2000-2014年西藏雅鲁藏布江流域积雪时空变化分析及对气候的响应研究

利用2000-2014年MODIS逐日无云积雪产品对雅鲁藏布江流域积雪特征的空间分布及变化、积雪随高程变化的规律进行了分析,并采用被动微波数据SMMR （1979-1987年）和SSM/I （1988-2008年）以及中国地面降水和气温0.5°×0.5°日值格点数据集,研究了雅鲁藏布江流域关键积雪参数对气候要素的响应等。结果表明：流域下游积雪日较大且变化剧烈;流域整体上呈显著减少的趋势;积雪日随高程的上升而增加;流域内降水呈不显著的增加趋势,而气温呈显著的增加趋势,最高气温对积雪变化影响最大;气温对积雪终日的影响明显高于积雪初日;在积雪消融期降水的增多促进了积雪的消融。     

基于MODIS数据的青藏高原冰川反照率时空分布及变化研究

冰川反照率对冰川融化具有重要影响,以2000-2013年MODIS的MOD10A1逐日积雪反照率数据资料为基础,分析了青藏高原冰川反照率的时空分布及变化。结果表明：冰川年平均反照率变化范围是0.42（枪勇冰川）~0.75（PT5冰川）,其中夏季平均反照率变化范围是0.45（来古冰川）~0.69（东绒布冰川和古里雅冰川）。冰川反照率空间分布并没有明显的规律性,而冰川反照率的变化速率空间分布规律明显——南部较大往北减小,北部反照率出现增大现象。研究区内大部分冰川反照率呈波动降低的趋势,年平均反照率和夏季平均反照率变化速率最大值都出现在枪勇冰川,分别是-0.015 a^-1和-0.019 a^-1。木吉和木孜塔格冰川年平均和夏季平均冰川反照率都增大,木吉冰川是由于2012年的高反照率引起的,而木孜塔格冰川主要与该地区气温降低、降水增多有关。     

Rice crop phenology mapping at high spatial and temporal resolution using downscaled MODIS time-series

Satellite data holds considerable potential as a source of information on rice crop growth which can be used to inform agronomy. However, given the typical field sizes in many rice-growing countries such as China, data from coarse spatial resolution satellite systems such as the Moderate Resolution Imaging Spectroradiometer (MODIS) are inadequate for resolving crop growth variability at the field scale. Nevertheless, systems such as MODIS do provide images with sufficient frequency to be able to capture the detail of rice crop growth trajectories throughout a growing season. In order to generate high spatial and temporal resolution data suitable for mapping rice crop phenology, this study fused MODIS data with lower frequency, higher spatial resolution Landsat data. An overall workflow was developed which began with image preprocessing, calculation of multi-temporal normalized difference vegetation index (NDVI) images, and spatiotemporal fusion of data from the two sensors. The Spatial and Temporal Adaptive Reflectance Fusion Model was used to effectively downscale the MODIS data to deliver a time-series of 30 m spatial resolution NDVI data at 8-day intervals throughout the rice-growing season. Zonal statistical analysis was used to extract NDVI time-series for individual fields and signal filtering was applied to the time-series to generate rice phenology curves. The downscaled MODIS NDVI products were able to characterize the development of paddy rice at fine spatial and temporal resolutions, across wide spatial extents over multiple growing seasons. These data permitted the extraction of key crop seasonality parameters that quantified inter-annual growth variability for a whole agricultural region and enabled mapping of the variability in crop performance between and within fields. Hence, this approach can provide rice crop growth data that is suitable for informing agronomic policy and practice across a wide range of scales.     

