| The forest vegetation, as an important component of the earth biosphere, exchanges water and heat with the soil and the atmosphere in sorts of forms over different dimensions. In the interactions between vegetation and hydrological aspects, the forest vegetation and the hydrological cycle consist of complicated interaction and feedback mechanism through vegetation hydrological processes mainly including woodland evapotranspiration, atmospheric precipitation interception, litter interception, surface runoff, soil infiltration and moisture storage. The eco-water layer is a vegetation-centered layer of the earth surface, and forms a special transition zone or buffer zone in the water cycle, which has direct consequences on the retention time and the dynamic distribution of the water resources in the water cycle. The eco-water layer acts as a relatively independent entity in the water cycle, being an important role in the forest hydrological cycle. However, its resources present to be macroscopic and spatially heterogenic, and thus it is difficult to acquire its resources by ordinary methods, especially in the inland river basin with dramatic spatial changes in ecological and hydrological process parameters distribution. Hence, that how to effectively utilize the multi-scale distributed data acquisition capability of remote sensing and other new technologies to deliver the quantitative observation of eco-water, is capable of providing theoretical and technical supports for determining the eco-water resources and its spatial and temporal distribution and change characteristics in the upper Minjiang River, which has important theoretical and practical implications for the regional water cycle studies.The earlier studies of eco-water demonstrated that the factors such as vegetation biophysical parameters and the soil moisture present the macroscopic reflection of spectral characteristics in multi-band and multi-temporal remotely sensed data. Consequently the remote sensing inversion model can be established for parts of the eco-water parameters. However, earlier eco-water researches mainly focused on the field of optical remote sensing. Because it is difficult to obtain the optical remotely sensed data in tough climate regions, the optical methods present certain limitations in eco-water researches especially for studying its spatial and temporal dynamics. In recent years, contributing to the full abilities in all weather and day or night of microwave remote sensing, its hydrological applications have been increasingly and effectively applied to hydrological variables such as atmospheric precipitation, soil moisture and snow cover, and vegetation ecological parameters such as leaf area index and vegetation classification information. Therefore, based on the earlier research achievements of eco-water, this paper studied the quantitative remote sensing inversion methods for eco-water parameters in plateau mountain areas from both optical and microwave remote sensing at the Maoergai area in the upper Minjiang River. Also the multi-temporal optical and microwave remotely sensed data were used in change monitoring of parts of eco-water parameters.The main research contents and innovations of this paper are as follows:(1) The soil moisture estimation model of the study area was built from the time series radar data set. The interval length of dry and wet end was simulated by AEIM for the relative soil moisture estimation model proposed by Wagner et al. The soil moisture estimation model was built to acquire the relative soil moisture which was then converted to volumetric soil moisture content. Meanwhile, based on the AIEM simulated results from a comparatively wide range of surface roughness and incidence angles, a 10 dB approximate interval length was determined for C-band and L-band. This conclusion provides practical meaning for regions where are difficult to acquire wet end value due to long-term drought or difficult to acquire dry end value due to long-term flood.(2) A land cover classification method based on multi-temporal radar remote sensing imagery is developed. Seven multi-temporal Synthetic Aperture Radar(SAR) images were used to handle effectively the speckle noise by image fusion technique. Also this paper developed a dual-visual-direction shadow compensation algorithm for maintain areas to improve the image quality. Four characteristic parameters of the backscattering coefficient, the texture feature, the coherence coefficient and the digital elevation were introduced into the Support Vector Machine(SVM) to implement the supervised classification with a total classification accuracy of 81.77%.(3) Based on the Van Genuchten soil water retention curve model, the mathematical form of the soil moisture saturation(SMS) was defined. Considering the research results about the strongly positive correlation between the volumetric soil water content and the radar backscatter coefficient with the scale independent, the soil moisture saturation(SMS) microwave remote sensing model was established under the assumption that the specific surface condition remains invariant when the specific backscattering coefficients of the model are obtained. Then the SMS microwave remote sensing model for the study area was built upon the time series radar data set.(4) The double ratios(MSI/SR) optical remote sensing inversion model of vegetation moisture content(VMC) in the study area was built from field spectral data and leaf equivalent water thickness(EWT). Applying the Temperature Vegetation Dryness Index(TVDI) to the study area, the EVI instead of NDVI was used to build the Ts/EVI space, where the surface temperature elevation correction model was introduced. Then the soil moisture content(SMC) optical remote sensing inversion model was established through regression analysis between the field data and the TVDI.(5) Using multi-temporal radar remote sensing data, the changes of the soil moisture content(SMC) and the soil moisture saturation(SMS) of the main land cover types were monitored in the study area. The results showed that the average SMC and SMS slightly decreased from July to September, and the average SMC and SMS of different types of vegetation decreased gradually from the evergreen woodland to the shrub land and then to the grassland. Using multi-temporal optical remote sensing data, the changes of the modulus of eco-water conservation(MEC) of the main land cover types were monitored in the study area. The results showed that the average MEC slightly decreased from June to September, and the average MEC of different types of vegetation decreased gradually from the evergreen woodland to the shrub land and then to the grassland. |
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