نوع مقاله : مقاله پژوهشی
عنوان مقاله English
نویسندگان English
Drought is among the most expensive natural occurrences in Iran. Agricultural drought is a climatic phenomenon that results in severe repercussions for food and economic resources. The key characteristic of this drought is the decline in soil moisture resulting from decreased rainfall or elevated temperatures, leading to lower farm output. The absence of climatic data hinders drought monitoring. The Soil-Adjusted Vegetation Index (SAVI) was employed in this study to track agricultural drought in the agricultural areas below the Sorkhab Dam, Ardabil Province. Statistics revealed a correlation of 0.61 between the SAVI index derived from agricultural lands and the SPI12 index. As a result of this correlation, it is anticipated that with rising rainfall levels, areas of land that receive adequate water will expand, specifically those with a SAVI index ranging from 0.5 to 0.7, whereas areas of land that receive inadequate water will contract, corresponding to a SAVI index of 0.2 to 0.5. Data from 1986 to 2024 (39 years), was compared to data from 2010 to 2024 (15 years). The long-term trend revealed that the area of low-water lands with low SAVI indexes remained relatively stable, at a rate of -0.08 hectares per time. Conversely, the short-term trend indicated a significant increase in these areas, with a rate of +13.1 hectares per unit of time. In both periods, the observed rainfall patterns exhibited an upward trend. The selected statistical periods revealed that the positive effects of rising observed rainfall, resulting from adjusted temperature increases and alterations in soil moisture, were largely offset by corresponding changes in vegetation cover, as reflected in the SAVI index values. The application of remote sensing data in agricultural drought research and tracking can be highly effective and easily implemented in various locations, particularly in regions with inadequate statistics or limited observational data.
کلیدواژهها English
. اکبری ازیرانی, ط. (2022) آشکارسازی و تحلیل روند خشکسالی حوضه آبخیز جازموریان در ارتباط با شاخصهای جوی- اقیانوسی. پژوهشهای تغییرات آب و هوایی 3(11), 1-16.
11.Aşık, Ş. (2020) Agricultural Drought Monitoring Using Surface Temperature and Vegetation Indices from Satellite Images. Journal of Agriculture Faculty of Ege University, 151-160.
12.Al Asri, A., Muhammad, H., Khairul, M. and Nela, W. (2024) Analysis of Vegetation Index (NDVI, SAVI, LAI) on Coffee Productivity in Bener Meriah District, Aceh Province, pp. 39-45, Atlantis Press.
13.Asgarzadeh, P., Hamedi, F. and Rojas, O. (2021) Assessment of agricultural drought using MODIS derived FAO's agriculture stress index system (ASIS) over the Iran croplands. Desert 26(1), 29-41
14.Binte Mostafiz, R., Noguchi, R. and Ahamed, T. (2021) Agricultural Land Suitability Assessment Using Satellite Remote Sensing-Derived Soil-Vegetation Indices. Land 10(2), 223.
15.Cumbie-Ward, R.V. and Boyles, R.P. (2016) Evaluation of a High-Resolution SPI for Monitoring Local Drought Severity. Journal of Applied Meteorology and Climatology 55(10), 2247-2262.
16.Duan, K. and Mei, Y. (2014) Comparison of Meteorological, Hydrological and Agricultural Drought Responses to Climate Change and Uncertainty Assessment. Water Resources Management 28, 5039-5054.
17.Gündoğdu, K.S. and Bantchina, B.B. (2018) Landsat uydu görüntülerinden NDVI değer dağılımının parsel bazlı değerlendirilmesi, Uludağ üniversitesi ziraat fakültesi çiftlik arazisi örneği. Bursa Uludağ Üniversitesi Ziraat Fakültesi Dergisi 32(2), 45-53.
18.Haboudane, D., Miller, J., Tremblay, N., Zarco-Tejada, P. and Dextraze, L. (2002) Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sensing of Environment 81, 416-426.
19.Heumann, B. (2011) Satellite remote sensing of mangrove forests: Recent advances and future opportunities. Progress in Physical Geography 35, 87-108.
