|
Determining the area under rice cultivation using machine learning methods and simultaneous use of optical satellite images and radar with virtual aperture (Case study: Babol city)
|
Ali لخخیشقظ   |
|
|
|
Abstract: (154 Views) |
Rice is one of the key agricultural products in Iran and the world, and accurate monitoring of its cultivated area, especially the separation of monoculture and double-cropping, plays a vital role in water resources management and production planning. Mazandaran province, as the country's rice production hub, requires new methods for monitoring agricultural lands due to the spatial dispersion of farms and diverse environmental conditions. In this study, a new framework based on the combination of deep convolutional networks (CNN) and recurrent networks (RNN) was developed, which identifies rice lands and distinguishes monoculture/double-cropping patterns at two levels. In the first step, the U-Net neural network architecture was used to identify rice lands in Babol County. This model was trained in two scenarios: (1) with Sentinel-2 optical data only and (2) with a combination of optical and radar data (Sentinel-2 and Sentinel-1). The results of the experiments showed that the model based on Sentinel-2 optical data was evaluated with an overall accuracy of 96.35% and an F1 score of 0.957. While it was expected that the combination of optical-radar data would improve the results, in the study area, due to the relatively cloudless conditions, the main advantage of SAR data was not very apparent. This, along with the need for more complex preprocessing of these data, could be the reason for the relative decrease in the accuracy of the combined model with an overall accuracy of 94.32 and an F1 score of 0.93. Three regions were selected to test the generalizability of the model: Sari County as an area close to the main area, Lahijan County as an area with a greater distance and more diverse vegetation, and south Tehran as an area without rice cultivation. The generalizability assessment showed that the model provided stable and accurate results in Sari County, while in Lahijan, the accuracy decreased but was still acceptable with an overall accuracy of over 88%. Testing the model in rice-free lands south of Tehran also showed a very low error rate (14.2%), indicating the high ability of the model to distinguish non-rice fields. In the second step, the GRU recurrent network was used to distinguish monoculture and biculture lands. This model was able to provide a very good performance in distinguishing monoculture and biculture rice fields with an overall accuracy of 98.4%, a kappa coefficient of 0.951, and an F1 score of 0.974. |
|
| Keywords: Rice Crop Area, Deep learning, CNN, RNN, Sentinel1, Sentinel2 |
|
|
Full-Text [PDF 15254 kb]
(49 Downloads)
|
Type of Study: Research |
Subject:
Photo&RS
Received: 2025/10/14 | Accepted: 2026/06/16
|
|
|
|
|
|
|
| References |
