Volume 4, Issue 1, June 2020, Page: 34-43
Remote Sensing Application in Mapping Agricultural Crop Areas and Monitoring Rice Maturity
Nguyen Quoc Hiep, Center for Water Resources Software, Vietnam Academy for Water Resources, Hanoi, Vietnam
Nguyen Anh Hung, Center for Water Resources Software, Vietnam Academy for Water Resources, Hanoi, Vietnam
Pham Quang Loi, Center for Water Resources Software, Vietnam Academy for Water Resources, Hanoi, Vietnam
Pham Thi Thu Hien, Center for Water Resources Software, Vietnam Academy for Water Resources, Hanoi, Vietnam
Nguyen Duy Khanh, Center for Water Resources Software, Vietnam Academy for Water Resources, Hanoi, Vietnam
Received: Apr. 17, 2020;       Accepted: May 5, 2020;       Published: May 19, 2020
DOI: 10.11648/j.stpp.20200401.16      View  261      Downloads  114
Abstract
Climate change has evolved in an unpredictable trend and droughts have occurred more and more severely in the central provinces of Vietnam. Determining the irrigated area and water requirement for various crops and the growth stage of each crop is an urgent need as water resources for irrigation are getting scarce year by year. This research examines the application of Sentinel-2 and Sentinel-1 images to map crop areas and identify the current development stage of paddy rice areas. The images are collected and pre-processed from 2017 to 2018 for Ha Tinh Province in Vietnam. The Maximus Likelihood method is used to interpret Sentinel-2 imagery for mapping agricultural crop distribution status. The research presents a new approach for identifying rice maturity using the Sentinel-1 image series. The Overall Accuracy (OA) and Kappa coefficient methods are used to evaluate the generated maps of the agricultural crop’s distribution status. This study shows the relationship between the Sentinel-1 VH band and the growth of rice. From the image bands, we could calculate the slope of the line correlating between the VH backscattering value and the growth time of rice. Along with the local planting schedule, rice life cycle, and simple deduction, we could determine the rice growth stage at each time of image acquisition. The results identifying the rice maturity progression are illustrated for Cam Hoa commune in Cam Xuyen district and Thach Hoi commune in Thach Ha district, Ha Tinh Province.
Keywords
Remote Sensing, Map of Agricultural Crop, Rice Maturity
To cite this article
Nguyen Quoc Hiep, Nguyen Anh Hung, Pham Quang Loi, Pham Thi Thu Hien, Nguyen Duy Khanh, Remote Sensing Application in Mapping Agricultural Crop Areas and Monitoring Rice Maturity, Science, Technology & Public Policy. Vol. 4, No. 1, 2020, pp. 34-43. doi: 10.11648/j.stpp.20200401.16
Copyright
Copyright © 2020 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Reference
[1]
Manuel Campos-Taberner, Francisco Javier García-Haro, Beatriz Martínez, Sergio Sánchez-Ruíz, María Amparo Gilabert. A Copernicus Sentinel-1 and Sentinel-2 Classification Framework for the 2020+ European Common Agricultural Policy: A Case Study in València (Spain). Agronomy 2019, 9, 556.
[2]
Stavros Patsalidis, Athos Agapiou, Diofantos G. Hadjimitsis. Random forest classification analysis of Sentinel-2 and Landsat-8 images over semi-arid environment in the Eastern Mediterranean. AGILE 2019 – Limassol, June 17-20, 2019.
[3]
Raziye Hale, Elif, Nebiye. Assessment of classification accuracies of Sentinel-2 and Landsat-8 data for land cover / use mapping. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLI-B8, 2016 XXIII ISPRS Congress, 12–19 July 2016, Prague, Czech Republic.
[4]
Neetu* and S. S. Ray. Exploring machine learning classification algorithms for crop classification using Sentinel-2 data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-3/W6, 2019 ISPRS-GEOGLAM-ISRS Joint Int. Workshop on “Earth Observations for Agricultural Monitoring”, 18–20 February 2019, New Delhi, India.
[5]
Licheng Zhao, Yun Shi, Bin Liu, Ciara Hovis, Yulin Duan, Zhongchao Shi. Finer Classification of Crops by Fusing UAV Images and Sentinel-2A Data. Remote Sens. 2019, 11, 3012.
[6]
Tian-Xiang Zhang, Jin-Ya Su, Cun-Jia Liu, Wen-Hua Chen. Potential Bands of Sentinel-2A Satellite for Classification Problems in Precision Agriculture, 2019, Springer. International Journal of Automation and Computing.
[7]
Wang, L.-F.; Kong, J. A.; Ding, K. H.; Le Toan, T.; Ribbes, F.; Floury, N. Electromagnetic scattering model for rice canopy based on monte carlo simulation. Prog. Electromagn. Res. 2005, 52, 153–171.
[8]
Bouvet, A.; LeToan, T. Use of ENVISAT/ASAR wide-swath data for timely rice fields mapping in the Mekong River Delta. Remote Sens. Environ. 2011, 115, 1090–1101.
[9]
Nguyen, D.; Clauss, K.; Cao, S.; Naeimi, V.; Kuenzer, C.; Wagner, W. Mapping Rice Seasonality in the Mekong Delta with Multi-Year Envisat ASAR WSM Data. Remote Sens. 2015, 7, 15868–15893.
[10]
Lopez-Sanchez, J. M.; Ballester-Berman, J. D.; Hajnsek, I. First Results of Rice Monitoring Practices in Spain by Means of Time Series of TerraSAR-X Dual-Pol Images. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2011, 4, 412–422.
[11]
Nelson, A.; Setiyono, T.; Rala, A.; Quicho, E.; Raviz, J.; Abonete, P.; Maunahan, A.; Garcia, C.; Bhatti, H.; Villano, L.; et al. Towards an Operational SAR-Based Rice Monitoring System in Asia: Examples from 13 Demonstration Sites across Asia in the RIICE Project. Remote Sens. 2014, 6, 10773–10812.
[12]
Clauss, K.; Ottinger, M.; Leinenkugel, P.; Kuenzer, C. Estimating rice production in the Mekong Delta, Vietnam, utilizing time series of Sentinel-1 SAR data. Int. J. Appl. Earth Obs. Geoinf. 2018, 73, 574–585.
[13]
Lasko, K.; Vadrevu, K. P.; Tran, V. T.; Justice, C. Mapping Double and Single Crop Paddy Rice with Sentinel-1A at Varying Spatial Scales and Polarizations in Hanoi, Vietnam. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2018, 11, 498–512.
[14]
Ndikumana, E.; HoTong Minh, D.; Baghdadi, N.; Courault, D.; Hossard, L. Deep Recurrent Neural Network for Agricultural Classification using multitemporal SAR Sentinel-1 for Camargue, France. Remote Sens. 2018, 10, 1217.
[15]
Emile Ndikumana, Dinh Ho Tong Minh, Hai Thu Dang Nguyen, Nicolas Baghdadi, Dominique Courault, Laure Hossard, Ibrahim El Moussawi. Estimation of Rice Height and Biomass Using Multitemporal SAR Sentinel-1 for Camargue, Southern France. Remote Sens. 2018, 10, 1394.
[16]
Hassan Bazzi, Nicolas Baghdadi, Mohammad El Hajj, Mehrez Zribi, Dinh Ho Tong Minh, Emile Ndikumana, Dominique Courault, Hatem Belhouchette. Mapping Paddy Rice Using Sentinel-1 SAR Time Series in Camargue, France. Remote Sens. 2019, 11, 887.
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