Advanced Applied Geology

Advanced Applied Geology

Phenological Monitoring and Mapping of Paddy Rice Cultivation Using Integrated Sentinel‑1 and Sentinel‑2 Imagery and Machine Learning in Google Earth Engine: Downstream Lands of the Karun River

Authors
Ahad Nazarpor
Arman Joveili
Department of Remote sensing,, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran
10.22055/aag.2026.52453.2577
Abstract
Accurate monitoring of the growth patterns and spatial distribution of paddy rice cultivation in areas dependent on surface water resources is essential for sustainable water-resource management and irrigation planning. The aim of this study was to develop a multi-source framework for phenological analysis and mapping of paddy rice cultivation by integrating Sentinel-2 optical and Sentinel-1 radar data within the Google Earth Engine (GEE) platform in northern Bavi County and the agricultural lands surrounding the Karun River. To this end, time series of NDVI, EVI, and LAI were analyzed over the period from 2022 to 2025 to characterize the temporal growth dynamics and phenological stages of paddy rice. Based on the resulting temporal patterns, the growth stages and peak greenness of paddy rice were identified, and an appropriate time window was selected for generating the final 2025 cultivation map. For classification, optical features were integrated with Sentinel-1 radar backscatter features, including VV and VH polarizations, and three machine-learning algorithms, namely Support Vector Machine (SVM), k-Nearest Neighbors (KNN), and Random Forest (RF), were evaluated. Model performance was assessed using an independent validation dataset. The results showed that Random Forest achieved the best classification performance, with an overall accuracy of 91.51% and a Kappa coefficient of 0.9310. Based on the final classification map, the paddy rice cultivation area within the study area was estimated at 7,457 ha in 2025. The proposed framework, through the integration of optical, radar, and temporal information, can support monitoring of cultivated areas, assessment of crop condition and water stress, and provision of spatial information for water-resource allocation, irrigation planning, and cropping-pattern management. When integrated with complementary information such as evapotranspiration, climate, soil, and irrigation data, the resulting rice distribution maps can also serve as a basis for assessing crop water requirements and supporting more informed water-resource management.


Subsequently, using time‑series analysis and image compositing, the seasonal growth pattern of paddy rice and the timing of peak greenness were determined. The results indicated that the maximum vegetation density occurs in late summer; therefore, the optimal temporal window for extracting the final map for 2025 was selected accordingly. To improve classification accuracy, radar data with co‑polarized and cross‑polarized channels were integrated with optical indices so that information related to surface moisture and canopy structure could also be incorporated into the classification process. Classification was performed using Support Vector Machine (SVM), k‑Nearest Neighbor (kNN), and Random Forest (RF) algorithms, and the accuracy of the models was evaluated using training samples and independent validation datasets. performance.
Keywords
Phenology
Remote Sensing
Karun River
Paddy Rice
Machine Learning
Subjects
Abdi-Dehkordi, M., Bozorg-Haddad, O., Salavitabar, A., 2021. Developing a sustainability assessment framework for integrated management of water resources systems using distributed zoning and system dynamics approaches. Environment, Development and Sustainability 23, 16246–16282. https://doi.org/10.1007/s10668-021-01340-0
Ahmed, Z., Jing, L., Naeem, M., Tahir, M., Qurat-ul-Ain, Zafar, M., 2024. Analyzing vegetation health dynamics across seasons and regions through NDVI and climatic variables. Scientific Reports 14, 11775. https://doi.org/10.3390/s41598-024-62464-7
