MAPPING PADDY RICE EXTENT AND CROPPING PATTERN IN IADA BARAT LAUT SELANGOR USING INTEGRATION OF SENTINEL-1 AND 2 DATA
DOI:
https://doi.org/10.46754/umtjur.v5i3.381Keywords:
Paddy rice fields, cropping calendar, food security, Google Earth Engine (GEE), Sentinel-1, Sentinel-2 Surface ReflectanceAbstract
Under the 12th Malaysia Plan (RMK-12), the Malaysian government has set a goal to achieve a self-sufficiency level (SSL) of 70% for rice production. Accurate and timely spatiotemporal information on harvested rice extent is required to measure progress towards achieving the SSL. Remote sensing technology has been widely used to provide rapid information on the extent of rice. This study aims to create maps of rice extent and cropping patterns in the IADA Barat Laut Selangor (BLS), combining Synthetic Aperture Radar (SAR) imagery data using Sentinel-1 and optical imagery data using Sentinel-2. The monthly composite data of VH polarization and the Normalized Difference Vegetation Index (NDVI) were stacked as bands in an imagery dataset, forming a time series dataset. The unsupervised K-means clustering method and the Google Earth Engine (GEE) cloud-based computing platform were used for the analysis of the data. The results allowed the classification of the rice and non-rice groups. The rice cropping patterns were generated from the temporal composites of VH backscatter values of Sentinel-1 data and NDVI of Sentinel-2 data. The map products were evaluated using accuracy measures using visual interpretation techniques applied to very high-resolution imagery acquired from Google Earth (GE). The rice extent map generated at 10-meter resolution exhibited excellent accuracy with an overall accuracy of 98% and a kappa coefficient of 0.95. The estimated rice parcel area in IADA BLS for 2021 was 17,864 ha, which is close to the recorded data (18,785 ha). The comparison of results in the irrigation block also indicated that the rice field area agreed well with the statistical data, with an R2 of 0.95, RMSE of 357 ha, and a relative discrepancy of 4.9%. The cropping pattern also showed satisfactory results as compared to the existing data. These findings demonstrate that the proposed methodology can provide high-accuracy rice extent map products and has the potential to be applied to rice fields across Malaysia and other tropical regions to address food security issues. Subsequent investigations should prioritize addressing the challenge of detecting conversions into other land uses, which were not addressed in this study, by exploring alternative data sources, advanced machine learning algorithms, and incorporating ground-based surveys.
References
Ansori, A. R., & Abdul Rahman, S. M. (2021). An assessment of rice seed quality at various physiological harvest maturity stages. GADING Journal of Science and Technology , 4(1). https://ir.uitm.edu.my/id/ eprint/46247/1/46247.pdf
Che Omar, S., Shaharudin, A., & Tumin, S. A. (2019). The status of the paddy and rice industry in Malaysia. Khazanah Research Institute.
Dong, J., & Xiao, X. (2016). Evolution of regional to global paddy rice mapping methods: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 119, 214–227. https://doi.org/https://doi. org/10.1016/j.isprsjprs.2016.05.010
Fatchurrachman, Rudiyanto, Soh, N. C., Shah, R. M., Giap, S. G. E., Setiawan, B. I., & Minasny, B. (2022). High-resolution mapping of paddy rice extent and growth stages across Peninsular Malaysia using a fusion of sentinel-1 and 2 time series data in Google Earth engine. Remote Sensing, 14(8). https://doi.org/10.3390/rs14081875
Hashim, S. (2015). Protection of new varieties of rice in Malaysia. http://eapvp.org/files/ report/docs/malaysia/04-5%20Country%20 Report_Malaysia2.pdf
Holben, B. N. (1986). Characteristics of maximum-value composite images from temporal AVHRR data. International Journal of Remote Sensing, 7(11), 1417–1434. https://doi. org/10.1080/01431168608948945
Houma, A. A., Kamal, M. R., Mojid, M. A., Abdullah, A. F. B., & Wayayok, A. (2021). Climate change impacts on rice yield of a large-scale irrigation scheme in Malaysia. Agricultural Water Management, 252, 106908. https://doi.org/https://doi. org/10.1016/j.agwat.2021.106908
IADA Barat Laut Selangor. (n.d.). Kawasan pembangunan pertanian bersepadu IADA Barat Laut Selangor. Retrieved January 2, 2022, from https://iadabls.mafs.gov.my/
Jiang, X., Luo, S., Gao, S., Fang, S., Wang, Y., Yang, K., Xiong, Q., & Li, Y. (2022). An automatic rice mapping method based on constrained feature matching exploiting Sentinel-1 data for arbitrary length time series. International Journal of Applied Earth Observation and Geoinformation, 114, 103032. https://doi.org/https://doi. org/10.1016/j.jag.2022.103032
Kuenzer, C., & Knauer, K. (2013). Remote sensing of rice crop areas. International Journal of Remote Sensing, 34(6), 2101– 2139. https://doi.org/10.1080/01431161.20 12.738946
Li, J., & Chen, B. (2020). Global revisit interval analysis of landsat-8 -9 and sentinel-2A -2B data for terrestrial monitoring. Sensors, 20(22). https://doi.org/10.3390/s20226631
Ministry of Agriculture and Food Industries. (2011). Dasar agromakanan negara 2011– 2020. Ministry of Agriculture and Food Industries.
