Spectral index method was widely applied in drought monitoring.Comprehensive use of multi-sensor data needs to be taken to improve precision and timeliness of drought detection.The effect of bandwidth on retrieval of leaf water content by spectral indices (including vegetation water indices and vegetation indices)was evaluated in order to monitor leaf water content from multiple remote sensing data.13 vegetation water indices and 10 vegetation indices were analyzed and compared based on data simulated by radiation transfer model PROSPECT.The results showed that both indices had good correlation with leaf water content in leaf scale (average correlation coefficient square:0.983,0.917).Then,sensitivity of indices to variations of leaf water content and bandwidth was analyzed and compared,and the results showed that vegetation water indices were sensitive to leaf water content variations and comparatively insensitive to bandwidth variations (except plant water index),while vegetation indices had low correlation with leaf water content and were strongly affected by bandwidth variation.Therefore,vegetation water indices instead of vegetation indices should be chosen in estimating leaf water content in leaf scale.The impact of bandwidth should be considered when choosing vegetation indices from multiple satellite data.Among 13 selected vegetation water indices,NDII and GVMI were proved to be most sensitive to leaf water content and most sensor-independent.

Spectral indices (SIs) method has been widely applied in the prediction of vegetation biochemical parameters. Take the diversity of spectral response of different sensors into consideration, this study aimed at researching spectral scale effect of SIs for estimating vegetation chlorophyll content (VCC). The 5 nm leaf reflectance data under 16 levels of chlorophyll content was got by the radiation transfer model PROSPECT and then simulated to multiple bandwidths spectrum (10-35 nm), using Gaussian spectral response function. Firstly, the correlation between SIs and VCC was studied. And then the sensitivity of SIs to VCC and bandwidth were analyzed and compared. Lastly, 112 samples were selected to verify the results above mentioned. The results show that Vegetation Index Based on Universal Pattern Decomposition Method (VIUPD) is the best spectral index due to its high sensitivity to VCC but low sensitivity to bandwidth, and can be successfully used to estimate VCC with coefficient of determination R2 of 0.99 and RMSE of 3.52 μg x cm(-2). Followed by VIUPD, Normalized Difference Vegetation Index (NDVI) and Simple Ratio Index (SRI) presented a comparatively good performance for VCC estimation (R2 > 0.89) with their prediction value of chlorophyll content was lower than the true value. The worse accuracy of other indices were also tested. Results demonstrate that spectral scale effect must be well-considered when estimating chlorophyll content, using SIs method. VIUPD introduced in the present study has the best performance, which reaffirms its special feature of comparatively sensor-independent and illustrates its potential ability in the area of estimating vegetation biochemical parameters based on multiple satellite data.

Spectral index method was widely applied to the inversion of crop chlorophyll content. In the present study, PSR3500 spectrometer and SPAD-502 chlorophyll fluorometer were used to acquire the spectrum and relative chlorophyll content (SPAD value) of winter wheat leaves on May 2nd 2013 when it was at the jointing stage of winter wheat. Then the measured spectra were resampled to simulate TM multispectral data and Hyperion hyperspectral data respectively, using the Gaussian spectral response function. We chose four typical spectral indices including normalized difference vegetation index (NDVD, triangle vegetation index (TVI), the ratio of modified transformed chlorophyll absorption ratio index (MCARI) to optimized soil adjusted vegetation index (OSAVI) (MCARI/OSAVI) and vegetation index based on universal pattern decomposition (VIUPD), which were constructed with the feature bands sensitive to the vegetation chlorophyll. After calculating these spectral indices based on the resampling TM and Hyperion data, the regression equation between spectral indices and chlorophyll content was established. For TM, the result indicates that VIUPD has the best correlation with chlorophyll (R2 = 0.819 7) followed by NDVI (R2 = 0.791 8), while MCARI/OSAVI and TVI also show a good correlation with R2 higher than 0.5. For the simulated Hyperion data, VIUPD again ranks first with R2 = 0.817 1, followed by MCARI/OSAVI (R2 = 0.658 6), while NDVI and TVI show very low values with R2 less than 0.2. It was demonstrated that VIUPD has the best accuracy and stability to estimate chlorophyll of winter wheat whether using simulated TM data or Hyperion data, which reaffirms that VIUPD is comparatively sensor independent. The chlorophyll estimation accuracy and stability of MCARI/OSAVI also works well, partly because OSAVI could reduce the influence of backgrounds. Two broadband spectral indices NDVI and TVI are weak for the chlorophyll estimation of simulated Hyperion data mainly because of their dependence on few bands and the strong influence of atmosphere, solar altitude, viewing angle of sensor, background and so on. In conclusion, the stability and consistency of chlorophyll estimation is equally important to the estimation accuracy by spectral index method. VIUPD introduced in the study has the best performance to estimate winter wheat chlorophyll, which illustrates its potential ability in the area of estimating vegetation biochemical parameters.

