Vegetation water inversion using MODIS satellite data

Abstract

The vegetation water condition is determined by soil water content. So soil water condition can be reflected by means of vegetation water condition indirectly. There have been many detailed studies on crop water stress index from the micrometeoological aspect before, for example, CWSI (crop water stress index) and crop water stress microclimatic model based on CWSI. The generation of these indices need many meteorological data and is not suitable to regional water stress study because of the difficulties in limitation of data acquisition of related data on the surface. Moreover, there are a few relevant studies using remote sensing techniques, such as WDI (water deficit index), VCI (vegetation condition index) and TCI (temperature condition index). Although these indices are suitable to regional study, they utilize the statistic value of remote sensing data for years and they are on the basis of pixel scale. Therefore, the precision of quantitatively assessing surface water is limited to some extent. In view of problems of these water stress indices, this article is going to discuss a not only simple but also reasonable method to derive vegetation water. Different vegetation water condition could cause the variation of vegetation spectrum, so vegetation water stress can be shown by remote sensing vegetation indices, such as vegetation condition index and anomaly vegetation index et al. In view of this, soil water content can be assessed indirectly. But vegetation indices neglect some environment factors, eg. temperature and precipitation. The less evaportranspiration, the higher vegetation and soil temperatures. Therefore the vegetation temperature is a direct indicator of vegetation water stressed and drought. In a word, the soil water is positive correlation with vegetation index and negative correlation with temperature. It takes the North-West semi-arid area - Xilingole district of Inner Mongolia as study area. Different degraded grasslands are selected as objects in this study. MODIS (moderate resolution imaging spectroradiometer) has thirty-six bands of visible/near-infrared and thermal infrared with abundant information. This study selects MODIS as data source. In consideration of the spectral character of MODIS data and different degraded grasslands, the reflective spectrum of vegetation is greatly affected by soil. MSAVI (modified soil- adjusted vegetation index) is selected to weaken the disturbanceof soil information. MSAVI and NDWI (Normalized Difference Water Index) are deduced using one visible band (0.66 mum) and two near-infrared bands (0.86 mum, 1.24 mum). In order to estimate the vegetation water more accurately. Vegetation component temperature is inversed using two thermal infrared bands (8.6 mum, 11 mum) according to the emissivity distribution of vegetation with the wavelength from eight to twelve micrometer and the correlation analysis of MODIS data. Sequentially, the vegetation water synthesis index is acquired by analyzing the coupling character of three indexes, which can reflect the vegetation water condition. Then the vegetation water content can be extracted using the synthesis index effectively. Lastly, the vegetation water content is validated using measured data. The matching results show that the synthesis index is directly proportional to the measured data. It proves that the synthesis index and the vegetation water content are credible and the method is reasonable. This study has discussed a new method to know the regional vegetation water condition from satellite remote sensing data directly and quickly.