MODIS雪产品的计算及其与NSIDC雪产品的比较--以青藏高原雪覆盖为例

以青藏高原上的两个区域、选择不同时相的MODIS影像，运用NDSI、NDVI等指标，结合MODIS云腌膜数据，计算得到MODIS的雪产品；并将其与美国NSIDC的雪产品进行比较。结果表明，计算得到的MODIS雪产品中雪的覆盖区域与NSIDC雪产品中的雪覆盖区域基本一致，但计算得到的雪覆盖面积比对应的NSIDC雪产品中的雪覆盖面积略大3％。计算得到的雪产品与NSIDC雪产品在同样经纬度范围内图像位置有偏差。范围越小，位置偏差越明显。对图像进行经度偏移后，取共同区域进行像素差值比较，结果，计算得到的雪产品与NSIDC雪产品判断一致的像素约83．12％。     

基于多时相MODIS监测冬小麦的种植面积

论文基于时相和波谱信息，利用MODIS数据监测了北京冬小麦的种植面积。首先，基于地形高度对地物光谱反射值的影响，借助DEM数据对研究区域进行划分。其次，在分析并提取北京地区主要农作物时间谱曲线特征的基础上，设计决策函数，成功提取了北京地区冬小麦的种植面积。最后，比较并分析了非遥感数据对监测精度的影响。研究结果表明，（1）时相信息可以极大的提高农作物种植面积的监测精度。（2）辅助数据的利用，使决策函数的设计更具有针对性，监测结果更可靠。（3）多源多时相遥感数据在农作物种植面积的提取中具有明显的技术优势和重要的应用潜力。     

Comparison of the influence factors on NDVI for CCD camera and WFI imager on CBERS-02

China Brazil Earth Resources Satellites (CBERS) have many payloads, among them there are a high resolution Charge Coupled Device (CCD) Camera and the Wide Field Image (WFI). CCD’s spatial resolution in nadir is 19.5 meters and its swath width is 113 kilometers. It has 4 wave bands and a panchromatic wave band in visible and near infra- red spectral band. Side looking is one of the main functions of CCD and the side looking range is ±32°. WFI has one visible band and one near inf…     

Combining vegetation index and model inversion methods for the extraction of key vegetation biophysical parameters using Terra and Aqua MODIS reflectance data

Accurate estimates of vegetation biophysical variables are valuable as input to models describing the exchange of carbon dioxide and energy between the land surface and the atmosphere and important for a wide range of applications related to vegetation monitoring, weather prediction, and climate change. The present study explores the benefits of combining vegetation index and physically based approaches for the spatial and temporal mapping of green leaf area index (LAI), total chlorophyll content (TC_ab), and total vegetation water content (VWC). A numerical optimization method was employed for the inversion of a canopy reflectance model using Terra and Aqua MODIS multi-spectral, multi-temporal, and multi-angle reflectance observations to aid the determination of vegetation-specific physiological and structural canopy parameters. Land cover and site-specific inversion modeling was applied to a restricted number of pixels to build multiple species- and environmentally dependent formulations relating the three biophysical properties of interest to a number of selected simpler spectral vegetation indices (VI). While inversions generally are computationally slow, the coupling with the simple and computationally efficient VI approach makes the combined retrieval scheme for LAI, TC_ab, and VWC suitable for large-scale mapping operations. In order to facilitate application of the canopy reflectance model to heterogeneous forested areas, a simple correction scheme was elaborated, which was found to improve forest LAI predictions significantly and also provided more realistic values of leaf chlorophyll contents.The inversion scheme was designed to enable biophysical parameter retrievals for land cover classes characterized by contrasting canopy architectures, leaf inclination angles, and leaf biochemical constituents without utilizing calibration measurements. Preliminary LAI validation results for the Island of Zealand, Denmark (57°N, 12°E) provided confidence in the approach with root mean square (RMS) deviations between estimates and in-situ measurements of 0.62, 0.46, and 0.63 for barley, wheat, and deciduous forest sites, respectively. Despite the independence on site-specific in-situ measurements, the RMS deviations of the automated approach are in the same range as those established in other studies employing field-based empirical calibration.Being completely automated and image-based and independent on extensive and impractical surface measurements, the retrieval scheme has potential for operational use and can quite easily be implemented for other regions. More validation studies are needed to evaluate the usefulness and limitations of the approach for other environments and species compositions.     

AVHRR derived phenological change in the Sahel and Soudan, Africa, 1982-2005

The Sahel region of Africa has experienced a decrease in rainfall from the early 1960s to mid 1990s. Recent studies have detected an increased in NDVI amplitude and growing season integrated NDVI for the region since 1982. However, these studies have not examined how plant phenology has changed. Phenology examines life cycle events such as bud burst and leaf senescence. Using the software TIMESAT to estimate phenological parameters from the GIMMS AVHRR NDVI dataset, we have found significant positive trends for the length of the growing and end of the growing season for the Soudan and Guinean regions, but significant trends in the Sahel could not be detected. The geographical extent of these trends contrasts with the more northern extent of positive trends of NDVI amplitude and growing season integrated NDVI. Results suggest two types of “greening” trends associated with rainfall change since the drought in the early 1980s.     

