing plays a vital role in monitoring and managing water resources. It helps in assessing water quality, monitoring changes in water bodies, and analyzing water availability and usage. Remote sensing data can assist in identifying potential areas of water stress, optimizing water allocation, and supporting sustainable water resource management strategies.

With high motivation, our research endeavors revolve around answering the following questions by utilizing diverse techniques such as deep learning, artificial intelligence (AI), and Physical, Semi-empirical modeling of the surface:

• What is the land cover and land use distribution? (Land cover and land use mapping)
• How is vegetation health? (Vegetation health assessment)
• What is the condition of agricultural crops? (Crop monitoring and assessment)
• What are the environmental impacts and trends? (Environmental monitoring and change detection)
• How can we monitor water resources and urban development? (Water resource management and urban monitoring)
• How can we manage natural resources sustainably? (Sustainable resource management)
• How can we assess and respond to natural disasters? (Disaster assessment and response)

**

Research Scholar

• Sumana Khamrai
• Bharat Kumar Prajapati
• Vivek Tiwari
• Sonu Kumar
• Muskan
• Subham Roy 
• Ahana Mukhopadhyay

M.Sc. and IDD Students - 20 

Former Scholar

• Dr. Shubham Kumar Singh (PDF USA)
• Dr. Bhagyashree Verma (PDF USA)
• Dr. Suraj A Yadav (PDF USA)
• Dr. Jyoti Sharma (Scientist, Indian Mereological Department, New Delhi)            
• Dr. Ajeet K Vishwakarma (PGT, Government College)
• Dr. Vijay P Yadav (Assistant Professor, Government Degree College, Obra, UP)
• Dr. Ruchi Bala (Assistant Professor, Ara University, Bihar)
• Dr. Varun Narayan Mishra (Assistant Professor at Amity University, Ghaziabad, UP)
• Dr. Pradeep Kumar (Dr. Kothari PDF, JNU, New Delhi)
• Dr. Deelip Kumar Gupta (Dr. Kothari PDF, BHU)
• Dr. Abhishek Pandey​​​

List of Publications

1.    Ruchi Bala, Vijay Pratap Yadav, D. Nagesh Kumar, Rajendra Prasad, Examining the relationship of major air pollutants with land surface parameters and its monthly variation in Indian cities using satellite data, Remote Sensing Applications: Society and Environment, Volume 35, 2024, 101232.

2.    Sharma, J., Prasad, R., Srivastava, P. K., Yadav, S. A., Singh, S. K., and Verma, B., “Development of a new vegetation modulated soil moisture index for the spatial disaggregation of SMAP soil moisture data product,” Physics and Chemistry of the Earth, Parts A/B/C, p. 103 594, (2024).

3.    Bhagyashree Verma, Prachi Singh, Rajendra Prasad. Prashant Kumar Srivastava, Rucha Dave, 2023, Leaf chlorophyll content retrieval for AVIRIS-NG imagery using different feature selection and wavelet analysis, Advances in Space research, https://doi.org/10.1016/j.asr.2023.06.005.

4.    Bala R., Yadav V. P., Kumara D. Nagesh., Prasad R., 2023, Assessment of Surface Energy Fluxes relation with land cover parameters in four distinct Indian cities using remote sensing data. Theoretical and applied climatology.

5.    Bala R., Yadav V. P., Kumara D. Nagesh., Prasad R., 2023, Quantification of Surface Urban Heat Island Intensity using MODIS satellite imagery in different Indian cities. Journal of the Indian Society of Remote Sensing. 

6.   Shubham Kumar Singh, Rajendra Prasad, Prashant Kumar Srivastava, Suraj A. Yadav, Vijay P. Yadav, Jyoti Sharma, 2023, Incorporation of first order backscattering power in Water Cloud Model for improving the leaf area index and soil moisture retrieval using dual-polarized Sentinel-1 SAR data, Remote sensing of Environment, Volume 296, 113756.

7.    Singh, S. K., Prasad, R., Vivek Tiwari & Srivastava, P. K., 2023, An improved volume power approach to estimate LAI from optimized dual-polarized SAR decomposition, International Journal of Remote Sensing, Volume 44, 2023 - Issue 18.

