Water status of intact plants has been optically estimated by measuring reflectance at the wavelengths 1,450 nm and 1,900 nm based on their signal strengths. Although another water band at 970 nm is considered to have very small signals, the band apparently lies within the detection range of inexpensive spectrometer and plain charge-coupled device (CCD) camera used in wild fire studies. However measurement of the reflectance at 970 nm has been rarely applied to estimate the water status of dead plant materials such as fallen branch, twig, and leaf. To test the possibility of applying water reflectance at 970 nm to estimate the water content (WC) in leaf litter, the reflectance in various WC values were measured in the leaf litter of three Quercus species (Q aliena, Q aliena, Q mongolica, and Quercus variabilis). The results showed that the WC in the leaf litter can be determined by reflectance water index (WI) in the three Quercus species (WC=1,450×WI-1,378.8, r=0.865). However, there was no interaction effect in the relationship between WI and WC among the litter of the three Quercus species.

Normalized Difference Vegetation Index (NDVI) has played an important role in assessing green plant biomass through remote sensing on global scale since the early 1970s. The concept of NDVI is based on the fact that green plants show higher reflection in near-infrared region than in visible region of the electromagnetic spectrum. However, it is well known that the relocation of chloroplasts in plant leaf cells may dramatically change the optical properties of plant leaves. In this study I traced the changes in the reflectance and transmittance properties of Tobacco leaves at the wavelengths of 660 and 800 nm after a sudden increase in light intensity. The results showed that NDVI of leaves gradually decreased from 72.7% to 69.9% when exposed to a sudden increase in light intensity from 30 to 1,200 μmol/m2 · s. This means that the error resulting from the physiological status of the plant should be accounted for a more precise understanding of ground truth corresponding to the data from the remotely acquired images.