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نوع مقاله : مقالات پژوهشی

نویسندگان

1 گروه علوم و مهندسی آب، دانشکده کشاورزی، دانشگاه فردوسی مشهد

2 مشهدگروه علوم و مهندسی آب، دانشکده کشاورزی، دانشگاه فردوسی مشهد

چکیده

وقوع خشکسالی در کشاورزی صرفا با اندازه‌گیری تغییرات بارش قابل رصد نیست بلکه متغیرهای دیگری همچون رطوبت خاک نیز در آن نقش دارند. در میان روش‌های مختلف دورسنجی، طیف الکترومغناطیس مایکروویو محدودیت‏های فیزیکی سایر امواج رادیومتری در اندازه‏گیری رطوبت خاک را ندارد. با این تفاوت که داده‏های مایکروویو رطوبت خاک غالبا دارای ابعاد پیکسل بسیار بزرگ (بیش از 10 کیلومتر) هستند و این موضوع کاربرد آنها در مقیاس‏های کوچک را با مشکل مواجه می‏سازد. در این پژوهش به منظور محاسبه شاخص خشکی کشاورزی در مقیاس مزرعه، ابتدا با استفاده از داده‏های اندازه‏گیری میدانی رطوبت در محدوده دشت نیشابور طی سال‏های 1396 تا 1398، واسنجی داده‏های بازیابی رطوبت خاک سنجنده AMSR2 انجام شد. سپس با کمک تصاویر سنجنده مودیس روابط خطی ریزمقیاس نمایی تصاویر رطوبت خاک استخراج شده و ابعاد تصویر از 25 کیلومتر به 1000 متر کاهش یافت. در گام بعدی از شاخص خشکی کشاورزی SMADI که تلفیقی از خصوصیات پوشش گیاهی، رطوبت خاک و دمای سطح زمین است برای پایش خشکی کشاورزی در مقیاس مزرعه استفاده شد. به منظور ارزیابی نتایج، شاخص‏های آماری ضریب تعیین ( )، میانگین قدرمطلق خطا (MAE) و ریشه میانگین مربعات خطا (RMSE) در سه کاربری اراضی منتخب شامل زراعت دیم (R1)، مرتع متوسط (R2) و مرتع فقیر (R3) بررسی شد. شاخص­های MAE و RMSE در بازه 1.6 تا 4 و شاخص  در بازه 0.73 تا 0.84 قرار گرفت. نتایج نشان داد که الگوریتم استفاده شده در ریزمقیاس نمایی و همچنین برآورد شاخص خشکی کشاورزی SMADI به خوبی قادر به بازتاب اندرکنش‏های بین بارش، رطوبت خاک، پوشش گیاهی و تغییرات پروفیل دمایی کانوپی است و این ویژگی کاربرد آن را در تحلیل‏های هواشناسی کشاورزی توجیه و تقویت می‏کند.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Field-Scale Agricultural Drought Monitoring Based on Microwave Imagery of Soil Moisture

نویسندگان [English]

  • M. Fashaee 1
  • S.H. Sanaei Nejad 1
  • M. Quchanian 2

1 Water Science and Engineering department, Agriculture faculty, Ferdowsi university of Mashhad, Mashhad, Iran

2 Water Science and Engineering department, Agriculture faculty, Ferdowsi university of Mashhad, Mashhad, Iran

چکیده [English]

