Results 21 to 30 of about 48,829 (304)

Land surface phenology from VEGETATION and PROBA-V data. Assessment over deciduous forests

International Journal of Applied Earth Observations and Geoinformation, 2020
Land surface phenology has been widely retrieved although no consensus exists on the optimal satellite dataset and the method to extract phenology metrics.
Kevin Bórnez, Adrià Descals, Aleixandre Verger, Josep Peñuelas   +3 more
doaj   +1 more source

Land Surface Snow Phenology Based on an Improved Downscaling Method in the Southern Gansu Plateau, China

Remote Sensing, 2022
Snow is involved in and influences water–energy processes at multiple scales. Studies on land surface snow phenology are an important part of cryosphere science and are a hot spot in the hydrological community.
Lei Wu, Changbin Li, Xuhong Xie, Jianan Lv, Songbing Zou, Xuan Zhou, Na Shen   +6 more
doaj   +1 more source

Global Assessment of Cumulative and Time-Lag Effects of Drought on Land Surface Phenology

GIScience & Remote Sensing, 2022
Increased frequency and intensity of droughts under climate change will have a significant impact on land surface phenology, however, the drought-phenology interactions that are associated with complex temporal effects are not well understood. This study
Ronglei Zhou, Yangyang Liu, Mengying Cui, Jinxin Lu, Haijing Shi, Hanyu Ren, Wei Zhang, Zhongming Wen   +7 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

Implementation of a new crop phenology and irrigation scheme in the ISBA land surface model using SURFEX_v8.1 [PDF]

Geoscientific Model Development, 2022
With an increase in the number of natural processes represented, global land surface models (LSMs) have become more and more accurate in representing natural terrestrial ecosystems.
A. Druel, A. Druel, S. Munier, A. Mucia, C. Albergel, C. Albergel, J.-C. Calvet   +6 more
doaj   +1 more source

Comparisons of numerical phenology models and machine learning methods on predicting the spring onset of natural vegetation across the Northern Hemisphere

Ecological Indicators, 2021
The timing of vegetation spring onset is largely influenced by climate factors, making it sensitive to climate variation. Robust models that predict vegetation spring onset via the climate forcing data are needed in the land surface models for ...
Wanjing Li, Qinchuan Xin, Xuewen Zhou, Zhicheng Zhang, Yongjian Ruan   +4 more
doaj   +1 more source

Mapping Crop Cycles in China Using MODIS-EVI Time Series [PDF]

, 2014
As the Earth’s population continues to grow and demand for food increases, the need for improved and timely information related to the properties and dynamics of global agricultural systems is becoming increasingly important.
Friedl, Mark A, Frolking, Steve, Gray, Josh M, Li, Le, pan, Yaozhong, Xin, Qinchuan   +5 more
core   +3 more sources

No Consistent Evidence for Advancing or Delaying Trends in Spring Phenology on the Tibetan Plateau [PDF]

, 2017
Vegetation phenology is a sensitive indicator of climate change and has significant effects on the exchange of carbon, water, and energy between the terrestrial biosphere and the atmosphere.
Badeck, Balzarolo, Chen, Chen, China Meteorological Administration, Coops, Dai, Deronde, Ding, Duan, Forkel, Fu, Ganjurjav, Guay, Guo, Holben, Hou, Huete, Jeong, Julien, Justice, Kljun, Knorr, Lasslop, Li, Li, Li, Liu, Liu, Macbean, McClain, Menzel, Penuelas, Piao, Pinzon, Reed, Richardson, Richardson, Richardson, Sen, Shen, Shen, Shen, Shen, Shi, Stöckli, Suzuki, Tian, Tucker, Turner, Wang, Wang, Wang, Wu, Wu, Xin, Xin, Xu, Yi, Yu, Yu, Zhang, Zhang, Zhang, Zhang, Zhao, Zhao, Zhong, Zhou, Zhou   +69 more
core   +3 more sources

Global land surface phenology trends from GIMMS database [PDF]

International Journal of Remote Sensing, 2009
A double logistic function has been used to describe global inventory mapping and monitoring studies (GIMMS) normalized difference vegetation index (NDVI) yearly evolution for the 1981 to 2003 period, in order to estimate land surface phenology parameter.
Y. Julien, J. A. Sobrino
openaire   +1 more source

Impact of Snowpack on the Land Surface Phenology in the Tianshan Mountains, Central Asia

Remote Sensing, 2022
The accumulation and ablation processes of seasonal snow significantly affect the land surface phenology in a mountainous ecosystem. However, the ability of snow to regulate the alpine land surface phenology in the arid regions is not well described in ...
Tao Yang, Qian Li, Qiang Zou, Rafiq Hamdi, Fengqi Cui, Lanhai Li   +5 more
doaj   +1 more source