Comparison of remote sensing indices and meteorological and agricultural drought index to determine drought status in regions with different climatic conditions

Document Type : Research Paper

Authors

1 P Department of Water Science and Engineering, Faculty of Agriculture, University of Birjand, Birjand, Iran

2 , Department of Water Science and Engineering, Faculty of Agriculture, University of Birjand

3 Department of Water Science and Engineering, Faculty of Agriculture, University of Birjand, Birjand

4 Department of Civil and Environmental Engineering, San Jose State University, San Jose, California, United States

Abstract

Effective and timely drought monitoring can contribute to the development of drought systems and the optimal management of water resources using these systems in turn can minimize the costs of drought. The purpose of this study is to investigate the drought using Landsat satellite data and meteorological and agricultural drought indices in three regions with different climatic conditions (Birjand, Shiraz and Rasht). For this purpose, drought indices based on satellite data including Normalized Difference Vegetation Index (NDVI), Soil Adjustment Vegetation Index (SAVI) and Simple Ratio (SR) were extracted from Landsat images for the period 2002, 2014 to 2020. Then the results of these indices were compared with the values of standard precipitation index (SPI) and Reconnaissance Drought Index (RDI). The study of indicators shows that the amount of indicators is high in all studied years in Rasht region. In Shiraz region, a significant decrease in the average value of indicators occurred in August and September from 2015 to 2020. Also, this decrease was seen in the average value of indicators in Birjand region from September 2002 to 2020. On the other hand, among the studied months, September 2015 in Rasht and Shiraz regions and 2014 (September) Birjand had the most drought in terms of remote sensing indicators. The results showed that in all three regions, remote sensing indices including NDVI and SAVI have a high correlation with SPI and RDI indices. The RDI index is superior to the SPI index for drought monitoring and prediction. As a result, the RDI index takes into account evapotranspiration in addition to rainfall and is more sensitive especially in dry areas such as Shiraz and Birjand where evapotranspiration is higher than rainfall.

