Modeling soil salinity in an arid salt-affected ecosystem is a difficult task when using remote sensing data because of the complicated soil context （vegetation cover, moisture, surface roughness, and organic matter） and the weak spectral features of salinized soil. Therefore, an index such as the salinity index （SI） that only uses soil spectra may not detect soil salinity effectively and quantitatively. The use of vegetation reflectance as an indirect indicator can avoid limitations associated with the direct use of soil reflectance. The normalized difference vegetation index （NDVI）, as the most common vegetation index, was found to be responsive to salinity but may not be available for retrieving sparse vegetation due to its sensitivity to background soil in arid areas. Therefore, the arid fraction integrated index （AFⅡ） was created as supported by the spectral mixture analysis （SMA）, which is more appropriate for analyzing variations in vegetation cover （particularly halophytes） than NDVI in the study area. Using soil and vegetation separately for detecting salinity perhaps is not feasible. Then, we developed a new and operational model, the soil salinity detecting model （SDM） that combines AFⅡ and SI to quantitatively estimate the salt content in the surface soil. SDMs, including SDM1 and SDM2, were constructed through analyzing the spatial characteristics of soils with different salinization degree by integrating AFⅡ and SI using a scatterplot. The SDMs were then compared to the combined spectral response index （COSRI） from field measurements with respect to the soil salt content. The results indicate that the SDM values are highly correlated with soil salinity, in contrast to the performance of COSRI. Strong exponential relationships were observed between soil salinity and SDMs （R2〉0.86, RMSE〈6.86） compared to COSRI （R2=0.71, RMSE=16.21）. These results suggest that the feature space related to biophysical properties combined with AFII and SI can effectively
In this paper,the performance of the classic snowmelt runoff model(SRM)is evaluated in a daily discharge simulation with two different melt models,the empirical temperature-index melt model and the energy-based radiation melt model,through a case study from the data-sparse mountainous watershed of the Urumqi River basin in Xinjiang Uyghur Autonomous Region of China.The classic SRM,which uses the empirical temperature-index method,and a radiation-based SRM,incorporating shortwave solar radiation and snow albedo,were developed to simulate daily runoff for the spring and summer snowmelt seasons from 2005 to 2012,respectively.Daily meteorological and hydrological data were collected from three stations located in the watershed.Snow cover area(SCA)was extracted from satellite images.Solar radiation inputs were estimated based on a digital elevation model(DEM).The results showed that the overall accuracy of the classic SRM and radiation-based SRM for simulating snowmeltdischarge was relatively high.The classic SRM outperformed the radiation-based SRM due to the robust performance of the temperature-index model in the watershed snowmelt computation.No significant improvement was achieved by employing solar radiation and snow albedo in the snowmelt runoff simulation due to the inclusion of solar radiation as a temperature-dependent energy source and the local pattern of snowmelt behavior throughout the melting season.Our results suggest that the classic SRM simulates daily runoff with favorable accuracy and that the performance of the radiation-based SRM needs to be further improved by more ground-measured data for snowmelt energy input.
Vegetation fractional coverage （VFC） is an important index to describe and evaluate the ecological system. The vegetation index is widely used to monitor vegetation coverage in the field of remote sensing （RS）. In this paper, the author conducted a case study of the delta oasis of Weigan and Kuqa rivers, which is a typical saline area in the Tarim River Watershed. The current study was based on the TM/ETM＋ images of 1989, 2001, and 2006, and supported by Geographic Information System （GIS） spatial analysis, vegetation index, and dimidiate pixel model. In addition, VBSl （vegetation, bare soil and shadow indices） suitable for TM/ETM＋ irrlages, constructed with FCD （forest canopy density） model principle and put forward by ITTO （International Tropical Timber Organization）, was used, and it was applied to estimate the VFC. The estimation accuracy was later prow^n to be up to 83.52%. Further, the study analyzed and appraised the changes in vegetation patterns and revealed a pattern of spatial change in the vegetation coverage of the study area by producing the map of VFC levels in the delta oasis. Forest, grassland, and farmland were the three main land-use types with high and extremely-high coverage, and they played an important role in maintaining the vegetation. The forest area determined the changes of the coverage area, whereas the other two land types affected the directions of change. Therefore, planting trees, protecting grasslands, reclaiming farmlands, and controlling unused lands should be included in a long-term program because of their importance in keeping regional vegetation coverage. Finally, the dynamic variation of VFC in the study area was evaluated according to the quantity and spatial distribution rendered by plant cover diigital images to deeply analyze the reason behind the variation.
Fei ZHANGTashpolat TIYIPJianLi DINGMamat SAWUTVerner Carl JOHNSONNigaraTASHPOLATDongWei GUI
