In situ hyperspectral data analysis for pigment content estimation of rice leaves.
ABSTRACT Analyses of the correlation between hyperspectral reflectance and pigment content including chlorophyll-a, chlorophyll-b and carotenoid of leaves in different sites of rice were reported in this paper. The hyperspectral reflectance of late rice during the whole growing season was measured using a Spectroradiometer with spectral range of 350-1050 nm and resolution of 3 nm. The chlorophyll-a, chlorophyll-b and carotenoid contents in rice leaves in rice fields to which different levels of nitrogen were applied were measured. The chlorophyll-a content of upper leaves was well correlated with the spectral variables. However, the correlation between both chlorophyll-b and caroteniod and the spectral variables was far from that of chlorophyll-a. The potential of hyperspectral reflectance measurement for estimating chlorophyll-a of upper leaves was evaluated using univariate correlation and multivariate regression analysis methods with different types of predictors. This study showed that the most suitable estimated model of chlorophyll-a of upper leaves was obtained by using some hyperspectral variables such as SD(r), SD(b) and their integration.
- SourceAvailable from: P.R.J. North[show abstract] [hide abstract]
ABSTRACT: Remotely sensed estimates of the foliar biochemical content of vegetation canopies could be used to derive indicators of ecosystem functioning at regional to global scales. In the past decade, a number of studies have reported strong correlations between the reflectance spectra of vegetation canopies and their foliar biochemical content. However, these studies have commonly employed multiple regression techniques or spectral indices to determine biochemical content, which have been found to be highly sensitive to variation in canopy architecture [such as leaf area index (LAI) and canopy closure] and understory. To date, these effects combined with the low signal-to-noise ratios of airborne spectrometers have inhibited the development of robust and portable spectral techniques for the estimation of canopy biochemical content. This paper reports on a theoretical study in which a leaf model, LIBERTY (leaf incorporating biochemicals exhibiting reflectance and transmittance yields), characterized specifically for conifer needles, was coupled with a hybrid geometric/radiative transfer bidirectional reflectance distribution function FLIGHT (forest light) model. By varying leaf biochemical content, LAI, canopy closure and understory, we analyzed the simulated canopy reflectance spectra to determine if the biochemical absorption features in leaf spectra were preserved at the canopy scale. Absorption features or wavelength regions that were both related to a specific biochemical of interest (water, lignin-cellulose) and persistent at the scale of both the leaf and the canopy were identified at a number of wavelengths or wavelength regions.Remote Sensing of Environment. 01/1999;
- [show abstract] [hide abstract]
ABSTRACT: An experiment was designed to determine whether chlorophyll and nitrogen concentrations could be predicted from reflectance (R) spectra of fresh bigleaf maple leaves in the laboratory, and, if so, whether the predictive spectral features could be correlated with chlorophyll and nitrogen concentration or content of simple canopies of maple seedlings. The best predictors for nitrogen and chlorophyll of fresh leaves appeared with first-difference transformations of log 1/R, and the bands selected were similar to those found in other studies. Shortwave infrared bands were best predictors for nitrogen, visible bands best for chlorophyll. In the shortwave infrared region, however, the absolute differences in reflectance at critical bands was extremely small, and the bands of high correlation were narrow. High spectral and radiance resolution are required to resolve these differences accurately. The best shortwave infrared bands from the leaf scale were not good predictors of chemical content or concentration at the canopy scale; variability in canopy reflectance in the shortwave infrared region was at least an order of magnitude beyond that necessary to detect signals from chemicals. The variability in first-difference log 1/R on the canopy scale was related to the arrangement of trees with respect to direct solar radiation, instrument noise, leaf fluttering, and small changes in atmospheric moisture.Remote Sensing of Environment. 01/1995;
- [show abstract] [hide abstract]
ABSTRACT: Seasonal changes in the density of photosynthetically active vegetation have been observed in derivative-based green vegetation index (DGVI) values derived from AVIRIS reflectance spectra of arid land shrub and saltgrass communities adjacent to Mono Lake, California. The study was conducted using AVIRIS datasets acquired in late August and early October of 1992. 2DZ_DGVI (second-order DGVI-derived in reference to zero baseline) showed a strong linear relationship (r2>0.93 for August and October) with green leaf area index (LAI) values of ten bitterbrush (Purshia tridentata) sample stands. After accounting for background errors introduced to the calculation of 1DL_DGVI (first-order DGVI-derived in reference to local rock-soil baseline), a modified 1DL_DGVI (1DL_MDGVI) exhibited a high linear correlation (r2> 0.92 for both seasons) with green LAI values of the bitterbrush stands. 2DZ_DGVI was applied to the two AVIRIS scenes acquired for the two periods to quantify and compare green vegetation cover. Areas covered by saltgrass (Distichlis spicata var. stricta) showed the largest change in 2DZ_DGVI from August to October. Shrubs, including bitterbrush and big sagebrush (Artemisia tridentata), changed less during the same period. The lowest seasonal change in 2DZ_DGVI occurred in barren areas and locations covered by Jeffrey pine (Pinus jeffreyi). The DGVI concept has potential for monitoring ecosystems in arid and semiarid lands where the influence of exposed rock–soil backgrounds reduces the effectiveness of broadband red-vs.-NIR vegetation indices.Remote Sensing of Environment - REMOTE SENS ENVIRON. 01/1998; 65(3):255-266.