Plant photosynthesis plays an essential role in regulating the global carbon cycle. Therefore, it is essential to understand the limitations imposed by climate on plant photosynthesis to comprehend the impacts of climate change on land carbon dynamics. In this study, taking gross primary productivity as a direct representation of photosynthesis, we employed a light use efficiency model (i.
View Article and Find Full Text PDFEarth observation satellites have facilitated the quantification of how vegetation phenology responds to climate warming on large scales. However, satellite image pixels may contain a mixture of multiple vegetation types or species with diverse phenological responses to climate variability. It is unclear how these mixed pixels affect the statistical relationships between satellite-derived vegetation phenology and climate factors.
View Article and Find Full Text PDFVegetation phenology has been viewed as the nature's calendar and an integrative indicator of plant-climate interactions. The correct representation of vegetation phenology is important for models to accurately simulate the exchange of carbon, water, and energy between the vegetated land surface and the atmosphere. Remote sensing has advanced the monitoring of vegetation phenology by providing spatially and temporally continuous data that together with conventional ground observations offers a unique contribution to our knowledge about the environmental impact on ecosystems as well as the ecological adaptations and feedback to global climate change.
View Article and Find Full Text PDFGuang Pu Xue Yu Guang Pu Fen Xi
May 2014
The present study aims to explore capability of different methods for winter wheat leaf area index inversion by integrating remote sensing image and synchronization field experiment. There were four kinds of LAI inversion methods discussed, specifically, support vector machines (SVM), discrete wavelet transform (DWT), continuous wavelet transform (CWT) and principal component analysis (PCA). Winter wheat LAI inversion models were established with the above four methods respectively, then estimation precision for each model was analyzed.
View Article and Find Full Text PDFGuang Pu Xue Yu Guang Pu Fen Xi
February 2014
Leaf area index (LAI) is one of the most important parameters for evaluating winter wheat growth status and forecasting its yield. Hyperspectral remote sensing is a new technical approach that can be used to acquire the instant information of vegetation LAI at large scale. This study aims to explore the capability of least squares support vector machines (LS-SVM) method to winter wheat LAI estimation with hyperspectral data.
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