Mapping cropland fallow areas in myanmar to scale up sustainable intensification of pulse crops in the farming system

Cropland fallows are the next best-bet for intensification and extensification, leading to increased food production and adding to the nutritional basket. The agronomical suitability of these lands can decide the extent of usage of these lands. Myanmar’s agricultural land (over 13.8 Mha) has the potential to expand by another 50% into additional fallow areas. These areas may be used to grow short-duration pulses, which are economically important and nutritionally rich, and constitute the diets of millions of people as well as provide an important source of livestock feed throughout Asia. Intensifying rice fallows will not only improve the productivity of the land but also increase the income of the smallholder farmers. The enhanced cultivation of pulses will help improve nutritional security in Myanmar and also help conserve natural resources and reduce environmental degradation. The objectives of this study was to use remote sensing methods to identify croplands in Myanmar and cropland fallow areas in two important agro-ecological regions, delta and coastal region and the dry zone. The study used moderate-resolution imaging spectroradiometer (MODIS) 250-m, 16-day normalized difference vegetation index (NDVI) maximum value composite (MVC), and land surface water index (LSWI) for one 1 year (1 June 2012–31 May 2013) along with seasonal field-plot level information and spectral matching techniques to derive croplands versus cropland fallows for each of the three seasons: the monsoon period between June and October; winter period between November and February; and summer period between March and May. The study showed that Myanmar had total net cropland area (TNCA) of 13.8 Mha. Cropland fallows during the monsoon season account for a meagre 2.4% of TNCA. However, in the winter season, 56.5% of TNCA (or 7.8 Mha) were classified as cropland fallows and during the summer season, 82.7% of TNCA (11.4 Mha) were cropland fallows. The producer’s accuracy of the cropland fallow class varied between 92 and 98% (errors of omission of 2 to 8%) and user’s accuracy varied between 82 and 92% (errors of commission of 8 to 18%) for winter and summer, respectively. Overall, the study estimated 19.2 Mha cropland fallows from the two major seasons (winter and summer). Out of this, 10.08 Mha has sufficient moisture (either from rainfall or stored soil water content) to grow short-season pulse crops. This potential with an estimated income of US$ 300 per hectare, if exploited sustainably, is estimated to bring an additional net income of about US$ 1.5 billion to Myanmar per year if at least half (5.04 Mha) of the total cropland fallows (10.08 Mha) is covered with short season pulses.     

Fusing MODIS with Landsat 8 data to downscale weekly normalized difference vegetation index estimates for central Great Basin rangelands,USA

Data fused from distinct but complementary satellite sensors mitigate tradeoffs that researchers make when selecting between spatial and temporal resolutions of remotely sensed data. We integrated data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor aboard the Terra satellite and the Operational Land Imager sensor aboard the Landsat 8 satellite into four regression-tree models and applied those data to a mapping application. This application produced downscaled maps that utilize the 30-m spatial resolution of Landsat in conjunction with daily acquisitions of MODIS normalized difference vegetation index (NDVI) that are composited and temporally smoothed. We produced four weekly, atmospherically corrected, and nearly cloud-free, downscaled 30-m synthetic MODIS NDVI predictions (maps) built from these models. Model results were strong with R² values ranging from 0.74 to 0.85. The correlation coefficients (r ≥ 0.89) were strong for all predictions when compared to corresponding original MODIS NDVI data. Downscaled products incorporated into independently developed sagebrush ecosystem models yielded mixed results. The visual quality of the downscaled 30-m synthetic MODIS NDVI predictions were remarkable when compared to the original 250-m MODIS NDVI. These 30-m maps improve knowledge of dynamic rangeland seasonal processes in the central Great Basin, United States, and provide land managers improved resource maps.     

Examining human heat stress with remote sensing technology

Heat stress as an environmental hazard which can seriously affect productivity, health, or even survival of individuals has long been studied. Despite the endeavors that have been made to address the issue quantitatively with various heat stress indices, they are often measured at scatter sites. This research devotes to revealing human heat stress within continuous space with remote sensing technology. The study began with the retrieval of dry-bulb temperature from land surface temperature (LST) with empirical models. As wet-bulb temperature was calculated from dry-bulb temperature and relative humidity, discomfort index (DI) as an indicator of heat stress was revealed for the study area at 1 km spatial resolution for three summer days. Results indicated that DI can be derived within continuous space with remotely sensed data, and its spatial distribution can be dramatically affected by relative humidity. Further comparison between DI and LST indicated that LST as a widely utilized indicator of surface thermal condition fails to address human heat stress as environmental factors such as relative humidity are not taken into account.     

