MODIS数据在海冰遥感中的应用

MODIS数据作为中国可见光遥感的重要数据源之一,目前已经在许多领域获得了重要应用.国家卫星海洋应用中心从2000年开始接收处理MODIS数据,并同时为海冰预报提供海冰遥感图像服务.本文利用MODIS卫星遥感的原始数据,通过对数据进行地理定位、辐射定标等处理,形成MODIS数据的一级产品,并利用海冰实验的实测数据和海冰光谱分析,对一级产品进行定量化处理,获得了海冰厚度、密集度和外缘线等海冰数值产品,逐步应用于海冰的预报服务.     

藏北高原那曲地区不同下垫面地表粗糙度的变化特征研究

利用2008年、2010年和2012年藏北高原那曲地区MODIS卫星数据和站点大气湍流观测数据,分别应用Massman反演模型和一种确定地表粗糙度的独立方法,计算并分析地表粗糙度的时空变化特征并对反演模型进行验证,分析Z0m存在着明显的季节性变化特征。2—8月伴随冰雪消融与植被生长,Z0m逐渐增大,站点Z0m最大可达4～5 cm。9月至次年2月由于高原季风的衰退期等原因,Z0m逐渐减小,站点Z0m减小至1～2 cm。异常年份的降雪是Z0m明显低于常年的主要原因。依据Z0m的由小到大可以将下垫面分为冰雪类、稀疏草地类、茂盛草地类、城镇类4类,其中茂盛草地类和稀疏草地类分别占区域62. 49%和33. 74%,为主要类别,其Z0m年变化分别在2～6 cm和1～4 cm之间。两种计算方法得出的结论相关性较好,由于平均滑动作用,反演资料较实测计算结果偏小。整体而言,利用卫星数据反演算法计算的Z0m是可行的,并可应用于改进陆面模式参数,提高模式模拟的准确性,能更好的揭示区域的热通量交换。     

基于MODIS数据的甘肃主要水库水体面积变化特征

掌握甘肃主要水库的水体面积变化规律,对"甘肃省国家生态安全屏障综合试验区"建设具有重要参考意义。基于2000—2014年MODIS的8 d合成地表反射率产品MOD09资料,利用WI1水体指数,对甘肃四大水库双塔、昌马、红崖山和刘家峡的水体面积进行监测,并与兰州区域气候中心实时接收的卫星资料目视解译结果进行对比、建立相关关系,进而对上述水库水体面积变化状况进行研究。结果表明:甘肃四大水库8 d合成产品MOD09反演的水体与卫星实时资料目视解译结果均具有很好的相关性,且变化趋势较一致,MOD09产品反演结果能够反映水体实时变化状况,可用于水库水体面积的变化特征研究。近10 a多,甘肃四大水库水体面积表现出春季或秋季较大、夏季最小的年内分布特征,各水库水体面积的年际变化趋势较复杂,疏勒河上的昌马和双塔水库春、夏、秋三季水体面积的年际变化趋势不明显,而红崖山和刘家峡水库水体面积均呈波动增加趋势,这对甘肃中西部农业灌溉极为有利。     

基于深度学习的FY3D/MERSI和EOS/MODIS云检测模型研究

针对FY3D/MERSI和EOS/MODIS的云检测问题,提出了一种基于深度学习技术的全自动云检测算法,首次将深度学习引入到卫星影像云检测领域。本算法使用深度全卷积神经网络(Deep Convolutional Neural Networks)作为核心结构,基于EOS/MODIS基本云检测原理选择合适的通道作为特性向量参数,针对不同的场景进行分类和网络模型的训练,最终得到基于深度学习的云检测模型。经过EOS/MODIS数据和FY3D/MERSI数据的测试,云检测的精度达到98%以上,可以看出基于深度学习的云检测算法能够用于云检测,该算法具有效率高、精度高等特点,云检测效果理想。     

A new method for generating a clear-sky Landsat composite for cropland from cloud-contaminated Landsat-7 and Landsat-8 images

A new method was developed in this study for producing a clear-sky Landsat composite for cropland from cloud-contaminated Landsat images acquired in a short time period. It used Thiel–Sen regression to normalize all Landsat scenes to a MODIS image to make all Landsat images radiometrically consistent and comparable. Pixel selection criteria combining the modified maximum vegetation index and the modified minimum visible reflectance selection methods were designed to enhance the pixel selection of land/water over cloud/shadow in the image compositing. The advantages of the method include (1) avoiding complicated atmospheric corrections but with reliable surface reflectance results, (2) being insensitive to errors induced by image co-registration uncertainties between Landsat and MODIS images, (3) avoiding the lack of samples for the regression analysis using the full Landsat scenes (rather than overlay regions), and (4) enhancing cloud/shadow detection. The composite image has MODIS-like surface reflectance, thus making MODIS algorithms applicable for retrieving biophysical parameters. The method was automatically implemented on a set of 13 cloud-contaminated (>39%) Landsat-7 (Scan-Line Corrector-Off) and Landsat-8 scenes acquired during peak growing season in a crop region of Manitoba, Canada. The result was a 95.8% cloud-free image. The method can also substantially increase the usage of cloud-contaminated Landsat data.     

