Spectral properties of a wheat canopy with vegetation fraction (VF) from 0% to 100% in visible and near-infrared (NIR) ranges of the spectrum were studied in order to devise a technique for remote estimation of VF. When VF was < 60%, from emergence till middle of the elongation stage, four distinct, and quite independent, spectral bands of reflectance existed in the visible range of the spectrum: 400 to 500 nm, 530 to 600 nm, near 670 nm, and around 700 nm. When VF was between 60% and 100%, reflectance in the NIR leveled off or even decreases with an increase of VF. The decreased reflectance in the NIR, occurring at or near the midseason, can be a limiting factor in the use of that spectral region for VF estimation. It was found that for VF > 60%, the information content of reflectance spectra in visible range can be expressed by only two independent pairs of spectral bands: (1) the blue from 400 to 500 nm and the red near 670 nm; (2) the green around 550 nm and the red edge region near 700 nm. We propose using only the visible range of the spectrum to quantitatively estimate VF. The green (as well as a 700-nm band) and the red (near 670 nm) reflectances were used in developing new indices, which were linearly proportional to wheat VF ranging from 0% to 100%. The Atmospherically Resistant Vegetation Index (ARVI) concept was used to correct indices for atmospheric effects. Visible Atmospherically Resistant Index in the form VARI= (R green À R red )/(R green + R red À R blue ) was found to be minimally sensitive to atmospheric effects allowing estimation of VF with an error of < 10% in a wide range of atmospheric optical thickness. Validation of the newly suggested technique was carried out using wheat independent data sets and reflectance data obtained for cornfields in Nebraska. Predicted green VF was compared with retrieved from digital images. Despite the fact that the reflectance contrast among the visible channels is much smaller than between the visible and NIR, the sensitivity of suggested indices to moderate to high values of VF is much higher than for the Normalized Difference Vegetation Index (NDVI), and the error in VF prediction did not exceed 10%. Suggested indices will complement the widely used NDVI, ARVI, Soil Adjusted Vegetation Index (SAVI) and others, which are based on the red and the NIR bands in VF estimation, and also Green Atmospherically Resistant Index (GARI), which is based on the green and the NIR bands. D

[1] Accurate estimation of spatially distributed chlorophyll content (Chl) in crops is of great importance for regional and global studies of carbon balance and responses to fertilizer (e.g., nitrogen) application. In this paper a recently developed conceptual model was applied for remotely estimating Chl in maize and soybean canopies. We tuned the spectral regions to be included in the model, according to the optical characteristics of the crops studied, and showed that the developed technique allowed accurate estimation of total Chl in both crops, explaining more than 92% of Chl variation. This new technique shows great potential for remotely tracking the physiological status of crops, with contrasting canopy architectures, and their responses to environmental changes. Citation: Gitelson,

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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.

[1] Accurate estimation of spatially distributed CO 2 fluxes is of great importance for regional and global studies of carbon balance. We applied a recently developed technique for remote estimation of crop chlorophyll content to assess gross primary production (GPP). The technique is based on reflectance in two spectral channels: the near-infrared and either the green or the red-edge. We have found that in irrigated and rainfed crops (maize and soybean), midday GPP is closely related to total crop chlorophyll content. The technique provided accurate estimations of midday GPP in both crops under rainfed and irrigated conditions with root mean square error of GPP estimation of less than 0.3 mg CO 2 /m 2 s in maize (GPP ranged from 0 to 3.1 mg CO 2 /m 2 s) and less than 0.2 mg CO 2 /m 2 s in soybean (GPP ranged from 0 to 1.8 mg CO 2 /m 2 s). Validation using an independent data set for irrigated and rainfed maize showed robustness of the technique; RMSE of GPP prediction was less than 0.27 mg CO 2 /m 2 s.