地物反射光谱对MODIS近红外波段水汽反演影响的模拟分析

在近红外辐射传输方程的基础上,利用近红外波段水汽的不同吸收属性,在MODTRAN的模拟下,深入分析了基于MODIS近红外数据的可降水汽反演算法,并着重讨论了地物反射光谱非线性在可降水汽反演中的影响。研究结果显示,当波段间反射率之比不等于1时,MODIS近红外波段反演水汽将存在较大偏差。同时,在地物光谱库基础上,计算了不同地物反射率比值,其分布表明,大部分地物波段反射率比值不等于1。研究表明,应用现有MODIS近红外波段水汽反演算法,如果不考虑地表反射率光谱变化的影响,由地表反射光谱造成的误差最大约为反射率比值与1偏差的15倍,同时,这一误差还与大气波段透过率之比有关。     

2000-2018年东亚地区云顶高度的时空变化特征

为了了解区域云顶高度对过去气候变化的响应,基于卫星搭载的MODIS传感器提供的2000年3月至2018年2月MOD03_08_v6.0数据,分析了东亚地区云顶高度2000—2018年的时空变化特征,并探讨其长期变化的原因。研究发现,东亚地区云顶高度呈西南高东北低的特征。云顶高度在东亚地区以0.020 km/a的变率增长,其中大陆东部云顶高度的年际变率为0.035 km/a,东部海域年际变率为0.034 km/a。在东部海域地区云顶高度的变化同海表温度的变化相关性较高,相关系数为0.68,这表明云顶高度的变化受下垫面的影响。在东亚地区30°~40°N区域内,年平均云顶高度的增加较为明显。此外,夏季云顶高度在长江中下游盆地、塔里木盆地、吐鲁番盆地以及四川盆地东北部呈-0.03 km/a的减少趋势,这是由于更多低云的形成降低了云顶高度;冬季云顶高度在东亚地区40°N以北呈下降趋势,而在40°N以南呈增加趋势。     

利用MODIS大气廓线产品计算空气相对湿度的方法研究

森林火灾遍及全球,损失巨大。火险天气指数（FWI）是森林火灾预警和管理的有效工具。相对湿度（RH）是计算天气火险指数一个非常重要的参数。然而,由插值方法得到的RH在远离气象站点的地方往往存在很大的误差。因此,尝试利用MODIS遥感数据计算基于像元的高分辨率RH。MODIS的MOD07大气廓线产品,可以提供大气可降水量和地表气温数据。基于地面水汽压和大气可降水量的经验关系,利用MODIS数据计算了地表空气的相对湿度,平均绝对误差小于5％。结果表明,利用MODIS数据能简单而有效地计算地表空气的相对湿度,拓展了遥感技术在森林火险管理中的应用。     

MODIS数据的水浇地提取

水浇地是土地覆盖分类系统中的一个重要类型, 依据水分亏缺指数(WDI)与土壤水分的相关性, 计算了监测时间内WDI的变化情况, 用降雨资料去除了由于降雨引起的WDI的变化, 进而提取了研究区的水浇地分布。结果表明：(1)提取出的水浇地在数量上与统计资料相比, 除山西省偏差相对较大外, 其余各省偏差均小于7%, 提取出的结果与统计资料具有可比性。(2)提取出的水浇地在空间上多成片出现在河流、湖泊、水库附近、灌区和绿洲上, 与已知的水浇地集中区一致。(3)利用TM影像对提取结果进行了初步判断, 其中山西省的正确率较低, 只有64%, 其余各省正确率均在70%以上, 新疆的正确率最高为92%。     

GF-2支持下的干旱区稀疏植被区植被覆盖度估算

现有像元二分模型MODIS植被覆盖度模型因其形式简单、适用性较强的特点被广泛应用于区域植被覆盖度(FVC)的估算。然而,研究表明在沙漠和低植被覆盖的西部干旱区,从250 m的影像上很难精准地获取NDVIveg(全植被覆盖植被指数)和NDVIsoil(全裸土区植被指数)参数。利用常用的直方图累计法获取模型所需参数NDVIveg和NDVIsoil,估算结果存在普遍高估现象。为此,本文首先引入同期获取的GF-2号卫星数据,从GF-2号影像上提取植被覆盖像元;然后,利用Pixel Aggregate方法重采样至250 m分辨率,获取250 m空间分辨率下纯植被和纯裸土像元;最后,将纯植被和纯裸土像元各自空间位置相对应的MODIS NDVI数据最大值作为模型所需NDVIveg和NDVIsoil参数,实现研究区内植被覆盖度的估算。试验通过与线性回归法、多项式回归法和直方图累计像元二分模型法估算结果进行精度对比,结果表明:利用GF-2影像辅助的像元二分模型,精准地获取了低植被覆盖区NDVIveg和NDVIsoil模型参数,提高了干旱区植被覆盖度的估算精度,并有效地抑制了受稀疏植被影响NDVI在干旱区普遍偏高问题导致的FVC高估的现象。     

A dynamic relationship between the leaf phenology and rainfall regimes of Hawaiian tropical ecosystems: A remote sensing approach