1. E. Elert, "Rice by the numbers: A good grain," Nature, vol. 514, no. 7524, pp. S50-S51, Oct. 2014, doi: 10.1038/514S50A; SUBJMETA. [ DOI:10.1038/514S50a] 2. K. P. Rijal, "Climate Change and its Impact on Rice Yield," Hydro Nepal: Journal of Water, Energy and Environment, vol. 16, pp. 23-27, Mar. 2015, doi: 10.3126/HN.V16I0.12215. [ DOI:10.3126/hn.v16i0.12215] 3. "FAO (2018). Rice Market Monitor. Volume XXI, Issue No. 1, April 2018. - References - Scientific Research Publishing." Accessed: Aug. 08, 2026. [Online]. Available: https://www.scirp.org/reference/referencespapers?referenceid=4055560 4. L. Chang, Y. T. Chen, J. H. Wang, and Y. L. Chang, "Rice-Field Mapping with Sentinel-1A SAR Time-Series Data," Remote Sensing 2021, Vol. 13, Page 103, vol. 13, no. 1, p. 103, Dec. 2020, doi: 10.3390/RS13010103. [ DOI:10.3390/rs13010103] 5. S. Ferreira, J. M. Sánchez, and J. M. Gonçalves, "A Remote-Sensing-Assisted Estimation of Water Use in Rice Paddy Fields: A Study on Lis Valley, Portugal," Agronomy 2023, Vol. 13, Page 1357, vol. 13, no. 5, p. 1357, May 2023, doi: 10.3390/AGRONOMY13051357. [ DOI:10.3390/agronomy13051357] 6. M. Saadat, M. Hasanlou, and S. Homayouni, "RICE CROP MAPPING USING SENTINEL-1 TIME SERIES IMAGES (CASE STUDY: MAZANDARAN, IRAN)," The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XLII-4-W18, no. 4/W18, pp. 897-904, Oct. 2019, doi: 10.5194/ISPRS-ARCHIVES-XLII-4-W18-897-2019. [ DOI:10.5194/isprs-archives-XLII-4-W18-897-2019] 7. S. Kumar et al., "Remote sensing for agriculture and resource management," Natural Resources Conservation and Advances for Sustainability, pp. 91-135, Jan. 2022, doi: 10.1016/B978-0-12-822976-7.00012-0. [ DOI:10.1016/B978-0-12-822976-7.00012-0] 8. M. H. Hussain, D. A. Abuhani, J. Khan, M. ElMohandes, I. Zualkernan, and T. Ali, "A Light-Weight Cropland Mapping Model Using Satellite Imagery," Sensors 2023, Vol. 23, Page 6729, vol. 23, no. 15, p. 6729, Jul. 2023, doi: 10.3390/S23156729. [ DOI:10.3390/s23156729] 9. F. Ramadhani, R. Pullanagari, G. Kereszturi, and J. Procter, "Automatic Mapping of Rice Growth Stages Using the Integration of SENTINEL-2, MOD13Q1, and SENTINEL-1," Remote Sensing 2020, Vol. 12, Page 3613, vol. 12, no. 21, p. 3613, Nov. 2020, doi: 10.3390/RS12213613. [ DOI:10.3390/rs12213613] 10. J. Cao et al., "Integrating Multi-Source Data for Rice Yield Prediction across China using Machine Learning and Deep Learning Approaches," Agric. For. Meteorol., vol. 297, p. 108275, Feb. 2021, doi: 10.1016/J.AGRFORMET.2020.108275. [ DOI:10.1016/j.agrformet.2020.108275] 11. M. Saadat, S. T. Seydi, M. Hasanlou, and S. Homayouni, "A Convolutional Neural Network Method for Rice Mapping Using Time-Series of Sentinel-1 and Sentinel-2 Imagery," Agriculture (Switzerland), vol. 12, no. 12, Dec. 2022, doi: 10.3390/AGRICULTURE12122083. [ DOI:10.3390/agriculture12122083] 12. D. Sudiana, S. H. Putri, D. Kushardono, A. S. Prabuwono, J. T. Sri Sumantyo, and M. Rizkinia, "CNN-Random Forest Hybrid Method for Phenology-Based Paddy Rice Mapping Using Sentinel-2 and Landsat-8 Satellite Images," Computers 2025, Vol. 14, Page 336, vol. 14, no. 8, p. 336, Aug. 2025, doi: 10.3390/COMPUTERS14080336. [ DOI:10.3390/computers14080336] 13. F. Marinello et al., "Monitorization through NDVI of a Rice (Oryza sativa L.) Culture Production in Ribatejo Region," Chemistry Proceedings 2022, Vol. 10, Page 3, vol. 10, no. 1, p. 3, Jan. 2022, doi: 10.3390/IOCAG2022-12170. [ DOI:10.3390/IOCAG2022-12170] 14. C. J. Tucker and