Zobrazeno 1 - 10
of 43
pro vyhledávání: '"Fangni, Lei"'
Autor:
Sangchul Lee, Dongho Kim, Gregory W. McCarty, Martha Anderson, Feng Gao, Fangni Lei, Glenn E. Moglen, Xuesong Zhang, Haw Yen, Junyu Qi, Wade Crow, In-Young Yeo, Liang Sun
Publikováno v:
Heliyon, Vol 10, Iss 10, Pp e30923- (2024)
Remotely sensed products are often used in watershed modeling as additional constraints to improve model predictions and reduce model uncertainty. Remotely sensed products also enabled the spatial evaluation of model simulations due to their spatial
Externí odkaz:
https://doaj.org/article/5c245e0aabef42588b0239e93d55047f
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 16, Pp 5629-5644 (2023)
A high spatial and temporal resolution global soil moisture product is essential for understanding hydrologic and meteorological processes and enhancing agricultural applications. Global navigation satellite system (GNSS) signals at L-band frequencie
Externí odkaz:
https://doaj.org/article/179dfc1a2cf74fdeb1f8bb4ea1b032c3
Publikováno v:
Nature Communications, Vol 13, Iss 1, Pp 1-8 (2022)
Summertime warm bias in the central United States persists in Earth System Models. This bias is dominated by land physics related to transpiration and evaporation partitioning. Improved land physics can constrain projected climate uncertainty.
Externí odkaz:
https://doaj.org/article/83edc18269b84d478277019e6aee8d1c
Publikováno v:
In Remote Sensing of Environment 15 September 2019 231
Autor:
Fan Chen, Fangni Lei, Kyle Knipper, Feng Gao, Lynn McKee, Maria del Mar Alsina, Joseph Alfieri, Martha Anderson, Nicolas Bambach, Sebastian J. Castro, Andrew J. McElrone, Karrin Alstad, Nick Dokoozlian, Felix Greifender, William Kustas, Claudia Notarnicola, Nurit Agam, John H. Prueger, Lawrence E. Hipps, Wade T. Crow
Publikováno v:
Irrigation Science. 40:779-799
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b28112b34bd8f4bc221108ef4a27a020
https://doi.org/10.1007/s00271-022-00789-9
https://doi.org/10.1007/s00271-022-00789-9
Autor:
Jing Hu, Dana M. Miles, Ardeshir Adeli, John P. Brooks, Frances A. Podrebarac, Renotta Smith, Fangni Lei, Xiaofei Li, Johnie N. Jenkins, Robert J. Moorhead
Publikováno v:
Environments
Volume 10
Issue 2
Pages: 19
Volume 10
Issue 2
Pages: 19
Agroecosystems, accounting for more than one-third of arable land worldwide, play an essential role in the terrestrial carbon (C) cycle. The development of agricultural practices, which maximize soil C sequestration from the atmosphere, is receiving
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e019f42683d86c29f69ab83a218cd3bd
Publikováno v:
Remote Sensing, Vol 12, Iss 21, p 3503 (2020)
This paper presents a machine learning (ML) framework to derive a quasi-global soil moisture (SM) product by direct use of the Cyclone Global Navigation Satellite System (CYGNSS)’s high spatio-temporal resolution observations over the tropics (with
Externí odkaz:
https://doaj.org/article/4aff107488bb48e482dfe1b2b3bac07a
Publikováno v:
Remote Sensing, Vol 12, Iss 7, p 1168 (2020)
Soil moisture (SM) derived from satellite-based remote sensing measurements plays a vital role for understanding Earth’s land and near-surface atmosphere interactions. Bistatic Global Navigation Satellite System (GNSS) Reflectometry (GNSS-R) has em
Externí odkaz:
https://doaj.org/article/a28ff60767cc409599377ea2209178ad
Publikováno v:
Remote Sensing, Vol 7, Iss 10, Pp 13448-13465 (2015)
Satellite-derived soil moisture products have become an important data source for the study of land surface processes and related applications. For satellites with sun-synchronous orbits, these products are typically derived separately for ascending
Externí odkaz:
https://doaj.org/article/43583ef29d4840ebbf24f35c0e4abd92
Autor:
Sangchul Lee, Dongho Kim, Gregory W. McCarty, Martha Anderson, Feng Gao, Fangni Lei, Glenn E. Moglen, Xuesong Zhang, Haw Yen, Junyu Qi, Wade Crow, In-Young Yeo, Liang Sun
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5d6bd7cc045e8128dedcd351199cccf3
https://doi.org/10.5194/hess-2022-187-supplement
https://doi.org/10.5194/hess-2022-187-supplement
