AMSR-E微波数据反演裸地地表温度算法研究

利用AMSR-E被动微波数据,以撒哈拉沙漠地区为研究对象,基于搭载在同一颗卫星上的AMSR-E传感器和MODIS传感器同步观测成像的特点,将MODIS数据反演得到的裸地地表温度产品进行尺度上推.作为AMSR-E像元尺度的地表真实温度.在分析AMSR-E不同通道微波探测值所包含的不同信息特点的基础上,选出反演裸地地表温度较为有效的探测通道,利用多元线性回归的方法,建立经尺度上推后对应AMSR-E尺度的MODIS地表温度与AMSR-E各通道亮温之间的对应关系同,从而实现利用AMSR-E被动微波数据反演裸地地表温度的算法.经验证,反演计算的裸地地表温度误差在±3 K之间.该方法不仅对时间和空间的变化有一定的适应性.而且具有一定的物理意义,演结果可以较好地反映裸地地表温度的变化情况.     

Classification of vegetation in North Tibet Plateau based on MODIS time-series data

Based on the 16d-composite MODIS （moderate resolution imaging spectroradiometer）-NDVI（normalized difference vegetation index） time-series data in 2004, vegetation in North Tibet Plateau was classified and seasonal variations on the pixels selected from different vegetation type were analyzed. The Savitzky-Golay filtering algorithm was applied to perform a filtration processing for MODIS-NDVI time-series data. The processed time-series curves can reflect a real variation trend of vegetation growth. The NDVI time-series curves of coniferous forest, high-cold meadow, high-cold meadow steppe and high-cold steppe all appear a mono-peak model during vegetation growth with the maximum peak occurring in August. A decision-tree classification model was established according to either NDVI time-series data or land surface temperature data. And then, both classifying and processing for vegetations were carried out through the model based on NDVI time-series curves. An accuracy test illustrates that classification results are of high accuracy and credibility and the model is conducive for studying a climate variation and estimating a vegetation production at regional even global scale.     

Estimation of net surface shortwave radiation from land surface temperature in regional scale

The method to estimate NSSR (net surface shortwave radiation) from LST (land surface temperature) in regional scale is discussed. First, an elliptical model between the time series of normalized LST and NSSR was developed using the daily evolution of LST and NSSR. Second, time series of LST and NSSR were simulated by common land model (CoLM) and were proved to be of high accuracy. On the basis of these, a non-linear least square ellipse fitting using the genetic algorithm method was used to fit the normalized LST and NSSR. Finally, LST was inverted using MODIS (moderate resolution imaging spectroradiometer) data with the split-window algorithm, and the regional NSSR was then estimated with LST and an elliptical model. The validation result shows that the derived average NSSR of 50×50 pixels of MODIS data was quite close to the observed data, and the distribution was reasonable, which indicates that the proposed method was capable of estimating NSSR on a regional scale.     

一种实时数据处理的软硬件算法研究

针对某中分辨率成像光谱仪遥感图像数据的特点,在研究基于局域纹理特征适合空间应用图像无损压缩技术的基础上,进行无损解压缩算法的设计,阐述了如何采用并行处理和流水线技术优化算法,实现算法到VLSI结构的优化映射,研制无损解压缩专用芯片,该芯片可以不低于16 Mbps的速率实时处理压缩数据,进行无损解压.     

Dynamic Drought Monitoring in Guangxi Using Revised Temperature Vegetation Dryness Index

Moderate resolution imaging spectroradiometer （MODIS） data are very suitable for vast extent, long term and dynamic drought monitoring for its high temporal resolution, high spectral resolution and moderate spatial resolution. The composite Enhanced Vegetation Index （EVI） and composite land surface temperature （Ts） obtained from MODIS data MOD11A2 and MOD13A2 were used to construct the EVI-Ts space. And Temperature Vegetation Dryness Index （TVDI） was calculated to evaluate the agriculture drought in Guangxi province, China in October of 2006. The results showed that the drought area in Guangxi was evidently increasing and continuously deteriorating from the middle of September to the middle of November. The TVDI, coming from the EVI-Ts space, could effectively indicate the spatial distribution and temporal evolution of drought, so that it could provide a strong technical support for the forecasting agricultural drought in south China.     

