Hyperspectral Vegetation Indices and Their Relationships with ...

180 Thenkabail et al.to-soil reflectance ratio, second only to 680 nm region tral narrow band NIR and red-based indices can be computed(Fig. 8). The k 12 1025 nm represents the steep and suddenby taking: (1) very narrow band centered at 682rise after the moisture sensitive “trough” of the NIR nm (k4 nm) for red and (2) narrow band centered atband, which combines with other wave bands to provide 920 nm (k20 nm) for NIR.optimal information. However, the dark current drift in These results demonstrate that the widely used redthe spectroradiometer can affect this portion of the spectrum.and NIR band combinations are not necessarily the bestThe NIR shoulder can be represented by a broad for two-band NDVI type indices for estimating crop vari-band or a narrow band centered at 845 nm (k 9 ) to completeables. Even when red and NIR bands are involved, theythe 12 bands. The twelve bands were selected often appear in unique narrow portions of the spectrumbased on their frequency of occurrence in various modelsthat are not selected by any existing satellite sensor. Forof crop variables as discussed throughout this paper. example, the index that has highest correlation for cottonThe distribution of the crop information clusters in differentWBM had band centers at 682 nm for red and 918 nmwaveband portions (Fig. 6) also shows an over- for NIR with band widths varying between 20 nm to 28whelming proportion of quantitative crop information in nm. In comparison Landsat-5 TM bands are centered atthe band centers of these 12 bands (Table 6). Many of 660 nm (k60 nm) for red (TM3) and at 830 nmthe optimum bands that were selected make physical (k140 nm) for NIR (TM4). The dominant role of thesense given what we know about plant chemistry, canopy waveband centered at 682 nm was partly due to the decreasestructure, and plant spectra (Table 6; Knipling, 1970; Elvidge,in leaf reflectance between 550 nm and 690 nm,1990). For example, 550 nm is centered over peak but is even more closely associated with the steeply risingchlorophyll reflection in the visible portion of the spectrum,soil reflectance in this range. The four-variableand 982 nm is centered over peak liquid water ab- OMNBR models performed marginally better than thesorption in the near-infrared.two-band NDVI type models. However, the OMNBRmodels do not offer the simplicity of the two-band NDVISUMMARY AND CONCLUSIONStype models. Also, a significant proportion of the twobandNDVI models perform nearly as well as four-variableThis research established the optimum number of hyperspectralOMNBR models. For example, the two-band NDVIbands, centers, and widths (Table 6) in the visi- models explained between 64% to 88% variability in differentble and near-infrared spectrum for establishing relationshipscrop variables compared to 64% to 92% variabilitywith agricultural crop biophysical characteristics. explained by OMNBR models. Most of this variability inThis recommendation is based on the study of five crops, OMNBR models is explained using the first two to fournine growth stages, and wide ranging crop characteristics narrow bands with addition of further bands adding only(Table 1). A remarkably strong relationship with crop small (often, statistically insignificant) incremental invariablesis located in specific narrow bands (Table 6) in creases in R 2 values. Additionally, it was established thatthe longer wavelength portion of the red, 650 nm to 700 when the ratio (M/N) of the number of independentnm, with secondary concentrations in the shorter wave- variables or narrow bands (M) to that of total number oflength portion of the green, 500 nm to 550 nm, in one field samples (N) exceeds anywhere between 0.15 toparticular section of the near-infrared, 900 nm to 940 0.20, “over-fitting” of OMNBR models will be a seriousnm, and in the moisture-sensitive near-infrared, centered problem. The most frequently appearing narrow bandsat 982 nm (Fig. 6). These band regions are followed by in the four-variable OMNBR models were (Table 3): redred-edge portion centered on 720 nm, and NIR shoulder (e.g., 682 nm), moisture-sensitive NIR (e.g., 982 nm),centered on 845 nm. An overwhelming proportion of green (e.g., 525 nm), and longer portion of NIR (920these channels had band widths that were classified as: nm). The narrow band models perform significantly bet-(1) very narrow (1 nm to 15 nm wide) or (2) narrow (16 ter than the broad bands. For example, when comparednm to 30 nm wide). Most were on the order of 10 nm with TM broad band NDVI-type models, the: (1) four-to 20 nm wide (narrow or very narrow). A rigorous pro- variable OMNBR models explained 3% to 40% additionalcedure adopted here to identify the best narrow bandvariability and (2) two narrow band NDVI-typeNDVI type models showed that the best two-band com- models explained an additional 3% to 16% additionalbinations often involve (Tables 4 and 6, Figs. 4, and Figurevariability. The narrow band transformed soil-adjusted5): (1) a very narrow red band and a narrow or a vegetation index (TSAVI) models are considered the bestbroad NIR band, or (2) a very narrow red band and a where site specific soil lines are available, but did notvery narrow or a narrow green band, or (3) a very narrow provide significant improvements over narrow bandred band and a narrow band in the moisture-sensitive NDVI models, except in one case involving potatoportion of the NIR, or (4) a very narrow red-edge band WBM. The best derivative indices performed significantlyand a narrow or broad NIR band. The NIR and redbasedpoorer than an overwhelming proportion of theNDVI indices are most widely used in remote narrow band NDVI, OMNBR, and even when comparedsensing. This study recommends that the best hyperspec- with most broad band NDVI models.