Zobrazeno 1 - 10
of 1 568
pro vyhledávání: '"moderate-resolution imaging spectroradiometer (MODIS)"'
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 14806-14822 (2024)
Land surface temperature (LST) is a crucial physical parameter for hydrological, meteorological, climatological, and climate change studies. To encourage the use of satellite-derived LST products in a wide range of applications, providing feedback on
Externí odkaz:
https://doaj.org/article/de2aa36a10004197b401830b0854ba7d
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 14762-14776 (2024)
Live fuel moisture content (LFMC) is a critical variable in improving fire risk estimations, which varies widely among different vegetation types and ecosystems. However, many regions do not have an operational LFMC mapping system, as such tools are
Externí odkaz:
https://doaj.org/article/12bb9fb3ee1341bb97c77cf423185b80
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 15222-15242 (2024)
The satellite-derived surface urban heat island is often not properly distinguished from the in situ derived canopy-layer urban heat island in the field of remote sensing of urban climates. Yet, some studies have investigated their differences, focus
Externí odkaz:
https://doaj.org/article/da5e90b186df4f7d8d0f96f47e79209c
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 12092-12111 (2024)
While it is crucial to monitor the spatio-temporal dynamics of forests at the subpixel scale, most available nonlinear methods are used to predict forest cover fraction maps only at the acquisition time of the training samples and are, thus, unable t
Externí odkaz:
https://doaj.org/article/4b72ba4f8b8a4862bc9fbefffaea494a
Publikováno v:
Geo-spatial Information Science, Pp 1-18 (2023)
Cotton is one of the most significant cash crops in the world, and it is also the main source of natural fiber for textiles. It is crucial for cotton management to identify the spatiotemporal distribution of cotton planting areas timely and accuratel
Externí odkaz:
https://doaj.org/article/b2e1f9dbe6af491782c06fce85b09354
Autor:
Blake Steiner, Russell L. Scott, Jia Hu, Natasha MacBean, Andrew Richardson, David J. P. Moore
Publikováno v:
Ecosphere, Vol 15, Iss 2, Pp n/a-n/a (2024)
Abstract Savannas are water‐limited ecosystems characterized by two dominant plant types: trees and an understory primarily made up grass. Different phenology and root structures of these plant types complicate how savanna primary productivity resp
Externí odkaz:
https://doaj.org/article/a96e520b00c14eabab5bd5b29ca7b401
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 16, Pp 9967-9976 (2023)
Land surface temperature (LST) plays a key role in surface-atmosphere interactions and energy exchange processes and is an important parameter indispensable for earth science research. The LST accuracy retrieved from the Advanced Geosynchronous Radia
Externí odkaz:
https://doaj.org/article/a3dd3bfe3d31454caf774ad16d94f6b0
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 16, Pp 4180-4194 (2023)
Land surface temperature (LST) plays a crucial role in the energy and water cycles of the Earth's climate system. The uncertainty of LST retrieval from satellites is a fundamental and long-standing issue, especially in plateau areas [such as the Tibe
Externí odkaz:
https://doaj.org/article/b40a2e306fef4b51a11dd3c4ccc645de
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 16, Pp 7124-7134 (2023)
To improve the finesse of the temperature-humidity index (THI), this study applies four machine learning methods in THI downscaling, including multiple linear regression, random forest (RF), support vector machine, and gradient boosting machine. The
Externí odkaz:
https://doaj.org/article/47c95e18c5194e5a94b6061f8cabe49d
Autor:
Jinyang Du, John S. Kimball, Steven K. Chan, Mario Julian Chaubell, Rajat Bindlish, R. Scott Dunbar, Andreas Colliander
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 16, Pp 4871-4881 (2023)
Fractional water (FW) correction of satellite microwave brightness temperature (Tb) observations is a prerequisite for accurate soil moisture (SM) mapping over mixed land and water areas. Here, we evaluated the FW impacts on NASA Soil Moisture Active
Externí odkaz:
https://doaj.org/article/6773a9f27f9946f5a8cb650e344421a9