ارزیابی روش‌های پیش‌بینی شاخص ترکیبی خشکسالی کشاورزی (CDI) براساس تصاویر ماهواره‌ای با روشهای یادگیری عمیق و یادگیری ماشین

نوع مقاله : مقالات پژوهشی

نویسندگان

1 گروه علوم و مهندسی آب، دانشگاه تبریز، تبریز، ایران

2 گروه سنجش از دور و GIS، دانشکده برنامه‌ریزی و علوم محیطی، دانشگاه تبریز، تبریز، ایران

3 گروه مهندسی کشاورزی، دانشکده کشاورزی، دانشگاه آنکارا، آنکارا، ترکیه

چکیده

باتوجه به بحران خشکیدگی دریاچه ارومیه، مطالعه وضعیت پوشش‌گیاهی و خشکسالی کشاورزی محدوده حوضه آبریز دریاچه ارومیه که یکی از شش حوضه اصلی ایران محسوب می‌شود، از اهمیت قابل توجهی برخوردار است. در این مطالعه ابتدا یک شاخص ترکیبی خشکسالی CDI (Combined Drought Index) مبتنی بر شاخص‌های وضعیت پوشش گیاهی (VCI)، وضعیت دمایی گیاهی (TCI) و شاخص تنش آبی محصول (CWSI) با‌استفاده از داده‌های سنجنده MODIS قرار‌گرفته در ماهواره TERRA معرفی و محاسبه گردید. سپس با روش‌های درخت تصمیم-طبقه‌بندی و درخت رگرسیون (DT-CART)، ماشین‌بردار پشتیان (SVM) و حافظه کوتاه مدت، بلند مدت (LSTM) و حافظه کوتاه مدت دو جهته (BiLSTM)، شاخص ترکیبی خشکسالی (CDI) معرفی و تخمین زده شد. در فرآیند مدل‌سازی شاخص ترکیبی خشکسالی، محصولات شاخص‌های پوشش گیاهی، تبخیر-‌‌تعرق، تبخیر-تعرق پتانسیل، دمای سطح زمین در روز و دمای سطح زمین در شب برگرفته از سنجنده MODIS به‌عنوان ورودی مدل‌ها استفاده شد. درنهایت بررسی عملکرد مدل‌ها براساس ترکیب‌های متفاوتی از ورودی مدل‌ها بااستفاده از معیارهای ارزیابی شامل ضریب همبستگی، جذر میانگین مربعات خطا و ضریب ناش ساتکلیف و همچنین به کمک نمودارهای کلوروگرام، تیلور و ویلونی بصورت بصری انجام‌شد. نتایج نشان‌داد که متغیر‌های دمای سطح زمین در روز، دمای سطح زمین در شب و تبخیر-تعرق موثرترین متغیرها برای مدل‌سازی شاخص ترکیبی خشکسالی (CDI) و مطالعه خشکسالی کشاورزی می‌باشند. همچنین مدل CART با ضریب همبستگی 96/0، میانگین جذر مربعات خطا برابر با 029/0 و ضریب ناش ساتکلیف 92/0 به‌عنوان بهترین مدل انتخاب گردید. نتایج بدست آمده نشان‌داد که روش‌های یادگیری ماشین و یادگیری عمیق ابزاری توانمند در مدل‌سازی و پیش‌بینی شاخص ترکیبی خشکسالی (CDI) بوده و در بررسی و ارزیابی خشکسالی کشاورزی به‌خصوص در حوضه‌های فاقد آمار با اطمینان کافی می‌تواند مورد استفاده قرار گیرد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Evaluation of Combined Agricultural Drought Index (CDI), Prediction Methods Based on Satellite Images via Deep Learning and Machine Learning Approaches

نویسندگان [English]

  • Nazila Shamloo 1
  • Mohammad Taghi Sattari 1
  • Khalil Valizadeh Kamran 2
  • Halit Apaydin 3
1 Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, Iran
2 Department of Remote Sensing and GIS, Faculty of Geography & Planning, University of Tabriz, Tabriz, Iran
3 Department of Agricultural, Faculty of Agriculture, University of Ankara, Ankara, Turkey
چکیده [English]

