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The purpose of this study is to estimate long-term SMC and find its relation with soil moisture (SM) of climate station in different depths and NDVI for the growing season. The study area is located in agricultural regions in the North of Mongolia. The Pearson’s correlation methodology was used in this study. We used MODIS and SPOT satellite data and 14 years data for precipitation, temperature and SMC of 38 climate stations. The estimated SMC from this methodology were compared with SM from climate data and NDVI. The estimated SMC was compared with SM of climate stations at a 10-cm depth (r2 = 0.58) and at a 50-cm depth (r2 = 0.38), respectively. From the analysis, it can be seen that the previous month’s SMC affects vegetation growth of the following month, especially from May to August. The methodology can be an advantageous indicator for taking further environmental analysis in the region. 相似文献
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P. Cristofanelli P. di Carlo A. D’. Altorio C. Dari Salisburgo P. Tuccella F. Biancofiore P. Stocchi G. P. Verza T. C. Landi A. Marinoni F. Calzolari R. Duchi Paolo Bonasoni 《Pure and Applied Geophysics》2013,170(11):1985-1999
Tropospheric ozone (O3) is an important atmospheric pollutant and climate forcer. The Mediterranean basin is a hot-spot region in terms of short-term O3 distribution, with frequent episodes of high tropospheric O3, especially during summer. To improve the characterisation of summer O3 variability in the Mediterranean area, during the period 6–27 August 2009 an experimental campaign was conducted at Campo Imperatore, Mt Portella (CMP), a high mountain site (2,388 m a.s.l.) located in the central Italian Apennines. As deduced from analysis of atmospheric circulation, the measurement site was significantly affected by air masses originating over the Mediterranean basin, which affected the measurement site for 32 % of the time. Analysis of average values and diurnal and day-to-day variability revealed that CMP O3 observations (average value 60.0 ± 5.1 ppbv) were comparable with measurements at other European mountain stations, indicating a prevalent effect of meteorological conditions and atmospheric transport on the synoptic scale. In fact, only a small “reverse” diurnal variation typically characterises diurnal O3 variability because of local thermal wind circulation, which sporadically favours transport of air masses rich in O3 from the foothill regions. Statistical analysis of five-day back-trajectory ensembles indicates that synoptic-scale air-mass transport from the Mediterranean Sea usually results in decreasing O3 concentrations at CMP, whereas the highest hourly O3 values are mostly associated with air masses from central continental Europe, eastern Europe, and northern Italy. High O3 concentrations are also related to downward air-mass transport from higher altitudes. Comparison of in-situ O3 variability with tropospheric O3 satellite-based measurements reveals similar features of the two data sets. Together with the results from back-trajectory analysis, this indicates that CMP measurements might usefully improve characterisation of broad-scale O3 variability over the central Mediterranean basin. 相似文献
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Enkhjargal Natsagdorj Martin Kappas Batchuluun Tseveen Chimgee Dari Oyunbileg Tsend 《地球空间信息科学学报》2017,20(1):46-55
Soil moisture (SM) content is one of the most important environmental variables in relation to land surface climatology, hydrology, and ecology. Long-term SM data-sets on a regional scale provide reasonable information about climate change and global warming specific regions. The aim of this research work is to develop an integrated methodology for SM of kastanozems soils using multispectral satellite data. The study area is Tuv (48°40′30″N and 106°15′55″E) province in the forest steppe zones in Mongolia. In addition to this, land surface temperature (LST) and normalized difference vegetation index (NDVI) from Landsat satellite images were integrated for the assessment. Furthermore, we used a digital elevation model (DEM) from ASTER satellite image with 30-m resolution. Aspect and slope maps were derived from this DEM. The soil moisture index (SMI) was obtained using spectral information from Landsat satellite data. We used regression analysis to develop the model. The model shows how SMI from satellite depends on LST, NDVI, DEM, Slope, and Aspect in the agricultural area. The results of the model were correlated with the ground SM data in Tuv province. The results indicate that there is a good agreement between output SM and SM of ground truth for agricultural area. Further research is focused on moisture mapping for different natural zones in Mongolia. The innovative part of this research is to estimate SM using drivers which are vegetation, land surface temperature, elevation, aspect, and slope in the forested steppe area. This integrative methodology can be applied for different regions with forest and desert steppe zones. 相似文献
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