P. J. Sellers, "Satellite remote sensing of primary production," Int. J. Remote Sens., vol. 7, no. 11, pp. 1395-1416, 1986, doi: 10.1080/01431168608948944. [ DOI:10.1080/01431168608948944] 15. M. Swoish, J. F. Da Cunha Leme Filho, M. S. Reiter, J. B. Campbell, and W. E. Thomason, "Comparing satellites and vegetation indices for cover crop biomass estimation," Comput. Electron. Agric., vol. 196, p. 106900, May 2022, doi: 10.1016/J.COMPAG.2022.106900. [ DOI:10.1016/j.compag.2022.106900] 16. S. Ferreira, J. M. Sánchez, and J. M. Gonçalves, "A Remote-Sensing-Assisted Estimation of Water Use in Rice Paddy Fields: A Study on Lis Valley, Portugal," Agronomy 2023, Vol. 13, Page 1357, vol. 13, no. 5, p. 1357, May 2023, doi: 10.3390/AGRONOMY13051357. [ DOI:10.3390/agronomy13051357] 17. M. Arii and H. Yamada, "Rice paddy monitoring by L-band MIMP SAR approach," International Geoscience and Remote Sensing Symposium (IGARSS), vol. 2017-July, pp. 2442-2445, Dec. 2017, doi: 10.1109/IGARSS.2017.8127486. [ DOI:10.1109/IGARSS.2017.8127486] 18. Y. Shao, J. Liao, X. Fan, and Y. Wang, "Analysis of temporal backscatter of rice: A comparison of RADARSAT observations with modeling results," International Geoscience and Remote Sensing Symposium (IGARSS), vol. 1, pp. 478-480, 2002, doi: 10.1109/IGARSS.2002.1025079. [ DOI:10.1109/IGARSS.2002.1025079] 19. V. Lalitha and B. Latha, "A review on remote sensing imagery augmentation using deep learning," Mater. Today Proc., vol. 62, pp. 4772-4778, Jan. 2022, doi: 10.1016/J.MATPR.2022.03.341. [ DOI:10.1016/j.matpr.2022.03.341] 20. C. Broni-Bediako, Y. Murata, L. H. B. Mormille, and M. Atsumi, "Searching for CNN Architectures for Remote Sensing Scene Classification," IEEE Transactions on Geoscience and Remote Sensing, vol. 60, 2022, doi: 10.1109/TGRS.2021.3097938. [ DOI:10.1109/TGRS.2021.3097938] 21. T. Fu, S. Tian, and J. Ge, "R-Unet: A Deep Learning Model for Rice Extraction in Rio Grande do Sul, Brazil," Remote Sens. (Basel)., vol. 15, no. 16, p. 4021, Aug. 2023, doi: 10.3390/RS15164021/S1. [ DOI:10.3390/rs15164021] 22. J. Qin et al., "Deep-Learning-Based Rice Phenological Stage Recognition," Remote Sensing 2023, Vol. 15, Page 2891, vol. 15, no. 11, p. 2891, Jun. 2023, doi: 10.3390/RS15112891. [ DOI:10.3390/rs15112891] 23. H. C. de C. Filho et al., "Rice Crop Detection Using LSTM, Bi-LSTM, and Machine Learning Models from Sentinel-1 Time Series," Remote Sensing 2020, Vol. 12, Page 2655, vol. 12, no. 16, p. 2655, Aug. 2020, doi: 10.3390/RS12162655. [ DOI:10.3390/rs12162655] 24. S. L. Salzberg, "C4.5: Programs for Machine Learning by J. Ross Quinlan. Morgan Kaufmann Publishers, Inc., 1993," Mach. Learn., vol. 16, no. 3, pp. 235-240, Sep. 1994, doi: 10.1007/BF00993309. [ DOI:10.1007/BF00993309] 25. L. Breiman, "Random forests," Mach. Learn., vol. 45, no. 1, pp. 5-32, Oct. 2001, doi: 10.1023/A:1010933404324. [ DOI:10.1023/A:1010933404324] |
|
| Add your comments about this article |
|
|
|
|
لخخیشقظ A. Determining the area under rice cultivation using machine learning methods and simultaneous use of optical satellite images and radar with virtual aperture (Case study: Babol city). JGST 2026; 15 (4) :85-102 URL: http://jgst.issgeac.ir/article-1-1241-en.html
|
