A Phenology Based Paddy Rice Mapping by Correlation Analysis on Sentinel-1/2 Imagery in Fragmented Lands Using GEE Platform

Namazi, Fateme; Ezoji, Mehdi; Ghanbari Parmehr, Ebadat

doi:10.22044/jadm.2025.15379.2639

Articles in Press

Document Type : Original/Review Paper

Authors

¹ Faculty of Electrical and Computer Engineering, Babol Noshirvani University of Technology, Babol, Iran.

² Department of Geomatics, Faculty of Civil Engineering, Babol Noshirvani University of Technology, Babol, Iran.

10.22044/jadm.2025.15379.2639

Abstract

Paddy fields in the north of Iran are highly fragmented, leading to challenges in accurately mapping them using remote sensing techniques. Cloudy weather often degrades image quality or renders images unusable, further complicating monitoring efforts. This paper presents a novel paddy rice mapping method based on phenology, addressing these challenges. The method utilizes time series data from Sentinel-1 and 2 satellites to derive a rice phenology curve. This curve is constructed using the cross ratio (CR) index from Sentinel-1, and the normalized difference vegetation index (NDVI) and land surface water index (LSWI) from Sentinel-2. Unlike existing methods, which often rely on analyzing single-point indices at specific times, this approach examines the entire time series behavior of each pixel. This robust strategy significantly mitigates the impact of cloud cover on classification accuracy. The time series behavior of each pixel is then correlated with this rice phenology curve. The maximum correlation, typically achieved around the 50-day period in the middle of the cultivation season, helps identify potential rice fields. A Support Vector Machine (SVM) classifier with a Radial Basis Function (RBF) kernel is then employed, utilizing the maximum correlation values from all three indices to classify pixels as rice paddy or other land cover types. The implementation results validate the accuracy of this method, achieving an overall accuracy of 99%. All processes were carried out on the Google Earth Engine (GEE) platform.

Keywords

Main Subjects

H.5. Image Processing and Computer Vision

References

[1] P. A. Seck, A. Diagne, S. Mohanty, and M. C. Wopereis, "Crops that feed the world 7: Rice," Food security, vol. 4, pp. 7-24, 2012.

[2] J. Dong, X. Xiao, M. A. Menarguez, G. Zhang, Y. Qin, D. Thau, C. Biradar, and B. Moore III, "Mapping paddy rice planting area in northeastern Asia with Landsat 8 images, phenology-based algorithm and Google Earth Engine," Remote Sensing of Environment, vol. 185, pp. 142-154, 2016

[3] N. Bhogapurapu, S. Dey, D. Mandal, A. Bhattacharya, L. Karthikeyan, H. McNairn, and Y. S. Rao, "Soil moisture retrieval over croplands using dual-pol L-band GRD SAR data," Remote Sensing of Environment, vol. 271, p. 112900, 2022.

[4] Y. Jiang, H. Qian, S. Huang, X. Zhang, L. Wang, L. Zhang, M. Shen, X. Xiao, F. Chen, H. Zhang, and C. Lu, "Acclimation of methane emissions from rice paddy fields to straw addition," Science Advances, vol. 5, no. 1, p. eaau9038, 2019.

[5] M. Gilbert, N. Golding, H. Zhou, G. W. Wint, T. P. Robinson, A. J. Tatem, S. Lai, S. Zhou, H. Jiang, D. Guo, and Z. Huang, "Predicting the risk of avian influenza A H7N9 infection in live-poultry markets across Asia," Nature Communications, vol. 5, no. 1, p. 4116, 2014.

[6] M. Zhang, H. Lin, G. Wang, H. Sun, and J. Fu, "Mapping paddy rice using a convolutional neural network (CNN) with Landsat 8 datasets in the Dongting Lake Area, China," Remote Sensing, vol. 10, no. 11, p. 1840, 2018.

[7] W. Zhang, H. Liu, W. Wu, L. Zhan, and J. Wei, "Mapping rice paddy based on machine learning with Sentinel-2 multi-temporal data: Model comparison and transferability," Remote Sensing, vol. 12, no. 10, p. 1620, 2020.

[8] H. C. de Castro Filho, O. A. de Carvalho Júnior, O. L. F. de Carvalho, P. P. de Bem, R. dos S. de Moura, A. O. de Albuquerque, C. R. Silva, P. H. G. Ferreira, R. F. Guimarães, and R. A. T. Gomes, "Rice crop detection using LSTM, Bi-LSTM, and machine learning models from Sentinel-1 time series," Remote Sensing, vol. 12, no. 16, p. 2655, 2020.

[9] X. Shi, Z. Chen, H. Wang, D.-Y. Yeung, W.-K. Wong, and W.-c. Woo, "Convolutional LSTM network: A machine learning approach for precipitation nowcasting," Advances in neural information processing systems, vol. 28, 2015.

[10] M. Rußwurm and M. Körner, "Convolutional LSTMs for cloud-robust segmentation of remote sensing imagery," arXiv preprint arXiv:1811.02471, 2018.

[11] G. Tian, H. Li, Q. Jiang, B. Qiao, N. Li, Z. Guo, J. Zhao, and H. Yang, "An automatic method for rice mapping based on phenological features with Sentinel-1 time-series images," Remote Sensing, vol. 15, no. 11, p. 2785, 2023.

[12] S. Park, J. Im, S. Park, C. Yoo, H. Han, and J. Rhee, "Classification and mapping of paddy rice by combining Landsat and SAR time series data," Remote Sensing, vol. 10, no. 3, p. 447, 2018.