Introduction
Drought is one of the greatest challenges of our time due to the dangers it poses to the world. In arid and semi-arid regions, it is necessary to continuously monitor agricultural systems that face water shortages and frequent droughts. Therefore, it is necessary to have large-scale information about agricultural systems and land use for managing and making decisions for the sustainability of food security. Continuous monitoring of drought requires a large amount of information to be processed with great speed and accuracy. Due to the complexity and impact of various factors on drought, in recent years, the methods of combining several factors to create a comprehensive drought index have received much attention. Machine learning and deep learning methods can provide a more accurate and efficient tool to predict droughts and be used in drought risk management. The review of sources shows that until now no studies have been conducted in the field of drought monitoring using deep learning approach and satellite images in the catchment area of Lake Urmia in Iran. A large part of its economic activities is dedicated to agriculture. The increase in temperature, the increase in evaporation-transpiration and the excessive use of water resources for agriculture have caused an upward trend in the frequency of droughts in this basin during consecutive years, one of the harmful effects of which is a significant decrease in the lake level. Therefore, for drought management in this basin, it is very important to identify drought behavior so It is very important to determine appropriate and reliable indicators to measure and predict the effects of droughts. According to the investigations, it was observed that most of the studies in the field of drought in this basin have been carried out from the meteorological point of view, or by individual plant indicators, so in this study, using the approach of principal component analysis, we tried to provide a composite drought index for drought modeling and forecasting.
 
Materials and Methods
In this research, satellite images and deep learning and machine learning methods have been used to predict the Combined Drought Index. For this purpose, satellite images were first obtained for the study area and pre-processing was done on the data. Then, all the data were converted to a scale with a spatial resolution of 500 meters, and the VCI index was calculated using NDVI data, the TCI index using the land surface temperature product, and the CWSI index using the Modis evapotranspiration product, and finally, CDI drought index was calculated using principal component analysis method. Then the correlation between CDI data and other meteorological variables including evapotranspiration, potential evapotranspiration, land surface temperature during the day, and land surface temperature at night was calculated. Finally, the CDI index is modeled using deep learning and machine learning methods.
 
Results and Discussion
This study modeled the Combined Drought Index based on a different combination of input variables and deep learning and machine learning methods. Examining the results showed that the variables of the normalized difference vegetation index, the land surface temperature during the day and at night, evapotranspiration, and potential evapotranspiration were the most influential parameters for modeling the CDI index, and all four methods with acceptable accuracy and error have been able to model the combined drought index. The CART model with a correlation coefficient of 0.96, RMSE equal to 0.029, and Nash Sutcliffe coefficient of 0.92 was chosen as the best model among the methods.
 
Conclusion
In this research, different combinations of input variables extracted from satellite image products were evaluated in the form of 6 independent scenarios to predict the Combined Drought Index. By examining the evaluation parameters including correlation coefficient, Nash Sutcliffe coefficient, and root mean square error, it was found that all four methods can estimate the combined drought index with acceptable accuracy and error. Among all the methods, the CART method performed better (R=0.96 and RMSE=0.029) than the other methods for predicting the time series of the Combined Drought Index. On the other hand, the SVM method has been able to model the combined drought index with acceptable accuracy (R=0.94 and RMSE=0.034). However, contrary to expectations, two deep learning methods were able to model the combined drought index with less accuracy than machine learning methods. In general, by examining the results, it was found that with the method presented in this research, it is possible to accurately predict the CDI combined drought index time series and predict drought in different periods of plant growth, and use its results for regional drought management and policies, especially in Basins without statistics.
 

کلیدواژه‌ها [English]

  • Agricultural drought
  • Combined Drought Index (CDI)
  • Deep learning and machine learning
  • Satellite images

©2023 The author(s). This is an open access article distributed under Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source.

