Document Type : Original Article
Authors
1 Assistant Professor, Department of Water Science and Engineering, Faculty of Agriculture and Natural Resources,Imam Khomeini International University, Qazvin, Iran
2 PhD in Irrigation and Drainage, Department of Water Science and Engineering, Faculty of Agriculture and Natural Resources, Imam Khomeini International University, Qazvin, Iran
3 Associate Professor, Department of Water Science and Engineering, Faculty of Agriculture and Natural Resources,Imam Khomeini International University, Qazvin, Iran
Abstract
Abstract
Accurate estimation of cultivated area is essential for agricultural planning and water resource management. The integration of remote sensing data and machine learning algorithms can enhance the accuracy of such estimations. This study evaluates the effectiveness of the Random Forest algorithm in estimating the dominant cultivated areas by integrating 10-meter resolution Sentinel-1 and Sentinel-2 data within the Qazvin Plain irrigation network. First, the Jeffries-Matusita separability test was conducted to assess the spectral discrimination of six land use classes (wheat-barley/maize, alfalfa, fallow, bare_land, and urban areas) during Winter and Spring cropping seasons. The results indicated high separability for some classes, while others exhibited spectral overlap. Subsequently, the combination of radar and optical data with the Random Forest algorithm significantly improved classification accuracy. In the Winter, the classification achieved a kappa coefficient of 0.99 and an overall accuracy of 99.69%, while in the spring cropping season, the kappa coefficient was 0.98, with an overall accuracy of 98.93%. An analysis of cultivated area trends for wheat-barley, maize, and alfalfa over the past decade revealed an increase in maize cultivation and a decline in alfalfa during the spring season. In the Winter, wheat and barley cultivation expanded, likely due to their relatively higher resilience to climatic fluctuations and economic incentives. The analysis of cultivated area changes within the irrigation network indicated a general shift towards increased maize cultivation, with a growing preference for spring cropping over Winter cropping.
Keywords
Main Subjects
Abdi, S., Ahmadee, M., Rustum, R., & Salmanpour, A. (2024). Monitoring Changes in Rice Cultivation Area using Multi-Temporal Satellite images (Case Study: Beiranshahr Region, Iran). Journal of Drought and climate change Research (JDCR), 2(2), 77-92. https://doi.org/10.22077/jdcr.2023.6743.1040.
Acevedo, M., Pixley, K., Zinyengere, N., Meng, S., Tufan, H., Cichy, K., Bizikova, L., Isaacs, K., Gezzi-Kopel, K., & Porciello, J. (2020). A scoping review of adoption of climate-resilient crops by small-scale producers in low- and middle-income countries. Nature Plants, 6(10), 1231-1241. https://doi.org/10.1038/s41477-020-00783-z.
Ahmad, I., Singh, A., Fahad, M., & Waqas, M. M. (2020). Remote sensing-based framework to predict and assess the interannual variability of maize yields in Pakistan using Landsat imagery. Computers and Electronics in Agriculture, 178, 105732. https://doi.org/10.1016/j.compag.2020.105732.
Akbari, M., Najafi Alamdarlo, H., & moosavi, s. h. (2019). Impacts of Climate Change and Drought on Income Risk and Crop Pattern in Qazvin Plain Irrigation Network. Journal of Water Research in Agriculture, 33(2), 265-281. https://doi.org/10.22092/jwra.2019.119742.
Aria, M., Cuccurullo, C., & Gnasso, A. (2021). A comparison among interpretative proposals for Random Forests. Machine Learning with Applications, 6, 100094. https://doi.org/10.1016/j.mlwa.2021.100094.
Ashktorab, N., Layani, G., & Soltani, G. R. (2015). Evaluating the Effects of Climate Changes and Government Policies on Yield and Cultivation Area of Maize in Iran: Panel Data Method. Journal of Agricultural Economics and Development, 29(1), 31-42. https://doi.org/10.22067/jead2.v0i0.37268.
Ashourloo, D., Nematollahi, H., Huete, A., Aghighi, H., Azadbakht, M., Shahrabi, H. S., & Goodarzdashti, S. (2022). A new phenology-based method for mapping wheat and barley using time-series of Sentinel-2 images. Remote Sensing of Environment, 280, 113206. https://doi.org/10.1016/j.rse.2022.113206.
Ayoub Shaikh, T., Rasool, T., & Rasheed Lone, F. (2022). Towards leveraging the role of machine learning and artificial intelligence in precision agriculture and smart farming. Computers and Electronics in Agriculture, 198, 107119. https://doi.org/10.1016/j.compag.2022.107119.
