scholarly journals Recent Advances in Land Surface Phenology Estimation with Multispectral Sensing

2021 ◽  
Author(s):  
Irini Soubry ◽  
Ioannis Manakos ◽  
Chariton Kalaitzidis
Keyword(s):  
Land Surface ◽  
Land Surface Phenology ◽  
Recent Advances ◽  
Multispectral Sensing

A Bayesian model to estimate land surface phenology parameters with harmonized Landsat 8 and Sentinel-2 images

2021 ◽  
Vol 261 ◽  
pp. 112471
Author(s):  
Chad Babcock ◽  
Andrew O. Finley ◽  
Nathaniel Looker
Keyword(s):  
Land Surface ◽  
Bayesian Model ◽  
Landsat 8 ◽  
Land Surface Phenology ◽  
Sentinel 2

scholarly journals Estimating and Monitoring Land Surface Phenology in Rangelands: A Review of Progress and Challenges

2021 ◽  
Vol 13 (11) ◽  
pp. 2060
Author(s):  
Trylee Nyasha Matongera ◽  
Onisimo Mutanga ◽  
Mbulisi Sibanda ◽  
John Odindi
Keyword(s):  
Land Surface ◽  
Vegetation Index ◽  
Critical Role ◽  
Spectral Characteristics ◽  
Vegetation Indices ◽  
Machine Learning Algorithms ◽  
Special Focus ◽  
Land Surface Phenology ◽  
Spectral Vegetation Indices ◽  
Satellite Sensors

Land surface phenology (LSP) has been extensively explored from global archives of satellite observations to track and monitor the seasonality of rangeland ecosystems in response to climate change. Long term monitoring of LSP provides large potential for the evaluation of interactions and feedbacks between climate and vegetation. With a special focus on the rangeland ecosystems, the paper reviews the progress, challenges and emerging opportunities in LSP while identifying possible gaps that could be explored in future. Specifically, the paper traces the evolution of satellite sensors and interrogates their properties as well as the associated indices and algorithms in estimating and monitoring LSP in productive rangelands. Findings from the literature revealed that the spectral characteristics of the early satellite sensors such as Landsat, AVHRR and MODIS played a critical role in the development of spectral vegetation indices that have been widely used in LSP applications. The normalized difference vegetation index (NDVI) pioneered LSP investigations, and most other spectral vegetation indices were primarily developed to address the weaknesses and shortcomings of the NDVI. New indices continue to be developed based on recent sensors such as Sentinel-2 that are characterized by unique spectral signatures and fine spatial resolutions, and their successful usage is catalyzed with the development of cutting-edge algorithms for modeling the LSP profiles. In this regard, the paper has documented several LSP algorithms that are designed to provide data smoothing, gap filling and LSP metrics retrieval methods in a single environment. In the future, the development of machine learning algorithms that can effectively model and characterize the phenological cycles of vegetation would help to unlock the value of LSP information in the rangeland monitoring and management process. Precisely, deep learning presents an opportunity to further develop robust software packages such as the decomposition and analysis of time series (DATimeS) with the abundance of data processing tools and techniques that can be used to better characterize the phenological cycles of vegetation in rangeland ecosystems.

scholarly journals Characterising the Land Surface Phenology of Europe Using Decadal MERIS Data

2015 ◽  
Vol 7 (7) ◽  
pp. 9390-9409 ◽  
Author(s):  
Victor Rodriguez-Galiano ◽  
Jadunandan Dash ◽  
Peter Atkinson
Keyword(s):  
Land Surface ◽  
Land Surface Phenology

Modeling and predicting spring land surface phenology of the deciduous broadleaf forest in northern China

2014 ◽  
Vol 198-199 ◽  
pp. 33-41 ◽  
Author(s):  
Xiangzhong Luo ◽  
Xiaoqiu Chen ◽  
Lingxiao Wang ◽  
Lin Xu ◽  
Youhua Tian
Keyword(s):  
Land Surface ◽  
Northern China ◽  
Broadleaf Forest ◽  
Land Surface Phenology ◽  
Deciduous Broadleaf Forest

Variations in land surface phenology and their response to climate change in Yangtze River basin during 1982–2015

2018 ◽  
Vol 137 (3-4) ◽  
pp. 1659-1674 ◽  
Author(s):  
Moxi Yuan ◽  
Lunche Wang ◽  
Aiwen Lin ◽  
Zhengjia Liu ◽  
Sai Qu
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Land Surface ◽  
Yangtze River ◽  
Yangtze River Basin ◽  
Land Surface Phenology

Land surface phenology along urban to rural gradients in the U.S. Great Plains

2015 ◽  
Vol 165 ◽  
pp. 42-52 ◽  
Author(s):  
J.J. Walker ◽  
K.M. de Beurs ◽  
G.M. Henebry
Keyword(s):  
Great Plains ◽  
Land Surface ◽  
Land Surface Phenology ◽  
The U.S
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