Mapping Paddy Rice Area and Yields Over Thai Binh Province in Viet Nam From MODIS, Landsat, and ALOS-2/PALSAR-2

2018 ◽  
Vol 11 (7) ◽  
pp. 2238-2252 ◽  
Author(s):  
Kaiyu Guan ◽  
Zhan Li ◽  
Lakshman Nagraj Rao ◽  
Feng Gao ◽  
Donghui Xie ◽  
...  
Keyword(s):  
Paddy Rice ◽  
Viet Nam ◽  
Rice Area

scholarly journals Application of DNDC Model for Mapping Greenhouse Gas Emission from Paddy Rice Cultivation in Nam Dinh Province

2019 ◽  
Vol 35 (2) ◽  
Author(s):  
Nguyen Le Trang ◽  
Bui Thi Thu Trang ◽  
Mai Van Trinh ◽  
Nguyen Tien Sy ◽  
Nguyen Manh Khai
Keyword(s):  
Greenhouse Gas ◽  
Agricultural Sector ◽  
Greenhouse Gas Emission ◽  
Agricultural Soils ◽  
Paddy Rice ◽  
Rice Cultivation ◽  
N2o Emissions ◽  
Viet Nam ◽  
Alkaline Soils ◽  
Dndc Model

Abstract: This study used the Denitrification-Decomposition (DNDC) model to calculate greenhouse gas emissions from a paddy rice cultivation in ​​Nam Dinh province. The results show that the total CH4 emission from paddy rice field in Nam Dinh province ranges from 404 to 1146kg/ha/year. Total N2O emissions range from 0.8 to 4.2 kg/ha/year; The total amount of CO2e varies between 10,000 and 30,000 kg CO2e / ha / year. CH4 emissions on typical salinealluvial soils, light mechanics are the highest and lowest on alkaline soils. Alluvium, alkaline soils have the highest N2O emissions and the lowest is the typical saline soils. The study has also mapped CH4, N2O and CO2e emissions for Nam Dinh province. Keywords: DNDC, Green house gas, agricultural sector, Nam Dinh,  GIS. References: [1] Bộ Tài nguyên và Môi trường, Báo cáo kỹ thuật kiểm kê quốc gia KNK của Việt Nam năm 2014, NXB Tài Nguyên Môi trường và Bản đồ Việt Nam, 2018.[2] D.L. Giltrap, C.Li, S. Saggar, DNDC: A process-based model of greenhouse gas fluxes from agricultural soils, Agriculture, Ecosystems & Environment,Volume 136 (2010), 292–300. https://doi:10.1016/j.agee.2009.06.014.[3] Viện Thổ nhưỡng Nông hóa, Báo cáo kết quả đề tài: “Nghiên cứu, đánh giá tài nguyên đất sản xuất nông nghiệp phục vụ chuyển đổi cơ cấu cây trồng chính có hiệu quả tại tỉnh Nam Định”, 2017.[4] Trung tâm Khí tượng thủy văn quốc gia – Bộ TN&MT, Số liệu thống kê khí tượng thủy văn các trạm khí tượng Văn Lý, Nam Định, Ninh Bình, Thái Bình năm 2014, 2015.[5] Niên giám thống kê tỉnh Nam Định, 2015.[6] T. Weaver, P. Ramachandran, L. Adriano, Policies for High Quality, Safe, and Sustainable Food Supply in the Greater Mekong Subregion. ADB: Manila. (2019) Chapter 7, 178-204.[7] Mai Văn Trịnh, Sổ tay hướng dẫn đo phát thải khí nhà kính trong canh tác lúa. NXB Nông nghiệp, 2016.    

scholarly journals Mapping a Paddy Rice Area in a Cloudy and Rainy Region Using Spatiotemporal Data Fusion and a Phenology-Based Algorithm

2021 ◽  
Vol 13 (21) ◽  
pp. 4400
Author(s):  
Rongkun Zhao ◽  
Yuechen Li ◽  
Jin Chen ◽  
Mingguo Ma ◽  
Lei Fan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Data Fusion ◽  
Survey Data ◽  
Paddy Rice ◽  
Spatiotemporal Data ◽  
Series Data ◽  
Landsat 8 ◽  
Rice Area ◽  
Sentinel 2

