The vegetation space-time variability during 1999-2010 in the North of the Apulian region (Southern Italy) was analysed using SPOT VEGETATION (VGT) sensor data. Three bands of VEGETATION (RED, NIR and SWIR) were used to implement the vegetation index named reduced simple ratio (RSR) to derive leaf area index (LAI). The monthly average LAI is an indicator of biomass and canopy cover, while the d...

Land cover maps are used widely to parameterize the biophysical properties of plant canopies in models that describe terrestrial biogeochemical processes. In this paper, we describe the use of supervised classification algorithms to generate land cover maps that characterize the vegetation types required for Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR) retrie...

Leaf area index (LAI) is a key variable for the understanding and modelling of several ecophysiological processes within a vegetation canopy. The LAI could thus provide vital information for the management of the environment and agricultural practices when estimated continuously over time and space thanks to remote sensing sensors such as CHRIS/PROBA. The spaceborne ESA-mission CHRIS/PROBA prov...

In this study, seasonal field measurements of the normalized difference vegetation index (NDVI), using a field spectroradiometer, and leaf area index (LAI), using a LI-COR LAI-2000 Plant Canopy Analyzer, were compared with above-ground phytomass data to investigate relationships between vegetation properties and spectral indices for four distinct tundra vegetation types at Ivotuk, Alaska (68.49...

Leaf area index (LAI) is one of the key biophysical indicators for characterizing growth and status vegetation also used in modeling earth system processes. Machine learning algorithms (MLAs) such as random forest regression (RFR), artificial neural network (ANNR) support vector (SVR) based on satellite data have been widely estimation LAI. However, selection input variables has a great impact ...

Leaf area index (LAI) is a key variable for the understanding and modeling of several ecophysiological processes within a vegetation canopy. The LAI could thus provide vital information for the management of the environment and agricultural practices when estimated continuously over time and space thanks to remote sensing sensors such as CHRIS/PROBA. The spaceborne ESA-mission CHRIS/PROBA provi...

Currently, multiple leaf area index (LAI) products retrieved from remote sensing data are widely used in crop growth monitoring, land-surface process simulation and studies of climate change. However, most LAI products are only retrieved from individual satellite observations, which may result in spatial-temporal discontinuities and low accuracy in these products. In this paper, a new method wa...

Land cover maps are widely used to parameterize the biophysical properties of plant canopies in models that describe terrestrial biogeochemical processes. In this paper, we describe the use of supervised classification algorithms to generate land cover maps that characterize the vegetation types required for LAI and FAPAR retrievals from MODIS and MISR. As part of this analysis, we examine the ...

Terrestrial isoprene emissions directly respond to leaf temperature, photosynthetically active radiation (PAR), soil moisture, and plant characteristics such as leaf area index (LAI). Prior work has estimated isoprene interannual variability at 5e25%, however the relative contributions of individual environmental factors have not been delineated. A biogenic isoprene emissions model (MEGAN) is c...

The amount of leaves in a plant canopy (measured as leaf area index, LAI) modulates key land–atmosphere interactions, including the exchange of energy, moisture, carbon dioxide (CO 2), and other trace gases, and is therefore an essential variable in predicting terrestrial carbon, water, and energy fluxes. The latest generation of Earth system 5 models (ESMs) simulate LAI, as well as provide pro...