8.    Yadav, S.A., Prasad, R., Srivastava, P.K., Singh, S.K., Sharma, J. and Khamrai, S., 2022. Time-series polarimetric bistatic scattering decomposition using comprehensive modified first-order radiative transfer model at C-band for vegetative terrain and validation. International Journal of Remote Sensing, 43(19-24), pp.7161-7180.

9.    Singh, S.K., Prasad, R., Yadav, V.P., Yadav, S.A., Sharma, J. and Srivastava, P.K., 2022. Synergy of dual–polarimetric radar vegetation descriptor and Gaussian processes regression algorithm for estimation of leaf area index. International Journal of Remote Sensing, 43(19-24), pp.6921-6935.

10.    Yadav, S. A., Prasad, R., Yadav, V. P., Verma, B., Singh, S. K., Sharma, J., & Srivastava, P. K. (2022). Far-field bistatic scattering simulation for rice crop biophysical parameters retrieval using modified radiative transfer model at X-and C-band. Remote Sensing of Environment, 272, 112959.

11.    Gupta, D.K., Srivastava, P.K., Pandey, D.K., Chaudhary, S.K., Prasad, R. and O’Neill, P.E., 2022. Passive only Microwave Soil Moisture Retrieval in Indian Cropping Conditions: Model Parameterization and Validation. IEEE Transactions on Geoscience and Remote Sensing. 

12.    Sharma, J., Prasad, R., Srivastava, P. K., Yadav, S. A., & Yadav, V. P. (2022). Improving Spatial Representation of Soil Moisture through different downscaling approaches. IEEE Transactions on Geoscience and Remote Sensing.  

13.    Verma, B., Prasad, R., Srivastava, P. K., Yadav, S. A., Singh, P., & Singh, R. K. (2022). Investigation of optimal vegetation indices for retrieval of leaf chlorophyll and leaf area index using enhanced learning algorithms. Computers and Electronics in Agriculture, 192, 106581.  

14.    Yadav, S.A., Prasad, R., Srivastava, P.K., Singh, S.K., Sharma, J. and Khamrai, S., 2022. Time-series polarimetric bistatic scattering decomposition using comprehensive modified first-order radiative transfer model at C-band for vegetative terrain and validation. International Journal of Remote Sensing, 43(19-24), pp.7161-7180.  

15.    Singh, S.K., Prasad, R., Yadav, V.P., Yadav, S.A., Sharma, J. and Srivastava, P.K., 2022. Synergy of dual–polarimetric radar vegetation descriptor and Gaussian processes regression algorithm for estimation of leaf area index. International Journal of Remote Sensing, 43(19- 24), pp.6921-6935. 

16.    Singh, P., Srivastava, P.K., Mall, R.K., Bhattacharya, B.K. and Prasad, R., 2022. A hyperspectral R based leaf area index estimator: model development and implementation using AVIRIS-NG. Geocarto International, pp.1-18. 

17.    Singh, R., Srivastava, P.K., Petropoulos, G.P., Shukla, S. and Prasad, R., 2022. Improvement of the “Triangle Method” for Soil Moisture Retrieval Using ECOSTRESS and Sentinel-2: Results over a Heterogeneous Agricultural Field in Northern India. Water, 14(19), p.3179. 

18.    Chaudhary, S.K., Srivastava, P.K., Gupta, D.K., Kumar, P., Prasad, R., Pandey, D.K., Das, A.K. and Gupta, M., 2022. Machine learning algorithms for soil moisture estimation using Sentinel-1: Model development and implementation. Advances in Space Research, 69(4), pp.1799-1812. 

19.    Chaudhary, S.K., Gupta, D.K., Srivastava, P.K., Pandey, D.K., Das, A.K. and Prasad, R., 2021. Evaluation of radar/optical based vegetation descriptors in water cloud model for soil moisture retrieval. IEEE Sensors Journal, 21(18), pp.21030-21037.
 
20.    Srivastava, P.K., Petropoulos, G.P., Prasad, R. and Triantakonstantis, D., 2021. Random forests with bagging and genetic algorithms coupled with least trimmed squares regression for soil moisture deficit using SMOS satellite soil moisture. ISPRS International Journal of GeoInformation, 10(8), p.507. 

21.    Sharma, J., Prasad, R., Srivastava, P.K., Singh, S.K., Yadav, S.A. and Yadav, V.P., 2021. Roughness characterization and disaggregation of coarse resolution SMAP soil moisture using single-channel algorithm. Journal of Applied Remote Sensing, 15(1), pp.014514-014514.  

