Assessment of the land surface wetness by using satellite remote sensing data over the Loess Plateau
摘要: 卫星遥感数据具有估算时空尺度上地表参量的优势，在陆地环境状况评估和监测等方面有很大的应用潜力.本文利用美国地球观测系统卫星搭载中等分辨率成像光谱仪（EOS/MODIS）在黄土高原2002-2010年期间获取的每16天归一化植被指数（NDVI）和每日地表温度（LST）数据，分析了黄土高原地区LST-NDVI空间的基本特征.结果发现：当研究区域足够大且遥感数据时间序列足够长时，LST-NDVI空间中（NDVI，LST）散点并非呈三角形或梯形分布.为了能够利用EOS/MODIS的NDVI和LST数据正确地评估陆面的干湿状况，本文给出了利用数据集合法确定LST-NDVI空间中干边和湿边的数值，即在LST-NDVI空间中，利用NDVI等值区间内LST最大值和最小值的集合代表干边和湿边的数值，并进一步证明了在LST-NDVI空间中干边和湿边数值并非呈线性关系.在分析LST-NDVI空间特征的基础上，通过构建地表温度-植被干旱指数（TVDI），探讨其在评估黄土高原地区陆面的干湿状况的应用潜力.结果表明：由TVDI距平表征的陆面的干湿程度与局地降水距平有很好的关联性，二者在时空分布上有较好的对应关系.在我国陇东黄土高原塬区，TDVI数值与地面观测的表层土壤湿度有很好的相关性，相关系数在0.67以上，并通过显著性为1%的检验.由此说明：如果合理选取干边和湿边的数值，TDVI可应用于区域陆面干湿程度的客观评估.Abstract: Due to its advantages in estimating regional and temporal land surface variables, satellite remote sensing has great potential in detecting and monitoring land surface wetness. In this paper, the preliminary characteristics of LST-NDVI space were analyzed by using land surface temperature (LST) and normalized difference vegetation index (NDVI) obtained from the Earth Observation System/MODerate-resolution Imaging Spectroradiometer (EOS/MODIS). The results indicated that when the study area was large enough, and the time series length of the datasets was long enough, the distribution of the points in the LST-NDVI space is not triangular or trapezoid shapes. Based on this fact, a method for estimating the values of the dry edge and wet edge was proposed, the values of dry edge and wet edge were the sets of maximum and minimum at the given NDVI internals, the NDVI and LST values on the dry edge and wet edge were not linear relationship. A land surface Temperature -Vegetation drought Difference Index (TVDI) was constructed based on the LST-NDVI space characteristics, its potential on land surface wetness assessment over the Loess Plateau was explored. The results showed that there was a relationship between the land surface wetness denoted by TVDI anomaly and meteorological drought cased by precipitation anomaly, both of them matched well at the spatial and temporal patterns. There was a good relationship between TVDI and 5.0 cm depth soil moisture over the Loess Plateau mesa, the correlation coefficient was above 0.67, it passed the test of significance at the significance level of 1 percent. Therefore, it could be concluded that TVDI is able to be used in assessing land surface wetness.
Carlson T N, Capehart W J, Gillies R R. 1995. A new look at the simplified method for remote sensing of daily evapotranspiration. Remote Sens. Environ., 54(2): 161-167.
Chen J A, Wang C Z, Jiang H, et al. 2011. Estimating soil moisture using Temperature-Vegetation Dryness Index (TVDI) in the Huang-Huai-Hai (HHH) plain. Int. J. Remote Sens., 32(4): 1165-1177.
Chen L W, Zhang Y J, Deng S Z, et al. 2012. Estimation of surface soil water content in source region of Yellow River based on TVDI. Adv. Sci. Technol. Water Resour. (in Chinese), 32(4): 6-9, 22.
Gao L, Qin Z H, Lu L P. 2007. An overview on agricultural drought monitoring models based on vegetation index and surface temperature feature space. Remote Sens. Land Resour. (in Chinese), 19(3): 1-7.
Gillies R R, Carlson T N, Kustas W P. 1997. A verification of the 'triangle’ method for obtaining surface soil water content and energy fluxes from remote measurements of the Normalized Difference Vegetation Index (NDVI) and surface radiant temperature. Int. J. Remote Sens., 18(15): 3145-3166.
Han L J, Wang P X, Wang J D, et al. 2005. Study on vegetation index-land surface temperature space. Sci. China (Series D) (in Chinese), 35(4): 371-377.
Jiang D, Fu J Y, Zhuang D F, et al. 2012. Dynamic drought-remote sensing monitoring in north China from 2008 to 2009. J Nat Disasters (in Chinese), 21(3): 92-101.