Baraj, B., Mishra, M., Sudarsan, D., da Silva, R. M., & Santos, C. A. G. (2024). Climate change and resilience, adaptation, and sustainability of agriculture in India: A bibliometric review. Heliyon. https://doi.org/ 10.1016/j.heliyon.2024.e29586.
Boryan, C., Yang, Z., Mueller, R., & Craig, M. (2011). Monitoring US agriculture: the US Department of Agriculture, National Agricultural Statistics Service, Cropland Data Layer Program. Geocarto International, 26(5), 341-358. https://doi.org/10.1080/10106049.2011.562309.
Breiman, L. (2001). Random forests. Machine Learning, 45, 5-32. https://doi.org/10.1023/A:1010933404324.
Chen, C., Liaw, A., & Breiman, L. (2004). Using Random Forest to learn imbalanced data. University of California, Berkeley.
Chen, Y., Lu, D., Moran, E., Batistella, M., Dutra, L. V., Sanches, I. D. A., da Silva, R. F. B., Huang, J., Luiz, A. J. B., de Oliveira, M. A. F. (2018). Mapping croplands, cropping patterns, and crop types using MODIS time-series data. International Journal of Applied Earth Observation and Geoinformation, 69, 133-147. https://doi.org/10.1016/j.jag.2018.03.005.
Darvish Hendi, M., & Amiri Tokaldany, E. (2024). Agricultural Water Management Challenges in Qazvin Plain Irrigation Network. Iranian Journal of Soil and Water Research, 54(12), 1945-1962. [In Persian]. https://doi.org/10.22059/ijswr.2023.365841.669581.
Elbasi, E., Zaki, C., Topcu, A. E., Abdelbaki, W., Zreikat, A. I., Cina, E., Shedefat, A., Saker, L. (2023). Crop Prediction Model Using Machine Learning Algorithms. Applied Sciences, 13(16), 9288. https://doi.org/10.3390/app13169288.
FAO. (2015). World program of the census of agriculture 2020 I: programme, concepts and definitions. FAO Statistical Development Series. FAO, Rome,, 204.
Fayazi, H., Zeinali, E., Soltani, A., & Torabi, B. (2022). The Effect of Climate Change on Yield Potential and Water Productivity of Forage Maize in Iran. Journal of Crops Improvement, 24(4), 1247-1263. [In Persian].https://doi.org/10.22059/jci.2022.334981.2648.
Ghodsi, Z., Kheirkhah Zarkesh, M. M., & Ghermezcheshmeh, B. (2021). Comparison of Accuracy Between Support Vector Machine and Random Forest Classifiers for Land Use and Crop Mapping Using Multi-Temporal Sentinel-2 Images. Iranian Journal of Remote Sensing & GIS, 12(4), 73-92. [In Persian]. https://doi.org/10.52547/gisj.12.4.73.
Gholami, Z., Ebrahimian, H., & Noory, H. (2018). Prioritization of Major Agricultural Crops Cultivation Considering the Energy and Water Costs in Qazvin Plain. Irrigation Sciences and Engineering, 41(1), 17-30. [In Persian].https://doi.org/10.22055/jise.2018.13447.
Gholamy, A., Kreinovich, V., & Kosheleva, O. (2018). Why 70/30 or 80/20 Relation Between Training and Testing Sets: A Pedagogical Explanation. International Journal of Intelligent Technologies and Applied Statistics, 11(2), 105-111. https://doi.org/10.6148/IJITAS.201806_11(2).0003.
Gilmanov, T. G., Wylie, B. K., Tieszen, L. L., Meyers, T. P., Baron, V. S., Bernacchi, C. J., Billesbach, D., Buba, G., Fishcher, M. L, Glenn, A. J., Hanan, N. P., Hatfield, J.L., Heuer, M. W., Hollinger, S.E., Howard, D. M., Matamala, R., Prueger, J. H., Tenuta, M., Young, D. G. (2013). CO2 uptake and ecophysiological parameters of the grain crops of midcontinent North America: Estimates from flux tower measurements. Agriculture, Ecosystems & Environment, 164, 162-175. https://doi.org/10.1016/j.agee.2012.09.017.
Gitz, V., Meybeck, A., Lipper, L., Young, C. D., & Braatz, S. (2016). Climate Change and Food Security: Risks and Responses. Food and Agriculture Organization of the United Nations (FAO) Report, 110, 3-36.
Goel, S., Singh, M., Phaugat, A., Grewal, S., Goel, M., & Mishra, A. K. (2023). Impact of COVID-19 lockdown on Indian agriculture: A review. Agricultural Reviews, 44(2), 215-222. https://doi.org/ 10.18805/ag.R-2188.
