The quick and accurate acquisition of crop growth parameters on a large scale is important for agricultural management and food security. The combination of photographic and wireless sensor network (WSN) techniques can be used to collect agricultural information, such as leaf area index (LAI), over long distances and in real time. Such acquisition not only provides farmers with photographs of c...

Leaf area index (LAI) is an important parameter of vegetation ecosystems, which can reflect the growth status of vegetation, and its inversion result has important significance on forestry system. The inversion values of forest LAI exists a certain deviation using traditional method. The airborne LiDAR technology adopts a new type of aerial earth observation method and makes it possible to esti...

Leaf area index (LAI) is a key biophysical variable in most process-based forest-ecosystem models. However, most such models require LAI as an input, typically obtained from empirical observations. We tested whether scaling principles based on trade-offs between single leaf and canopy properties could be effectively used to model LAI, thereby obviating the need for empirical observations. To do...

A description and analysis is given of a wetness duration experiment, carried out in a potato field in the centre of the Netherlands in September 2005. The observations are used to design and evaluate a within-canopy dew model which provides the leaf wetness distribution within the canopy caused by dew processes and by precipitation. This withincanopy dew model consists of three layers (bottom,...

Leaf area index (LAI) is an important biophysical descriptor of crop canopies. Accurate measurements of LAI are laborious and time-consuming. Many methods of measuring LAI of corn (Zea mays L.) have been reported and vary greatly in their accuracy, preclslon, bias, and ease of measurement. We examined the magnitude of plant-to-plant variability of leaf area of corn plants selected from uniform ...

Characterizing Leaf Area Index (LAI) and Vertical Foliage Profile (VFP) over the United States H. Tang, S. Ganguly, G. Zhang, M. A. Hofton, R. F. Nelson, and R. Dubayah Department of Geographical Sciences, University of Maryland, College Park, Maryland, USA Bay Area Environmental Research Institute (BAERI)/NASA Ames Research Center, Moffett Field, California, USA Biospheric Sciences Branch, Cod...

Citation: Hedley JD, Russell BJ, Randolph K, Pérez-Castro MÁ, Vásquez-Elizondo RM, Enríquez S and Dierssen HM (2017) Remote Sensing of Seagrass Leaf Area Index and Species: The Capability of a Model Inversion Method Assessed by Sensitivity Analysis and Hyperspectral Data of Florida Bay. Front. Mar. Sci. 4:362. doi: 10.3389/fmars.2017.00362 Remote Sensing of Seagrass Leaf Area Index and Species:...