Canopy reflectance, photosynthesis and transpiration

Abstract

Abstract. A two-stream approximation model of radiative transfer is used 10 calculate values of hemispheric canopy reflectance in the visible and near-infrared wavelength intervals. Simpleleaf modelsof photosynthesis and stomatal resistance' are integrated over leaforientation and canopy depth to obtain estimatesof canopy photosynthesis and bulk stomatal or canopy resistance. The ratio of near-infrared and visiblereflectances ispredicted to bea near linear indicator of minimumcanopy resistance and photosynthetic capacity but a poor predictor of leaf area index or biomass.

The use of remote sensing in light use efficiency based models of gross primary production: A review of current status and future requirements

“…Since the mid-1980's there has been an increase in the development of physical approaches to determining f PAR , due partly to global diagnostic studies using satellite data (Tucker and Sellers, 1986), plot scale field studies (Asrar et al, 1984;Tucker et al, 1981), large field experiments (Sellers et all., 1992;Hall et al, 1992;Sellers et al, 1997;Running et al, 1999) and theoretical work (Myneni et al, 1992;Hall et al, 1990;Sellers 1985;1987;Sellers et al, 1996a,b). This research formed the basis for physical models of canopy reflectance, transferring reflectance and biophysical property relationships from the leaf level, where they can be easily measured and related to leaf composition and structure, to the pixel level, where leaf optics interact with canopy structure, understorey characteristics, background reflectance, view and illumination geometry to produce a complicated relationship among pixel-level reflectance, stand structural, biophysical and leaf optical properties .…”

“…For a given time, r and t are a function of the leaf surface area (Sellers, 1985) parameterized by the leaf area index (LAI) defined as half the total foliage area per unit ground surface area (Chen and Black, 1992). Because of temporal variations in solar irradiance, chlorophyll content (Dawson et al, 2003) and leaf-sun geometry (Chen and Black, 1992) the amount of solar radiation being absorbed by a plant canopy varies diurnally as well as seasonally (Chen, 1996).…”

“…Starting in the early 1980s, numerous studies have shown substantial evidence of a close relationship between f PAR and the top of the canopy reflectance measurements in the visible and near infrared region (Tucker 1979;Daughtry et al 1983;Asrar et al 1984), and theoretical work (Sellers, 1985;1987) has given this relationship a solid basis as a measure of the solar photosynthetically active radiation absorbed by the canopy. Various linear and non-linear relationships between satellite-derived SVIs and f PAR have been found for different vegetation types and climatic conditions (e.g., Asrar et al, 1984;Badhwar et al, 1986;Fassnacht and Gower, 1997) where NIR and Red is the reflectance in the near infrared and red, respectively (Tucker 1979).…”

Reconstructed Solar‐Induced Fluorescence: A Machine Learning Vegetation Product Based on MODIS Surface Reflectance to Reproduce GOME‐2 Solar‐Induced Fluorescence

“…GPP can be written as GPP = LUE × fPAR Ch  × PAR, with LUE the light use efficiency, fPAR Ch the fraction of photosynthetically active radiation absorbed by chlorophyll, and PAR the incoming photosynthetically active radiation (Monteith, 1972). Plants have distinct spectral signatures with low reflectance in the visible range and high reflectance in the near infrared, which is directly related to fPAR (Sellers, 2007; Tucker, 1979). One natural proxy was thus the Normalized Difference Vegetation Index (NDVI), which takes advantage of such spectral signature to monitor vegetation changes (Tucker, 1979).…”

Remote sensing of evapotranspiration and carbon uptake at Harvard Forest

“…Researches are focused on parameterizations of the exchange processes, such as canopy resistance, that may be remotely sensed by satellites [Nishida et al 2003]. Most of existing satellite remote sensing techniques for ET estimations are based on measurements at visible and near-infrared wavelengths, such as the normalized difference vegetation index (NDVI), as the spectral measurements are highly correlated to the absorbed fraction of photosyntheticallyactive radiation (PAR) [Asrar et al 1984;Sellers 1985;Myneni et al 1995;Granger 2000;Jiang and Islam, 2003;Nishida et al, 2003]. Since these measurements are strongly influenced by clouds and aerosols, their capability of monitoring ET under all-weather conditions and accurately estimating the spring onset and growing season duration is limited.…”