
AGRICULTURAL DROUGHT MONITORING BASED ON LANDSAT 8 DERIVED VEGETATION HEALTH INDICES: CASE STUDY OF MEZŐHEGYES, SOUTH-EASTERN HUNGARY
Khilola Amankulova,Jamol Urinov,Sojida Ishmurodova , Department of Geoinformatics, Physical and Environmental Geography, University of Szeged, Egyetem utca 2, Szeged 6722, HungaryAbstract
Agricultural drought causes many economic and social problems in various communities. Advanced Geographic Information Systems (GIS) and remote sensing techniques play a significant role in the mapping and monitoring of agricultural drought. The main objective of this paper is to monitor agricultural drought using a remote sensing-based vegetation health index (VHI) derived from Landsat 8 during 2020 and 2021. VHI was calculated based on temperature condition (VCI) and thermal condition of vegetation (TCI). Both indices were generated from Normalized Difference Vegetation Index and land surface temperature (LST) data respectively. The result indicates that there is no any extreme drought occurred in the research area. The VHI classes demostrates that large area of study site faced drought from moderate to mild from 2020 to 2021. Mild drought, covering an area, 50.2 percent in 2020 while 64.5 percent in 2021 was registered all across Mezohegyes. This is due to the rising of NDVI from 0.46 in 2020 to 0.55 in 2021 whereas LST decreased slightly from 62oC to 51oC respectively.
Keywords
remote sensing, drought, agriculture, Landsat, VCI, LST.
References
Brema, J., Rahul, T.S., Julius, J.J., 2019. Study on Drought Monitoring Based on Spectral Indices in Noyyal River Sub-watershed Using Landsat-8 Imageries, in: Rao, P.J., Rao, K.N., Kubo, S. (Eds.), Proceedings of International Conference on Remote Sensing for Disaster Management, Springer Series in Geomechanics and Geoengineering. Springer International Publishing, Cham, pp. 473–482. https://doi.org/10.1007/978-3-319-77276-9_42
Chander, G., Markham, B.L., Helder, D.L., 2009. Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sens. Environ. 113, 893–903. https://doi.org/10.1016/j.rse.2009.01.007
Gu, Y., Brown, J.F., Verdin, J.P., Wardlow, B., 2007. A five-year analysis of MODIS NDVI and NDWI for grassland drought assessment over the central Great Plains of the United States. Geophys. Res. Lett. 34, L06407. https://doi.org/10.1029/2006GL029127
Gulácsi, A., Kovács, F., 2018. Drought monitoring of forest vegetation using MODIS-based normalized difference drought index in Hungary. Hung. Geogr. Bull. 67, 29–42. https://doi.org/10.15201/hungeobull.67.1.3
Hu, X., Ren, H., Tansey, K., Zheng, Y., Ghent, D., Liu, X., Yan, L., 2019. Agricultural drought monitoring using European Space Agency Sentinel 3A land surface temperature and normalized difference vegetation index imageries. Agric. For. Meteorol. 279, 107707. https://doi.org/10.1016/j.agrformet.2019.107707
Kogan, F., Stark, R., Gitelson, A., Jargalsaikhan, L., Dugrajav, C., Tsooj, S., 2004. Derivation of pasture biomass in Mongolia from AVHRR-based vegetation health indices. Int. J. Remote Sens. 25, 2889–2896. https://doi.org/10.1080/01431160410001697619
Kogan, F.N., 1995. Application of vegetation index and brightness temperature for drought detection. Adv. Space Res. 15, 91–100. https://doi.org/10.1016/0273-1177(95)00079-T
Masroor, M., Sajjad, H., Rehman, S., Singh, R., Hibjur Rahaman, M., Sahana, M., Ahmed, R., Avtar, R., 2022. Analysing the relationship between drought and soil erosion using vegetation health index and RUSLE models in Godavari middle sub-basin, India. Geosci. Front. 13, 101312. https://doi.org/10.1016/j.gsf.2021.101312
Mishra, A.K., Ines, A.V.M., Das, N.N., Prakash Khedun, C., Singh, V.P., Sivakumar, B., Hansen, J.W., 2015. Anatomy of a local-scale drought: Application of assimilated remote sensing products, crop model, and statistical methods to an agricultural drought study. J. Hydrol. 526, 15–29. https://doi.org/10.1016/j.jhydrol.2014.10.038
Rojas, O., Vrieling, A., Rembold, F., 2011. Assessing drought probability for agricultural areas in Africa with coarse resolution remote sensing imagery. Remote Sens. Environ. 115, 343–352. https://doi.org/10.1016/j.rse.2010.09.006
Wang, H., Lin, H., Liu, D., 2014. Remotely sensed drought index and its responses to meteorological drought in Southwest China. Remote Sens. Lett. 5, 413–422. https://doi.org/10.1080/2150704X.2014.912768
Zeng, J., Zhang, R., Qu, Y., Bento, V.A., Zhou, T., Lin, Y., Wu, X., Qi, J., Shui, W., Wang, Q., 2022. Improving the drought monitoring capability of VHI at the global scale via ensemble indices for various vegetation types from 2001 to 2018. Weather Clim. Extrem. 35, 100412. https://doi.org/10.1016/j.wace.2022.10041
Article Statistics
Downloads
Copyright License

This work is licensed under a Creative Commons Attribution 4.0 International License.