Introduction
 Drought analysis in agriculture can not only be achieved by measuring precipitation changes but also by using other parameters such as soil moisture. Due to the fact that soil moisture affects plant growth and yield, it is often considered for monitoring agricultural drought. Remote sensing data are often provided from three sources: microwave, visible and thermal. Most satellite soil moisture-based algorithms rely on passive microwave images, active microwaves, or a combination of data from several different sensors. Among the various remote sensing methods, the microwave electromagnetic spectrum has fewer physical limitations than other spectrum in measuring soil moisture. However, microwave soil moisture data often have very large pixel dimensions (more than 10 km), making it difficult to use them on a small scale.
Materials and Methods
 In this study, in order to calculate the agricultural drought index at the field-scale, AMSR2 Retrieval data were calibrated first using field moisture measurement data in the Neishabour plain during 2017 to 2019. During the research period, 560 soil samples (20 samples in 28 shifts) were collected and soil moisture was measured in the laboratory of the Department of Water Science and Engineering, Ferdowsi University of Mashhad. LPRM_AMSR2_ SOILM3_001 is one of the third level products of the AMSR2 sensor, which is produced on a daily basis with a spatial resolution of 25 × 25 km2. Land surface parameters including surface temperature, surface soil moisture and plant water availability were obtained by passive microwave data using the Land parameter Retrieval Method (LPRM). Then, by using Modis sensor images (NDVI and LST), linear downscaling equations were extracted. The dimensions of the AMSR2 images were reduced from 25 kilometers to 1000 meters using these equations. In next step, SMADI Agricultural Drought Index, which is a combination of vegetation characteristics, soil moisture and land surface temperature, was used to monitor agricultural drought at the field-scale. Statistical indicators such as coefficient of determination (R^2), mean absolute error (MAE) and root mean square error (RMSE) were also used to evaluate the statistical performance.
Results and Discussion
By visual analysis of the role of vegetation and land unevenness, it was found that these two factors affect the regression relationships extracted for calibration of remote sensing data. The RMSE and MAE values for the regression equations used in the calibration process were calculated in the range of 1.6 to 4%, which can be considered acceptable in comparison with the mean values of the soil moisture data (15 to 20). The results showed that changes in SMADI index in three land use zones including rainfed cultivation (R1), medium rangeland (R2) and poor rangeland (R3) have experienced a similar trend to precipitation changes, illustrating that precipitation is one of the most effective factors in major changes in SMADI agricultural drought index fluctuations. It was also observed that SMADI index changes with a delay of 1 to 8 days compared to the precipitation changes in all three zones. In all three zones, the SMADI index followed a similar trend to in-situ soil moisture changes. At mot 80% of the changes in SMADI-R1 index can be explained by in-situ SM-R1, and the rest of the changes were related to other environmental factors or measurement error. This decreases to 68% in the R3 zone. It should be noted that soil moisture monitoring can more accurately reflect the impact of environmental factors on the changes in agricultural drought index such as SMADI than other variables; because the rainfall recorded at the meteorological station does not necessarily occur uniformly throughout the study area. On the other hand, any amount of precipitation will not necessarily lead to an effective change in soil moisture storage. This also renders assessment of the performance of agricultural drought indicators difficult.
Conclusion
 Examination of statistical indices of coefficient of determination (R2), mean absolute error value (MAE) and root mean square error (RMSE) showed that the algorithm used in downscaling as well as estimating SMADI agricultural drought index is well able to reflect the interactions between precipitation, soil moisture, vegetation and changes in canopy temperature profile. This feature justifies and strengthens its application in agrometeorological analysis.
 

کلیدواژه‌ها [English]