Almalki, R., Khaki, M., Saco, P. M., Rodriguez, J.F., 2022. Monitoring and Mapping Vegetation Cover Changes in Arid and Semi-Arid Areas Using Remote Sensing Technology: A Review. Remote Sensing 14(20), 5143. https://doi.org/10.3390/rs14205143
Bahiraie, M., Hosseini, S.M., Hossein-Panahi, B., 2025. Groundwater resources exploitation management in response to water scarcity challenges in Khuzestan Province, Iran. Journal of Groundwater Science and Engineering 13(3), 268-285.   https://doi.org/10.26599/JGSE.2025.9280054
Bauer-Marschallinger, B., Cao, S., Navacchi, C., Reimer, C., Pfeil, I., Hochstöger, S., Paulik, C., Brombacher, J., Melzer, T., Milovac, J., Cisneros, S., Sabel, D., Schlaffer, S., Haub, M., Hochschild, J., Wallisch, A., Brocca, L., Salach, A., Milenkovic, M., Notarnicola, C., Angileri, V., Wagner, W., 2021. The normalised Sentinel-1 Global Backscatter Model, mapping Earth’s land surface with C-band microwaves. Scientific Data 8, 277. https://doi.org/10.1038/s41597-021-01059-7
Bhattacharya, A., 2022. Effect of Low-Temperature Stress on Germination, Growth, and Phenology of Plants: A Review. In: Physiological Processes in Plants Under Low Temperature Stress. Springer, Singapore, pp. 1–106. https://doi.org/10.1007/978-981-16-9037-2_1
Caruso, G., Palai, G., Tozzini, L., D’Onofrio, C., Gucci, R., 2023. The role of LAI and leaf chlorophyll on NDVI estimated by UAV in grapevine canopies. Scientia Horticulturae 322, 112398. https://doi.org/10.1016/j.scienta.2023.112398
Dakhili, M., Zahiri, J., Cheraghi, M., Moradi, S., 2025. Assessment of agricultural drainage water quality for irrigation reuse using hydrochemical indices (case study: Bavi County, Khuzestan). Iranian Journal of Soil and Water Research 56(7), 1949–1966. https://doi.org/10.22059/ijswr.2025.394995.669936
Eyni-Nargeseh, H., Asgharipour, M.R., Rahimi-Moghaddam, S., Gilani, A., Damghani, A. M., Azizi, K., 2023. Which rice farming system is more environmentally friendly in Khuzestan province, Iran? A study based on emergy analysis. Ecological Modelling 481, 110373. https://doi.org/10.1016/j.ecolmodel.2023.110373
Falcioni, R., Santos, G.L.A.A.d., Crusiol, L. G.T., Antunes, W.C., Chicati, M.L., Oliveira, R.B.d., Demattê, J.A.M., Nanni, M.R., 2023. Non−Invasive Assessment, Classification, and Prediction of Biophysical Parameters Using Reflectance Hyperspectroscopy. Plants 12(13), 2526. https://doi.org/10.3390/plants12132526
Foody, G.M., 2023. Challenges in the real-world use of classification accuracy metrics: From recall and precision to the Matthews correlation coefficient. PLOS ONE 18(10), e0291908. https://doi.org/10.1371/journal.pone.0291908
Gharibreza, M., Soleimani, F., Soozangar, F., 2023. Environmental geochemistry and ecological risk for aquatic life and human health of the Karun River (Iran). International Journal of Environmental Science and Technology 20, 1801–1822. https://doi.org/10.1007/s13762-022-04703-w
Gong, Y., Yang, K., Lin, Z., Chen, X., Zhao, C., 2021. Remote estimation of leaf area index (LAI) with unmanned aerial vehicle (UAV) imaging for different rice cultivars throughout the entire growing season. Plant Methods 17, 88. https://doi.org/10.1186/s13007-021-00789-4
Granata, F., Zhu, S., Di Nunno, F., 2025. Hydrological extremes in the Mediterranean basin: interactions, impacts, and adaptation in the face of climate change. Regional Environmental Change 25, 100. https://doi.org/10.1007/s10113-025-02432-7
Han, J., Zhu, J., Cao, X., Xi, L., Qi, Z., Li, Y., Wang, X., Zou, J., 2025. Extraction of sparse vegetation cover in deserts based on UAV remote sensing. Remote Sensing 17(15), 2665. https://doi.org/10.3390/rs17152665
Hassan, M.A., Chang, C.Y.Y., 2025. PhenoGazer: A high-throughput phenotyping system to track plant stress responses using hyperspectral reflectance, nighttime chlorophyll fluorescence and RGB imaging in controlled environments. Plant Phenomics 7(2), 100047. https://doi.org/10.1016/j.plaphe.2025.100047
Huang, S., Tang, L., Hupy, J.P., 2021. A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing. Journal of Forestry Research 32, 1–6. https://doi.org/10.1007/s11676-020-01155-1
