Spatial variations of LST and NDVI in Muzaffarpur district, Bihar using Google earth engine (GEE) during 1990-2020


  • BHARTENDU SAJAN Centre for Climate Change and Water Research, Suresh Gyan Vihar University, Jaipur-302017, Rajasthan, India
  • SHRUTI KANGA Department of Geography, School of Environment and Earth Sciences, Central University of Punjab, VPO-Ghudda, Bathinda 151401, Punjab, India
  • SURAJ KUMAR SINGH Centre for Sustainable Development, Suresh Gyan Vihar University, Jaipur 302017, Rajasthan, India
  • VARUN NARAYAN MISHRA Amity Institute of Geoinformatics & Remote Sensing (AIGIRS), Amity University, Sector 125, Noida 201313, Gautam Buddha Nagar, India
  • BOJAN DURIN Department of Civil Engineering, University North, Varaždin 42000, Croatia



Land Use/Land Cover, Land Surface Temperature, NDVI, Correlation


The aim of this study is to analyze land cover changes and their effects on land surface temperature (LST) and normalized difference vegetation index (NDVI) in Muzaffarpur district, Bihar, India. The research utilized Landsat 5 and 8 satellite images taken every five years from 1990 to 2020 to classify seven land cover types, namely built-up areas, wetlands, fallow lands, croplands, vegetation, and water bodies, using the Artificial Neural Network technique in ENVI 5.1. The resulting land cover maps reveal a significant decrease in cropland area during the studied period, while fallow land area decreased from 48.06% to 35.79%. Analysis of LST and NDVI data showed a strong negative correlation (R2 < -0.0057) for all years, except for a weak positive correlation (R2 > 0.006). NDVI values were highest in agricultural lands with the lowest LST values, while fallow land areas showed the opposite trend. The study suggests that vegetation and fallow land are crucial determinants of the spatial and temporal variations in NDVI and LST, relative to urban and water cover categories.



Alemu, M. M. (2019). Analysis of Spatio-temporal Land Surface Temperature and Normalized Difference Vegetation Index Changes in the Andassa Watershed, Blue Nile Basin, Ethiopia. J. Res. Eco., 10(1): 77-85. doi:10.5814/j.issn.1674-764x.2019.01.010

Bhagia, N, Oza, M.P., Rajak, D.R., and Dadhwal, V.K. (2005). Wheat Yield Forecast Models Using Temperature Based Simple and Weighted Indices for Punjab and Western Uttar Pradesh. J. Agrometeorol., 7 (1): 115–19.

Chaudhari, K.N., Tripathy, R., and Patel, N.K. (2010). Spatial Wheat Yield Prediction Using Crop Simulation Model, GIS, Remote Sensing and Ground Observed Data. J. Agrometeorol., 12 (2): 174–80.

Chen, X. L., Zhao, H. M., Li, P. X., and Yin, Z. Y. (2006). Remote sensing image-based analysis of the relationship between. Remote Sens. Environ., 104(2006), 133-146. DOI: 10.1016/j.rse.2005.11.016

Dadhwal, V. K., and Yamini Bhat. (2023). Revisiting statistical spectral-agrometeorological wheat yield models for Punjab using MODIS EVI and NCMRWF re-analysis temperature data. J. Agrometeorol., 25(1):10–17.

Dash, P., Göttsche, F.-M., Olesen, F.-S., and Fischer, H. (2002). Land surface temperature and emissivity estimation from passive sensor data: theory and practice; current trends. International J. Remote Sens., 23(13): 2563. doi: 10.1080/01431160110115041

Gorgani, A. S., Panahi, M., and Rezaie, F. (2013). The Relationship between NDVI and LST in the urban area of Mashhad, Iran. International Conference on Civil Engineering Architecture & Urban Sustain. Devel., 1-7.

Kumar, D.A., Neelima, T., Srikanth, P., Devi, M.U., Suresh, K. and Murthy, C. (2022). Maize yield prediction using NDVI derived from Sentinal 2 data in Siddipet district of Telangana state. J. Agrometeorol., 24 (2): 165-168.

Lambin, E., Turner, B., Geist, H. J., Agbola, S., Angelsen, A., Bruce, J., and Xu, J. (2001). The causes of land-use and land-cover change: moving beyond the myths. Global Environ. Change, 11: 261-269.

Lin, B. B., Philpott, S. M., and Jha, S. (2015). The future of urban agriculture and biodiversity-ecosystem services: Challenges and next steps. Gfo Ecol. Soc. Germany, Austria and Switzerland, 16(3): 189-201.

Masek, J.G., Vermote, E.F., Saleous, N., Wolfe, R., Hall, F.G., Huemmrich, F., Gao, F., Kutler, J., and Lim, T.K. (2017). LEDAPS Landsat Calibration, Reflectance, Atmospheric Correction Preprocessing Code. Orn. Dac. NASA, 2.

Rakib, A. A., Akter, S. K., Rahman, M. N., Arpi, S., and Kafy, A. A. (2020). Analyzing the Pattern of Land Use Land Cover Change and its Impact on Land Surface Temperature: A Remote Sensing Approachin Mymensingh, Bangladesh. (B. University of Dhaka, Ed.) Dhaka University Research Society, 1-10.

Richards, D. R., and Belcher, R. N. (2019). Global Changes in Urban Vegetation Cover. Remote Sen., 12(1): 23.

Zhu, Z., and Curtis E, W. (2012). Object-based cloud and cloud shadow detection in Landsat imagery. Remote Sens. Environ., 118:83-94.

Zhu, Z., Wang S., and Curtis, W. E. (2015). Improvement and expansion of the Fmask algorithm: Cloud, cloud shadow, and snow detection for Landsats 4-7, 8, and Sentinel 2 images. Remote Sens. Environ., 159: 269-277.




How to Cite

BHARTENDU SAJAN, SHRUTI KANGA, SURAJ KUMAR SINGH, VARUN NARAYAN MISHRA, & BOJAN DURIN. (2023). Spatial variations of LST and NDVI in Muzaffarpur district, Bihar using Google earth engine (GEE) during 1990-2020. Journal of Agrometeorology, 25(2), 262–267.