مراجع

  1. AghaKouchak, A., Farahmand, A., Melton, F.S., Teixeira, J., Anderson, M.C., Wardlow, B.D., & Hain, C. R. (2015). Remote sensing of drought: Progress, challenges and opportunities. Reviews of Geophysics, 53(2), 452-480. https://doi.org/10.1002/2014RG000456
  2. Arslan, N., & Sekertekin, A. (2019). Application of long short-term memory neural network model for the reconstruction of MODIS Land Surface Temperature images. Journal of Atmospheric and Solar-Terrestrial Physics, 194, 105100. https://doi.org/10.1016/j.jastp.2019.105100
  3. Babajafari, H., Paimazd, Sh., Moghadasi, M., & Hosseinivardanjani, M. (2022). Assessment monitoring spatio-temporal of drought lake Urmia basin using ETDI remote sensing index and SPI ground index. Journal of Water and Soil Science, 26(3), 281-302
  4. Balti, H., Abbes, A.B., Mellouli, N., Farah, I.R., Sang, Y., & Lamolle, M. (2020). A review of drought monitoring with big data: Issues, methods, challenges and research directions. Ecological Informatics, 60, 101136. https://doi.org/10.1016/j.ecoinf.2020.101136
  5. Boser, B.E., Guyon, I.M., & Vapnik, V.N. (1992). A training algorithm for optimal margin classifiers. Proceedings of the fifth annual workshop on Computational learning theory, https://doi.org/10.1145/130385.130401
  6. Breiman, L., Friedman, J., Stone, C.J., & Olshen, R.A. (1984). Classification and regression trees. CRC press.
  7. Crisóstomo de Castro Filho, H., Abílio de Carvalho Júnior, O., Ferreira de Carvalho, O.L., Pozzobon de Bem, P., dos Santos de Moura, R., Olino de Albuquerque, , Rosa Silva, C., Guimarães Ferreira, P.H., Fontes Guimarães, R., & Trancoso Gomes, R.A. (2020). Rice crop detection using LSTM, Bi-LSTM, and machine learning models from Sentinel-1 time series. Remote Sensing, 12(16), 2655. https://doi.org/10.3390/rs12162655
  8. Cunha, R.L., Silva, B., & Netto, M.A. (2018). A scalable machine learning system for pre-season agriculture yield forecast. 2018 IEEE 14th International Conference on e-Science (e-Science), https://doi.org/1109/eScience.2018.00131
  9. Dang, C., Liu, Y., Yue, H., Qian, J., & Zhu, R. (2020). Autumn crop yield prediction using data-driven approaches:-support vector machines, random forest, and deep neural network methods. Canadian Journal of Remote Sensing, 1-20. https://doi.org/10.1080/07038992.2020.1833186
  10. Elbeltagi, A., Kumari, N., Dharpure, J.K., Mokhtar, A., Alsafadi, K., Kumar, M., Mehdinejadiani, B., Ramezani Etedali, H., Brouziyne, Y., & Towfiqul Islam, A.R.M. (2021). Prediction of combined terrestrial evapotranspiration index (CTEI) over large river basin based on machine learning approaches. Water, 13(4), 547. https://doi.org/10.3390/w13040547
  11. Fan, Y., Qian, Y., Xie, F.-L., & Soong, F.K. (2014). TTS synthesis with bidirectional LSTM based recurrent neural networks. Fifteenth annual conference of the international speech communication association.
  12. Guzmán, S.M., Paz, J.O., Tagert, M.L.M., Mercer, A.E., & Pote, J.W. (2018). An integrated SVR and crop model to estimate the impacts of irrigation on daily groundwater levels. Agricultural Systems, 159, 248-259. https://doi.org/10.1016/j.agsy.2017.01.017
  13. Heim Jr, R.R., & Brewer, M.J. (2012). The global drought monitor portal: The foundation for a global drought information system. Earth Interactions, 16(15), 1-28. https://doi.org/10.1175/2012EI000446.1
  14. Hintz, J.L., & Nelson, R.D. (1998). Violin plots: A box plot-density trace synergism. The American Statistician, 52(2), 181-184.
  15. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780. https://doi.org/1162/neco.1997.9.8.1735