Kabelizadeh, M., Rangzan, K., Rashidian, M., Delfan, H., 2018. Estimation of total dissolved solids and turbidity of water in Karkheh and Dez dams and the Great Karun River using Sentinel-2 satellite images. Advanced Applied Geology 8(4), 17-27. https://doi.org/10.22055/aag.2019.24426.1807
Karimi, M., Ahmadi Nadoushan, M., Chavoshi, E., 2025. A decade-long spatial analysis of opportunistic rice cultivation in a water-stressed agricultural landscape. Environmental Monitoring and Assessment 197, 216. https://doi.org/10.1007/s10661-025-13656-9
Kior, A., Sukhov, V., Sukhova, E., 2021. Application of Reflectance Indices for Remote Sensing of Plants and Revealing Actions of Stressors. Photonics 8(12), 582. https://doi.org/10.3390/photonics8120582
Kurbanov, R.K., Zakharova, N.I., 2020. Application of vegetation indexes to assess the condition of crops. Agricultural Machinery and Technologies 14(4), 4–11. https://doi.org/10.22314/2073-7599-2020-14-4-4-11
Liu, S., Jin, X., Nie, C., Wang, S., Yu, X., Cheng, M., Yang, G., Liu, Y., 2021. Estimating leaf area index using unmanned aerial vehicle data: shallow vs. deep machine learning algorithms. Plant Physiology 187(3), 1551–1576. https://doi.org/10.1093/plphys/kiab322
Naeem, S., Ali, A., Anam, S., Ahmed, M.M., 2023. An unsupervised machine learning algorithm: Comprehensive review. International Journal of Computing and Digital Systems 13(1), 1224-1233. https://doi.org/10.12785/ijcds/130172
Podani, J., Schmera, D., Bagella, S., 2023. Correlating variables with different scale types: A new framework based on matrix comparisons. Methods in Ecology and Evolution 14 (4), 1049–1060. https://doi.org/10.1111/2041-210X.14074
Pommé, L.-E., Bourqui, R., Giot, R., Auber, D., 2022. Relative Confusion Matrix: Efficient Comparison of Decision Models. 26th International Conference on Information Visualisation (IV), 98–103. https://doi.org/10.1109/IV56949.2022.00025
Qiao, K., Zhu, W., Xie, Z., Wu, S., Li, S., 2024. New three red-edge vegetation index (VI3RE) for crop seasonal LAI prediction using Sentinel-2 data. International Journal of Applied Earth Observation and Geoinformation 130, 103894. https://doi.org/10.1016/j.jag.2024.103894
Rabie, A.B., Elhag, M., Subyani, A. 2025. Remote sensing, GIS, and machine learning in water resources management for arid agricultural regions: A review. Water, 17(21), 3125. DOI: https://doi.org/10.3390/w17213125  
Raschka, S., Patterson, J., Nolet, C., 2020. Machine learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence. Information 11 (4), 193. https://doi.org/10.3390/info11040193
Rastmanesh, F., Hasna, Z., Zarasvandi, A., 2019. Enrichment, origin, and health risk of heavy metals in rice fields: A case study of Ahvaz and Bavi counties. Advanced Applied Geology 9(1), 75-84. https://doi.org/10.22055/aag.2019.28395.1935
Richards, J.A., 2022. Supervised classification techniques. In: Richards, J. A. (Ed.), Remote Sensing Digital Image Analysis. Springer International Publishing, Cham, pp. 263–367. https://doi.org/10.1007/978-3-030-82327-6_8 
Salman, H.A., Kalakech, A., Steiti, A., 2024. Random forest algorithm overview. Babylonian Journal of Machine Learning, 69–79. https://doi.org/10.58496/BJML/2024/007
Salman, H.H., Jazaa, H.S., 2023. Shatt Al-Arab River and the seawater intrusion: causes and solutions. Al-Bahir 2 (1), 6. https://doi.org/10.55810/2313-0083.1018
Sarker, I.H., 2021. Machine learning: Algorithms, real-world applications and research directions. SN Computer Science 2 (3), 1–21. https://doi.org/10.1007/s42979-021-00592-x
Shetty, S.H., Shetty, S., Singh, C., Rao, A., 2022. Supervised machine learning: algorithms and applications. In: Fundamentals and Methods of Machine and Deep Learning: Algorithms, Tools and Applications. Wiley, pp. 1–16. https://doi.org/10.1002/9781119821908.ch1
Singh, N.K., Nagahara, M., 2024. LightGBM-, SHAP-, and Correlation-Matrix-Heatmap-Based approaches for analyzing household energy data: Towards electricity self-sufficient houses. Energies 17 (17), 4518. https://doi.org/10.3390/en17174518