Spectral index method was widely applied to the inversion of crop chlorophyll content. In this study, PSR3500 spectrometer and SPAD-502 chlorophyll fluorometer were used to acquire the spectrum and relative chlorophyll content (SPAD value) of winter wheat leaves on May 2nd 2013 when it was at the jointing stage of winter wheat. Then the measured spectra were resampled to simulated TM multispectral data and Hyperion hyperspectral data respectively, using the Gaussian spectral response function. We chose four typical spectral indices including Normalized Difference Vegetation Index (NDVI), Triangle Vegetation Index (TVI), the ratio of Modified Transformed Chlorophyll Absorption Ratio Index(MCARI) to Optimized Soil Adjusted Vegetation Index(OSAVI) (MCARI/OSAVI) and Vegetation Index Based on Universal Pattern Decomposition Method (VIUPD), which were constructed with the feature bands sensitive to the vegetation chlorophyll. After calculating these spectral indices based on the resampling TM and Hyperion data, the regression equation between spectral indices and chlorophyll content was established. For TM, the result indicates that VIUPD has the best correlation with chlorophyll (R2=0.8197) followed by NDVI (R2=0.7918), while MCARI/OSAVI and TVI also show a good correlation with R2 higher than 0.5. For the simulated Hyperion data, VIUPD again ranks first with R2=0.8171, followed by MCARI/OSAVI (R2=0.6586), while NDVI and TVI show very low values with R2 lesser than 0.2. It is demonstrated that VIUPD has the best accuracy and stability to estimate chlorophyll of winter wheat whether using simulated TM data or Hyperion data, which reaffirms that VIUPD is comparatively sensor independent.

The vegetation index (VI) and vegetation water index (VIw) have long been used for plant water stress detection indiscriminately, without considering the effects of differences in their band selection. To address this, this study quantitatively compared the difference of sensor dependence for the two indices based on canopy/atmospheric radiative transfer model. Five different bandwidths at canopy and top-of-atmosphere scale were simulated separately for 23 classic indices. The results show that VIws exhibited better correlation with vegetation water content (VWC) at both scale (R-2 : 0.835; 0.812) in comparison with VIs (R-2 : 0.474; 0.475). To quantitatively describe the uncertainty caused by bandwidth, a new index variability was established. VIws and VIs performed entirely differently: at canopy scale, the uncertainty caused by bandwidths for VIws and VIs is 13.703% and 43.451%, respectively. However, at top-of-atmosphere scale, the uncertainty for VIws and VIs is 32.021% and 41.265%. VIws exhibited less dependence on bandwidth and were more affected by atmospheric effect than VIs. We attribute these differences to differences in band selection: VIws based on water absorption features are more sensitive to not only variation of VWC but also atmospheric conditions. Conversely, as chlorophyll absorption features which VIs are calculated on effectively avoid atmospheric absorption features and are located in red edge region, VIs are found less affected by the atmosphere condition and extremely sensitive to bandwidth. Results figure out the differences we should focus on when we choose VI or VIw from different sensors for VWC retrieval.

LAI is a crucial parameter and a basic quantity indicating crop growth situation. Empirical models comprising spectral indices (SIs) and LAI have widely been applied to the retrieval of LAI. SI method already has exhibited feasibility in the estimation of vegetation LAI. However, it is largely subject to the inconsistency from different remote sensors which have varied specifications, such as spectral response features and central wavelength. To address this issue, a new vegetation index (VIUPD) based on the universal pattern decomposition method was proposed. It is expressed as a linear sum of the pattern decomposition coefficients and features in sensor-independency. The aim of this study was to evaluate the prediction accuracy and stability of VIUPD for estimating LAI, compared with three other common-used SIs. In this study, the measured spectra were resampled to simulated TM multispectral data and Hyperion hyperspectral data respectively, using the Gaussian spectral response function. The three typical SIs chosen were including NDVI, TVI and MCARI, which were constructed with the sensitive bands to the LAI. Finally, the regression equations between four selected SIs and LAI were established. The best index evaluated using the simulated TM data was VIUPD which exhibits the best correlation with LAI (R2=0.92) followed by NDVI (R2=0.80). For the simulated Hyperion data, VIUPD again ranks first with R2=0.89, followed by TVI (R2=0.63). Meanwhile, the consistence of VIUPD also was studied based on simulated TM and Hyperion sensor data and the R2 reached to 0.95. It is demonstrated that VIUPD has the best accuracy and stability to estimate LAI of winter wheat whether using simulated TM data or Hyperion data, which reaffirms that VIUPD is comparatively sensor independent.

In previous studies of the universal pattern decomposition method (UPDM), spectral shifts, which are very common in hyperspectral imaging spectrometers, were not taken into account when calculating standard spectral pattern vectors. This study evaluated the effect of spectral shifts on the sensor dependence of the vegetation index based on the UPDM (VIUPD) and 11 other vegetation indices (VIs). Spectral shifts were calculated using Gao's spectrum-matching method. The influences of smoothing techniques (moving average and Savitzky–Golay filters) on the consistency of these VIs were also evaluated and compared. Data from the typical narrowband imaging spectrometers, Hyperion and the Compact High Resolution Imaging Spectrometer (CHRIS), were chosen for the study. For all VIs, both smoothing and spectral calibration changed the consistency between Hyperion and CHRIS. Spectral calibration had a positive effect on the majority of VIs, whereas smoothing improved the performance of some VIs but decreased the consistency of others. When compared with spectral calibration and Savitzky–Golay smoothing, moving average generated greater variations within the results. Among the smoothing techniques employed, moving average smoothing exhibited a larger distortion of VI sensor dependency than that of Savitzky–Golay smoothing of the same order. VIUPD based on narrowband hyperspectral data was sensitive to spectral operations (spectral calibration and smoothing). For VIUPD, spectral calibration increased its sensor independence, whereas smoothing had a negative effect. After spectral calibration, VIUPD was more sensor independent than any other VI examined in this study.