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References

Abedzadeh, M., Arastoo, B., Nankeli, H.R. (2018). Agricultural Drought Risk Analysis Using Remote Sensing Techniques and GIS (Case Study: Semnan Province). Journal of GIS & RS Application in Planning. 6(1): 18-36. [In Persian].
Abramowitz, M., Stegun, I.A. (1965). Handbook of Mathematical Functions. Dover Publications, New York.
Adeyeri, O.E., Akinsanola, A.A., Ishola, K.A. (2017). Investigating surface urban heat island characteristics over Abuja, Nigeria: relationship between land surface temperature and multiple vegetation indices. Remote Sensing Applications: Society and Environment. 7, 57–68.
Alawi Panah, S.K., Matin Far, H.R., Rafi'I Imam. A. (2008). Application of information technology in earth sciences (digital soil science). First Edition, University of Tehran Press,. 457 pages. [In Persian].
Amin, M., Riaz Khan, M., Shah Hassan, Sh., Ahmad Khan, A., Imran, M., Arif Goheer, M., Mahlaqa Hina, S., Perveen, A. (2020.) Monitoring agricultural drought using geospatial techniques: a case study of Thal region of Punjab, Pakistan. Journal of Water & Climate Change. https://doi.org/10.2166/wcc.2020.232.
Aznarul, I., Balai, C.D., Sadik, M., Palash, GH., Suman, D.B., Biplab, S. (2021). Chapter 29 - Deforestation and its impact on sediment flux and channel morphodynamics of the Brahmani River Basin, India. Forest Resources Resilience and Conflicts. 377-415. https://doi.org/10.1016/B978-0-12-822931-6.00029-0
Barati, S., Rayegani, B., Saati, M., Sharifi, A., Nasri, M. (2011). Comparison the accuracies of different spectral indices for estimation of vegetation cover fraction in sparse vegetated areas. The Egyptian Journal of Remote Sensing and Space Sciences. 14, 49-56.
Bazrafshan, J. (2002). A comparative study of some meteorological drought indices in some climatic samples of Iran. Master Thesis. University of Tehran. [In Persian].
Bazrafshan, J., Hijabi, S. (2017). Drought Monitoring methods (along with applications in MATLAB programming environment). University of Tehran Press, Second Edition. [In Persian].
Bernstein, L., Jin, X., Gregor, B., Adler-Golden, S. (2012). Quick atmospheric correction code: Algorithm description and recent upgrades. Optical Engineering. 51. 1719. https://doi.org/10.1117/1.OE.51.11.111719
Birth, G., McVey, G. (1968). Measuring the color of growing turf with a reflectance spectrophotometer. Agronomy Journal. 60, 640-643.
Bokusheva, R., Kogan, F., Vitkovskaya, I., Conradt, S., Batyrbayeva, M. (2016). Satellite-based vegetation health indices as a criteria for insuring against druoght-related yield losses. Agricultural and Forest Meteorology. 220: 200-206.
Darwish, T., Faour, G. (2008). Rangeland degradation in two watersheds of Lebanon. Lebanese Sci. J. 9: 71-80.
EbrahimiKhusfi, M., Darvishzade, R., Matkan, A. A., Ashourloo, D. (2010). Drought Assessment in Arid Regions Using Vegetation Indices - a Case Study of "Shirkooh of Yazd" in Central Iran. Journal of Environmental Sciences, 7 (4), 59 -72. [In Persian].
Fadaei, H. (2018). Advanced land observing satellite data to identify ground vegetation in a juniper forest, northeast Iran. Journal of Forestry Research. 31, 531-539. http://doi.org/10.1007/s11676-018-0812-5
Fathi Taperasht, A., Shafizadeh-Moghadam, H., & Kouchakzadeh, M. (2022). Spatial-temporal analysis of Iran's climatic classification based on Domarten method and Mann-Kendall test in the statistical period of 1995-2019. Environmental Sciences, 20(3), -. doi: 10.52547/envs.2021.
Fazel Dehkordi, L., Sohrabi, T.S., Mahmoodi Kohan, F. (2015). Drought monitoring by Using of MODIS Satellite Images in Dry land (Case study: YAZD Rangelands). Desert Ecosystem Engineering Journal. 4(9): 81-94. [In Persian].
Ghabaei Sough A, Mosaedi M. )2012(. Design process of selecting appropriate drought index based on monitoring multivariate meteors in some stations of arid and semi-arid regions, Iran. Journal of Water and Soil. 26(2): 414–426. [In Persian].
Hamzeh, S., Farahani, Z., Mahdavi, Sh., Chhtarabgun, O., Gholamnia, M. (2017). Temporal and spatial monitoring of agricultural drought using remote sensing data: Central Province of Iran. Journal of Spatial Analysis of Natural Hazards, 4 (3): 53-70. [In Persian].
Heim, R.R. (2002). A review of 20th century drought indices used in united states. Bulletin of the American Meteorological Society. 84, 1149-1165.
Hosseini, F. (2009). Investigating the effects of climate change in Karkheh catchment, Sharif University of Technology, Tehran, M.Sc. Thesis. [In Persian].
Huete, H. (1988). A Soil-Adjusted Vegetation Index (SAVI). Remote Sensing of Environment, 25: 295-309.
Jamshidi, H., Khalili, D., Zadeh, M., Hosseinipour, E. (2011). Assessment and comparison of SPI and RDI meteorological drought indices in selected synoptic stations of Iran. World Environmental and Water Resources Congress. 1161–1173. doi: 10.1061/41173(414)120 .[In Persian].
Karimi, M., Shahedi, K. (2018). Investigation of meteorological, hydrological and agricultural drought using drought indices (Case study: Gharehsou watershed). RS & GIS for Natural Resources. 9(2): 1-16. [In Persian].
