Tạp chí: International Conference on GeoInformatics for Spatial - Infracstructure Development in Earth & Allied Sciences at Can Tho University, 22nd – 25th November 2018
Rice monitoring and rice production estimation play an important role in agricultural sector. Understanding about growth conditions such as start of season, peak of season as well as rice extent and rice production are essential. The objective of this study is to combine Sentinel-1 data and rice yield estimation model (Oryza) to monitor rice area and estimate rice production in An Giang province. The study used Sentinel-1 data with 20m spatial resolution with 12-day temporal resolution together with Oryza model for monitoring and estimating rice extent and rice production of the Summer-Autumn season in 2016 in An Giang province. The approach used in this study is based on the analysis of changes in the scattered properties (sigma nought values) of rice growth conditions from April 2016 to September 2016. In addition, other parameters for instance rice variety, soil type, fertilizer rate, irrigation are used as the inputs to Oryza model for estimating rice yield. Results showed that in the Summer-Autumn 2016 season, rice area in An Giang province was 232,970 ha, this result is compared with the official statistical data and it showed good agreement with 97%. The rice extent has also validated base on 139 ground truth points and it showed very high overall accuracy (93.5%, Kappa index is 0.87). The results of rice production estimation indicated that in this season, the average yield was 5.67 tons per hectare, which is about 1% difference from the statistical data. The research had showed there is a great potential of using remote sensing data in combination with rice yield estimation models to estimate the rice extent and its production at the provincial level, and this result provides important information for policy decisions ensuring food security and reducing vulnerability of farmers in the Mekong Delta.
Trích dẫn: Võ Quốc Tuấn, Phạm Quốc Việt và Nguyễn Văn Thọ, 2020. Tích hợp ảnh radar và ảnh quang học xây dựng bản đồ hiện trạng sử dụng đất thành phố Cần Thơ. Tạp chí Khoa học Trường Đại học Cần Thơ. 56(5A): 20-29.
Trích dẫn: Võ Quốc Tuấn, Đặng Hoàng Khải, Huỳnh Thị Kim Nhân và Nguyễn Thiên Hoa, 2018. Phát triển thuật toán giám sát lũ lụt vùng Đồng bằng sông cửu Long dựa vào nền tảng Google Earth Engine. Tạp chí Khoa học Trường Đại học Cần Thơ. 54(9A): 29-36.
Tạp chí khoa học Trường Đại học Cần Thơ
Lầu 4, Nhà Điều Hành, Khu II, đường 3/2, P. Xuân Khánh, Q. Ninh Kiều, TP. Cần Thơ
Điện thoại: (0292) 3 872 157; Email: tapchidhct@ctu.edu.vn
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