Assessing the potential for satellite image monitoring of seagrass thermal dynamics: for inter- and shallow sub-tidal seagrasses in the inshore Great Barrier Reef World Heritage Area,Australia

Seagrass meadows are at increasing risk of thermal stress and recent work has shown that water temperature around seagrass meadows could be used as an indicator for seagrass condition. Satellite thermal data have not been linked to the thermal properties of seagrass meadows. This work assessed the covariation between 20 in situ average daily temperature logger measurement sites in tropical seagrass meadows and satellite derived daytime SST (sea surface temperature) from the daytime MODIS and Landsat sensors along the Great Barrier Reef coast in Australia. Statistically significant (R²?=?0.787–0.939) positive covariations were found between in situ seagrass logger temperatures and MODIS SST temperature and Landsat sensor temperatures at all sites along the reef. The MODIS SST were consistently higher than in situ temperature at the majority of the sites, possibly due to the sensor’s larger pixel size and location offset from field sites. Landsat thermal data were lower than field-measured SST, due to differences in measurement scales and times. When refined significantly and tested over larger areas, this approach could be used to monitor seagrass health over large (10⁶?km²) areas in a similar manner to using satellite SST for predicting thermal stress for corals.     

近地表大气逆温条件下的地表温度遥感反演与验证

为了减少近地表大气逆温对地表温度遥感反演精度的影响,提出在晴空的地表温度"通用劈窗算法"模型中增加一个温度改正项来实现。在建立该误差改正项时,利用正常条件下的通用劈窗算法系数和具有不同逆温强度的逆温廓线,并结合大气辐射传输模型MODTRAN计算,得到近地表大气逆温条件下的地表温度反演误差,并在分析了该误差值与相应的逆温强度的关系后,发现该温度改正项可以表示为近地表大气逆温强度的二次项函数。为了进一步提高地表温度的反演精度,将地表温度和大气水汽含量进行分组,分别针对每个分组来确定温度改正项方程的系数。模拟结果表明,在逆温强度为1.7 K/100m时,该温度改正项可以使地表温度的反演精度提高0.44 K。利用内蒙古海拉尔试验站的实测数据对地表温度反演结果进行了验证,在近地表大气存在逆温的条件下,该方法能提高地表温度的遥感反演精度0.47K。但是,由于本文提出的方法需要已知大气温度廓线来计算大气逆温强度,因此在实际应用中该方法受到了一定的限制。     

Study of thermal IR phenomena associated with 27 February 2010 Chile Mw 8.8 earthquake using MODIS data

Stresses acting on fault systems before major earthquakes can produce thermal anomalies; these abnormalities can be observed using multi-sensor satellite data. Moderate resolution spectro-radiometer on board the terra and aqua satellites can provide thermal infrared (TIR) imaging data for land and ocean. These TIR data have recorded short-lived thermal anomaly prior to major earthquakes. It is suggested by others that these electromagnetic (EM) phenomena relate to stress build up before earthquakes. The objective of this study is to find an association between spatial extent and temporal evolution of thermal anomalies and known major earthquakes near the boundary of Nazca plate and South American plate. Our approach is to map the TIR transient fields from polar orbiting satellites and analysing those data using time series temperature plots to detect the abnormal thermal trends before the earthquake. This study concentrated on marine earthquakes to detect the changes in both land and ocean before seismic activity.     

Reconstruction of time series MODIS EVI data using de-noising algorithms

Long-term Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) data have inherent noise due to clouds and poor atmospheric conditions that limit its applicability for environmental applications. This study was carried out with an objective of noise removal and reconstruction of time series MODIS EVI data (16 day) for the period 2010–2014 using de-noising algorithms. Relative evaluation of de-noising algorithms for smoothing temporal data with ideal noise free data is not possible in actual scenario. Hence, synthetic signals were generated and introduced Gaussian noise at different variance levels for evaluation purpose. Spatial analysis was carried out by introducing noise at different variance levels into the noise free EVI images from the raw EVI stacked image. Spatio-temporal analyses of noise signals in the reconstructed EVI images were evaluated in terms of performance indicators, namely Peak Signal-to-Noise Ratio and Mean Square Error.     

Local variability in the timing and intensity of tropical dry forest deciduousness is explained by differences in forest stand age

Tropical Dry Forest deciduousness is a behavioral response to climate conditions that determines ecosystem-level carbon uptake, energy flux, and habitat conditions. It is regulated by factors related to stand age, and landscape scale variability in deciduous phenology may affect ecosystem functioning in forests throughout the tropics. This study determines whether observed phenological differences are explainable by forest age in the southern Yucatán Peninsula in Mexico, where forest clearing for shifting cultivation has created a mosaic of forest stands of varying age. Matched-pair statistical tests compare neighboring forest pixels of different age class (12–22 years versus 22+ years) and detect significant differences in Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI)-derived metrics related to the timing and intensity of deciduousness during three dry seasons (2008–2011). In all seasons, young forests exhibit significantly more intense deciduousness, measured as total seasonal change of EVI normalized by annual maximum EVI (p < 0.001), and larger normalized EVI change during successive dry season months relative to start-of-dry-season EVI (p < 0.001), than neighboring older forests subject to similar environmental conditions.