| 基于MODIS数据新疆土壤干旱特征分析 |
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| 引用本文： | 何建村,白云岗,张严俊.基于MODIS数据新疆土壤干旱特征分析[J].干旱区地理,2015,38(4):735-742. |
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| 作者姓名： | 何建村 白云岗 张严俊 |
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| 作者单位： | 新疆水利水电科学研究院, 新疆 乌鲁木齐 830049 |
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| 基金项目： | 新疆自治区科技支撑项目(201331104) |
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| 摘 要： | 干旱是一种常见的自然灾害,严重影响着新疆的农业生产。利用中分辨率成像光谱仪MODIS影像MOD11A2数据和MOD13A2数据,数字高程模型(DEM)对Ts进行了纠正,提取归一化植被指数(NDVI)和地表温度(Ts)构建NDVI-Ts特征空间,并依据特征空间计算的温度植被干旱指数(TVDI)作为监测土壤湿度指标,反演了新疆2013年5、6、7三个月每16 d的土壤湿度。较好地反映地表图层土壤湿度,分析了新疆土壤湿度的时空分布特征,新疆北部地区土壤湿度高于南部,西部的土壤湿度高于东部,且土壤湿度由西北向东南逐步减小,依次表现为湿润>正常>轻旱>中旱>重旱>极旱;由5月到7月土壤湿度不断增大,这与新疆降水量分布和实地土壤含水率十分吻合,监测结果可信,能够为决策部门防旱抗旱提供有力的信息支持。
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| 关 键 词： | 归一化植被指数 地表温度 温度干旱植被指数 遥感监测 |
| 收稿时间： | 2014-12-17 |
Soil drought characteristics in Xinjiang with remote sensing data |
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| HE Jian-cun,BAI Yun-gang,ZHANG Yan-jun.Soil drought characteristics in Xinjiang with remote sensing data[J].Arid Land Geography,2015,38(4):735-742. |
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| Authors: | HE Jian-cun BAI Yun-gang ZHANG Yan-jun |
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| Institution: | Xinjiang Institute of Water Resources and Hydropower Research, Urumqi 830049, Xinjiang, China |
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| Abstract: | Drought is a common natural disaster, which seriously impacts on China's agricultural production. As a complex natural phenomena commonly worldwide occurrence with wide spread and long duration, it is one of the most serious natural disasters for agricultural production and human life, also a direct result in huge economic, social and environmental losses, causing famine, epidemics and large population movements. In recent years in China there has been more large-scale, prolonged drought on the region, which has a serious impact on social-economy and agricultural production. Therefore, it is necessary to build a real-time, dynamic drought monitoring method. The traditional method was using drought monitoring ground point data through statistical analysis of drought monitoring, such method could neither get timely information on drought quickly nor accurately estimate the lag defects. But the space monitoring method, with satellite remote sensing technology which constantly evolved and matured, can be used as a long-term dynamic monitoring with a wide range of fast, low cost. So remote sensing technology and its application became the primary means of regional-scale drought monitoring. Combined with the Moderate Resolution Imaging Spectrometer data MOD11A2 and MOD13A2, the paper uses digital elevation model(DEM)for correcting Ts, extracting normalized difference vegetation index(NDVI)and surface temperature(Ts)to construct Ts-NDVI feature space, and adopts the calculated temperature vegetation dryness index(TVDI)as indicators of soil moisture monitoring based on the feature space to have inversion calculation of every 16 d soil moisture of Xinjiang on May, June, July, 2013, which reflects the surface layer soil moisture perfectely. With analysis of spatial and temporal characteristics of soil moisture in Xinjiang, the paper found that soil moisture in northern part of Xinjiang is higher than that of southern, western higher than eastern, and soil moisture decreases gradually from northwest to southeast, followed by performance as humid> normal> light dry> in dry> severe drought> extremely dry; From May to July soil moisture is increasing, which is very consistent with the distribution of precipitation in Xinjiang, monitoring results credible, able to provide strong information support for drought and drought decision-making departments. As the satellite sensor FOV affects the information receiving, thus affecting the image quality to a certain extent, a reflection of the vegetation will produce some error message, this paper does not consider the impact on the NDVI and Ts perspective, a certain error will exist, on the other hand, if the NDVI is small or large, the linear relationship between NDVI and vegetation coverage is not good, it will reduce the accuracy of NDVI inversion method, we can consider using other vegetation index instead of NDVI, or integrated multi-vegetation index inversion NDVI, such as in different seasons with different vegetation index,that the monitoring study for further improvement needs to be further studied on this issue. |
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| Keywords: | normalized difference vegetation Index(NDVI) land surface temperature(LST) Temperature-vegetation dryness index(TVDI) Remote sensing monitoring |
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