Journal ArticleDOI
A new model of gross primary productivity for North American ecosystems based solely on the enhanced vegetation index and land surface temperature from MODIS
Daniel A. Sims,Abdullah F. Rahman,Vicente D. Cordova,Bassil El-Masri,Dennis D. Baldocchi,Paul V. Bolstad,Lawrence B. Flanagan,Allen H. Goldstein,David Y. Hollinger,Laurent Misson,Russell K. Monson,Walter C. Oechel,Hans Peter Schmid,Steven C. Wofsy,Liukang Xu +14 more
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TLDR
In this paper, the authors presented a new model, termed the Temperature and Greenness (TG) model, which combines enhanced vegetation index (EVI) and land surface temperature (LST) from MODIS.About:
This article is published in Remote Sensing of Environment.The article was published on 2008-04-15. It has received 400 citations till now. The article focuses on the topics: Enhanced vegetation index & Vegetation.read more
Citations
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Journal ArticleDOI
Development of a two-band enhanced vegetation index without a blue band
TL;DR: In this paper, the authors developed and evaluated a 2-band enhanced vegetation index (EVI2), without a blue band, which has the best similarity with the 3-band EVI, particularly when atmospheric effects are insignificant and data quality is good.
Journal ArticleDOI
Analysis of monotonic greening and browning trends from global NDVI time-series
Rogier de Jong,Sytze de Bruin,Allard de Wit,Michael E. Schaepman,Michael E. Schaepman,David Dent +5 more
TL;DR: In this article, the authors applied harmonic analysis and non-parametric trend tests to the GIMMS NDVI dataset (1981-2006) to detect greening and browning trends; especially the global coverage of time-series normalized difference vegetation index (NDVI) data which are available from 1981.
Journal ArticleDOI
The photochemical reflectance index (PRI) and the remote sensing of leaf, canopy and ecosystem radiation use efficiencies: A review and meta-analysis
Martín F. Garbulsky,Martín F. Garbulsky,Josep Peñuelas,John A. Gamon,Yoshio Inoue,Iolanda Filella +5 more
Journal ArticleDOI
Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms
Gianluca Tramontana,Martin Jung,Christopher R. Schwalm,Kazuhito Ichii,Gustau Camps-Valls,Botond Ráduly,Markus Reichstein,M. Altaf Arain,Alessandro Cescatti,Gerard Kiely,Lutz Merbold,Penélope Serrano-Ortiz,Sven Sickert,Sebastian Wolf,Dario Papale +14 more
TL;DR: In this article, the authors performed a cross-validation experiment for predicting carbon dioxide, latent heat, sensible heat and net radiation fluxes across different ecosystem types with 11 machine learning (ML) methods from four different classes (kernel methods, neural networks, tree methods, and regression splines).
Journal ArticleDOI
Large seasonal swings in leaf area of Amazon rainforests
Ranga B. Myneni,Wenze Yang,Wenze Yang,Ramakrishna R. Nemani,Alfredo Huete,Robert E. Dickinson,Yuri Knyazikhin,Kamel Didan,Rong Fu,Robinson I. Negrón Juárez,S. S. Saatchi,Hirofumi Hashimoto,Kazuhito Ichii,N.V. Shabanov,Bin Tan,P. Ratana,Jeffrey L. Privette,Jeffrey L. Privette,Jeffrey T. Morisette,Eric Vermote,Eric Vermote,David P. Roy,Robert E. Wolfe,Mark A. Friedl,Steven W. Running,Petr Votava,Nazmi El-Saleous,Sadashiva Devadiga,Yin Su,Vincent V. Salomonson +29 more
TL;DR: There are reported seasonal swings in green leaf area of ≈25% in a majority of the Amazon rainforests, which may be critical to initiation of the transition from dry to wet season, seasonal carbon balance between photosynthesis gains and respiratory losses, and litterfall nutrient cycling in moist tropical forests.
References
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Journal ArticleDOI
Overview of the radiometric and biophysical performance of the MODIS vegetation indices
TL;DR: In this paper, the authors evaluated the performance and validity of the MODIS vegetation indices (VI), the normalized difference vegetation index (NDVI) and enhanced vegetation index(EVI), produced at 1-km and 500-m resolutions and 16-day compositing periods.
Journal ArticleDOI
FLUXNET: A New Tool to Study the Temporal and Spatial Variability of Ecosystem-Scale Carbon Dioxide, Water Vapor, and Energy Flux Densities
Dennis D. Baldocchi,Eva Falge,Lianhong Gu,Richard J. Olson,David Y. Hollinger,Steven W. Running,P. M. Anthoni,Ch. Bernhofer,Kenneth J. Davis,Robert G. Evans,Jose D. Fuentes,Allen H. Goldstein,Gabriel G. Katul,Beverly E. Law,Xuhui Lee,Yadvinder Malhi,Tilden P. Meyers,William Munger,Walter C. Oechel,Kim Pilegaard,Hans Peter Schmid,Riccardo Valentini,Shashi B. Verma,Timo Vesala,Kell B. Wilson,S. C. Wofsy +25 more
TL;DR: The FLUXNET project as mentioned in this paper is a global network of micrometeorological flux measurement sites that measure the exchanges of carbon dioxide, water vapor, and energy between the biosphere and atmosphere.
FLUXNET: A New Tool to Study the Temporal and Spatial Variability of Ecosystem-Scale Carbon Dioxide, Water Vapor, and Energy Flux Densities
Dennis D. Baldocchi,Eva Falge,Lianhong Gu,Richard J. Olson,David Y. Hollinger,Steven W. Running,P. M. Anthoni,Christian Bernhofer,Kenneth J. Davis,Robert G. Evans +9 more
Journal ArticleDOI
Photosynthetic Response and Adaptation to Temperature in Higher Plants
Joseph A. Berry,Olle Björkman +1 more
TL;DR: A comparison of plants from contrasting thermol regimes in Thermally Contrasting Climates and adoptive responses in the heat stability 0/ the photosynthetic apparatus highlights the need to understand more fully the role of photosynthesis in climate change.
Journal ArticleDOI
Terrestrial ecosystem production: A process model based on global satellite and surface data
Christopher S. Potter,James T. Randerson,Christopher B. Field,Pamela A. Matson,Peter M. Vitousek,Harold A. Mooney,Steven Klooster +6 more
TL;DR: In this paper, the authors present a modeling approach aimed at seasonal resolution of global climatic and edaphic controls on patterns of terrestrial ecosystem production and soil microbial respiration using satellite imagery (Advanced Very High Resolution Radiometer and International Satellite Cloud Climatology Project solar radiation), along with historical climate (monthly temperature and precipitation) and soil attributes (texture, C and N contents) from global (1°) data sets as model inputs.
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