Estimation of diurnal air temperature using MSG SEVIRI data in West Africa

Spatially distributed air temperature data with high temporal resolution are desired for several modeling applications. By exploiting the thermal split window channels in combination with the red and near infrared channels of the geostationary MSG SEVIRI sensor, multiple daily air temperature estimates can be achieved using the contextual temperature-vegetation index method. Air temperature was estimated for 436 image acquisitions during the 2005 rainy season over West Africa and evaluated against in situ data from a field test site in Dahra, Northern Senegal. The methodology was adjusted using data from the test site resulting in RMSE = 2.55 K, MBE = − 0.30 K and R² = 0.63 for the estimated versus observed air temperatures. A spatial validation of the method using 12 synoptic weather stations from Senegal and Mali within the Senegal River basin resulted in overall values of RMSE = 2.96 K, MBE = − 1.11 K and R² = 0.68. The daytime temperature curve is interpolated using a sine function based on the multiple daily air temperature estimates from the SEVIRI data. These estimates (covering the 8:00-20:00 UCT time window) were in good agreement with observed values with RMSE = 2.99 K, MBE = − 0.70 K and R² = 0.64. The temperature-vegetation index method was applied as a moving window technique to produce distributed maps of air temperature with 15 min intervals and 3 km spatial resolution for application in a distributed hydrological model.     

基于NDVI的人工植被碳储量估算

人工植被是吸收CO²维护生态系统健康的重要生物成分,干旱区人工碳汇林在CO²减排方面具有重要的作用。应用2009年8月TM数据,提取克拉玛依人工减排林生态景观格局信息,并应用NDVI指数估算植被碳密度。通过测定乔木层及草本层生物量,估算出人工植被乔木层及草本层碳密度。结果表明,克拉玛依人工减排林乔木层的平均碳密度值为37.04 mg/hm²,1 m×1 m样方内草本层平均碳密度为59.65 g/m²,地上植被碳密度约为37.64 mg/hm²,植被层碳储量为250 915.5 mg;随着植被的生长发育及生物量累积效应的发挥,人工植被的碳汇功能还将进一步增大。     

基于亮温—植被指数—气溶胶光学厚度的MODIS火点监测算法研究

MODIS火灾产品的火点检测算法主要以4和11μm通道亮温数据来识别火点,在应用于不同地区和不同季节时有一定局限性。在分析MODIS现有火点检测算法的基础上,对算法相关阈值及参数进行适当调整,同时考虑火灾前后NDVI的变化,以及林火燃烧过程中伴生烟羽使火点下风方气溶胶光学厚度明显增加的特点,构建了基于亮温—植被指数—气溶胶光学厚度的火点识别算法,并应用多次火灾个例对本算法进行验证。结果表明:算法提高了对高温热点和低温焖烧火点的识别能力,有效降低了高温热点的误报率和低温火点的漏报率,使火点检测算法在不同环境的适应性有所增强。     

Detection of foliage conditions and disturbance from multi-angular high spectral resolution remote sensing

Disturbance of forest ecosystems, an important component of the terrestrial carbon cycle, has become a focus of research over recent years, as global warming is about to increase the frequency and severity of natural disturbance events. Remote sensing offers unique opportunities for detection of forest disturbance at multiple scales; however, spatially and temporally continuous mapping of non-stand replacing disturbance remains challenging. First, most high spatial resolution satellite sensors have relatively broad spectral ranges with bandwidths unsuitable for detection of subtle, stress induced, features in canopy reflectance. Second, directional and background reflectance effects, induced by the interactions between the sun-sensor geometry and the observed canopy surface, make up-scaling of empirically derived relationships between changes in spectral reflectance and vegetation conditions difficult. Using an automated tower based spectroradiometer, we analyse the interactions between canopy level reflectance and different stages of disturbance occurring in a mountain pine beetle infested lodgepole pine stand in northern interior British Columbia, Canada, during the 2007 growing season. Directional reflectance effects were modelled using a bidirectional reflectance distribution function (BRDF) acquired from high frequency multi-angular spectral observations. Key wavebands for observing changes in directionally corrected canopy spectra were identified using discriminant analysis and highly significant correlations between canopy reflectance and field measured disturbance levels were found for several broad and narrow waveband vegetation indices (for instance, r²_NDVI = 0.90; r²_CHL3 = 0.85; p < 0.05). Results indicate that multi-angular observations are useful for extraction of disturbance related changes in canopy reflectance, in particular the temporally and spectrally dense data detected changes in chlorophyll content well. This study will help guide and inform future efforts to map forest health conditions at landscape and over increasingly coarse scales.     

The Yearly Land Cover Dynamics (YLCD) method: An analysis of global vegetation from NDVI and LST parameters

NDVI (Normalized Difference Vegetation Index) has been widely used to monitor vegetation changes since the early eighties. On the other hand, little use has been made of land surface temperatures (LST), due to their sensitivity to the orbital drift which affects the NOAA (National Oceanic and Atmospheric Administration) platforms flying AVHRR sensor. This study presents a new method for monitoring vegetation by using NDVI and LST data, based on an orbital drift corrected dataset derived from data provided by the GIMMS (Global Inventory Modeling and Mapping Studies) group. This method, named Yearly Land Cover Dynamics (YLCD), characterizes NDVI and LST behavior on a yearly basis, through the retrieval of 3 parameters obtained by linear regression between NDVI and normalized LST data. These 3 parameters are the angle between regression line and abscissa axis, the extent of the data projected on the regression line, and the regression coefficient. Such parameters characterize respectively the vegetation type, the annual vegetation cycle length and the difference between real vegetation and ideal cases. Worldwide repartition of these three parameters is shown, and a map integrating these 3 parameters is presented. This map differentiates vegetation in function of climatic constraints, and shows that the presented method has good potential for vegetation monitoring, under the condition of a good filtering of the outliers in the data.