  • AMSR2
  • Downscaling
  • Land Surface Temperature
  • Passive Microwav
  • Water Deficit
  1. AdegokeO., and Carleton A.M. 2002. Relations between Soil Moisture and Satellite Vegetation Indices in the U.S. Corn Belt. Journal of Hydrometeorology 3: 395-405. https://doi.org/10.1175/1525-7541(2002)003%3c0395:RBSMAS%3e2.0.CO;2.
  2. Adnan, Merwade V., and Yu Z. 2016. Multi-objective calibration of a hydrologic model using spatially distributed remotely sensed / in-situ soil moisture. Journal of Hydrology 536: 192–207. https://doi.org/10.1016/j.jhydrol.2016.02.037.
  3. AghaKouchak 2014. A baseline probabilistic drought forecasting framework using Standardized Soil Moisture Index: Application to the 2012 United States drought. Hydrol. Earth Systematic Science 18(7): 2485–2492. https://doi.org/10.5194/hess-18-2485-2014,%202014.
  4. BokenK. 2005. Monitoring and Predicting Agricultural Drought: A Global Study. Oxford Univ. Press. New York. 496. https://doi.org/10.1093/oso/9780195162349.001.0001.
  5. Brocca, Ciabatta L., Massari C., Camici S., and Tarpanelli A. 2017. Soil moisture for hydrological applications: open questions and new opportunities. Water 9(2): 140. https://doi.org/10.3390/w9020140.
  6. Carrão, Russo S., Sepulcre-canto G., and Barbosa P. 2016. An empirical standardized soil moisture index for agricultural drought assessment from remotely sensed data, International Journal of Applied Earth Observation and Geoinformation. International Journal Apply Earth Obs Geoinf 48:74-84. https://doi.org/10.1016/j.jag.2015.06.011.
  7. Chen, Wen J., and Tian H. 2016. Representativeness of the ground observational sites and up-scaling of the point soil moisture measurements. Journal of Hydrology 533: 62–73. https://doi.org/10.1016/j.jhydrol.2015.11.032.
  8. Chen, and Wang L. 2018. Recent advance in earth observation big data for hydrology. Big Earth Data. 1–22. https://doi.org/10.1080/20964471.2018.1435072.
  9. De Jue D., and Owe M. 2014. AMSR2/GCOM-W1 surface soil moisture (LPRM) L3 1 day 25 km x 25 km ascending V001, Edited by Goddard Earth Sciences Data and Information Services Center (GES DISC) (Bill Teng). Goddard Earth Sciences Data and Information Services Center (GES DISC). Greenbelt. MD. USA. https://doi.org/10.5067/CGDEOBASZ178.
  10. Dostálová, Doubková M., Sabel D., Bauer-Marschallinger B., and Wagner W. 2014. Seven years of advanced synthetic aperture radar (ASAR) global monitoring (GM) of surface soil moisture over Africa. Remote Sens 6(8): 7683–7707. https://doi.org/10.3390/rs6087683.
  11. Entekhabi, Reichle R.H., Koster R.D., and Crow W.T. 2010. Performance metrics for soil moisture retrievals and application requirements. Journal Hydrometeorology 11(3): 832–840. https://doi.org/10.1175/2010JHM1223.1.
  12. Fang, and Lakshmi V. 2014. Soil moisture at watershed scale : Remote sensing techniques. Journal of Hydrology 516:2014: 258–272. https://doi.org/10.1016/j.jhydrol.2013.12.008.
  13. Fang, Lakshmi V., Bindlish R., Jackson T.J., Cash M., and Basara J. 2013. Passive Microwave Soil Moisture Downscaling Using Vegetation Index and Skin Surface Temperature. Vadose Zone Journal https://doi.org/10.2136/vzj2013.05.0089.
  14. Farokhi, Ansari H., and Faridhosseini A. 2019. Integration of retrievals of the AMSR2 sensor with MODIS products to estimate soil moisture at high resolution. Iranian Journal of Irrigation and Drainage 13(3): 687-698. (In Persian with English abstract)
  15. Fashaee, and Sanaei Nejad S.H. 2021. Downscaling Retrievals of the AMSR2 Passive Microwavee Soil Moisture Imagery to Farm Scale. Iranian Journal of Irrigation and Drainage 14(6): 1973–1983. (In Persian with English abstract). https://dorl.net/dor/20.1001.1.20087942.2021.14.6.28.9.
  16. Ghafari, Davari K., and Farid Hosseini A. 2020. Development of Improved Algorithms for Downscaling SMAP-Derived Soil Moisture Using Visible/Inferred satellite observations. Iranian Journal of Irrigation and Drainage 14(2): 650–660. (In Persian with English abstract)
  17. Jagdhuber, Konings A.G., McColl K.A., Alemohammad S.H., Das N.N., Montzka C., Link M., Akbar R., and Entekhabi D. 2019. Physics-Based Modeling of Active and Passive Microwave Covariations Over Vegetated Surfaces. IEEE Transactions on Geoscience and Remote Sensing. 57: 788-802. https://doi.org/10.1109/TGRS.2018.2860630.
  18. Kim, and Hogue T. 2012. Improving spatial soil moisture representation through integration of AMSR-E and MODIS products. IEEE Trans. Geosci. Remote Sens 50(2): 446–460. https://doi.org/10.1109/TGRS.2011.2161318.