20.Huete, A.R. (1988) A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment 25(3), 295-309.
21.Javed, T., Li, Y., Rashid, S., Li, F., Hu, Q., Feng, H., Chen, X., Ahmad, S., Liu, F. and Pulatov, B. (2020) Performance and relationship of four different agricultural drought indices for drought monitoring in China's mainland using remote sensing data. Science of The Total Environment.
22.Jensen, J.R. (1996) Introductory digital image processing: a remote sensing perspective, Prentice-Hall Inc., Upper Saddle River.
23.Keikhosravi, G. and Rastgar Alaleh Gurabi, S. (2021) Monitoring agricultural drought in Iran using time series of vegetation health index. Sustainable Earth Trends 1(4), 52-61.
24.Lee, S.-J., Kim, N. and Lee, Y. (2021) Development of Integrated Crop Drought Index by Combining Rainfall, Land Surface Temperature, Evapotranspiration, Soil Moisture, and Vegetation Index for Agricultural Drought Monitoring. Remote Sensing 13(9), 1778.
25.Li, J., Zhang, X., Rousta, I., Olafsson, H. and Krzyszczak, J. (2024) Evaluating vegetation dynamics in the Yangtze river basin in relation to climatological parameters using remote sensing data from 2001 to 2022. International Agrophysics 38, 407-422.
26.Li, L., Su, X., Zhang, Y., Lin, Y. and Li, Z. (2015) Trend Modeling for Traffic Time Series Analysis: An Integrated Study. IEEE Transactions on Intelligent Transportation Systems 16(6), 3430-3439.
27.Mariod, A., Abdalla, N., Gaiballa, A., Kätsch, C. and Suliman, M. (2015) Using MODIS- Derived NDVI and SAVI to Distinguish Between Different Rangeland Sites According to Soil Types in Semi-Arid Areas of Sudan (North Kordofan State). International Journal of Life Science and Engineering 1, 150-164.
28.McKee, T.B., Doesken, N.J. and Kleist, J. (1993) The relationship of drought frequency and duration to time scales, pp. 179-183, Boston.
29.Meseret Alemu, M. (2019) Analysis of Spatio-temporal Land Surface Temperature and Normalized Difference Vegetation Index Changes in the Andassa Watershed, Blue Nile Basin, Ethiopia. Journal of Resources and Ecology 10(1), 77-85.
30.Mo, K. and Lyon, B. (2015) Global Meteorological Drought Prediction Using the North American Multi-Model Ensemble. Journal of Hydrometeorology 16, 150310071054006.
31.Monforte, P. and Ragusa, M.A. (2022) Temperature Trend Analysis and Investigation on a Case of Variability Climate. Mathematics 10(13), 2202.
32.Mudelsee, M. (2010) Climate Time Series Analysis: Classical Statistical and Bootstrap Methods, Springer Netherlands.
33.Mudelsee, M. (2019) Trend analysis of climate time series: A review of methods. Earth-Science Reviews 190, 310-322.
34.Mullapudi, A., Vibhute, A.D., Mali, S. and Patil, C.H. (2023) A review of agricultural drought assessment with remote sensing data: methods, issues, challenges and opportunities. Applied Geomatics 15(1), 1-13.
35.Nanasaheb, A.A., V., P.R. and Paswan, P.K. (2020) Soil adjusted vegetation index (SAVI), in the Uben river basin of Gujarat. International Journal of Chemical Studies 8, 693-698.
36.Perez, G., Macapagal, M., Olivares, R., Macapagal, E. and Comiso, J. (2016) FORECASTING AND MONITORING AGRICULTURAL DROUGHT IN THE PHILIPPINES. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B8, 1263-1269.
37.Pieper, P., Düsterhus, A. and Baehr, J. (2020) A universal Standardized Precipitation Index candidate distribution function for observations and simulations. Hydrol. Earth Syst. Sci. 24(9), 4541-4565.