[13] Y. He, J. Dong, X. Liao, L. Sun, Z. Wang, N. You, Z. Li, and P. Fu, "Examining rice distribution and cropping intensity in a mixed single-and double-cropping region in South China using all available Sentinel 1/2 images," International Journal of Applied Earth Observation and Geoinformation, vol. 101, p. 102351, 2021.

[14] Z. He, S. Li, Y. Wang, L. Dai, and S. Lin, "Monitoring rice phenology based on backscattering characteristics of multi-temporal RADARSAT-2 datasets," Remote Sensing, vol. 10, no. 2, p. 340, 2018.

[15] J. Li and D. P. Roy, "A global analysis of Sentinel-2A, Sentinel-2B and Landsat-8 data revisit intervals and implications for terrestrial monitoring," Remote Sensing, vol. 9, no. 9, p. 902, 2017.

[16] L. Liu, X. Xiao, Y. Qin, J. Wang, X. Xu, Y. Hu, and Z. Qiao, "Mapping cropping intensity in China using time series Landsat and Sentinel-2 images and Google Earth Engine," Remote Sensing of Environment, vol. 239, p. 111624, 2020.

[17] L. Pan, H. Xia, X. Zhao, Y. Guo, and Y. Qin, "Mapping winter crops using a phenology algorithm, time-series Sentinel-2 and Landsat-7/8 images, and Google Earth Engine," Remote sensing, vol. 13, no. 13, p. 2510, 2021.

[18] F. Namazi, M. Ezoji, and E. G. Parmehr, "Paddy Rice mapping in fragmented lands by improved phenology curve and correlation measurements on Sentinel-2 imagery in Google earth engine," Environmental Monitoring and Assessment, vol. 195, no. 10, p. 1220, 2023.

[19] F. Gascon, C. Bouzinac, O. Thépaut, M. Jung, B. Francesconi, J. Louis, V. Lonjou, B. Lafrance, S. Massera, A. Gaudel-Vacaresse, and F. Languille, "Copernicus Sentinel-2A calibration and products validation status," Remote Sensing, vol. 9, no. 6, p. 584, 2017.

[20] X. Xiao, S. Boles, J. Liu, D. Zhuang, S. Frolking, C. Li, W. Salas, and B. Moore III, "Mapping paddy rice agriculture in southern China using multi-temporal MODIS images," Remote Sensing of Environment, vol. 95, no. 4, pp. 480-492, 2005.

[21] C. J. Tucker, "Red and photographic infrared linear combinations for monitoring vegetation," Remote sensing of Environment, vol. 8, no. 2, pp. 127-150, 1979.

[22] Y. Kim and J. J. Van Zyl, "A time-series approach to estimate soil moisture using polarimetric radar data," IEEE Transactions on Geoscience and Remote Sensing, vol. 47, no. 8, pp. 2519-2527, 2009.

[23] Y. Jiang, Z. Lu, S. Li, Y. Lei, Q. Chu, X. Yin, and F. Chen,"Large-scale and high-resolution crop mapping in China using Sentinel-2 satellite imagery," Agriculture, vol. 10, no. 10, p. 433, 2020.

[24] Y. Liu, Q. Yu, Q. Zhou, C. Wang, S. D. Bellingrath-Kimura, and W. Wu, "Mapping the complex crop rotation systems in Southern China considering cropping intensity, crop diversity, and their seasonal dynamics," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 15, pp. 9584-9598, 2022.

[25] J. Tian, Y. Tian, Y. Cao, W. Wan, and K. Liu, "Research on Rice fields extraction by NDVI difference method based on sentinel data," Sensors, vol. 23, no. 13, p. 5876, 2023.

[26] R. Zhao, Y. Li, and M. Ma, "Mapping paddy rice with satellite remote sensing: A review," Sustainability, vol. 13, no. 2, p. 503, 2021.

[27] C. Huang, S. You, A. Liu, P. Li, J. Zhang, and J. Deng, "High-Resolution National-Scale Mapping of Paddy Rice Based on Sentinel-1/2 Data," Remote Sensing, vol. 15, no. 16, p. 4055, 2023.

[28] P. Wei, H. Ye, S. Qiao, R. Liu, C. Nie, B. Zhang, L. Song, and S. Huang, "Early crop mapping based on Sentinel-2 time-series data and the random forest algorithm," Remote Sensing, vol. 15, no. 13, p. 3212, 2023.

[29] A. Htitiou, A. Boudhar, A. Chehbouni, and T. Benabdelouahab, "National-scale cropland mapping based on phenological metrics, environmental covariates, and machine learning on Google Earth Engine," Remote Sensing, vol. 13, no. 21, p. 4378, 2021.

[30] Z. Jiang, A. R. Huete, K. Didan, and T. Miura, "Development of a two-band enhanced vegetation index without a blue band," Remote Sensing of Environment, vol. 112, no. 10, pp. 3833-3845, 2008.

Journal of AI and Data Mining

A Phenology Based Paddy Rice Mapping by Correlation Analysis on Sentinel-1/2 Imagery in Fragmented Lands Using GEE Platform

References

References

Articles in Press, Accepted Manuscript
Available Online from 03 May 2025

A Phenology Based Paddy Rice Mapping by Correlation Analysis on Sentinel-1/2 Imagery in Fragmented Lands Using GEE Platform

References

References

Articles in Press, Accepted Manuscript Available Online from 03 May 2025

Articles in Press, Accepted Manuscript
Available Online from 03 May 2025