Kang Y, Li Z C, Tian H, et al. 2011. Trend of vegetation evaluation and its responses to climate change over the source region of the Yellow River. Climate Environ. Res. (in Chinese), 16(4): 505-512.
Li Z C, Wei Z G, Wen J, et al. 2008. Analyses on climate variation characteristics over the Loess Plateau in recent 50 years. J. Arid Land Resour. Environ. (in Chinese), 22(3): 57-62.
Li Z L, Stoll M P, Zhang R H, et al. 2000. Study on decomposition temperature of soil and plant using ATSR data. Sci. China (Series E) (in Chinese), 30(S1): 27-38.
Liu Q H, Xin J F, Xin X Z, et al. 2007. Monitoring agricultural drought by vegetation index and remotely sensed temperature. Sci. Technol. Rev. (in Chinese), 25(6): 12-18.
Liu R, Wen J, Wei Z G, et al. 2010. Vegetation water content retrieval using MERIS and AATSR data over the Loess Plateau. Sci. Cold Arid Reg., 2(3): 250-258.
Ma Z G, Fu C B. 2005. Decadal variations of arid and semiarid boundary in China. Chinese J Geophys. (in Chinese), 48(3): 519-525.
Price J C. 1990. Using spatial context in satellite data to infer regional scale evapotranspiration. IEEE Trans. Geosci. Remote Sens., 28(5): 940-948.
Qi S H, Li G C, Wang C Y, et al. 2005. Study on monitoring drought in China with MODIS product. Adv. Water Sci. (in Chinese), 16(1): 56-61.
Qin Z H, Gao M F, Qin X M, et al. 2005. Methodology to retrieve land surface temperature from MODIS data for agricultural drought monitoring in China. J. Nat.Disasters (in Chinese), 14(4): 64-71.
Sandholt I, Rasmussen K, Andersen J. 2002. A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status. Remote Sens. Environ., 79(2-3): 213-224.
Song X N, Zhao Y S. 2004. Study on vegetation-temperature-water synthesis index using MODIS satellite data. Geog. Geo-Information Sci. (in Chinese), 20(2): 13-17.
Sun L, Sun R, Jia C G, et al. 2008. A comparion of split-window algorithm to retrieve land surface temperature from MODIS data. J. Beijing Normal University (in Chinese), 44(4), 434-438.
Sun L, Sun R, Li X W, et al. 2012. Monitoring surface soil moisture status based on remotely sensed surface temperature and vegetation index information. Agr. Forest Meteor., 166-167: 175-187.
Wan Z, Wang P, Li X. 2004. Using MODIS land surface temperature and normalized difference vegetation index products for monitoring drought in the southern Great Plains, USA. Int. J. Remote Sens., 25(1): 61-72.
Wang P X, Wan Z M, Gong J Y, et al. 2003. Advances in drought monitoring by using remotely sensed normalized difference vegetation index and land surface temperature products. Adv. Earth. Sci. (in Chinese), 18(4): 527-533.
Wang X P, Guo N. 2003. Some research advances and methods on drought monitoring by remote sensing. J. Arid Meteor. (in Chinese), 21(4): 76-81.
Wang Y R, Lü S H. 2008. Sensitivity analysis of the response of precipitation to climate change over China Loess Plateau. J. Glaciol. Geocryol. (in Chinese), 30(1): 43-51.
Wen J, Wang L, Wei Z G. 2009. An overview of the Loess Plateau mesa region land surface process field experiment series (LOPEXs). Hydrol. Earth Sys. Sci., 13(6): 945-951.
Xin Z B, Xu J X, Zheng W. 2007. The impacts of climate change and human activity on the vegetation cover change over the Loess Plateau. Sci. China (Series E) (in Chinese), 37(11): 1504-1514.
Yang X L, Yang X G. 2007. Research on meteorological drought severity model for Loess Plateau in Gansu. J. Nat. Disasters (in Chinese), 16(5): 30-36.
Yao C S, Zhang Z X, Whang X. 2004. Evaluating soil moisture status in Xinjiang using the temperature vegetation dryness index (TVDI). Remote Sens. Techn. Appl. (in Chinese), 19(6): 473-478.
Zhang J Y, Yang Y, Lu G H, et al. 2005. Study and application on quantitative survey of surface drought characters based on remote sensing technique. Adv. Water Sci. (in Chinese), 16(4): 541-545.
Zhang T T, Wen J, Su Z B, et al. 2009. Soil moisture mapping over Chinese Loess Plateau Using ENVISAT/ASAR data. Adv. Space Res., 43(7): 1111-1117.
- 文章访问数: 879
- PDF下载数: 2637
- 施引文献: 0