Sishodia, R.P., Ray, R.L., Singh, S.K., 2020. Applications of remote sensing in precision agriculture: A review. Remote Sensing 12 (19), 3136. https://doi.org/10.3390/rs12193136
Suyal, M., Goyal, P., 2022. A review on analysis of k-nearest neighbor classification machine learning algorithms based on supervised learning. International Journal of Engineering Trends and Technology 70 (7), 43–48. https://doi.org/10.14445/22315381/IJETT-V70I7P205
Varoquaux, G., Colliot, O., 2023. Evaluating machine learning models and their diagnostic value. In: Machine Learning for Brain Disorders. Springer, pp. 601–630. https://doi.org/10.1007/978-1-0716-3195-9_20
Verma, P., Raghubanshi, A., Srivastava, P.K., Raghubanshi, A.S., 2020. Appraisal of kappa-based metrics and disagreement indices of accuracy assessment for parametric and nonparametric techniques used in LULC classification and change detection. Modeling Earth Systems and Environment 6 (2), 1045–1059. https://doi.org/10.1007/s40808-020-00740-x
Vijith, H., Dodge-Wan, D., 2020. Applicability of MODIS land cover and Enhanced Vegetation Index (EVI) for the assessment of spatial and temporal changes in strength of vegetation in tropical rainforest region of Borneo. Remote Sensing Applications: Society and Environment 18, 100311. https://doi.org/10.1016/j.rsase.2020.100311
Wang, Z., Jia, Y., Niu, C., Liu, J., Jin, J., Liao, Z., Zhang, J., 2025. Spatiotemporal variation in fractional vegetation coverage and quantitative analysis of its driving forces: A case study in the Tabu River Basin, Northern China, 1986–2023. Remote Sensing 17 (14), 2490. https://doi.org/10.3390/rs17142490
Wu, B., Zhang, M., Zeng, H., Tian, F., Potgieter, A.B., Qin, X., Loupian, E., 2023. Challenges and opportunities in remote sensing-based crop monitoring: A review. National Science Review 10 (4), nwac290. https://doi.org/10.1093/nsr/nwac290
Yang, C.Y., Yang, M.D., Tseng, W.C., Hsu, Y.C., Li, G.S., Lai, M.H., Lu, H.Y., 2020. Assessment of rice developmental stage using time series UAV imagery for variable irrigation management. Sensors 20 (18), 5354. https://doi.org/10.3390/s20185354 
Yang, X., Zuo, X., Xie, W., Li, Y., Guo, S., Zhang, H., 2022. A correction method of NDVI topographic shadow effect for rugged terrain. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 15, 8456–8472. https://doi.org/10.1109/JSTARS.2022.3193419
Yazdanpanah Dero, Q., Sharif, M., Nikzad Samarin, A., Naseri, A.A., Mohammadi, H.R., 2025. Monitoring of water resources based on remote sensing and ground data, a comprehensive analysis of human and climate impact. Modeling Earth Systems and Environment 11 (1), 24. https://doi.org/10.1007/s40808-024-02193-y
Zeng, Y., Hao, D., Huete, A., Dechant, B., Berry, J., Chen, J.M., Chen, M., 2022. Optical vegetation indices for monitoring terrestrial ecosystems globally. Nature Reviews Earth & Environment 3 (7), 477–493. https://doi.org/10.1038/s43017-022-00298-5
Zhang, G., Ming, B., Shen, D., Xie, R., Hou, P., Xue, J., Li, S., 2021. Optimizing grain yield and water use efficiency based on the relationship between leaf area index and evapotranspiration. Agriculture 11 (4), 313. https://doi.org/10.3390/agriculture11040313
Zhang, R., Bao, X., Hong, R., He, X., Yin, G., Chen, J., Liu, G., 2024. Soil moisture retrieval over croplands using novel dual-polarization SAR vegetation index. Agricultural Water Management 306, 109159. https://doi.org/10.1016/j.agwat.2024.109159
Zhao, M., Lin, Y., Chen, H., 2020. Improving nutritional quality of rice for human health. Theoretical and Applied Genetics 133(5), 1397-1413. https://doi.org/10.1007/s00122-019-03530-x
Zheng, Z., Wu, Z., Chen, Y., Guo, C., Marinello, F., 2022. Instability of remote sensing based ecological index (RSEI) and its improvement for time series analysis. Science of the Total Environment 814, 152595. https://doi.org/10.1016/j.scitotenv.2021.152595
Advanced Applied Geology
Volume 16, Issue 2
Summer 2026
Pages 121-146