Zobrazeno 1 - 10
of 30
pro vyhledávání: '"Feini Huang"'
Autor:
Shijie Jiang, Lily‐belle Sweet, Georgios Blougouras, Alexander Brenning, Wantong Li, Markus Reichstein, Joachim Denzler, Wei Shangguan, Guo Yu, Feini Huang, Jakob Zscheischler
Publikováno v:
Earth's Future, Vol 12, Iss 7, Pp n/a-n/a (2024)
Abstract Interpretable Machine Learning (IML) has rapidly advanced in recent years, offering new opportunities to improve our understanding of the complex Earth system. IML goes beyond conventional machine learning by not only making predictions but
Externí odkaz:
https://doaj.org/article/eb521b00c73e49509e141f1f17007bc6
Autor:
Zili Xiong, Wei Shangguan, Vahid Nourani, Qingliang Li, Xingjie Lu, Lu Li, Feini Huang, Ye Zhang, Wenye Sun, Hua Yuan, Xueyan Li
Publikováno v:
Climate, Vol 11, Iss 10, p 205 (2023)
Land carbon fluxes play a critical role in ecosystems, and acquiring a comprehensive global database of carbon fluxes is essential for understanding the Earth’s carbon cycle. The primary methods of obtaining the spatial distribution of land carbon
Externí odkaz:
https://doaj.org/article/5215023e49f7476f8633992d5fe54d04
Autor:
Wei Shangguan, Zili Xiong, Vahid Nourani, Qingliang Li, Xingjie Lu, Lu Li, Feini Huang, Ye Zhang, Wenye Sun, Yongjiu Dai
Publikováno v:
Forests, Vol 14, Iss 5, p 913 (2023)
Global carbon fluxes describe the carbon exchange between land and atmosphere. However, already available global carbon fluxes datasets have not been adjusted by the available site data and deep learning tools. In this work, a global carbon fluxes da
Externí odkaz:
https://doaj.org/article/ecae54d6c220437bb443fcb99eb7ff7e
Publikováno v:
Agriculture, Vol 13, Iss 5, p 971 (2023)
Soil moisture (SM) is a key variable in Earth system science that affects various hydrological and agricultural processes. Convolutional long short-term memory (Conv-LSTM) networks are widely used deep learning models for spatio-temporal SM predictio
Externí odkaz:
https://doaj.org/article/4c3ce0194c944892a591b1537eb0d44e
Publikováno v:
Environmental Research Letters, Vol 18, Iss 7, p 074002 (2023)
Determination of the dominant factors which affect soil moisture (SM) predictions for drought analysis is an essential step to assess the reliability of the prediction results. However, artificial intelligence (AI) based drought modelling only provid
Externí odkaz:
https://doaj.org/article/1ba658250f624cf08097967ba344b1b5
Publikováno v:
Remote Sensing, Vol 15, Iss 2, p 366 (2023)
Soil moisture (SM) has significant impacts on the Earth’s energy and water cycle system. Remote sensing, such as the Soil Moisture Active Passive (SMAP) mission, has delivered valuable estimations of global surface soil moisture. However, it has a
Externí odkaz:
https://doaj.org/article/bf5a2f3fa71840a9aeb2f91999a7e170
Autor:
Taoning Mao, Wei Shangguan, Qingliang Li, Lu Li, Ye Zhang, Feini Huang, Jianduo Li, Wei Liu, Ruqing Zhang
Publikováno v:
Remote Sensing, Vol 14, Iss 16, p 3858 (2022)
Remote sensing soil moisture (SM) has been widely used in various earth science studies and applications, but their low resolution limits their usage and downscaling of them is needed. In this study, we proposed a spatial downscaling method for SM ba
Externí odkaz:
https://doaj.org/article/5c3ad2e99ba44842a8226438f1a1cfd7
Publikováno v:
Land, Vol 11, Iss 4, p 502 (2022)
Accurate assessment of agricultural drought is useful for ecosystem services. This is a successive work of our previous study that assessed agricultural drought using the soil water deficit index (SWDI) based on ERA5-Land in the four southern provinc
Externí odkaz:
https://doaj.org/article/08012d9d770648f1880c3af0993e4b4c
Autor:
Ruqing Zhang, Lu Li, Ye Zhang, Feini Huang, Jianduo Li, Wei Liu, Taoning Mao, Zili Xiong, Wei Shangguan
Publikováno v:
Agriculture, Vol 11, Iss 5, p 411 (2021)
It is important to accurately assess agricultural drought because of its harmful impacts on the ecosystem and economy. Soil moisture reanalysis datasets provide an important way to assess agricultural drought. In this study, the ERA5-Land surface and
Externí odkaz:
https://doaj.org/article/f0159e1ad0a945d6affc369a1c971d4f
Autor:
Qingliang Li, Gaosong Shi, Wei Shangguan, Vahid Nourani, Jianduo Li, Lu Li, Feini Huang, Ye Zhang, Chunyan Wang, Dagang Wang, Jianxiu Qiu, Xingjie Lu, Yongjiu Dai
High-quality gridded soil moisture products are essential for many Earth system science applications, while the recent reanalysis and remote sensing soil moisture data are often available at coarse resolution and remote sensing data are only for the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::dc8bd4b37a549e11cf556a81bd3e27df
https://essd.copernicus.org/articles/14/5267/2022/
https://essd.copernicus.org/articles/14/5267/2022/