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 (R<sup>2</sup>=0.92) followed by NDVI (R<sup>2</sup>=0.80). For the simulated Hyperion data, VIUPD again ranks first with R<sup>2</sup>=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 R<sup>2</sup> 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.
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 ( R2 : 0.835; 0.812) in comparison with VIs ( R2 : 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.
The urban heat island (UHI) effect caused by urbanization generally leads to adverse economic and environmental impacts. Thus, a detailed assessment of the thermal properties over individual land cover types at different spatial resolutions is required to better understand the establishment of UHI. Two scale levels are utilized to examine surface thermal characteristics in the case of Shijiazhuang, China. At the regional level, Landsat TM data are used to extract subpixel impervious surface area (ISA) and to inverse land surface temperature (LST) by means of multiple endmember spectral mixture analysis. Urban thermal characteristics are analyzed by relating the LST to normalized difference vegetation index (NDVI) and ISA. On the other hand, thermal airborne spectrographic imager data, with a high spatial resolution, are employed to describe the spatial distribution of urban thermal patterns at the local level. Results indicate that there is an approximate linear relationship among LST, NDVI, and ISA. In addition, the thermal characteristics over each land cover type are consistent at both levels, suggesting UHI is evident at Shijiazhuang and impervious surface is contributing most to this phenomenon. This confirms that different spatial scales are requested in UHI studies.