Goldblatt, R., You, W., Hanson, G., & Khandelwal, A. K. (2016). Detecting the boundaries of urban areas in india: A dataset for pixel-based image classification in google earth engine. Remote Sensing, 8(8), 634. https://doi.org/10.3390/rs8080634.
Habib-ur-Rahman, M., Ahmad, A., Raza, A., Hasnain, M. U., Alharby, H. F., Alzahrani, Y. M., Bamagoos, A., Hakeem, K. R., Ahmad, S., Nasim, W., Ali, S., Mansour, F., El Sabagh, A. (2022). Impact of climate change on agricultural production; Issues, challenges, and opportunities in Asia. Frontiers in Plant Science, 13, 925548. https://doi.org/10.3389/fpls.2022.925548.
Hou, H., Wang, R., & Murayama, Y. (2019). Scenario-based modelling for urban sustainability focusing on changes in cropland under rapid urbanization: A case study of Hangzhou from 1990 to 2035. Science of The Total Environment, 661, 422-431. https://doi.org/10.1016/j.scitotenv.2019.01.208.
Lu, T., Gao, M., & Wang, L. (2023). Crop classification in high-resolution remote sensing images based on multi-scale feature fusion semantic segmentation model. Frontiers in Plant Science, 14, 1196634. https://doi.org/10.3389/fpls.2023.1196634.
Mahmoodi, A., & Parhizkari, A. (2016). Economic Analysis of the Climate Change Impacts on Products Yield, Cropping Pattern and Farmer's Gross Margin (Case Study: Qazvin Plain). Economic Growth and Development Research, 5(17(3)), 40-25. [In Persian]
Meng, X., Zhu, Y., Yin, M., & Liu, D. (2021). The impact of land use and rainfall patterns on the soil loss of the hillslope. Scientific Reports, 11(1), 16341. https://doi.org/10.1038/s41598-021-95819-5.
moosazadeh, n., & Tavakkoli, J. (2023). The investigations of the impact of the outbreak of the Covid-19 pandemic on the supply chain of agricultural products in the villages of Kermanshah province. Economic Geography Research, 4(12), 25-47. [In Persian]
Moradhaseli, S., Ataei, P., Karimi, H., & Hajialiani, S. (2022). Typology of Iranian farmers' vulnerability to the COVID-19 outbreak. Frontiers in Public Health, 10, 1018406. https://doi.org/10.3389/fpubh.2022.1018406.
Neetu, & Ray, S. (2019). Exploring machine learning classification algorithms for crop classification using Sentinel 2 data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 42, 573-578. https://doi.org/10.5194/isprs-archives-XLII-3-W6-573-2019.
Novelli, A., Aguilar, M. A., Aguilar, F. J., Nemmaoui, A., & Tarantino, E. (2017). AssesSeg—A Command Line Tool to Quantify Image Segmentation Quality: A Test Carried Out in Southern Spain from Satellite Imagery. Remote Sensing, 9(1). https://doi.org/10.3390/rs9010040.
Portmann, F. T., Siebert, S., & Döll, P. (2010). MIRCA2000—Global monthly irrigated and rainfed crop areas around the year 2000: A new high‐resolution data set for agricultural and hydrological modeling. Global biogeochemical cycles, 24(1). https://doi.org/10.1029/2008GB003435.
Rad, A. K., Shamshiri, R. R., Azarm, H., Balasundram, S. K., & Sultan, M. (2021). Effects of the COVID-19 Pandemic on Food Security and Agriculture in Iran: A Survey. Sustainability, 13(18), 10103. https://doi.org/10.3390/su131810103.
Rahimzadegan, M., & Pourgholam, M. (2017).Identification of the area under cultivation of Saffron using Landsat-8 temporal satellite images (Case study: Torbat Heydarieh). Journal of Applied RS and GIS techniques in natural resources science, 7(4), 97-115. [In Persian]
Ramezani Etedali, H., & Ahmadi, M. (2024). Investigating the relationship between drought indices and maize yield using random forest method (case study: Qazvin plain irrigation network). Nivar, 48(126), 127-137. https://doi.org/10.30467/nivar.2024.467444.1299.
Rezaei, M., Amiri, E., & Kamali, M. (2024). Estimating the area under rice cultivation in Guilan province using remote sensing technology and GEE. Iranian Journal of Soil Research, 38(2), 113-124. https://doi.org/10.22092/ijsr.2024.366254.750.
Sen, R., Goswami, S., & Chakraborty, B. (2019). Jeffries-Matusita distance as a tool for feature selection. International Conference on Data Science and Engineering (ICDSE), 15-20. https://doi.org/10.1109/ICDSE47409.2019.8971800.