Montero, D., Aybar, C., Mahecha, M. D., Martinuzzi, F., Söchting, M., & Wieneke, S. (2023). A standardized catalogue of spectral indices to advance the use of remote sensing in Earth system research. Scientific Data, 10(1), 197. https://doi.org/10.1038/s41597- 023-02096-0
Muda Agriculture Development Authority (MADA). (n.d.). New rice variety. Retrieved February 16, 2023, from https://www.mada. gov.my/?page_id=13023&lang=en
Nguyen, D. B., Gruber, A., & Wagner, W. (2016). Mapping rice extent and cropping scheme in the Mekong Delta using Sentinel- 1A data. Remote Sensing Letters, 7(12), 1209–1218. https://doi.org/10.1080/21507 04X.2016.1225172
Ni, R., Tian, J., Li, X., Yin, D., Li, J., Gong, H., Zhang, J., Zhu, L., & Wu, D. (2021). An enhanced pixel-based phenological feature for accurate paddy rice mapping with sentinel-2 imagery in Google Earth Engine. ISPRS Journal of Photogrammetry and Remote Sensing, 178, 282–296. https://doi.org/https://doi.org/10.1016/j. isprsjprs.2021.06.018
Ramli, A. (2015). Rice breeding, production and distribution of rice in Malaysia. MARDI. http://eapvp.org/files/report/docs/malaysia/ 04-4%20Country%20Report_Malaysia1. pdf
Rudiyanto, Minasny, B., Shah, M. R., Soh, N. C., Arif, C., & Setiawan, B. I. (2019). Automated near-real-time mapping and monitoring of rice extent, cropping patterns, and growth stages in Southeast Asia using sentinel-1 time series on a Google Earth Engine Platform. Remote Sensing, 11(14), 1666. https://doi.org/10.3390/rs11141666
Shang, R., & Zhu, Z. (2019). Harmonizing landsat 8 and sentinel-2: A time-series-based reflectance adjustment approach. Remote Sensing of Environment, 235, 111439. https://doi.org/https://doi.org/10.1016/j. rse.2019.111439
Singha, M., Dong, J., Zhang, G., & Xiao, X. (2019). High resolution paddy rice maps in cloud-prone Bangladesh and Northeast India using sentinel-1 data. Scientific Data, 6(1), 26. https://doi.org/10.1038/s41597- 019-0036-3
Son, N.-T., Chen, C.-F., Chen, C.-R., & Minh, V.-Q. (2018). Assessment of Sentinel- 1A data for rice crop classification using random forests and support vector machines. Geocarto International, 33(6), 587–601. https://doi.org/10.1080/1010604 9.2017.1289555
Son, N.-T., Chen, C.-F., Chen, C.-R., Toscano, P., Cheng, Y.-S., Guo, H.-Y., & Syu, C.- H. (2021). A phenological object-based approach for rice crop classification using time-series Sentinel-1 Synthetic Aperture Radar (SAR) data in Taiwan. International Journal of Remote Sensing, 42(7), 2722– 2739. https://doi.org/10.1080/01431161.20 20.1862440
Suhaila, J., Mohd Deni, S., Wan Zin, W. Z., & Jemain, A. A. (2010). Trends in Peninsular Malaysia rainfall data during the southwest monsoon and northeast monsoons seasons: 1975-2004. Sains Malaysiana, 39(4), 533– 542.
The European Space Agency. (n.d.-a). Sentinel-1. Retrieved February 15, 2023, from https://sentinels.copernicus.eu/web/ sentinel/missions/sentinel-1
The European Space Agency. (n.d.-b). Sentinel-2. Retrieved February 15, 2023, from https://sentinels.copernicus.eu/web/ sentinel/missions/sentinel-2
Torres, R., Snoeij, P., Geudtner, D., Bibby, D., Davidson, M., Attema, E., Potin, P., Rommen, B., Floury, N., Brown, M., Traver, I. N., Deghaye, P., Duesmann, B., Rosich, B., Miranda, N., Bruno, C., L’Abbate, M., Croci, R., Pietropaolo, A., … & Rostan, F. (2012). GMES Sentinel-1 mission. Remote Sensing of Environment, 120, 9–24. https://doi.org/https://doi.org/10.1016/j. rse.2011.05.028
Velasco, A., Rabus, B., & Beg, M. F. (2021). Comparison of speckle noise filters on crop classification based on sentinel-1 sar time-series. 2021 IEEE International India Geoscience and Remote Sensing Symposium (InGARSS), 61–64. https://doi.org/10.1109/ InGARSS51564.2021.9791938
Zhao, R., Li, Y., & Ma, M. (2021). Mapping paddy rice with satellite remote sensing: A review. Sustainability, 13(2). https://doi. org/10.3390/su13020503
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