Results 31 to 40 of about 7,403 (276)

Comparing land surface phenology of major European crops as derived from SAR and multispectral data of Sentinel-1 and -2

Remote Sensing of Environment, 2021
Guido Lemoine, Michele Meroni, Astrid Verhegghen   +2 more
exaly   +2 more sources

Characterizing Spring Phenological Changes of the Land Surface across the Conterminous United States from 2001 to 2021

Remote Sensing, 2023
Monitoring land surface phenology plays a fundamental role in quantifying the impact of climate change on terrestrial ecosystems. Shifts in land surface spring phenology have become a hot spot in the field of global climate change research.
Wei Wu, Qinchuan Xin
doaj   +1 more source

Phenological Responses to Snow Seasonality in the Qilian Mountains Is a Function of Both Elevation and Vegetation Types

Remote Sensing, 2022
In high-elevation mountains, seasonal snow cover affects land surface phenology and the functioning of the ecosystem. However, studies regarding the long-term effects of snow cover on phenological changes for high mountains are still limited.
Yantao Liu, Wei Zhou, Si Gao, Xuanlong Ma, Kai Yan   +4 more
doaj   +1 more source

Near Real-time Fine-resolution Land Surface Phenological Prediction Using Convolutional Neural Network and Data Fusion [PDF]

E3S Web of Conferences, 2022
Near real-time fine-resolution land surface phenology (LSP) prediction is essential for understanding surface attributes and ecosystem functions, and solving important ecological processes related to phenology at the landscape scale.
Xiao Kun, Wang Yidan, Wu Wei, Xin Qinchuan   +3 more
doaj   +1 more source

ChinaCropPhen1km: a high-resolution crop phenological dataset for three staple crops in China during 2000–2015 based on leaf area index (LAI) products [PDF]

Earth System Science Data, 2020
Crop phenology provides essential information for monitoring and modeling land surface phenology dynamics and crop management and production. Most previous studies mainly investigated crop phenology at the site scale; however, monitoring and modeling ...
Y. Luo, Z. Zhang, Y. Chen, Z. Li, F. Tao, F. Tao   +5 more
doaj   +1 more source

A deep learning method to predict soil organic carbon content at a regional scale using satellite-based phenology variables

International Journal of Applied Earth Observations and Geoinformation, 2021
Obtaining the spatial distribution information of soil organic carbon (SOC) is significant to quantify the carbon budget and guide land management for migrating carbon emissions.
Lin Yang, Yanyan Cai, Lei Zhang, Mao Guo, Anqi Li, Chenghu Zhou   +5 more
doaj   +1 more source

The blsp R package with a Bayesian land surface phenology model [PDF]

, 2022
This software is an R package providing the Bayesian land surface phenology (BLSP) model that quantifies long-term annual land surface phenology from temporally sparse optical remote sensing time series (originally developed for 30 m Landsat time series).
Xiaojie Gao, Isabella Hinks, Owen Smith, Matt Shisler, Ian R. McGregor   +4 more
core   +1 more source

Quantitative Assessment of the Spatial Scale Effects of the Vegetation Phenology in the Qinling Mountains

Remote Sensing, 2022
Vegetation phenology reflects the temporal dynamics of vegetation growth and is an important indicator of climate change. However, differences consistently exist in land surface phenology derived at different spatial scales, which hinders the ...
Minfei Ma, Jianhong Liu, Mingxing Liu, Wenquan Zhu, Clement Atzberger, Xiaoqing Lv, Ziyue Dong   +6 more
doaj   +1 more source

Vegetation Phenology Influenced by Rapid Urbanization of The Yangtze Delta Region

Remote Sensing, 2020
Impacts of urbanization and climate change on ecosystems are widely studied, but these drivers of change are often difficult to isolate from each other and interactions are complicated.
Haiyong Ding, Luming Xu, Andrew J. Elmore, Yuli Shi   +3 more
doaj   +1 more source

Spatiotemporal Characteristics and Heterogeneity of Vegetation Phenology in the Yangtze River Delta

Remote Sensing, 2022
Vegetation phenology and its spatiotemporal driving factors are essential to reflect global climate change, the surface carbon cycle and regional ecology, and further quantitative studies on spatiotemporal heterogeneity and its two-way driving are needed.
Cancan Yang, Kai Deng, Daoli Peng, Ling Jiang, Mingwei Zhao, Jinbao Liu, Xincai Qiu   +6 more
doaj   +1 more source