Shahrin, F., Zahin, L., Rahman, R., Hossain, A. J., Kaf, A. H., & Azad, A. K. M. A. M. (2020). Agricultural Analysis and Crop Yield Prediction of Habiganj using Multispectral Bands of Satellite Imagery with Machine Learning. 11th International Conference on Electrical and Computer Engineering (ICECE), 21-24, https://doi.org/ 10.1109/ICECE51571.2020.9393066.
Shayanmehr, S., Porhajašová, J. I., Babošová, M., Sabouhi Sabouni, M., Mohammadi, H., Rastegari Henneberry, S., & Shahnoushi Foroushani, N. (2022). The Impacts of Climate Change on Water Resources and Crop Production in an Arid Region. Agriculture, 12(7). https://doi.org/10.3390/agriculture12071056.
Skendžić, S., Zovko, M., Živković, I. P., Lešić, V., & Lemić, D. (2021). The impact of climate change on agricultural insect pests. Insects, 12(5), 440. https://doi.org/ 10.3390/insects12050440.
Snevajs, H., Charvat, K., Onckelet, V., Kvapil, J., Zadrazil, F., Kubickova, H., …Batrlova, I. (2022). Crop Detection Using Time Series of Sentinel-2 and Sentinel-1 and Existing Land Parcel Information Systems. Remote Sensing, 14(5), 1095. https://doi.org/10.3390/insects12050440.
Soleymani Nejad, S., Dourandish, A., Sabouhi, m., & Banayan Aval, M. (2019). The Effects of Climate Change on Cropping Pattern (Case Study: Mashhad Plain). Iranian Journal of Agricultural Economics and Development Research, 50(2), 249-263. [In Persian]. https://doi.org/10.22059/ ijaedr.2019.237998.668461.
Song, X.-P., Potapov, P. V., Krylov, A., King, L., Di Bella, C. M., Hudson, A., Khan, A., Adusei, B., Steman, S.V., Hansen, M. C. (2017). National-scale soybean mapping and area estimation in the United States using medium resolution satellite imagery and field survey. Remote Sensing of Environment, 190, 383-395. https://doi.org/10.1016/j.rse.2017.01.008.
Tahernia, M., & hasanvand, a. (2020). Economic consequences of Covid-19 disease on the Iranian economy; With an emphasis on employment [Research]. Quarterly Journal of Nersing Management, 9(3), 43-58. [In Persian]
Tariq, A., Yan, J., Gagnon, A. S., Riaz Khan, M., & Mumtaz, F. (2023). Mapping of cropland, cropping patterns and crop types by combining optical remote sensing images with decision tree classifier and random forest. Geo-Spatial Information Science, 26(3), 302-320. https://doi.org/10.1080/10095020.2022.2100287.
Teluguntla, P., Thenkabail, P. S., Oliphant, A., Xiong, J., Gumma, M. K., Congalton, R. G., Yadav, K., Huete, A. (2018). A 30-m landsat-derived cropland extent product of Australia and China using random forest machine learning algorithm on Google Earth Engine cloud computing platform. ISPRS Journal of Photogrammetry and Remote Sensing, 144, 325-340. https://doi.org/10.1016/j.isprsjprs.2018.07.017.
Torabi, O., Karimi, N., shesh angosht, s., Rashtbari, M., & Sarbazvatan, A. h. (2023). Investigating the effect of radar images in classifying land use classes in machine learning based algorithms. Human Ecology, 2(2), 141-154. https://doi.org/10.22034/el.2023.392546.1011. [In Persian]
Waldner, F., Fritz, S., Di Gregorio, A., Plotnikov, D., Bartalev, S., Kussul, N., Gong, P., Thenkabail, P., Gerard, H., Klein, I., Low, F., Miettinen, J., Dadhwal, V. K., Lamarche, C., Bontemps, S., Defourny, P. (2016). A Unified Cropland Layer at 250 m for Global Agriculture Monitoring. Data, 1(1), 3. https://doi.org/10.3390/data1010003.
Wang, Y., Qi, Q., & Liu, Y. (2018). Unsupervised Segmentation Evaluation Using Area-Weighted Variance and Jeffries-Matusita Distance for Remote Sensing Images. Remote Sensing, 10(8), 1193. https://doi.org/10.3390/rs10081193.
Youssef, A. M., Pourghasemi, H. R., Pourtaghi, Z. S., & Al-Katheeri, M. M. (2016). Landslide susceptibility mapping using random forest, boosted regression tree, classification and regression tree, and general linear models and comparison of their performance at Wadi Tayyah Basin, Asir Region, Saudi Arabia. Landslides, 13(5), 839-856. https://doi.org/10.1007/s10346-015-0614-1.
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