Investigating Agricultural Drought Changes Using Remote Sensing and Meteorological Indicators: A Case study of North Khorasan Province

Authors

‎10.22052/deej.2021.10.33.51

Abstract

Introduction
As a natural phenomenon that characterizes the climate system, drought exerts devastating effects on agricultural products, including reducing cultivation levels, decreasing crop yield, and changing cropping patterns, thus threatening the food security of humans and other creatures. Moreover, it brings about some social and environmental consequences such as threatening human health security, spreading diseases, increasing malnutrition, increasing the migration of rural residents, influencing the moisture, health, soil erosion, and vegetation, developing desertification, decreasing the water resources quality, increasing air pollution, and affecting groundwater reserves, wildlife, and biodiversity. These disastrous effects have turned drought into the subject of different investigations worldwide. on the other hand, as Iran is located in arid and semi-arid regions of Asia and suffers from inappropriate rainfall distribution, it faces some problems in terms of cultivating agricultural lands. Located in eastern Iran, North Khorasan province is not an exception in this regard, being considered as a region susceptible to different types of droughts, especially agricultural ones, due to its geographical location. Furthermore, recent droughts in the province have led to reduced water reservoirs' volume, severe groundwater depletion, reduced discharge of wells' water, damages to rainfed crops, and reduced irrigated crops in the province. Therefore, this study sought to investigate the relationship between drought and meteorological conditions in North Khorasan province throughout a 19-year period using remote sensing indices.
Materials and Methods: This study used the data collected from synoptic stations in North Khorasan province, and MODIS imagery data including 16-day MOD13A1 V6 products (500 m spatial resolution) to measure the NDVI index and 8-day MOD11A2 V6 products (500 m spatial resolution) to measure ground surface temperature (LST). Extracted from MODIS images in ARC GIS software, a set of remote sensing indices including NDVI, VCI, LST, VSWI, TCI, VDI, and VHI were also used to monitor the drought. Then, SPI, PNI, MCZI, and ZSI were measured to investigate the relationship between meteorological and remote sensing indices. Finally, Pearson correlations were measured between SPI, PNI, MCZI, and ZSI, and VCI, VSWI, TCI, VDI, and VHI using the SPSS software.

Results: The results of analyzing the Pearson correlation coefficients indicated a strong correlation between SPI, ZSI and MCZI, and VCI, VHI, VDI, and VSWI in Maneh and Samolghan, Shirvan, Esfarayen, Garmeh, and Farouj stations, moderate correlations between the indices in Raz, Jirgalan, and Bojnourd stations, and no significant correlation between the indices in Jajrum station. As for the status of vegetation in North Khorasan province, it was found that the northern, eastern, northeastern, western, and northwestern parts of the province had favorable vegetation, while the province's southern, southeastern, and southwestern regions were covered with sparse vegetation. The results also showed that sparse vegetation played a major role in drought occurrence, with many of the severe and very severe droughts occurring in the southern part of the province according to the TCI index, while the north, west, northwest, east, and northeast regions of the province were in the favorable condition in terms of drought where most of the droughts were of the mild type. Furthermore, based on VCI and VHI, north, northwest, northeast, east, and west parts of North Khorasan province possessed the highest areas with mild drought, with the mild drought, persisted during the 19-year period throughout the province, where the highest area affected by the drought belonged to Shirvan, and Maneh and Samalqan cities in 2000 and 2013, respectively.
However, most areas in the south, southeast, and southwest of the province were found to have experienced moderate drought during the study period. According to the VHI (used to displays the combined effects of vegetation and land temperature surface when monitoring the drought), most areas of the North Khorasan province were found to be affected by moderate and mild droughts, with merely the southern regions of the province (Esfarayen and Jajarum counties) experiencing severe droughts. It was also revealed that Farooj, Shirvan, Bojnord, and Raz and Jorgelan were in favorable conditions in terms of drought, VSWI, and VDI. However, the south, southeast, and southwest parts of the province were covered by the most drought areas based on VSWI, and VDI throughout the study period, while normal and optimal conditions were mostly observed in the north, northwest, northeast, west, and east regions of the province.

