A comparison of methods to relate grass reflectance to soil metal contamination - Kooistra, L.; Leuven, R. S. E. W.; Wehrens, R.; Nienhuis, P. H.; Buydens, L. M. C. (International Journal of Remote Sensing, 2003)
Grass-dominated vegetation covers large areas of the Dutch river floodplains. Remotely sensed data on the conditions under which this vegetation grows may yield information about the degree of soil contamination. This paper explores the relationship between grassland canopy reflectance and zinc (Zn) contamination in the soil under semi-field conditions. A field radiometer was used to record reflectance spectra of perennial ryegrass (Lolium perenne) in an experimental field with Zn concentrations in the soil ranging from 32 to 1800mgkg?1. Several spectral vegetation indices (VIs) and a multivariate approach using partial least squares (PLS) regression were investigated to evaluate their potential use in estimating Zn contamination levels. Compared to the best PLS model (RMSEP = 181.4 mg kg?1), the narrow band vegetation index MSAVI2mm performed better (RMSEP = 162.9 mg kg?1). Both MSAVI2mm and PLS gave a high user accuracy for the strongly contaminated soil class (100% and 91%, respectively), while the total accuracy was satisfactory (60% and 55%, respectively). Results from this feasibility study indicate the potential of using remote sensing techniques for the classification of contaminated areas in river floodplains. But as the results from this study may be both resolution- and location-dependent, research on field and image scale is now required to test the established relations and to assess their susceptibility to seasonal influences, species heterogeneity, and increased levels of spectral noise.

An investigation into robust spectral indices for leaf chlorophyll estimation - Main, Russell; Cho, Moses Azong; Mathieu, Renaud; OâKennedy, Martha M.; Ramoelo, Abel; Koch, Susan (ISPRS Journal of Photogrammetry and Remote Sensing, 2011)
Quantifying photosynthetic activity at the regional scale can provide important information to resource managers, planners and global ecosystem modelling efforts. With increasing availability of both hyperspectral and narrow band multispectral remote sensing data, new users are faced with a plethora of options when choosing an optical index to relate to their chosen or canopy parameter. The literature base regarding optical indices (particularly chlorophyll indices) is wide ranging and extensive, however it is without much consensus regarding robust indices. The wider spectral community could benefit from studies that apply a variety of published indices to differing sets of species data. The consistency and robustness of 73 published chlorophyll spectral indices have been assessed, using leaf level hyperspectral data collected from three crop species and a variety of savanna tree species. Linear regression between total leaf chlorophyll content and bootstrapping were used to determine the leafpredictive capabilities of the various indices. The indices were then ranked based on the prediction error (the average root mean square error (RMSE)) derived from the bootstrapping process involving 1000 iterative resampling with replacement. The results show two red-edge derivative based indices (red-edge position via linear extrapolation index and the modified red-edge inflection point index) as the most consistent and robust, and that the majority of the top performing indices (in spite of species variability) were simple ratio or normalised difference indices that are based on off-chlorophyll absorption centre wavebands (690â730 nm).
Keywords: Leaf level reflectance; Leaf chlorophyll; Red-edge; Vegetation indices; Photosynthetic activity

Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages - Sims, Daniel A.; Gamon, John A. (Remote Sensing of Environment, 2002)
Leaf pigment content can provide valuable insight into the physiological performance of leaves. Measurement of spectral reflectance provides a fast, nondestructive method for pigment estimation. A large number of spectral indices have been developed for estimation of leaf pigment content. However, in most cases these indices have been tested for only one or at most a few related species and thus it is not clear whether they can be applied across species with varying leaf structural characteristics. Our objective in this study was to develop spectral indices for prediction of leaf pigment content that are relatively insensitive to species and leaf structure variation and thus could be applied in larger scale remote-sensing studies without extensive calibration. We also quantified the degree of spectral interference between pigments when multiple pigments occur within the same leaf tissue. We found that previously published spectral indices provided relatively poor correlations with leaf chlorophyll content when applied across a wide range of species and plant functional types. Leaf surface reflectance appeared to be the most important factor in this variation. By developing a new spectral index that reduces the effect of differences in leaf surface reflectance, we were able to significantly improve the correlations with chlorophyll content. We also found that an index based on the first derivative of reflectance in the red edge region was insensitive to leaf structural variation. The presence of other pigments did not significantly affect estimation of chlorophyll from spectral reflectance. Previously published carotenoid and anthocyanin indices performed poorly across the whole data set. However, we found that the photochemical reflectance index (PRI, originally developed for estimation of xanthophyll cycle pigment changes) was related to carotenoid/chlorophyll ratios in green leaves. This result has important implications for the interpretation of PRI measured at both large and small scales. Our results demonstrate that spectral indices can be applied across species with widely varying leaf structure without the necessity for extensive calibration for each species. This opens up new possibilities for assessment of vegetation health in heterogeneous natural environments.

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