22.    Gupta, D.K., Srivastava, P.K., Singh, A., Petropoulos, G.P., Stathopoulos, N. and Prasad, R., 2021. Smap soil moisture product assessment over wales, uK, using observations from the wsmn ground monitoring network. Sustainability, 13(11), p.6019. 

23.    Srivastava, P.K., Gupta, M., Singh, U., Prasad, R., Pandey, P.C., Raghubanshi, A.S. and Petropoulos, G.P., 2021. Sensitivity analysis of artificial neural network for chlorophyll prediction using hyperspectral data. Environment, Development and Sustainability, 23, pp.5504-5519. 

24.    Vijay Pratap Yadav, R. Prasad, R. Bala, Prashant K. Srivastava (2020). ‘Synergy of Vegetation and Soil Microwave Scattering Model for Leaf Area Index Retrieval Using C- Band Sentinel-1A Satellite Data’. IEEE Geoscience and Remote Sensing Letters, DOI: 10.1109/LGRS.2020.3034420.  

25.    Vijay Pratap Yadav, Rajendra Prasad, Ruchi Bala & Prashant K. Srivastava (2021). ‘Assessment of red-edge vegetation descriptors in a modified water cloud model for forward modelling using Sentinel – 1A and Sentinel – 2 satellite data.’ International Journal of Remote Sensing, 42:3, 794-804. 

26.    Kumar Pradeep, Pratap Vineet, Kumar Akhilesh, Choudhary Arti, Prasad Rajendra, Shukla Anuradha, Singh R.P., Singh Abhay Kumar (2020), Assessment of atmospheric aerosols  over Varanasi: Physical, optical and chemical properties and meteorological applications. Journal of Atmospheric and Solar-Terrestrial Physics., https://doi.org/10.1016/j.jastp. 2020.105424.  

27.    Suraj A. Yadav, Rajendra Prasad, A.K. Vishwakarma, Jyoti Sharma, Bhagyashree Verma, Prashant K. Srivastava, (2020), Optimization of dual-polarized bistatic specular scatterometer for studying microwave scattering response and vegetation growth parameters retrieval of paddy crop using a machine learning algorithm," Computers and Electronics in Agriculture, Volume 175,105592.  

28.    Yadav, Vijay Pratap, Rajendra Prasad, Ruchi Bala, and A. K. Vishwakarma. (2020) "An improved inversion algorithm for spatio-temporal retrieval of soil moisture through modified water cloud model using C-band Sentinel-1A SAR data." Computers and Electronics in Agriculture 173, 105447.  

29.    Ruchi Bala, Rajendra Prasad, Vijay Pratap Yadav. (2020), “A comparative analysis of day and night land surface temperature in two semi-arid cities using satellite images sampled in different seasons.” Advances in Space Research, Vol. 66 (2), 412-425.  

30.    Bala, R., Prasad, R. & Yadav, V.P. (2020), “Thermal sharpening of MODIS land surface temperature using statistical downscaling technique in urban areas.” Theoretical & Applied Climatology. vol .141, pp 935 -946.  

31.    Singh, U., Prashant K. Srivastava, Pandey, D.K., Chaurasia, S., Gupta, D.K., Chaudhary, S.K., Prasad, R. and Raghubanshi, A.S., (2019). ScatSat-1 Leaf Area Index Product: Models Comparison, Development, and Validation Over Cropland. IEEE Geoscience and Remote Sensing Letters, 17(4), pp.563-567.  

32.    Vijay Pratap Yadav, Rajendra Prasad, Ruchi Bala (2019). “Leaf area index estimation of wheat crop using modified water cloud model from the time-series SAR and optical satellite data.” Geocarto International, DOI: 10.1080/10106049.2019.1624984.  

33.    Bala, R., Prasad, R., & Yadav, V. P. (2019). Disaggregation of MODIS land surface temperature in urban areas using improved thermal sharpening techniques. Advances in Space Research. Vol. 64, pp. 591-602  

34.    Vishwakarma, A. K., and Prasad, R. (2019). Bistatic specular scattering measurements for the estimation of rice crop growth variables using fuzzy inference system at X-, C-, and L- bands. Geocarto International, 1-17. DOI: 10.1080/10106049.2019.1576777.  