Conclusion: It could generally be argued that applying various indicators can provide a better understanding of the drought situation, as each indicator examines the drought status based on a specific parameter (such as TCI and VCI) or a combination of several parameters (such as the VHI index). Moreover, evaluation of the correlation coefficients of indicators could be very effective in providing accurate results, considering the fact that some satellite-driven indicators such as the meteorological ones have a direct relationship with the drought status, whose accuracy can better be determined if their relationship with remote sensing indices is investigated. Therefore, considering this study's results regarding the Pearson correlation coefficients between meteorological and satellite drought indices, it is recommended that future relevant studies use VCI, VHI, VSWI, and VDI to monitor the status of drought.

Keywords


  1. Artis, D. A. and Carnahan, W. H., 1982. Survey of emissivity variability in thermography of urban areas. Remote Sensing of Environment 12, 313-329
  2. Baaqideh, M., Alikhani, B. and Ziaian, P., 2011. Evaluating the possibility of using the NDVI index to analyze and monitor droughts in Esfahan Province. Journal of Arid Regions Geographic Studies 1, 1-16
  3. Baaqideh, M., Alikhani, B. and Ziaian, P., 2011. Evaluating the possibility of using the NDVI index to analyze and monitor droughts in Esfahan Province. Journal of Arid Regions Geographic Studies 1, 1-16
  4. Barkey, B. and Bailey, R. T., 2017. Estimating the Impact of Drought on Groundwater Resources of the Marshall Islands. Water 9, 1-12.
  5. Belesova, K., Agabiirwe, C. N., Zou, M., Phalkey, R. and Wilkinson, P., 2019. Drought exposure as a risk factor for child undernutrition in low- and middle-income countries: A systematic review and assessment of empirical evidence. Environment International 131, 1-18
  6. Brito, Y. M.A., Rufino, I.A.A., Braga, C.F.C. and Mulligan, K., 2021. The Brazilian drought monitoring in a multi-annual perspective. Environmental Monitoring and Assessment193, 1-17.
  7. Boroghani, M., Moradi, H. and Zangane Asadi, M., 2015. Zoning and determination of the best index in khorasan razavi. Arid Regions Geographic Studies 5, 70-84
  8. Cheval, S, 2015, The Standardized Precipitation Index an overview. Romanian journal of meteorology 12, 17-64
  9. Chamanpira, Gh., Zehtabian, Gh., Ahmadi, H. and Malekian, A., 2014. Effect of drought on groundwater resources in order to optimize utilization management, case study: Plain Alashtar. Journal of Watershed Engineering and Management 6, 10-20
  10. Crocetti, L., Forkel, M., Fischer, M., Jurečka, F., Grlj, A., Salentinig, A., Trnka, M., Anderson, M., Ng, W. T., Kokalj, Z., Bucur, A. and Dorigo, W., 2020. Earth Observation for agricultural drought monitoring in the Pannonian Basin (southeastern Europe): current state and future directions. Regional Environmental Change 20, 1-17

