A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling

During the last 50 years, the management of agroecosystems has been undergoing major changes to meet the growing demand for food, timber, fibre and fuel. As a result of this intensified use, the ecological status of many agroecosystems has been severely deteriorated. Modeling the behavior of agroecosystems is, therefore, of great help since it allows the definition of management strategies that maximize (crop) production while minimizing the environmental impacts. Remote sensing can support such modeling by offering information on the spatial and temporal variation of important canopy state variables which would be very difficult to obtain otherwise.In this paper, we present an overview of different methods that can be used to derive biophysical and biochemical canopy state variables from optical remote sensing data in the VNIR-SWIR regions. The overview is based on an extensive literature review where both statistical–empirical and physically based methods are discussed. Subsequently, the prevailing techniques of assimilating remote sensing data into agroecosystem models are outlined. The increasing complexity of data assimilation methods and of models describing agroecosystem functioning has significantly increased computational demands. For this reason, we include a short section on the potential of parallel processing to deal with the complex and computationally intensive algorithms described in the preceding sections.The studied literature reveals that many valuable techniques have been developed both for the retrieval of canopy state variables from reflective remote sensing data as for assimilating the retrieved variables in agroecosystem models. However, for agroecosystem modeling and remote sensing data assimilation to be commonly employed on a global operational basis, emphasis will have to be put on bridging the mismatch between data availability and accuracy on one hand, and model and user requirements on the other. This could be achieved by integrating imagery with different spatial, temporal, spectral, and angular resolutions, and the fusion of optical data with data of different origin, such as LIDAR and radar/microwave.     

On the flow structure and turbulence during sweep and ejection events in a wind-tunnel model canopy

Particle image velocimetry (PIV) data obtained in a wind-tunnel model of a canopy boundary layer is used to examine the characteristics of mean flow and turbulence. The vector spacing varies between 1.7 and 2.5 times the Kolmogorov scales. Conditional sampling based on quadrants, i.e. based on the signs of velocity fluctuations, reveals fundamental differences in flow structure, especially between sweep and ejection events, which dominate the flow. During sweeps, the downward flow generates a narrow, highly turbulent, shear layer containing multiple small-scale vortices just below canopy height. During ejections, the upward flow expands this shear layer and the associated small-scale flow structures to a broad region located above the canopy. Consequently, during sweeps the turbulent kinetic energy (TKE), Reynolds stresses, as well as production and dissipation rates, have distinct narrow peaks just below canopy height, whereas during ejections these variables have broad maxima well above the canopy. Three methods to estimate the dissipation rate are compared, including spectral fits, measured subgrid-scale (SGS) energy fluxes at different scales, and direct measurements of slightly underresolved instantaneous velocity gradients. The SGS energy flux is 40–60% of the gradient-based (direct) estimates for filter sizes inside the inertial range, while decreasing with scale, as expected, within the dissipation range. The spectral fits are within 5–30% of the direct estimates. The spectral fits exceed the direct estimates near canopy height, but are lower well above and below canopy height. The dissipation rate below canopy height increases with velocity magnitude, i.e. it has the highest values during sweep and quadrant 1 events, and is significantly lower during ejection and quadrant 3 events. Well above the canopy, ejections are the most dissipative. Turbulent transport during sweep events acts as a source below the narrow shear layer within the canopy and as a sink above it. Transport during ejection events is a source only well above the canopy. The residual term in the TKE transport equation, representing mostly the effect of pressure–velocity correlations, is substantial only within the canopy, and is dominated by sweeps.     

雷暴云电结构与闪电关系初探

利用雷暴云偶极性电结构理论,结合卫星和雷达资料分析了不同荷电结构下地面电场。结果表明:当正电荷中心高度越高、负电荷中心高度越低,形成的地面负电场越大,越有利于地闪的形成;负地闪发生后或云砧下方,地面电场成正极性,易引发正地闪;地闪分布与雷达回波、卫星云图中雷暴云位置基本一致,结合雷达和卫星资料可初步判断正/负地闪发生位置。     

2005年登陆我国热带气旋特征分析

热带气旋是影响我国沿海的主要灾害性天气系统之一, 它产生的狂风、暴雨、巨浪和风暴潮, 给沿岸地区人民生命和国家财产安全带来严重威胁。而严重的台风灾害, 往往是台风登陆引起的。为进一步研究登陆热带气旋的活动规律, 总结了2005年登陆我国热带气旋的特点, 结果表明:2005年登陆我国热带气旋具有登陆季节短、登陆地点分布异常、台风比例异常偏高、灾害损失极为严重的特点。同时, 还讨论了2005年登陆我国热带气旋异常的气候原因, 并指出未来几年登陆热带气旋和台风的年频数处在上升趋势中。     

POS数据辅助的航空影像变化检测方法研究

提出了一种利用机裁定位定向系统（POS）数据辅助航空影像进行影像匹配和变化检测的方法。首先利用带POS数据的老影像解求新影像的外方位元素，然后在老立体影像上提取特征点，根据前方交会和共线条件方程得到新影像上同名点的近似位置，再与新影像进行匹配，寻找匹配不好的点作为变化区域的初始位置。以此为基础选择精检测窗口，进行边缘提取和跟踪，并进行链码匹配，最终确定发生变化的区域。试验证实，本文方法是可行的。     

利用非线性扩散的半自动纹理图像分割

介绍了一种非线性扩散过滤器法从包含纹理的图像中获取灰度、尺度和方向信息，并形成经过耦合保边平滑的5通道向量值图像，对纹理图像的分割变为对此向量值图像的分割。针对半自动操作的特点，在合理的假设前提下，采用了多通道统计区域分割法。试验结果表明，本文的纹理分割方法能有效地利用重要纹理特征与灰度的混合信息，是一种半自动的无监督纹理图像分割方法。     

基于正则MAP模型的遥感影像亚像元定位

为了更好地解决亚像元的定位问题，基于超分辨率影像重建的技术，结合亚像元定位理论，提出了一种应用于亚像元定位的正则MAP估计模型，并且通过真实数据进行了检验。实验表明，该模型是一种简单、有效地解决亚像元定位问题的方法。     

一种基于图像金字塔光流的特征跟踪方法

推导并实现了一种基于图像金字塔光流的角点特征跟踪方法。实验结果表明,该方法在不同运动幅度和运动方式下的检测跟踪性能较好,能够有效地应用于长序列图像的特征跟踪。     

基于多分辨率分析的灰度调制影像融合方法研究

在遥感图像处理中,由于应用的需要,已经有很多算法可以用来融合高分辨率的全色影像和低分辨率的多光谱影像。本文阐述了小波变换的Mallat算法和a′Trous（多孔）算法,在此基础上,作者提出了一种基于多分辨率分析的灰度调制影像融合方法（MRAGM算法）,以SPOT全色影像与TM多光谱影像融合为例,详细给出了算法和解算步骤,最后分别用三种方法对SPOT高分辨率全色影像和TM低分辨率多光谱影像进行了融合,并且对融合后的影像从定性和定量方面都进行了评价,可以得出MRAGM方法比Mallat算法和a′Trous（多孔）算法的融合效果要好。     

投影寻踪学习网络的遥感影像分类

采用投影寻踪(projection pursuit,PP)学习网络方法建立了一种新的遥感影像分类模型。该方法结合了统计学中投影寻踪算法节点函数灵活的非参数估计特点和人工神经网络的自学习功能,具有简捷的网络结构和良好的鲁棒性能。利用苏州市TM影像进行了分类实验,将分类结果与BP神经网络和最大似然法的分类结果相比较,投影寻踪学习网络的分类精度较高,具有一定的实用性。