Khadempour, F., Bakhtiari, B., Golestani, S. (2017). Sensitivity Analysis of FAO Penman-Monteith Model in Daily Reference Evapotranspiration Estimation and Zoning Sensitivity Coefficients across Iran. Journal of Water and Soil (Agricultural Science and Technology). 31(4), 1046-1059. [In Persian].
Khosravi, M., Akbari, M. (2009). Investigation of drought characteristics of South Khorasan province. Geography and development. 14: 68-51. [In Persian].
Kogan, F.N. (1993). United States droughts of late 1980’s as seen by NOAA polar orbiting satellites. International Geoscience and Remote Sensing Symposium. 1, 197-199.
Manandhar, R., Odeh, I.O.A., Ancev, T. (2009). Improving the Accuracy of land use and land cover classiication of Landsat data using post- classification enhancement. Remote Sensing. 1, 330-344.
Marumbwa, F.M., Cho, M.A., Chirwa, P. W. (2020). An assessment of remote sensing-based drought index over different land cover types in southern Africa. International Journal of Remote Sensing. 41(19): 1-15.
McKee, T. B., Doesken, N. J., Kleist, J. (1993). The relationship of drought frequency and duration to time scales. Proc 8th Conference on Applied Climatology, American Meteorological Society, Boston, pp: 179-184.
Melillos, G., Hadjimitsis, D. (2020). Using simple ratio (SR) vegetation index to detect deep man-made infrastructures in Cyprus. Proc. SPIE 11418, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXV, 114180E. https://doi.org/10.1117/12.2557893
MirMousavi, H., Babaei, G., Karimi, S. (2010). Estimate the Amount of Vegetation Cover Using Different Indicators in Satellite Images and Comparing Them With the Index NDVI in the Region of Geshlag – Sanandaj. Journal of Geographical Notion. 4(7): 66-88. [In Persian].
Nohtani, M., Ajorlo, M., Sarhadi, M. (2018). Zoning Drought with Standardized Precipitation Index and Reconnaissance Drought Index in Sistan and Baluchestan Province, Southeastern Iran. ECOPERSIA. 6(2):111-119.
Rahnama, S., Shahidi, A., Yaghoobzadeh, M., Mehran, A.A. (2021). Investigation of drought in Birjand plain using SPI index. The 5th National Congress of Irrigation and Drainage of Iran. 23 and 24 June.
Rhee, J., Im, J., Carbone, G.J. (2010). Monitoring agricultural drought for arid and humid regions using multi-sensor remote sensing data. Remote Sensing of Environment. 114(12), 2875-2887.
Rouse, J.W., Haas, R.H., Schell, J.A., Deering, D.W. (1973). Monitoring vegetation systems in the Great Plains with ERTS, Third ERTS Symposium, NASA, SP- 351, 309-317.
Sharifan, H., Rahimi, L. (2014). Drought monitoring based on SPI index, deciles and normal. Irrigation and Drainage Association of Iran, Islamic Azad University, Khorsgan Branch, Isfahan, 6 February. [In Persian].
Shokoohi, A., Morovati, R. (2014). An investigation on the Urmia Lake Basin drought using RDI
and SPI indices. Watershed Engineering and Management. 3(6): 232–246. [In Persian].
Solimani, K., Darvishi, Sh., Shokrian, F. (2019). Analysis of agricultural drought using remote sensing indices (Case study: Marivan city). RS & GIS for Natural Resources. 10(2): 15-33. [In Persian].
Soltani, M., Soltani, A., Kole Hui, M., Soleimani, K. (2019). Regional drought monitoring using Landsat images of the study area: Kermanshah city. Scieniic - Research Quarterly of Geographical Data (SEPEHR), 28 (109): 137-146. [In Persian].
Sun, X., Wang, M., Li, G., Wang, Y. (2020). Regional-scale drought monitor using synthesized index based on remote sensing in northeast China. Open Geosciences. 12(1): 163-173.
Sur, K., Lunagaria, M.M. (2020). Association between drought and agricultural productivity using remote sensing data: a case study of Gujarat state of India. Journal of Water & Climate Change. https://doi.org/10.2166/wcc.2020.157
Tsakiris, G., Vangelis, H. (2005). Establishing a drought index incorporating evapotranspiration. European Water, 9(10), 3-11.
Tsakiris, G., Pangalou, D., Vangelis, H. (2007). Regional drought assessment based on the Reconnaissance Drought Index (RDI). Water Resources Management, 21, 821-833.
Wilson, N.R., Norman, L. M., Villarreal, M., Gass, L., Tiller, R., Salywon, A. (2016). Comparison of remote sensing indices for monitoring of desert cienegas, Arid Land and Research and Management, 30(4): 460-478.
Yaghoobzadeh, M., Barani, G., Akbarpour, A. (2009). Comparison of vegetation maps prepared from Landsat and IRS satellite images with the help of NDVI and VI indices. First International Water Crisis Conference. University of Zabol. [In Persian].
Zarei, R., Sarajian, M., Bazgeer, S. (2013). Monitoring Meteorological Drought in Iran Using Remote Sensing and Drought Indices. DESERT. 18: 89-97.
Zou, L., Cao, S., Sanchez-Azofeifa, A. (2020). Evaluating the utility of various drought indices to monitor meteorological drought in Tropical Dry Forests. International Journal of Biometeorology. 64(4): 701-711.
Iranian Journal of Soil and Water Research
Volume 53, Issue 10
January 2023
Pages 2383-2398

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