Remote Sensing of Vegetation: Potentials, Limitations, Developments and Applications

Abstract

Earth observation, i.e., gaining information of Earth’s physical, chemical and biological characteristics by remote sensing methods, can be used to make a range of quantitative measurements related to vegetation canopy structure and function. The capabilities of Earth observation for mapping, even indirectly, canopy state and function over wide areas and over decadal time-scales allow for studies of phenology, disturbance, anthropogenic impacts and responses to climate change. Key limitations of Earth observation measurements are discussed, in particular how their indirect nature makes them potentially hard to interpret and relate to physically-measurable quantities, as well as assumptions that are made to derive information from Earth observation data. Various Earth observation measurements of vegetation routinely provided from satellite data are introduced and a radiative transfer framework for developing, understanding and exploiting these measurements is outlined. This framework is critical in that it allow us to chart a consistent route from measurements made at the top-of-the atmosphere to estimates of canopy state and function. The impacts of assumptions required to solve the canopy radiative transfer problem in practical applications are discussed. New developments in radiative transfer theory and modelling are introduced, in particular focusing on how incorporating the vegetation structure in these models is key to interpreting many Earth observation measurements. These new techniques help to unpick the nature of the canopy signal from Earth observation measurements. The (key) issue of ‘effective’ model parameters that are often used to interpret and exploit observations is raised. These simplified or approximate manifestations of measurable physical properties permit development of practical, rapid models of the sort required for global applications but potentially introduce inconsistency between Earth observation measurements and models of vegetation productivity. Methods to overcome these limitations are discussed, such as data assimilation, which is being used to provide consistent model-data frameworks and make best use of both. Lastly, new remote sensing measurements are described that are providing information on 3D canopy structure, from lidar particularly, and canopy function from fluorescence. These measurements, along with other Earth observation data and model-data fusion techniques are providing new insights into canopy state and function on global scales.