35.    Mishra, V. N., Prasad, R., Rai, P. K., Vishwakarma, A. K., & Arora, A. (2019). Performance evaluation of textural features in improving land use/land cover classification accuracy of heterogeneous landscape using multi-sensor remote sensing data. Earth Science Informatics, 12 (1), 71-86.  

36.    Yadav, V. P., Prasad, R., Bala, R., Vishwakarma, A. K., & Yadav, S. A. (2018). Estimation of biophysical parameters of wheat crop through modified water cloud model using satellite data. ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences, Vol. 4 Issue 5, p239-244.  

37.    Bala, R., Prasad, R., Yadav, V. P., & Sharma, J. (2018). a Comparative Study of Land Surface Temperature with Different Indices on Heterogeneous Land Cover Using Landsat 8 Data. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. XLII-5, pp.389-394.  

38.    Yadav, S. A., Prasad, R., Vishwakarma, A. K., & Yadav, V. P. (2018). Random forest regression for the estimation of leaf area index of okra crop using ground based bistatic scatterometer. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences. Vol. XLII-5, pp. 719-725.  

39.    Sharma, J., Prasad, R., Mishra, V. N., Yadav, V. P., & Bala, R. (2018). Land Use and Land  Cover Classification of Multispectral LANDSAT-8 Satellite Imagery Using Discrete Wavelet Transform. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. XLII-5, pp. 703-706.  

40.    V.N. Mishra, P.K. Rai, R. Prasad, M. Punia, M.M. Nistor (2018). Prediction of spatiotemporal land use/land cover dynamics in rapidly developing Varanasi district of Uttar Pradesh, India using Geospatial approach: A comparison of hybrid models. Applied Geomatics. Vol. 10, pp. 257-276.  

41.    A. K. Vishwakarma, R. Prasad, D.K. Gupta, P. Kumar, V.N. Mishra (2018). Ground based bistatic scatterometer measurement for the estimation of growth variables of ladyfinger crop at X-band. Journal of the Indian Society of Remote Sensing. Vol. 46, pp. 973-980.  

42.    Gupta DK, Prasad R, Kumar P, Srivastava PK, Islam T (2018) Robust machine learning techniques for rice crop variables estimation using multiangular bistatic scattering coefficients. J Appl Remote Sens 12(03):1.  

43.    P. Kumar, R. Prasad, A. Choudhary, D.K. Gupta, V.N. Mishra, A.K. Vishwakarma, A. K. Singh, P. K. Srivastava (2018). Comprehensive evaluation of soil moisture retrieval models under different crop cover types using C-band synthetic aperture radar data. Geocarto International. https://doi.org/10.1080/10106049.2018.1464601.  

44.    P. Kumar, R. Prasad, D.K. Gupta, V.N. Mishra, A.K. Vishwakarma, V.P. Yadav, R. Bala, A. Choudhary, R. Avtar (2017), Estimation of winter wheat crop growth parameters using time series Sentinel-1A SAR data, Geocarto International, pp. 1-15.  

45.    P. Kumar, R. Prasad, A. Choudhary, V.N. Mishra, D.K. Gupta, P.K. Srivastava, (2017), A statistical significance of differences in classification accuracy of crop types using different classification algorithms, Geocarto International, 32 (206-224).  

46.    D.K. Gupta, R. Prasad, P. Kumar, A.K. Vishwakarma, P.K. Srivastava (2017), Vegetation water content retrieval using scatterometer data at X-band, Geocarto International, pp. 1-10.  

47.    V.N. Mishra, R. Prasad, P. Kumar, D.K. Gupta, S. Agrawal, A. Gangwal, (2017), Assessment of spatio-temporal changes in land use/land cover over a decade (2000- 2014) using earth observation datasets: a case study of Varanasi district, India, Iranian Journal of Science and Technology Transactions of Civil Engineering (In Press).  

48.    D.K. Gupta, R. Prasad, Pradeep Kumar, A.K. Vishwakarma (2017), Soil moisture retrieval using ground based bistatic scatterometer data at X-band, Advances in Space Research, 59 (996-1007).  

49.    V.N. Mishra, R. Prasad, P. Kumar, D.K. Gupta, P.K. Srivastava (2017), Dual-polarimetric Cband SAR data for land use/land cover classification by incorporating textural information, Environmental Earth Sciences, 76 (1-16).  