11. Emadodin, I., Reinsch, T. and Taube, F., 2019. Drought and Desertification in Iran. Hydrology 6, 1-12.

  1. Ensafimoghadam, T., 2007. An Investigation and assessment of climatological indices and determination of suitable index for climatological droughts in the Salt Lake Basin of Iran. Iranian Journal of Range and Desert Research 14, 271-288
  2. Fang, B., Kansara, P., Dandridge, Ch. and Lakshmi, V., 2021. Drought monitoring using high spatial resolution soil moisture data over Australia in 2015–2019. Journal of Hydrology 594, 1-12
  3. Ghaleb, F., Mario, M. and Sandra, A. N., 2015. Regional Landsat-Based Drought Monitoring from 1982 to 2014. journal of Climate 3, 563-577
  4. Guerrero, M.D.P., Nauditt, A., Muñoz-Robles, C., Ribbe, L. and Meza, F., 2020. Drought impacts on water quality and potential implications for agricultural production in the Maipo River Basin, Central Chile. Hydrological Sciences Journal 65, 1005-1021
  5. Gulácsi, A. and Kovács, F., 2015. Drought monitoring with spectral indices calculated from MODIS satellite images in Hungary. Journal of Environmental Geography 8, 11–20
  6. Haeffner, M., Baggio, J. A. and Galvin, K., 2018. Investigating environmental migration and other rural drought adaptation strategies in Baja California Sur, Mexico. Regional Environmental Change18, 1495–1507
  7. Hamzeh S, Farahani Z, Mahdavi S, Chatrobgoun O. and Gholamnia M., 2017. Spatiotemporal monitoring of agricultural drought using remotely sensed data (Case study of Markazi province of Iran). Journal of Spatial Analysis Environmental Hazards 4, 53-70
  8. Heim, R. J., 2002. A review of twentieth-century drought indices used in the United States. American meteorological society 83, 1149–1165
  9. Hu , Y., Wang, Sh., Yang, X., Kang, Y., Ning, G. and Du, H., 2019. Impact of winter droughts on air pollution over Southwest China. Science of The Total Environment 664, 724-736
  10. Iran Meteorological Organization, 2020. Meteorological data, Available at: https://data.irimo.ir/
  11. Javan, Kh., 2021. Investigation of meteorological drought in Urmia using SPI under climate change scenarios (RCP). Journal of Climate Change Research 2, 81-94
  12. Karimi, V., Habibnejadrooshan, M. and Abkar, A., 2011. Investigation of meteorological drought Indixes in Mazandaran synoptic Stations. Journal of Irrigation and Water Engineering 2, 15-25.
  13. Kabiri, K., 2001. Investigating the Effects of Drought on Iran vegetation in the 1990s, using NOAA satellite imagery, MSc Thesis, Toosi University, 170 pp.
  14. Khosravi, M., Movaqqari, A. and Mansouri Daneshvar, M. R., 2012. Evaluating the PNI, RAI, SIP and SPI Indices in Mapping Drought Intensity of Iran: Comparing the Interpolation Method and Digital Elevation Model (DEM). Journal of Geography and Sustainability of Environment 2, 53-70
  15. Kogan, F.N., 1995. Droughts of the late 1980s in the United States as derived from NOAA polar-orbiting satellite data. Bulletin of the American Meteorology Society 76, 655–668
  16. Li, S., Ren, H. D., Xue, L., Chang, J. and Yao, X. H., 2014. Influence of bare rocks on surrounding soil moisture in the karst rocky desertification regions under drought conditions. CATENA 116, 157-162
  17. Liu, S. and Li, W., 2011. “The study on drought monitoring based on multi-source remotely sensed images”. Eighth International Conference on Fuzzy Systems and Knowledge Discovery. Shanghai China.
  18. Liu, X., Zhu, X., Pan, Y., Li, S., Liu, Y. and Ma, Y., 2016. Agricultural drought monitoring: Progress, challenges, and prospects. Journal of Geographical Sciences26,750–767
  19. McVicar, T. R. and Bierwirth P. N., 2010. Rapidly assessing the 1997 drought in Papua New Guinea using composite AVHRR imagery. International Journal of Remote Sensing 22, 2109-2128
  20. Morid, S. and Paymozd, S., 2008. Comparison of Hydrological and Meteorological Methods for Daily Drought Monitoring: A Case Study, the 1998-2000 Drought Spell of Tehran Iran. Journal of Water and Soil Science 11, 325-333
  21. Mosley, L. M., 2015. Drought impacts on the water quality of freshwater systems; review and integration. Earth-Science Reviews 140, 203-214
  22. Mokarram, M. and Mahmoodi, A, R, 2015. Using SPI and PN indices for determination of drought in the Fars Province. Journal of Applied Hydrology 2, 49-60
  23. Navabi, N., Moghaddasi, M. and Gangi, N., 2021. Assessment of Agricultural Drought Monitoring Using Various Indices based on Ground-based and Remote Sensing Data (Case Study:Lake Urima Basin). Journal of Watershed Engineering and Management 13, 1-12.
  24. Nohegar, A., Mahmoodabadi, S. and Norouzi, A., 2015. Comparison the Suitability of SPI, PNI and DI Drought Index in Kahurestan Watershed (Hormozgan Province/South of Iran). Journal of Environment and Earth Science 5, 71-76
  25. Peters, A.J., Shea, E.W., Lei, J. and Svoboda, M., 2002. Drought Monitoring with NDVI-Based Standardized Vegetation Index. Photogrammetric Engineering & Remote Sensing 68, 71-75
  26. Preece, C., Verbruggen, E., Liu, L., Weedon, J. T. and Peñuelas, J., 2019. Effects of past and current drought on the composition and diversity of soil microbial communities. Soil Biology and Biochemistry 131, 28-39
  27. Prieto, P., Penuelas, J., Lloret, F., Llorens, L. and Estiarte, M., 2009. Experimental drought and warming decrease diversity and slow down post-fire succession in a Mediterranean shrubland. Ecography 32: 623-636
  28. Regional Water Company of north Khorasan, 2016, Statistical information of North Khorasan Province. Available at: http://www.wnkh.ir/SC.php?type=static&id=19
  29. Rousta, I., Olafsson, H., Moniruzzaman, M., Zhang, H., Liou, Y. A., Mushore, T. D. and Gupta, A., 2020. Impacts of Drought on Vegetation Assessed byVegetation Indices and Meteorological Factorsin Afghanistan, Remote Sens 12, 1-21.
  30. Saberi, A., Soltani-Gerdefaramarzi, S. and Miryaghoubzadeh, M., 2018. Study of drought using meteorological and remote sensing data (Azarbaijan province). Journal of the Earth and Space Physics 44, 439-461
  31. Salehvand, I., Montazeri, M., Gandomka, A. and Momeni, M., 2015. Evaluation of Meteorological Drought Index for Drought Assessment and Mapping in Lorestan Province in Iran. Journal of Geography Environment and Earth Science International 2, 24-36