38.Quille-Mamani, J.A., Huayna, G., Pino-Vargas, E., Chucuya-Mamani, S., Vera-Barrios, B., Ramos-Fernandez, L., Espinoza-Molina, J. and Cabrera-Olivera, F. (2024) Spatio-Temporal Evolution of Olive Tree Water Status Using Land Surface Temperature and Vegetation Indices Derived from Landsat 5 and 8 Satellite Imagery in Southern Peru. Agriculture 14(5), 662.
39.Rhyma, P.P., Norizah, K., Hamdan, O., Faridah-Hanum, I. and Zulfa, A.W. (2020) Integration of normalised different vegetation index and Soil-Adjusted Vegetation Index for mangrove vegetation delineation. Remote Sensing Applications: Society and Environment 17, 100-280.
40.Rousta, I., Izadian, S., Olafsson, H., Siedliska, A. and Krzyszczak, J. (2024) Investigation of vegetation dynamics with a focus on agricultural land cover and its relation with meteorological parameters based on the remote sensing techniques: a case study of the Gavkhoni watershed. International Agrophysics 38, 213-229.
41.Rousta, I., Mansourmoghaddam, M., Olafsson, H., Krzyszczak, J., Baranowski, P., Zhang, H. and Tkaczyk, P. (2022a) Analysis of the recent trends in vegetation dynamics and its relationship with climatological factors using remote sensing data for Caspian Sea watersheds in Iran. International Agrophysics 3, 139-153.
42.Rousta, I., Moniruzzaman, M., Olafsson, H., Zhang, H., Baranowski, P., Tkaczyk, P., Lipińska, H., Kępkowicz, A. and Krzyszczak, J. (2022b) Investigation of the Vegetation Coverage Dynamics and its Relation to Atmospheric Patterns in Kabul River Basin in Afghanistan. Pure and Applied Geophysics 179.
43.Rousta, I., Olafsson, H., Zhang, H., Moniruzzaman, M., Krzyszczak, J. and Baranowski, P. (2020) Anthropogenic Factors Affecting the Vegetation Dynamics in the Arid Middle East.
44.Roy, D., Wulder, M., Loveland, T., Woodcock, C., Allen, R., Anderson, M., Helder, D., Irons, J.R., Johnson, D., Kennedy, R., Scambos, T., Schaaf, C.B., Schott, J.R., Sheng, Y., Vermote, E., Belward, A., Bindschadler, R., Wohen, W.B., Gao, F. and Zhu, Z. (2014) Landsat-8: Science and Product Vision for Terrestrial Global Change Research. Remote Sensing of Environment 2014, 154-172.
45.Schneider, D.P., Deser, C., Fasullo, J. and Trenberth, K.E. (2013) Climate Data Guide Spurs Discovery and Understanding. Eos, Transactions American Geophysical Union 94(13), 121-122.
46.Wu, H., Hayes, M.J., Wilhite, D.A. and Svoboda, M.D. (2005) The effect of the length of record on the standardized precipitation index calculation. International Journal of Climatology 25(4), 505-520.
47.Wu, H., Svoboda, M.D., Hayes, M.J., Wilhite, D.A. and Wen, F. (2007) Appropriate application of the standardized precipitation index in arid locations and dry seasons. International Journal of Climatology 27(1), 65-79.
48.Wu, X., Xu, H., He, H., Wu, Z., Lu, G. and Liao, T. (2024) Agricultural Drought Monitoring Using an Enhanced Soil Water Deficit Index Derived from Remote Sensing and Model Data Merging. Remote Sensing 16(12), 2156.
49.Wulder, M., Masek, J., Cohen, W., Loveland, T. and Woodcock, C. (2012) Opening the archive: How free data has enabled the science and monitoring promise of Landsat. Remote Sensing of Environment 122, 2-10.
50.Zhou, L., Wu, J., Zhang, J., Zhao, F., Liu, M. and Zhao, L. (2010) Assessing the drought monitoring characteristic of timeseries NDVI indices in crop growing season.