50.    P. Kumar, R. Prasad, D.K. Gupta, A.K. Vishwakarma, & A. Choudhary, Retrieval of rice crop growth variables using multi-temporal RISAT-1 remotely sensed data. International Journal of Russian Agricultural Sciences. Vol. 43, pp. 461-465.  

51.    V.N. Mishra, R. Prasad, P. Kumar, P.K. Srivastava, P.K. Rai (2017), Knowledge- based decision tree approach for mapping spatial distribution of rice crop using C-band SAR derived information, Journal of Applied Remote Sensing11 (4), 046003.  

52.    Srivastava, P.K.; Han, D.; Yaduvanshi, A.; Petropoulos, G.P.; Singh, S.K.; Mall, R.K.; Prasad, R. Reference Evapotranspiration Retrievals from a Mesoscale Model Based Weather Variables for Soil Moisture Deficit Estimation. Sustainability 2017, 9, 1971.  

53.    V.N. Mishra, P.K. Rai, P. Kumar, R. Prasad (2016), Evaluation of land use/land cover classification accuracy using multi-resolution remote sensing images, Forum Geographic, 15 (45-53).  

54.    D.K. Gupta, R. Prasad, P. Kumar, V.N. Mishra (2016). Estimation of crop variables using bistatic scatterometer data and artificial neural network trained by empirical models. Computers and Electronics in Agriculture, 123: 64-73.  

55.    P. Kumar, R. Prasad, V.N. Mishra, D.K. Gupta & S.K. Singh (2016). Artificial neural network for crop classification using C-band RISAT-1 satellite datasets. International Journal of Russian Agricultural Sciences. 42: 281-284.  

56.    P. Kumar, D. K. Gupta, V. N. Mishra and R. Prasad (2015), Comparison of support vector machine, artificial neural network, and spectral angle mapper algorithms for crop classification using LISS IV data, International Journal of Remote Sensing, 36(6):1604-1617.  

57.    P. Kumar, R. Prasad, D.K. Gupta, V.N. Mishra, and A. Choudhary, (2015). Support vector machine for classification of various crops using high resolution LISS-IV Imagery Bulletin of Environmental and Scientific Research. Vol. 4, Issue (3), pp.1-5.  

58.    D.K. Gupta, P. Kumar, V.N. Mishra, R. Prasad, P.K.S. Dikshit, S.B. Dwivedi, A. Ohri, R.S. Singh, V. Srivastava, (2015) Bistatic measurements for the estimation of rice crop variables using artificial neural network, Advances in Space research, 55 (6): 1613-1623.  

59.    Pandey, A., Prasad, R., Singh, V. P., Jha, S.K., and Shukla, K.K., (2013) Crop Parameters Estimation by Fuzzy Inference System Using X-band Scatterometer Data, Advances in Space Research, 51: 905–911.  

60.    Prasad, R., Pandey, A., Singh, K.P., Singh, V.P., Mishra, R.K., Singh, D., (2012), Retrieval of Spinach Crop Parameters By Microwave Remote Sensing With Back Propagation Artificial Neural Networks: A Comparison of Different Transfer Functions, Advances in Space Research, 50: 363–370.  

61.    Pandey A., Khem B. Thapa, R. Prasad and K.P. Singh, (2012) General Regression Neural Network and Radial Basis Neural Network for the Estimation of Crop Variables of Lady Finger, Journal of Indian Society of Remote Sensing, DOI 10.1007/s12524-011-0197-9.  

62.    Pandey, A., Prasad, R., Srivastava, J.K., Singh,V.P., (2012) Retrieval of Soil Moisture by Artificial Neural Network Using X-band Ground Based Data, International Journal of Russian Agricultural Sciences, Vol. 38, No. 3, pp. 230–233.  

63.    Prasad, R., (2011), Estimation of crop variables of kidney-bean using ground based scatterometer data at 9.89 GHz, International Journal of Remote Sensing, Vol. 32, Issue 1, pp. 31–48.  

64.    Pandey, A., Prasad, R. and Jha, S.K., (2010), Classification of two Different Rough  Soil Surface by Using Microwave X-band Data Through Support Vector Machine (SVM), International Journal of Russian Agricultural Sciences, Vol. 36, N0. 2, pp. 141- 145.  

65.    Pandey, A., Jha, S.K. and Prasad, R., (2010), Retrieval of Crop Parameters of Spinach by Radial Basis Neural Network Approach Using X-band Scatterometer Data, International Journal of Russian Agricultural Sciences, Vol. 36, No. 4, pp. 312–315.  

