The SWIR portion of the electromagnetic spectrum is a region over which leaf water content influences the reflectance response from vegetation canopies, enabling one to assess the drought status of crops and vegetation (Tucker, 1980a). However, the operational use of the SWIR region for drought studies has been restricted by problems in separating the effects of canopy structure and geometry from those of water status. Ceccato et al. (2001) have shown that by combining the NIR and SWIR data into
NDVI-like formulations, one can retrieve variations induced by leaf water contents. Gao (1996) proposed a normalized difference water index (NDWI) for detecting and monitoring vegetation liquid water content using narrow-band (<10 nm) data. With the launch of new satellite sensor systems, more research can be expected on the development of operational and global drought monitoring approaches involving vegetation water status using the SWIR region.
Combining land-surface temperatures (Ts) with vegetation indices (VI) is also of great interest in drought monitoring studies (Nemani and Running, 1989). Under drought conditions, soil moisture is reduced, and hence evapotranspiration declines, causing leaf temperature to rise. Therefore, thermal infrared energy emitted from the vegetation canopy can be used to detect increased temperature of leaves to monitor drought conditions. Vegetation and crop stress indices, such as the surface moisture index (SMI;
Nemani et al., 1993) and the water deficit index (WDI; Moran et al., 1994) use thermal and vegetation index measurements of vegetated surfaces to estimate the soil moisture status through the rate of evaporative water loss.
The SMI is based on the slope of the AVHRR-NDVI and maximum temperature values. Gillies et al. (1997) later applied inversion techniques to improve the estimates of available soil moisture from VI-Ts scatterplots. These concepts, however, need to be incorporated into vegetation indices to be suitable for global monitoring of droughts.
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