The timely and accurate mapping of paddy rice is important to ensure food security and to protect the environment for sustainable development. Existing paddy rice mapping methods are often remote sensing technologies based on optical images. However, the availability of high-quality remotely sensed paddy rice growing area data is limited due to frequent cloud cover and rain over the southwest China. In order to overcome these limitations, we propose a paddy rice field mapping method by combining a spatiotemporal fusion algorithm and a phenology-based algorithm. First, a modified neighborhood similar pixel interpolator (MNSPI) time series approach was used to remove clouds on Sentinel-2 and Landsat 8 OLI images in 2020. A flexible spatiotemporal data fusion (FSDAF) model was used to fuse Sentinel-2 data and MODIS data to obtain multi-temporal Sentinel-2 images. Then, the fused remote sensing data were used to construct fusion time series data to produce time series vegetation indices (NDVI\LSWI) having a high spatiotemporal resolution (10 m and ≤16 days). On this basis, the unique physical characteristics of paddy rice during the transplanting period and other auxiliary data were combined to map paddy rice in Yongchuan District, Chongqing, China. Our results were validated by field survey data and showed a high accuracy of the proposed method indicated by an overall accuracy of 93% and the Kappa coefficient of 0.85. The paddy rice planting area map was also consistent with the official data of the third national land survey; at the town level, the correlation between official survey data and paddy rice area was 92.5%. The results show that this method can effectively map paddy rice fields in a cloudy and rainy area.

scholarly journals A Scalable Machine Learning Pipeline for Paddy Rice Classification Using Multi-Temporal Sentinel Data

2021 ◽  
Vol 13 (9) ◽  
pp. 1769
Author(s):  
Vasileios Sitokonstantinou ◽  
Alkiviadis Koukos ◽  
Thanassis Drivas ◽  
Charalampos Kontoes ◽  
Ioannis Papoutsis ◽  
...  
Keyword(s):  
Machine Learning ◽  
Satellite Data ◽  
High Performance ◽  
Large Scale ◽  
Paddy Rice ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Supervised Machine Learning ◽  
Rice Area ◽  
Multi Temporal

The demand for rice production in Asia is expected to increase by 70% in the next 30 years, which makes evident the need for a balanced productivity and effective food security management at a national and continental level. Consequently, the timely and accurate mapping of paddy rice extent and its productivity assessment is of utmost significance. In turn, this requires continuous area monitoring and large scale mapping, at the parcel level, through the processing of big satellite data of high spatial resolution. This work designs and implements a paddy rice mapping pipeline in South Korea that is based on a time-series of Sentinel-1 and Sentinel-2 data for the year of 2018. There are two challenges that we address; the first one is the ability of our model to manage big satellite data and scale for a nationwide application. The second one is the algorithm’s capacity to cope with scarce labeled data to train supervised machine learning algorithms. Specifically, we implement an approach that combines unsupervised and supervised learning. First, we generate pseudo-labels for rice classification from a single site (Seosan-Dangjin) by using a dynamic k-means clustering approach. The pseudo-labels are then used to train a Random Forest (RF) classifier that is fine-tuned to generalize in two other sites (Haenam and Cheorwon). The optimized model was then tested against 40 labeled plots, evenly distributed across the country. The paddy rice mapping pipeline is scalable as it has been deployed in a High Performance Data Analytics (HPDA) environment using distributed implementations for both k-means and RF classifiers. When tested across the country, our model provided an overall accuracy of 96.69% and a kappa coefficient 0.87. Even more, the accurate paddy rice area mapping was returned early in the year (late July), which is key for timely decision-making. Finally, the performance of the generalized paddy rice classification model, when applied in the sites of Haenam and Cheorwon, was compared to the performance of two equivalent models that were trained with locally sampled labels. The results were comparable and highlighted the success of the model’s generalization and its applicability to other regions.

Study on Extraction Methods of Paddy Rice Area Based on GF-6 Satellite Image

2021 ◽  
Author(s):  
Jie Shan ◽  
Lin Qiu ◽  
Miao Tian ◽  
Jingjing Wang ◽  
Zhiming Wang ◽  
...  
Keyword(s):  
Satellite Image ◽  
Extraction Methods ◽  
Paddy Rice ◽  
Rice Area

Large increases of paddy rice area, gross primary production, and grain production in Northeast China during 2000–2017

2020 ◽  
Vol 711 ◽  
pp. 135183 ◽  
Author(s):  
Fengfei Xin ◽  
Xiangming Xiao ◽  
Jinwei Dong ◽  
Geli Zhang ◽  
Yao Zhang ◽  
...  
Keyword(s):  
Primary Production ◽  
Northeast China ◽  
Gross Primary Production ◽  
Paddy Rice ◽  
Grain Production ◽  
Rice Area

Optimal MODIS data processing for accurate multi-year paddy rice area mapping in China

2020 ◽  
Vol 57 (5) ◽  
pp. 687-703
Author(s):  
Li Liu ◽  
Jingfeng Huang ◽  
Qinxue Xiong ◽  
Huijuan Zhang ◽  
Peiling Song ◽  
...  
Keyword(s):  
Data Processing ◽  
Paddy Rice ◽  
Modis Data ◽  
Rice Area
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