Zobrazeno 1 - 10
of 23
pro vyhledávání: '"Ruoque Shen"'
Publikováno v:
International Journal of Applied Earth Observations and Geoinformation, Vol 128, Iss , Pp 103725- (2024)
Accurate mapping of paddy rice cropping intensity (PRCI) affects precision agriculture, water use management, and informed decision-making. Many PRCI mapping approaches have been developed and achieved remarkable performance. However, large-scale and
Externí odkaz:
https://doaj.org/article/0bc9f515911e4b2d951e4164f433975f
Publikováno v:
Scientific Data, Vol 10, Iss 1, Pp 1-18 (2023)
Abstract China is the world’s second-largest maize producer, contributing 23% to global production and playing a crucial role in stabilizing the global maize supply. Therefore, accurately mapping the maize distribution in China is of great signific
Externí odkaz:
https://doaj.org/article/a323a27cb20c49fe8fd27e0b0ff3748a
Autor:
Xi Zhang, Ruoque Shen, Xiaolin Zhu, Baihong Pan, Yangyang Fu, Yi Zheng, Xuebing Chen, Qiongyan Peng, Wenping Yuan
Publikováno v:
Frontiers in Environmental Science, Vol 11 (2023)
Introduction: Timely and accurately mapping the spatial distribution of rice is of great significance for estimating crop yield, ensuring food security and freshwater resources, and studying climate change. Double-season rice is a dominant rice plant
Externí odkaz:
https://doaj.org/article/b1b3e7627462442784b4bd40752af29b
Publikováno v:
Science of Remote Sensing, Vol 7, Iss , Pp 100081- (2023)
Machine learning algorithms are a frequently used crop classification method and have been applied to identify the distribution of various crops over regional and national scales. Previous studies have underscored that the number of training samples
Externí odkaz:
https://doaj.org/article/4611a6de40c94a248d2844655e34513d
Autor:
Qiongyan Peng, Ruoque Shen, Jie Dong, Wei Han, Jianxi Huang, Tao Ye, Wenzhi Zhao, Wenping Yuan
Publikováno v:
Frontiers in Environmental Science, Vol 10 (2023)
Introduction: Using satellite data to identify the planting area of summer crops is difficult because of their similar phenological characteristics.Methods: This study developed a new method for differentiating maize from other summer crops based on
Externí odkaz:
https://doaj.org/article/48bea32c2d944bd3928d862839b9d4da
Publikováno v:
Remote Sensing in Ecology and Conservation, Vol 7, Iss 4, Pp 685-699 (2021)
Abstract Autumn phenology plays an important role in regulating ecosystem carbon and water cycling, but it has received less attention than spring phenology. Satellite‐based methods have been widely applied in monitoring autumn phenology at large s
Externí odkaz:
https://doaj.org/article/9fdddf69df9b4d42824152cda71ba60c
Publikováno v:
Science of Remote Sensing, Vol 5, Iss , Pp 100043- (2022)
The prediction of vegetation growth under climate change has become much more important in recent years, but is still a challenge. This study developed a machine learning method to predict vegetation growth, indicated by satellite-based normalized di
Externí odkaz:
https://doaj.org/article/5a4b15d5763d41a4b05031551ff8b9f2
Publikováno v:
Journal of Remote Sensing, Vol 2022 (2022)
As the second largest producer of maize, China contributes 23% of global maize production and plays an important role in guaranteeing maize markets stability. In spite of its importance, there is no 30 m spatial resolution distribution map of maize f
Externí odkaz:
https://doaj.org/article/d57b78adea7d45d08de94ee637645847
Autor:
Qian Li, Xiuzhi Chen, Wenping Yuan, Haibo Lu, Ruoque Shen, Shengbiao Wu, Fanxi Gong, Yuhang Dai, Liyang Liu, Qingling Sun, Chaoqun Zhang, Yongxian Su
Publikováno v:
Earth's Future, Vol 9, Iss 9, Pp n/a-n/a (2021)
Abstract Climatic drivers for canopy leaf shedding and flush of evergreen broadleaved forest biome are still unclear at the continental scale across tropical and subtropical region. This imposes a challenge for modeling pantropical photosynthesis sea
Externí odkaz:
https://doaj.org/article/117aa2fe40894344b3bd38e67c5774f2
Publikováno v:
Remote Sensing, Vol 13, Iss 22, p 4609 (2021)
Although China is the largest producer of rice, accounting for about 25% of global production, there are no high-resolution maps of paddy rice covering the entire country. Using time-weighted dynamic time warping (TWDTW), this study developed a pixel
Externí odkaz:
https://doaj.org/article/8febfd005a4c4248a2b75dfab46a8216