This paper reports results of an experiment LRVE (Leaf Area Index Retrieval and Validation Experiment) that was conducted over agricultural areas of Central India during winter season of 2001-02, aimed at relating field measurements of LAI to space borne IRS LISS-III data, preparation of site-level LAI maps and validation of MODIS-based 1 km LAI global fields,. Measurements of fieldlevel LAI, a...

Leaf Area Index (LAI) represents the total surface area of leaves above a unit area of ground and is a key variable in any vegetation model, as well as in climate models. New high resolution LAI satellite data is now available covering a period of several decades. OPEN ACCESS Remote Sens. 2013, 5 4820 This provides a unique opportunity to validate LAI estimates from multiple vegetation models. ...

When crops senescence, leaves remain until they fall off or are harvested. Hence, leaf area index (LAI) stays high even when chlorophyll content degrades to zero. Current LAI approaches from remote sensing techniques are not optimized for estimating LAI of senescent vegetation. In this paper a two-step approach has been proposed to realize simultaneous LAI mapping over green and senescent cropl...

Leaf area index (LAI) is an important essential biodiversity variable due to its role in many terrestrial ecosystem processes such as evapotranspiration, energy balance, and gas exchanges as well as plant growth potential. A novel approach presented here is the retrieval of LAI using thermal infrared (8–14 μm, TIR) measurements. Here, we evaluate LAI retrieval using TIR hyperspectral data. Cano...

Leaf area index (LAI) is a key variable for modeling energy and mass exchange between the land surface and the atmosphere. Inversion of physically based radiative transfer models is the most established technique for estimating LAI from remotely sensed data. This study aims to evaluate the suitability of the PROSAIL model for LAI estimation of three typical row crops (maize, potato, and sunflow...

High-quality leaf area index (LAI) products retrieved from satellite observations are urgently needed for crop growth monitoring and yield estimation, land-surface process simulation and global change studies. In recent years, sequential assimilation methods have been increasingly used to retrieve LAI from time series remote-sensing data. However, the inherent characteristics of these sequentia...

A major challenge in hydrologic modeling remains the mapping of vegetation dynamics in an urban landscape. The impact of vegetation on interception storage varies over time and needs to be quantified in order to enable proper management of water resources in urban areas. However, the heterogeneity and complexity of the urban landscape makes it challenging to monitor urban vegetation. A more det...

Leaf area index (LAI) is an important surface biophysical parameter as an input to many process-oriented ecosystem models. Remote sensing technology provides a practical way to estimate LAI at a large spatial scale, and hence, considerable effort has been expended in developing LAI estimation models from remotely sensed imagery. LAI estimation models were usually formulated using multi-spectral...

We assessed the accuracy with which the LAI-2000 plant canopy analyzer measured changes in leaf area index (LAI) and plant area index (PAI) in a 25-year-old Scots pine (Pinus sylvestris L.) stand. Stand density was 2100 stems ha(-1) and mean tree height was 8.7 m. Needle and branch areas of the stand were reduced progressively to zero by the stepwise removal of branches on all trees growing in ...