66.    Pandey, A., Jha, S. K., Srivastava, J. K. and Prasad, R., (2010), Artificial Neural Network for the Estimation of Soil Moisture and Surface Roughness, International Journal of Russian Agricultural Sciences, Vol. 36, No. 6, pp. 428–432.  

67.    Prasad, R., Pandey, A., Jha, S. K., Singh, K.P. and Yadav, G.S., (2010), Classification of Fields having Different Soil Moisture Content by SVM Technique using Bistatic Scatterometer, International Journal of Recent Research in Science and Technology, 3(1): 105-113.  

68.    Prasad, R., (2009), Retrieval of Crop Variables with Field-based X-band Microwave Remote Sensing of Ladyfinger, International Journal of Advances in Space Research, Vol. 43, pp. 1356–1363.  

69.    Prasad, R., Kumar, R. and Singh, D., (2009), A Radial Basis Function Approach to Retrieve Soil Moisture and Crop Variables from X-band Scatterometer Observations, International Journal of Progress in Electromagnetics Research B, Vol. 12, pp. 201–217.  

70.    Singh, D., Choudhary, N. K., Tiwari, K. C. and R. Prasad, (2009), Shape of Shallow Buried Metallic Objects at X-band using ANN Image Analysis Techniques Recognition, International Journal of Progress in Electromagnetics Research B, Vol. 13, pp. 257–273.  

71.    Prasad, R., Singh, D. and Singh, K.P., (2009), Microwave Response of Vegetation Lady's finger by Bistatic Scatterometer, Asian J Phys, Vol. 17, ISBN No.: 0971-3093, No 2, pp.303306.  

72.    V.N Mishra, R. Prasad, Pradeep Kumar, D.K. Gupta, A.K. Vishwakarma, Analysis of land use and land cover changes using multitemporal Landsat images, National conference on Managing soil resource for environmental sustainability: challenges and perspectives held during 9-10th December 2016 at Institute of environment and sustainable development.  

73.    Pradeep Kumar, R. Prasad, D.K. Gupta V.N. Mishra, A.K. Vishwakarma, Potential of Sentinel-1A SAR data for the estimation of winter wheat crop biophysical parameters, National Symposium on “Recent Advances in Remote Sensing and GIS with Special Emphasis on Mountain Ecosystems” & Annual Conventions of Indian Society of Remote Sensing & Indian Society of Geomatics held during December 7 - 9, 2016 at Dehradun, India.  

74.    V.N. Mishra, R. Prasad, Pradeep Kumar, D.K. Gupta, P.K. Rai, A remote sensing-based study for analyzing land use/land cover changes in Varanasi district, India, published in the proceedings of National Conference on advancements in applications of remote sensing and geospatial technology (AARSGT-2016) organized by the Department of remote sensing, Birla Institute of Technology Meshra, Ranchi, during May 19-21 (2016).  

75.    Pradeep Kumar, R. Prasad, A. Choudhary, D.K. Gupta, V.N. Mishra, P.K. Srivastava, Backscattering and vegetation water content response of paddy crop at C-band using RISAT-1 satellite data, Abstract published in European Geosciences Union General Assembly 2016 scheduled during 17-22 April 2016 in Vienna, Austria. 
 
76.    P. Kumar, R. Prasad, V.N. Mishra, D.K. Gupta, A. Choudhary, P.K. Srivastava (2015). Artificial neural network with different learning parameters for crop classification using multispectral datasets. International conference on microwave, optical and communication engineering (ICMOCE-2015) organized by IIT Bhubaneswar, Odisha India during December18-20. Accepted for publication in IEEE Xplore digital library.  

77.    V.N. Mishra, R. Prasad, P. Kumar, D.K. Gupta, P.K.S. Dikshit, S.B. Dwivedi, A. Ohri, (2015). Evaluating the effects of spatial resolution on land use and land cover classification accuracy. International conference on microwave, optical and communication engineering (ICMOCE-2015) organized by IIT Bhubaneswar, Odisha India during December18-20. Accepted for publication in IEEE Xplore digital library.  

