Use of a green channel in remote sensing of global vegetation from EOS-MODIS - Gitelson, Anatoly A.; Kaufman, Yoram J.; Merzlyak, Mark N. (Remote Sensing of Environment, 1996)
Most animals use a âgreenâ spectral range to remotely sense the presence and vitality of vegetation. While humans possess the same ability in their eyes, man-made space-borne sensors that sense evolution of global vegetation, have so far used a combination of the red and near infrared channels instead. In this article we challenge this approach, using measurements of reflectance spectra from 400 nm to 750 nm with spectral resolution of 2 nm, with simultaneous determination of pigment concentrations of mature and autumn senescing leaves. We show that, for a wide range of leaf greenness, the maximum sensitivity of reflectance coincides with the red absorption maximum of chlorophyll-a (Chl-a) at 670 nm. However, for yellow-green to green leaves (with Chl-a more than 3â5 μg/cm2), the reflectance near 670 nm is not sensitive to chlorophyll concentration because of saturation of the relationship of absorptions versus chlorophyll concentration. Maximum sensitivity of Chl-a concentration for a wide range of its variation (0.3â45 μg/cm2) was found, not surprisingly so, around the green band from 520 nm to 630 nm and also near 700 nm. We found that the inverse of the reflectance in the green band was proportional to Chl-a concentration with correlation r2 > 0.95. This band will be present on several future satellite sensors with a global view of vegetation (SeaWiFS to be launched in 1996, Polder on ADEOS-1 also in 1996, and MODIS on EOS in 1998 and 2000). New indexes that use the green channel and are resistant to atmospheric effects are developed. A green NDVI = (ϱnir − ϱgreen(ϱnir + ϱgreen) was tested for a range of Chl-a from 0.3 μg/cm2 to 45 μg/cm2, and found to have an error in the chlorophyll a derivation at leaf level of less than 3 μg/cm2. The new index has wider dynamic range than the NDVI and is, on average, at least five times more sensitive to Chl-a concentration. A green atmospherically resistant vegetation index (GARI), tailored on the concept of ARVI (Kaufman and TanrÃ©, 1992), is developed and is expected to be as resistant to atmospheric effects as ARVI but more sensitive to a wide range of Chl-a concentrations. While NDVI and ARVI are sensitive to vegetation fraction and to rate of absorption of photosynthetic solar radiation, a green vegetation index like GARI should be added to sense the concentration of chlorophyll, to measure the rate of photosynthesis and to monitor plant stress.

Remote estimation of leaf area index and green leaf biomass in maize canopies - Gitelson, Anatoly A.; ViÃ±a, AndrÃ©s; Arkebauer, Timothy J.; Rundquist, Donald C.; Keydan, Galina: Leavitt, Bryan (Geophys. Res. Lett., 2003)
Leaf area index (LAI) is an important variable for climate modeling, estimates of primary production, agricultural yield forecasting, and many other diverse studies. Remote sensing provides a considerable potential for estimating LAI at local to regional and global scales. Several spectral vegetation indices have been proposed, but their capacity to estimate LAI is highly reduced at moderate-to-high LAI. In this paper, we propose a technique to estimate LAI and green leaf biomass remotely using reflectances in two spectral channels either in the green around 550 nm, or at the red edge near 700 nm, and in the NIR (beyond 750 nm). The technique was tested in agricultural fields under a maize canopy, and proved suitable for accurate estimation of LAI ranging from 0 to more than 6.
Keywords: Remote sensing, Instruments and techniques. General or miscellaneous

The IndexDatabase - Henrich, V.; Krauss, G.; Götze, C.; Sandow, C. (2011)