  19. Kim, Liu Y.Y., Johnson F.M., Parinussa R.M., and Sharma A. 2015. A global comparison of alternate AMSR2 soil moisture products: Why do they differ?. Remote Sensing of Environment 161: 43–62. https://doi.org/10.1016/j.rse.2015.02.002.
  20. Martinez-Fernández, González Zomora A., Sánchez N., and Gumuzzio A. 2016. Remote Sensing of Environment Satellite soil moisture for agricultural drought monitoring: Assessment of the SMOS derived Soil Water De fi cit Index. Remote Sensing of Environment 177: 277–286. https://doi.org/10.1016/j.rse.2016.02.064.
  21. MladenovaE., Bolten J.D., Crow W., Sazib N., and Reynolds C. 2020. Agricultural Drought Monitoring via the Assimilation of SMAP Soil Moisture Retrievals Into a Global Soil Water Balance Model. Frontiers in Big Data 3: 10. https://doi.org/10.3389/fdata.2020.00010
  22. Modanesi, Massari C., Camici S., Brocca L., and Amarnath G. 2019. Do Satellite Surface Soil Moisture Observations Better Retain Information About Crop‐Yield Variability in Drought Conditions?. Water Resources Research 56(2). https://doi.org/10.1029/2019WR025855.
  23. MohantyP., Cosh M.H., Lakshmi V., and Montzka C. 2017. Soil moisture remote sensing: state-of-the-science. Vadose Zone Journal 16(1): 1–9. https://doi.org/10.2136/vzj2016.10.0105.
  24. Montzka, Bogena H.R., Zreda M., Monerris A., Morrison R., Muddu S., and Vereecken H. 2017. Validation of spaceborne and modelled surface soil moisture products with cosmic-ray neutron probes. Remote Sens 9(2): 103–136. https://doi.org/10.3390/rs9020103.
  25. Niazi, Talebi A., Mokhtari M.H., and Vazifedoust M. 2018. Presenting a soil moisture-based drought index derived from Global Land Data Assimilation System (GLDAS-SMDI) in central Iran. Scientific Research Quarterly of Geographical Data (SEPEHR) 27(107): 179–191. http://doi.org/10.22131/SEPEHR.2018.33574.
  26. NOAA Office of Satellite and Product Operation (OSPO) website, About AMSR-2, https://www.ospo.noaa.gov/Products/atmosphere/gpds/about_amsr2.html. Date of Visit: 2020-05-13.
  27. Paredes-trejo , and Barbosa H. 2017. Evaluation of the SMOS-Derived Soil Water Deficit Index as Agricultural Drought Index in Northeast of Brazil. https://doi.org/10.3390/w9060377.
  28. Peng, and Loew A. 2017. Recent advances in soil moisture estimation from remote sensing. Water 9(7): 530–534. https://doi.org/10.3390/w9070530.
  29. Saha, Patil M., Goyal V., and Rathore DS. 2018. Evaluation W. Assessment and Impact of Soil Moisture Index in Agricultural Drought Estimation Using Remote Sensing and GIS Techniques. 3rd International Electronic Conference on Water Sciences (ECWS-3), 2018; (November). https://doi.org/10.3390/ECWS-3-05802.
  30. Sánchez, González-zamora Á., Piles M., and Martínez-fernández J. 2016. SMADI Integrating MODIS and SMOS Products: A Case of Study over the Iberian Peninsula. https://doi.org/10.3390/rs8040287.
  31. Sur, Park S., Kim T., and Lee J. 2019. Remote Sensing-based Agricultural Drought Monitoring using Hydrometeorological Variables. KSCE Journal of Civil Engineering 23: 5244–5256. https://doi.org/10.1007/978-3-319-43744-6_7.
  32. Takada, Mishima Y., and Natsume S. 2009. Estimation of surface soil properties in peatland using ALOS/PALSAR. Landscape Ecology Engineering 5(1): 45–58. http://dx.doi.org/10.1007/s11355-008-0061-4.
  33. Velayati 2000. The most important factors affecting the qualitative changes of Neishabour plain aquifer. Geographical Researches Journal 149: 119-134. (In Persian)
  34. WilhiteA. 2005. Drought and Water Crises: Science, Technology, and Management Issues. CRC Press 86: 432. https://doi.org/10.1201/9781420028386.
  35. Wang, and Qu J.J. 2009. Satellite remote sensing applications for surface soil moisture monitoring: A review. Front. Earth Science Chin 3(2): 237–247. https://doi.org/10.1007/s11707-009-0023-7.
  36. Wang, Ling Z., Wang Y., and Zeng H. 2016. ISPRS Journal of Photogrammetry and Remote Sensing Improving spatial representation of soil moisture by integration of microwave observations and the temperature–vegetation–drought index derived from MODIS products. ISPRS Journal of Photogrammetry and Remote Sensing 113: 144–154. https://doi.org/10.1016/j.isprsjprs.2016.01.009.
  37. Zhang, Li Z.L., Tang R., Tang B.H., and Wu H. 2014. A remote sensing technique to determine the soil moisture saturation index. Institute of Geographic Sciences and Natural Resources Research 978-1-4799-5775-0/14. 3280. https://doi.org/10.1109/IGARSS.2014.6947180.
  38. Zhu, Luo Y., Xu Y., Tian Y., and Yang T. 2019. Satellite Soil Moisture for Agricultural Drought Monitoring: Assessment of SMAP-Derived Soil Water Deficit Index in Xiang River Basin. Remote Sensing 11(3): 362. https://doi.org/10.3390/rs11030362.
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