  16. Hu, Y., & Dong, Y. (2018). An automatic approach for land-change detection and land updates based on integrated NDVI timing analysis and the CVAPS method with GEE support. ISPRS Journal of Photogrammetry and Remote Sensing, 146, 347-359. https://doi.org/10.1016/j.isprsjprs.2018.10.008
  17. Jackson, R.D., Kustas, W.P., & Choudhury, B.J. (1988). A reexamination of the crop water stress index. Irrigation Science, 9, 309-317.
  18. Karimzadegan, S., Behmanesh, J., & Rezaie, H. (2018). Application of bayesian network and LS-SVM methods in predicting water surface level of Urmia Lake. Journal of Water and Soil Conservation, 25(3), 2018.
  19. Kazempour Choursi, S., Erfanian, M., & Ebadi Nehari, Z. (2019). Evaluation of MODIS and TRMM satellite data for drought monitoring in the Urmia lake basin. Journal of Geography and Environmental Planning, 30(2), 74.
  20. 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 Meteorological Society, 76(5), 655-668. https://doi.org/10.1175/1520-0477
  21. Komasi, M., & Sharghi, S. (2020). Drought forecasting using wavelet-support vector machine and standardized precipitation index (Case study: Urmia Lake-Iran). Journal of Environmental Science and Technology, 22(7), 83-101.
  22. Khosravi, I., Akhundzadeh, M., & Khoshgovtar, M. (2015). Modeling and forecasting the time series of drought indicators with machine learning methods in order to manage risks (case study: Eastern region of Isfahan). Environmental Risk Management, 2(1), 51-65. (In Persian with English abstract)
  23. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.
  24. Li, X., Yuan, W., & Dong, W. (2021). A machine learning method for predicting vegetation indices in China. Remote Sensing, 13(6), 1147. https://doi.org/10.3390/rs13061147
  25. Maroufpoor, S., Maroufpoor, E., Bozorg-Haddad, O., Shiri, J., & Yaseen, Z.M. (2019). Soil moisture simulation using hybrid artificial intelligent model: Hybridization of adaptive neuro fuzzy inference system with grey wolf optimizer algorithm. Journal of Hydrology, 575, 544-556. https://doi.org/10.1016/j.jhydrol.2019.05.045
  26. Mohammadi, H., Nasiri Kashani, K., Maleki, S., & Rostami, H. (2018). Identify and prioritize he factors affecting on the drying of Urmia Lake with Integrated Fuzzy DEMATEL; Analytic Network Process (F.D.ANP). Emergency Management, 7(1), 13-26.
  27. Nash, J.E., & Sutcliffe, J.V. (1970). River flow forecasting through conceptual models part I—A discussion of principles. Journal of Hydrology, 10(3), 282-290. https://doi.org/10.1016/0022-1694(70)90255-6
  28. Nay, J., Burchfield, E., & Gilligan, J. (2018). A machine-learning approach to forecasting remotely sensed vegetation health. International Journal of Remote Sensing, 39(6), 1800-1816. https://doi.org/10.1080/01431161.2017.1410296
  29. Niasati, Z., Ebadi, H., & Kayani, A. (2021). Estimation of reference evaporation and transpiration using remote sensing data in Hamadan Bahar Plain. Iran Water Research, 15(4), 45- (In Persian with English abstract)
  30. Prodhan, F.A., Zhang, J., Hasan, S.S., Sharma, T.P.P., & Mohana, H.P. (2022). A review of machine learning methods for drought hazard monitoring and forecasting: Current research trends, challenges, and future research directions. Environmental Modelling & Software, 149, 105327.
  31. Qian, Y., Zhou, W., Yan, J., Li, W., & Han, L. (2015). Comparing machine learning classifiers for object-based land cover classification using very high resolution imagery. Remote Sensing, 7(1), 153-168. https://doi.org/10.3390/rs70100153
  32. Rao, C. (1973). 10.1002/9780470316436, John Wiley and Sons, New York.
