Remote Sensing of Leaf Area Index (LAI) and Other Vegetation Parameters

Dear Colleagues,

Monitoring of vegetation structure and functioning is critical to modeling terrestrial ecosystems and energy cycles. In particular, leaf area index (LAI) is an important structural property of vegetation used in many land-surface vegetation, climate, and crop production models. Canopy structure (LAI, fCover, plant height and biomass) and biochemical parameters (leaf pigmentation and water content) directly influence the radiative transfer process of sunlight in vegetation, determining the amount of radiation measured by passive sensors in the visible and infrared portions of the electromagnetic spectrum.

Optical remote sensing (RS) methods build relationships exploiting in situ measurements and/or as outputs of physical canopy radiative transfer models. The increased availability of passive (radar and LiDAR) RS data has fostered their use in many applications for analysis of land surface properties and processes, thanks also to their insensitivity to weather conditions and the capability to exploit rich structural and texture information. Data fusion and multi-sensor integration techniques are pressing topics to fully exploit the information conveyed by both optical and microwave bands.

This Special Issue will review the state of the art in the retrieval of LAI and other vegetation parameters and its usage in a wide variety of applications (e.g., ecology, carbon cycle, agriculture, forestry and food security). Articles covering recent research about the following topics are invited for this Special Issue:

•         Field methods to measure LAI and other vegetation parameters

•         Multiple methods for the retrieval of LAI and other structural parameters (e.g., fCover, plant height, biomass) from satellite and airborne sensors

•         Methods to estimate vegetation status, dynamic (FAPAR, GPP, stage/phenology) and condition (e.g., pigmentation, leaf water content, water stress)

•         Improvement of radiative transfer models, and input data needed for the retrieval of vegetation parameters.

•         LiDAR and microwave Remote Sensing

•         Evaluations of recent missions (e.g., Landsat-8, Sentinel-1 and -2) to improve the spatial and temporal resolutions of retrieved maps

•         LAI and vegetation parameter retrieval from Unmanned Autonomous Vehicle (UAV)

•         Processing of big remote sensing data, e.g., data fusion and multi-sensor data integration techniques

•         Calibration/validation activities for LAI maps and other biophysical products

•         Environmental applications, e.g. detection and mapping of vegetation diseases and stress condition

•         Forest applications, e.g., mapping and monitoring of forest disturbance, degradation and regrowth.

•         Agriculture applications, e.g., crop growth cycle and crop condition, yield predictions, precision agriculture.

•         Assimilation of remote sensing data with vegetation models for forest and agriculture applications

Review articles covering one or more of these topics

 

Guest Editor

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