基于MOD09GA产品的草地生物量遥感估算模型

利用忖南州2006-2008年的草地地上生物量外业实测数据和EOS Terra卫星中分辨率成像光谱仪MODIS每日地表反射率产品MOD09GA,提取了年实测点上的7个通道的光谱反射率值,计算了17种植被指数,综合研究了植被指数与生物量之间的相关性及草地地上生物量遥感反演模型,采用植被指数最大合成算法,计算了2005-2007年各旬、月及年最大植被指数及草地地上生物量密度时空变化动态特征.研究表明适宜甘南州草地地上生物量估产的植被指数EVI,其估产模型是乘幂模型(y=13583x<1.6652>,R=0.798),模型平均测产精度达76.72%.甘南州草地地上生物量密度除了2005年在7月上旬达到最大值外,其他年份均在7月下旬达到最大值.从月度变化可以看出,除了2005年温性草甸草原、2006年低平地草甸、2007年暖性草丛的月生物量密度分别在6,9,6月达到最大值外,甘南州不同类型草地基本上在7月达到最大值.2006年和2007年甘南草地地上生物量密度的年变化比较相近,但2005年的草地地上生物量密度与其他两年相比差异较大,且多高于三年的平均值.     

MODIS在青藏高原大范围积雪制图中的应用及存在的问题

积雪是地球表面重要的组成部分之一,精确监测积雪覆盖及其动态变化是地球科学的一个重要研究方向。本文以我国主要积雪区之一的青藏高原作为研究区域,研究了MODIS数据在青藏高原大范围积雪制图中的应用。使用基于NDSI阈值法的积雪像元识别方法,对2002-2003年度青藏高原的积雪状况进行了监测。对监测结果的分析表明,虽然这种算法目前还存在一些不足,但对青藏高原大区域的积雪监测仍是一种极为有效的方法。     

巢湖蓝藻水华预警监控方案研究

文章根据近年来巢湖蓝藻水华预警监测期间的水质监测结果、水华暴发情况和MODIS蓝藻影像图,研究制定了巢湖蓝藻水华预警监控方案;将巢湖划分为饮用水源地、河流入湖区和湖区3个蓝藻水华预警监控区域,设置蓝色、黄色和红色三级预警响应级别;优化了巢湖蓝藻水华预警监测布点方案、监测时段、监测频次和监测项目,为今后巢湖蓝藻水华研究提供科学依据。     

A feasibility study on diagnosing wheat water status using spectral reflectance

A total of 110 wheat leaf samples were collected in the field and their spectral reflectances were measured with a spectroradiometer in laboratory. After a spectral normalizing technique, the spectral absorption feature parameters such as the absorption depth and area, were extracted from each leaf spectrum. The relative water content (RWC) was measured for samples. The experimental results indicated that the spectral absorption depth and area of wheat leaves at 1 450 nm were correlated with their RWC. So we can diagnose wheat water status by using their spectral reflectances. Furthermore, we discuss the possibility of developing new instruments based on the analysis of the spectroradiometer data for non-destructive and instantaneous measurement of the wheat water status in the field.     

The study of estimation method of broadband emissivity from EOS/MODIS data

The broadband emissivity is an important parameter for estimating the energy balance of the Earth.This study focuses on estimating the window(8-12 |xm) emissivity from the MODIS(moderate-resolution imaging spectroradiometer) data,and two methods are built.The regression method obtains the broadband emissivity from MOD11B1 5KM product,whose coefficient is developed by using 128 spectra,and the standard deviation of error is about 0.0118 and the mean error is about0.0084.Although the estimation accuracy is very high while the broadband emissivity is estimated from the emissivity of bands 29,31 and 32 obtained from MOD11B1 5KM product,the standard deviations of errors of single emissivity in bands 29,31,32 are about 0.009 for MOD11B1_5KM product,so the total error is about 0.02 and resolution is about 5km×5km.A combined radiative transfer model with dynamic learning neural network method is used to estimate the broadband emissivity from MODIS 1B data.The standard deviation of error is about 0.016,the mean error is about0.01,and the resolution is about 1km ×1km.The validation and application analysis indicates that the regression is simpler and more practical,and estimation accuracy of the dynamic learning neural network method is higher.Considering the needs for accuracy and practicalities in application,one of them can be chosen to estimate the broadband emissivity from MODIS data.