  33. Rezaei Moghadam, M.H., Valizadehkamran, Kh., Rostamzadeh, H., & Rezaei, A. (2012) Evaluation of theefficiency of MODIS sensor data in estimating drought (Case study: Urmia Lake catchment). Geographyand Environmental Sustainability, 5: 37-52.
  34. Reddy, D.S., & Prasad, P.R.C. (2018). Prediction of vegetation dynamics using NDVI time series data and LSTM. Modeling Earth Systems and Environment, 4(1), 409-419.
  35. Rhif, M., Abbes, A.B., Martinez, B., & Farah, I.R. (2020). A deep learning approach for forecasting non-stationary big remote sensing time series. Arabian Journal of Geosciences, 13(22), 1-11.
  36. Sattari, M.T., Apaydin, H., Ozturk, F., & Baykal, N. (2012). Application of a data mining approach to derive operating rules for the Eleviyan irrigation reservoir. Lake and Reservoir Management, 28(2), 142-152. https://doi.org/10.1080/07438141.2012.678927
  37. Sharifipour, L., Ghanei-Bafghi, M., Kosari, M., & Sharifipour, S. (2021). Comparing the efficiency of four artificial intelligence methods in drought prediction. Scientific Journal System, 8(3), 139-156. (In Persian with English abstract)
  38. Shamloo, N., Sattari, M.T., & Apaydin, H. (2022). Agricultural drought survey using MODIS-based image indices at the regional scale: case study of the Urmia Lake Basin, Iran. Theoretical and Applied Climatology, 149(1-2), 39-51.
  39. Shen, R., Huang, A., Li, B., & Guo, J. (2019). Construction of a drought monitoring model using deep learning based on multi-source remote sensing data. International Journal of Applied Earth Observation and Geoinformation, 79, 48-57. https://doi.org/10.1016/j.jag.2019.03.006
  40. Shao, Y., & Lunetta, R.S. (2012). Comparison of support vector machine, neural network, and CART algorithms for the land-cover classification using limited training data points. ISPRS Journal of Photogrammetry and Remote Sensing, 70, 78-87. https://doi.org/10.1016/j.isprsjprs.2012.04.001
  41. Smith, L. (2002). A Tutorial on Principal Components Analysis. Page 1-26.
  42. Taylor, K.E. (2001). Summarizing multiple aspects of model performance in a single diagram. Journal of Geophysical Research: Atmospheres, 106(D7), 7183-7192.https://doi.org/10.1029/2000JD900719
  43. Tucker, C.J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2), 127-150. https://doi.org/10.1016/0034-4257(79)90013-0
  44. Vapnik, V.N. (1995). The nature of statistical learning. Theory.
  45. Willmott, C.J., & Matsuura, K. (2005). Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate Research, 30(1), 79-82. https://doi.org/10.3354/cr030079
  46. Winkler, K., Gessner, U., & Hochschild, V. (2017). Identifying droughts affecting agriculture in Africa based on remote sensing time series between 2000–2016: rainfall anomalies and vegetation condition in the context of ENSO. Remote Sensing, 9(8), 831. https://doi.org/10.3390/rs9080831
  47. Yin, J., Zhan, X., Hain, C.R., Liu, J., & Anderson, M.C. (2018). A method for objectively integrating soil moisture satellite observations and model simulations toward a blended drought index. Water Resources Research, 54(9), 6772-6791. https://doi.org/10.1029/2017WR021959
  48. Zhu, X.X., Tuia, D., Mou, L., Xia, G.-S., Zhang, L., Xu, F., & Fraundorfer, F. (2017). Deep learning in remote sensing: A comprehensive review and list of resources. IEEE Geoscience and Remote Sensing Magazine, 5(4), 8-36. https://doi.org/1109/MGRS.2017.2762307
ارسال نظر در مورد این مقاله
CAPTCHA Image
آب و خاک
دوره 37، شماره 5 - شماره پیاپی 91
آذر و دی 1402
صفحه 787-807

فایل ها

سابقه مقاله

  • تاریخ دریافت: 17 خرداد 1402
  • تاریخ بازنگری: 05 شهریور 1402
  • تاریخ پذیرش: 29 شهریور 1402
  • تاریخ اولین انتشار: 01 مهر 1402

هم رسانی

ارجاع به این مقاله

آمار