基于多源卫星数据的云底高度反演算法在中国近海的应用研究

本文利用直减率反演云底高度的计算方法,联合星载主、被动探测资料开展了中国东海、南海上空暖云云底高度反演研究,同时对暖云的分布特征进行了统计。结果表明受大气层结稳定程度影响,夜间的反演效果优于白天。两种资料的云顶高度较一致时,反演效果好,该方法具有可行性。     

WRF模式中不同土地利用数据对新疆一次高温天气模拟的影响

本研究在WRF(v3.8.1)中分别使用MODIS 21类和USGS 24类土地利用类型数据,模拟了新疆2017年7月9日的极端高温天气,并在对模拟温度进行了高度订正的基础上,对比了两种土地利用数据对2 m温度预报的影响。结果表明:(1)MODIS和USGS在新疆地区的土地利用差异主要在阿尔泰山、天山以及南疆西部的昆仑山北部海拔3000 m以上的高山带,相应地,使用USGS模拟的这些高山带2 m气温明显高于使用MODIS的模拟值,最高偏高12 K左右,是全疆范围内两者偏差的极大值。(2)就新疆区域而言,使用USGS模拟的2 m气温整体优于使用MODIS的模拟值,且USGS模拟的2 m温度整体低于MODIS模拟的2 m温度。两者与实况的偏差多在2 K以内。(3)在伊犁河谷,MODIS土地利用类型主要为"旱地/草地",USGS为"草地"和"农田/林地马赛克"。伊犁河谷代表站点2 m温度模拟多以高温偏低、低温偏高为主。(4)与MODIS相比,USGS中哈密地区"农田/林地马赛克"所占比重明显增大。哈密地区多数代表站点高、低温均以偏低为主。(5)站点温度的高度订正多以调低为主,调低幅度最大值为1.9 K,出现在伊犁河谷的尼勒克站。站点2 m温度的调整幅度整体上明显大于MODIS和USGS模拟2 m温度的差值,由此可见温度高度订正的必要性。     

Study of Dongting Lake area variation and its influence on water level using MODIS data / Etude de la variation de la surface du Lac Dongting et de son influence sur le niveau d’eau,grâce à des données MODIS

Abstract

Abstract MODerate-resolution Imaging Spectroradiometer (MODIS) is a new generation remote sensing (RS) sensor and its applications in hydrology and water resources have attracted much attention. To overcome the problems of slow response in flood disaster monitoring based on traditional RS techniques in China, the Flood Disaster Monitoring and Assessing System (FDMAS), based on MODIS and a Geographic Information System (GIS), was designed and applied to Dongting Lake, Hunan Province, China. The storage curve of Dongting Lake for 1995 was obtained using 1:10 000 topographic map data and then a relationship between water level at the Chenglingji hydrological station and lake area was derived. A new relationship between water level and lake area was obtained by processing MODIS images of Dongting Lake from April 2002 to April 2003 and the influence of lake area variation on water level was analysed with the 1996 flood data. It was found that the water level reduction reached 0.64 m for the 1996 flood if the original lake area curve was replaced with the area curve of 2002. This illustrates that the flood water level has been considerably reduced as a result of the increased area of Dongting Lake since the Chinese Central Government’s ?return land to lake? policy took effect in 1998.     

MODIS NDVI时间序列在三江平原湿地植被信息提取中的应用

以三江平原为研究区,利用多时相的中分辨率成像光谱仪(Moderate Resolution Imaging Spectroradiometer,MODIS)影像数据,采用一种基于归一化植被指数(Normalized Difference Vegetation Index,NDVI)时间序列的监督分类方法获取了研究区湿地植被的分布数据。监督分类以NDVI时间序列的波形所反映出的植被物候特征作为分类器,将离散的傅立叶变换应用于NDVI时间序列以减少高频噪声对分类的影响,并运用傅立叶变换后波形幅度和相位的相似性来确定像素的归属类别。根据研究区植被的物候特征的差异,区分出7种地表(沼泽、沼泽化草甸、滩地、水田、旱地、灌木和林地)的植被类型,得到三江平原2005年湿地植被的分布数据。该方法的总体分类精度达到79.67%,Kappa系数为0.752 5。研究表明,基于MODIS多时相NDVI数据,采用基于傅立叶组分的相似度分类方法可以客观、经济、快速的提取湿地植被分布数据。