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Remote estimation of leaf area index and biomass in corn and soybean
The importance of studying vegetation dynamics has been recognized for decades. A key driver has been the interest in understanding the patterns of terrestrial vegetation productivity and its relationships with global biogeochemical cycles. Since the first Landsat satellite, launched in 1972, the study of terrestrial vegetation dynamics has been one of the most important applications of remote sensing. Remote sensing is the technology by which the electromagnetic energy emitted or reflected by the earth's surface, is recorded by sensors on the ground, aircrafts and spacecrafts. Data recorded by such sensors can be used to infer the nature and state of the earth's surface, their patterns of change through time and space, as well as to measure vegetation productivity. These capabilities, in combination with the synoptic view provided by imaging sensors, made remote sensing an attractive and powerful way of analyzing vegetation, at scales ranging from local to global. A multitude of algorithms have been developed for the remote estimation of canopy biophysical characteristics, in terms of combinations of spectral bands, derivatives of reflectance spectra, neural networks, inversion of radiative transfer models, and several multi-spectral statistical approaches. But by far, the most widespread type of algorithm is the mathematical combination of red and near-infrared reflectance bands, in the form of spectral vegetation indices. Applications of such vegetation indices have ranged from leaves to the entire globe, but in many instances their applicability is specific to species, vegetation types or local conditions. The general objective of this study was to devise a new approach for the remote estimation of green leaf area index and green leaf biomass in crop canopies, that is robust across different species, with different canopy architectures and leaf structures. The model was based on radiative transfer in the canopy through the application of the Kubelka-Munk theory, and was designed for estimating the amount of chlorophyll present in the crop canopy per unit of ground area. This characteristic is a proxy of crop canopy photosynthetic rates, and thus above-ground net primary productivity. The model described in this study proved to be robust for the remote estimation of green leaf area index and green leaf biomass in crops with different canopy architectures (e.g. corn and soybean). Thus, it can be used to assess seasonal dynamics, inter-annual variability, stresses, phenology and primary productivity of agro-ecosystems, by means of the synoptic view provided by remote sensing techniques. ^
Environmental Sciences|Remote Sensing
Vina, Andres, "Remote estimation of leaf area index and biomass in corn and soybean" (2004). ETD collection for University of Nebraska - Lincoln. AAI3131566.