43. Santra, A. and Mitra , S. S., 2020. Space-Time Drought Dynamics and Soil Erosion in Puruliya District of West Bengal, India A Conceptual Design. Journal of the Indian Society of Remote Sensing 48, 1191–1205

  1. Sholihah, R.I., Trisasongko, B.H., Shiddiq, D., LaOde S I., Kusdaryantoa, S. and ManijoaPanuju, D.R., 2016. Identification of agricultural drought extent based on vegetation health indices of Landsat data: case of Subang and Karawang, Indonesia. Procedia Environmental Sciences 33,14-20
  2. Shaw, S., Khan, J. and Paswan, B., 2020. Spatial modeling of child malnutrition attributable to drought in India. International Journal of Public Health65, 281–290
  3. Sheffield, J., Goteti, G., Wen, F. and Wood, E. F., 2004. A simulated soil moisture based drought analysis for the United States. Climate and Dynamics 109, 1-19
  4. Solaimani, K., Darvishi, Sh. and Shokrian, F., 2019. Analysis of agricultural drought using remote sensing indices (Case study: Marivan city). Journal of RS and GIS for Natural Resources 10,15-33.
  5. Sruthi, S. and Mohammed Aslam, M.A., 2015., Agricultural Drought Analysis Using the NDVI and Land Surface Temperature Data a Case Study of Raichur District. Aquatic Procedia 4, 1258-1264
  6. Sun, H., Zheo, X., Chen, Y., Gong, A. and Yang, j., 2013. A new agricultural drought monitoring index combining MODIS NDWI and day–night land surface temperatures: a case study in China. International Journal of Remote Sensing 34, 8986-9001.
  7. US Geological Survey website, 2020. MODIS images. Available at: https://earthexplorer.usgs.gov
  8. Zarei, R., Serajian, M., Bazgeer, S., 2013. Monitoring Meteorological Drought in Iran Using Remote Sensing and Drought Indices. Journal of Desert 18, 89-97.
  9. Zhang, A. and Jia, G., 2013. Monitoring meteorological drought in semiarid regions using multi-sensor microwave remote sensing data. Remote Sensing of Environment 134, 12-23.