Hypertemporal MODIS time series data provide a unique opportunity to investigate a dynamic relationship between leaf phenology and the climatic pattern of diverse, cloud‐prone Hawaiian ecosystems. Harmonic analysis summarized the complex greenness signals of Hawaiian tropical ecosystems into two main phenological wave forms – a moisture‐limited and a light‐limited type. Greenness maximums occurred during the wet season in dry and mesic ecosystems, and during the dry season in wet forests. The magnitude and periodicity of greenness fluctuations were also rainfall‐dependent. The annual greenness amplitude increased with increasing mean annual precipitation (MAP) in dry and mesic ecosystems. In wetter environments where MAP was greater than 3000 mm, however, annual greenness amplitude decreased with MAP. Annual greenness periodicity was stronger in drylands than in wet forests, and it weakened as annual precipitation increased. This result shows that rainfall is less important as a limiting factor in wet forests than it is in drylands. Therefore, leaf phenology is not governed by rainfall seasonality as forest wetness increases in the region.     

基于MODIS数据的2000-2010年乌梁素海"黄苔"时空变化

利用2000-2010年每年5-9月MODIS数据根据比值算法提取乌梁素海湖区黄苔的面积和空间分布信息并进行统计分析,探求乌梁素海黄苔产生的时空分布规律及特征,从而为黄苔的预防和治理提供支持.结果表明:(1)黄苔面积变化的年际和月际特征方面,2000、2001、2005、2006、2008、2010年黄苔面积超过了多年平均值(24 km2).5—7月份黄苔面积较小,保持在20 km2左右;8月黄苔面积迅速增长(约28 km2),9月黄苔面积最大,达到40 km2左右.(2)黄苔发生频率方面,2001年黄苔的规模和频率最高,发生频率达到0.58;2005、2006、2010年次之,发生频率在0.25附近波动(多年年均黄苔暴发频率为0.19);其他年份黄苔的发生频率处于低于0.10的水平.黄苔发生规模较大、次数较多的月份集中在8、9月,发生频率分别达到0.27、0.52,超过多年月均黄苔暴发频率0.19;其他月份黄苔的发生频率处于低于0.10的水平.(3)黄苔出现的空间分布方面,西大滩为东大滩的北部至中部,以及乌梁素海南部明水区排干口附近的西部沿岸是黄苔出现频率较高的区域.(4)2个月前的日均温度、降雨和营养盐浓度及当月风速与黄苔的产生具有极显著相关性;营养盐含量(TN、TP)的空间分布与黄苔的空间分布表现出较好的相关性.乌梁素海黄苔面积的年际变化受人类活动特别是生态补水的影响明显.     

Statistical data fusion of multi-sensor AOD over the Continental United States

This article illustrates two techniques for merging daily aerosol optical depth (AOD) measurements from satellite and ground-based data sources to achieve optimal data quality and spatial coverage. The first technique is a traditional Universal Kriging (UK) approach employed to predict AOD from multi-sensor aerosol products that are aggregated on a reference grid with AERONET as ground truth. The second technique is spatial statistical data fusion (SSDF); a method designed for massive satellite data interpolation. Traditional kriging has computational complexity O(N³), making it impractical for large datasets. Our version of UK accommodates massive data inputs by performing kriging locally, while SSDF accommodates massive data inputs by modelling their covariance structure with a low-rank linear model. In this study, we use aerosol data products from two satellite instruments: the moderate resolution imaging spectrometer and the geostationary operational environmental satellite, covering the Continental United States.     

运用动态谐波回归模型对MODIS叶面积指数时间序列产品的分析与预测

运用动态谐波回归模型(Dynamic Harmonic Regression,DHR)对MODIS的长时间序列的LAI产品进行分析,可以从中分离出LAI随时间变化的多年趋势、季节变化及残差等主要成分,通过建立的模型实现LAI年间变化的短时预测。本文将所述DHR模型分析方法试用于遥感数据产品随时间变化的信息提取,对LAI年间变化的预测结果证明该方法用于遥感像元尺度LAI产品的时间序列分析与预测的效果良好。     

Is remote sensing useful for finding and monitoring urban farms?

As the world becomes increasingly urbanized, the need for fresh fruits and vegetables in urban areas grows while the difficulty of bringing these perishable products to these areas also increases. Small-scale agriculture located in urban areas is a highly effective and profitable way to provide these products to communities that are far from extensive commercial agricultural areas. Here we describe how remote sensing can be used with data mining approaches to monitor urban and peri-urban farms within cities in both developed and developing countries. Using very high resolution satellite imagery together with moderate and coarse resolution imagery and information from social media and the web, we analyze the usefulness of different methods to identify farms within urban boundaries in four countries. The analysis shows how a mixed-method approach is necessary in order to identify where urban farming is occurring and to monitor its change through time. Although remote sensing-based vegetation and water indices were useful, without ancillary data they are not effective at remotely mapping the locations of urban farms. However, remote sensing is a good way to monitor vegetation condition in locations where actively managed urban farms are known to exist.