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Vegetation supply water index based on MODIS data Analysis of the in Yunnan in spring of 2012

机译:基于MODIS数据的云南省2012年春季植被补给水指标分析

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Today, drought has become a world problem, which is affecting agriculture more and more seriously. China is a large agricultural country, so drought is nor ignored for China. Drought hazards in agriculture growing, so timely and accurate monitoring of the development of drought is necessary to reduce the harm of drought. Over the years, many scholars established a variety of drought monitoring models, aiming at better monitoring of drought. Southwest China has been the drought-prone areas and Yunnan Province is the most serious area. [Method]: In this paper, in February 2012-May period of remote sensing data obtained with moderate-resolution imaging spectrometer (MODIS) conducts dynamic monitoring of drought in Yunnan. Normalized Difference Vegetation Index (NDVI) is calculated with the first bang and the second bang of MODIS data and Negative NDVI values that are representative of the water and snow should be removed. By split window algorithm method, land surface temperature (LST) in Yunnan is retrieved with 31-band and 32-band of MODIS data, but some places in where there is much cloud are removed. Then the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) calculated Vegetation Supply Water Index (VSWI) that dynamically monitors the drought in Yunnan. this model's results are validated based on 10 cm-shallow soil moisture data measured by the meteorological stations at the 0.05 confidence level and passed the correlation test which indicates that the results calculated by the model is sensitive to changes in drought. In the soil moisture data, some data collection sites have been irrigated, so these data should be removed. So this drought index can be used to monitor drought developments and we can get real-time, accurate drought. These monitoring results as a basis, We can make timely and correct drought policy to reduce losses caused by the drought; [Conclusions] :from spring drought monitoring results can be seen that Northwest- Yunnan has been in a semi- arid and arid state. In February and May, it is more serious drought, drought affecting a wide range. There are two reasons about development of drought: First, the terrain reasons, northwest, eastern and Central Yunnan are the highland area, low rainfall, dry climate, so the severe drought in these areas; Second, the climatic reason, the spring belongs to the dry season, Rainfall is relatively small and its distribution is not balance. Relative to previous years, three months and it rarely rains in March and May, so it is severe drought. In recent years, natural hazards occur frequently on the world in witch drought is most. Drought is characterized by a wide spread range, long duration and great harm. China is a large agricultural country so it be harmed when drought occurred witch hinders the development of society. So it is necessity of Real-time, dynamic and accurate monitoring of drought. Only in this way, can we minimize the damage.
机译:如今,干旱已成为世界性问题,对农业的影响越来越严重。中国是一个农业大国,因此干旱对中国也不容忽视。农业中的干旱危害日益严重,因此有必要对干旱的发展进行及时,准确的监测,以减少干旱的危害。多年来,许多学者建立了各种干旱监测模型,旨在更好地监测干旱。中国西南地区是干旱多发地区,云南省是最严重的地区。 [方法]:本文采用中分辨率成像光谱仪(MODIS)在2012年2月至5月期间对云南干旱进行动态监测。归一化植被指数(NDVI)是使用MODIS数据的第一个爆炸和第二个爆炸计算的,代表水和雪的NDVI负值应删除。通过分窗算法,利用31波段和32波段的MODIS数据反演了云南的地表温度(LST),但去除了云量较大的地方。然后,归一化植被指数(NDVI)和地表温度(LST)计算出的植被供水指数(VSWI)可动态监测云南的干旱。该模型的结果基于气象站在10置信水平下测量的10 cm浅层土壤水分数据进行了验证,并通过了相关检验,表明模型计算的结果对干旱变化敏感。在土壤湿度数据中,已经灌溉了一些数据收集站点,因此应删除这些数据。因此,该干旱指数可用于监视干旱的发展,并且我们可以获得实时,准确的干旱。以这些监测结果为依据,可以制定及时正确的干旱政策,减少干旱造成的损失; [结论]:从春季干旱监测结果可以看出,滇西北一直处于半干旱和干旱状态。在二月和五月,干旱更加严重,干旱影响范围广泛。造成干旱的原因有两个:一是地形原因,西北,东部和云南中部是高原地区,降雨少,气候干燥,因此这些地区干旱严重。其次,由于气候原因,春季属于干旱季节,降雨量相对较小,分布不平衡。与往年相比,三个月,三月和五月很少下雨,所以是严重的干旱。近年来,世界上自然灾害以巫婆干旱最为频繁。干旱的特点是传播范围广,持续时间长,危害大。中国是一个农业大国,因此干旱会损害社会发展。因此,有必要对干旱进行实时,动态和准确的监测。只有这样,我们才能将损失降到最低。

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