78.    D.K. Gupta, R. Prasad, P. Kumar, V.N. Mishra, A.K. Vishwakarma, R.S. Singh, V. Srivastava (2015). Spatial modeling of SPAD values for different type of crops using LISS-IV satellite imagery. International conference on microwave, optical and communication engineering (ICMOCE-2015) organized by IIT Bhubaneswar, Odisha, India during December18-20. Accepted for publication in IEEE Explore digital library.  

79.    D.K. Gupta, R. Prasad, P. Kumar, V.N. Mishra, P.K.S. Dikshit, S.B. Dwivedi, A. Ohri, R.S. Singh, V. Srivastava, P.K. Srivastava (2015). Crop variables estimation by adaptive neuro- fuzzy inference system using bistatic scatterometer data. II International conference on microwave and photonics (ICMAP-2015) organized by ISM Dhanbad, India during December11-13. Accepted for publication in IEEE Xplore digital library.  

80.    V.N. Mishra, R. Prasad, P. Kumar, D.K. Gupta, P.K.S. Dikshit, S.B. Dwivedi, R.S. Singh, V. Srivastava (2015). Supervised algorithms for classification of remotely sensed satellite image using open-source support. Published in Proceedings of National Conference on Open-Source GIS: Opportunities and Challenges, Department of Civil Engineering, IIT (BHU), Varanasi October 9-10. ISBN: 978-81-931-2500-7.  

81.    D.K. Gupta, R. Prasad, P. Kumar, V.N. Mishra, A.K. Vishwakarma, P.K. Srivastava (2015). Support vector regression for retrieval of soil moisture using bistatic scatterometer data at x- band. International Journal of Environmental, Chemical, Ecological, Geological and Geophysical Engineering, 9(10). 

82.    P. Kumar, R. Prasad, A. Choudhary, D.K. Gupta, V.N. Mishra, P.K. Srivastava (2016). “Backscattering and vegetation water content response of paddy crop at C-band using RISAT1 satellite data”. Accepted in European Geosciences Union General Assembly 2016 scheduled to be held during April17-22 in Vienna, Austria.  

83.    Gupta, D. K., Kumar, P., Mishra, V. N., R. Prasad, (2014), Soil moisture Estimation by ANN using Bistatic Scatterometer Data, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume II-8, Symposium, 09–12 December 2014, Hyderabad, India.  

84.    Mishra, V. N. Kumar, P., Gupta, D. K., R. Prasad, (2014), Classification of Various Land Features using RISAT-1 Dual Polarimetric Data, The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume II- 8, Symposium, 09–12, December 2014, Hyderabad, India.  

85.    Pandey, A., Prasad, R. and Rajput, N. S., (2009), Study of Vegetable Okra by Microwave Remote Sensing at X- Band, IEEE Explorer available online, ISBN No.: 978-1-4244-4846- 3, pp. 212-215.  
 

Book Chapters

• Dileep Kumar Gupta, Rajendra Prasad, Prashant K. Srivastava (2021). Bistatic scatterometer for the retrieval of soil moisture, Agricultural Water Management, Theories and Practices, Academic Press, Elsevier, USA, Pages 279-305
• Sumit Kumar Chaudhary, Jyoti Sharma Dileep Kumar Gupta, Rajendra Prasad, Prashant K. Srivastava, Dharmendra Pandey, (2020). Artificial neural network for the estimation of soil moisture using earth observation datasets, Agricultural Water Management, Theories and Practices, Academic Press, Elsevier, USA, DOI: 10.1016/B978-0-12-812362-1.00012-6.
• Dileep Kumar Gupta, Rajendra Prasad, Prashant K. Srivastava, Tanvir Islam and Manika Gupta (2015). Fuzzy logic for the Retrieval of Soil Moisture using Bistatic Scatterometer data. Geospatial Technology for Water Resource Applications CRC Press, Taylor and Francis, USA, Chapter 17, pp. 272-288.
• D. K. Gupta, R. Prasad, P.K. Srivastava and T. Islam (2015). Nonparametric model for the retrieval of soil moisture by microwave remote sensing. Satellite soil Moisture Retrieval. Academic Press, Elsevier, USA, Chapter 08, pp. 1-10.
• P.K. Srivastava, T. Islam, S.K Singh, M. Gupta, D.K Gupta, W.Z. Wan Jaafar and R. Prasad (2015). Soil moisture deficit estimation through SMOS soil moisture and MODIS land surface temperature. Academic Press, Elsevier, USA, Chapter 17, pp. 1-15.

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