月表热参数模型与月壤厚度的被动微波遥感理论分析

利用被动微波遥感亮度温度数据反演月壤厚度是“嫦娥”探月工程的科学目标之一，也是人类探测月壤厚度的一种新的尝试。深入研究月表太阳辐射、月球内部热流以及月表温度的分布和变化规律，是解译遥感数据，反演月壤厚度的前提条件，也为进一步开展月球探测、开发利用月球资源乃至建立月球基地相关研究工作提供必要的参考。 本文根据月表有效太阳辐照度与太阳常数、日月距离和太阳辐射入射角的关系，建立了月表有效太阳辐照度的实时模型如下： (1) 其中， (2) (3) 通过对月表有效太阳辐照度实时模型的各个参数分析发现，影响月表有效太阳辐照度变化的主要因素是日地距离和太阳辐射入射角的变化。对模型的误差分析表明，从1950年到2050年的100年内，月表有效太阳辐照度计算结果的误差百分比小于0.28％，能更准确地反映月表有效太阳辐照度的变化情况。从2007年月表有效太阳辐照度的计算结果发现，该年内的月表有效太阳辐照度变化在1321.5～1416.6 W•m-2之间，平均为1368.0 W•m-2，一个月内的变化最小幅度为6.0 W•m-2，最大幅度为23.6 W•m-2。 在月表有效太阳辐照度的实时模型基础上，根据能量守恒和Stefan-Boltzmann定律，本文还得出了月表温度分布模型如下： (4) 其中，初始条件由下式决定， (5) 通过与月表温度实际观测结果的比较发现，当月表反射率、热发射率和热惯量分别取0.127、0.94和125 J•m-2•s-1/2•K-1时，模型的计算结果与实际观测值比较符合，能较好地预测理想条件下的月表温度。 月表热参数研究的一个重要应用就是解译对月被动微波遥感的亮度温度数据。在对月被动微波遥感探测中，辐射计获得的亮度温度反映了月球表层的热辐射特性。月球表层的热辐射与其自身的热状况紧密相关，结合文中建立的月表热参数模型，根据辐射传播理论进一步分析了对月微波遥感探测中，月球表层在不同情况下对亮度温度的贡献，确定了亮度温度随月表温度和月壤厚度的变化关系，对被动微波遥感探测月壤厚度的可能性和可能达到的精度进行了估算。 对月球表层的热辐射传播的分析发现，对月被动微波遥感探测获得的亮度温度受月球表层热辐射的控制，与月壤厚度具有指数相关性，并受到月表温度的影响。当月壤和月岩的复介电常数分别为2 + 0.005 j和9 + 1 j、相对磁导率均为1时，对应3.0GHz、7.8GHz、19.35GHz和37.0GHz四个频率的亮度温度与月壤厚度及月表温度的关系可分别近似表示为， 3.0GHz亮度温度： (6) 7.8GHz亮度温度： (7) 19.35GHz亮度温度： (8) 37.0GHz亮度温度： (9) 当月壤厚度和月表温度分别在0.5m~30m和100K~400K之间变化时，上述四个频率的亮度温度变化范围分别在212.5K～252.8K、207.4 K～266.7K、193.8 K～288.6K和174.0 K～310.9K之间。对于较低频率的被动微波遥感，亮度温度随月壤厚度的增大逐渐增大并趋于稳定；对较高频率的被动微波遥感，亮度温度随月壤厚度的增大会产生起伏波动，不利于用单波段反演月壤厚度。亮度温度梯度在频率较高时梯度较大，在很小的月壤厚度范围内很快就趋于0，不利于厚度较大时的月壤厚度反演，但对于厚度较小时的月壤厚度反演精度较高；同时，除3.0GHz外，7.8GHz、19.35GHz和37.0GHz三个频率的亮度温度梯度随月表温度的升高降幅较大，尤其是19.35GHz，适合在夜间对月壤厚度较小的地区进行更精确的探测。对于3.0GHz，其亮度温度梯度受月表温度变化的影响很小，能反映出较深层月壤厚度的信息，可以对月球进行全球全天时探测。若辐射计的分辨率为0.02K，3.0GHz频率对10m厚月壤的判别精度达到0.07m；对于20m厚月壤的精度为1.4m。当月壤厚度小于0.5m时，随着月壤厚度从0到0.5m增加，月球表层的亮度温度贡献呈先减小后增大的趋势，从而使某一亮度温度值可能对应存在两种不同的月壤厚度。因此，对于月壤厚度小于0.5m的区域，利用单波段被动微波遥感亮度温度反演月壤厚度是比较困难的。 在对月被动微波遥感探测中，可以利用月球夜晚时段的亮度温度数据判别月壤厚度是否小于0.5m。当月表温度为100K时，3.0GHz、7.8GHz、19.35GHz和37.0GHz四个频率的亮度温度判别参考值分别为212.9K、207.4K、193.5K和174.1K；月表温度为240K时，上述四个频率的亮度温度判别参考值分别为220.8K、226.8K、234.1K和237.2K。当亮度温度小于参考值时表示月壤厚度小于0.5m，反之，表示月壤厚度大于0.5m。更进一步地，可以根据月表温度的影响系数对月岩是否裸露于月表进行判断。当3.0GHz、7.8GHz、19.35GHz和37.0GHz四个频率的月表温度影响系数接近0.77、0.82、0.84和0.85时，可以认为月岩直接暴露于月表。

Application of effective medium approximation theory to ocean remote sensing under wave breaking

Based on the effective medium approximation theory of composites, the empirical model proposed by Pandey and Kakar is remedied to investigate the microwave emissivity of sea surface under wave breaking driven by strong wind. In the improved model, the effects of seawater bubbles, droplets and difference in temperature of air and sea interface (DTAS) on the emissivity of sea surface covered by whitecaps are discussed. The model results indicate that the effective emissivity of sea surface increases with DTAS increasing, and the impacts of bubble structures and thickness of whitecaps layer on the emissivity are included in the model by introducing the effective dielectric constant of whitecaps layer. Moreover, a good agreement is obtained by comparing the model results with the Rose's experimental data.

Application of Dempster-Shafer evidence theory to unsupervised classification in multisource remote sensing

The aim of this paper is to show that Dempster-Shafer evidence theory may be successfully applied to unsupervised classification in multisource remote sensing. Dempster-Shafer formulation allows for consideration of unions of classes, and to represent both imprecision and uncertainty, through the definition of belief and plausibility functions. These two functions, derived from mass function, are generally chosen in a supervised way. In this paper, the authors describe an unsupervised method, based on the comparison of monosource classification results, to select the classes necessary for Dempster-Shafer evidence combination and to define their mass functions. Data fusion is then performed, discarding invalid clusters (e.g. corresponding to conflicting information) thank to an iterative process. Unsupervised multisource classification algorithm is applied to MAC-Europe'91 multisensor airborne campaign data collected over the Orgeval French site. Classification results using different combinations of sensors (TMS and AirSAR) or wavelengths (L- and C-bands) are compared. Performance of data fusion is evaluated in terms of identification of land cover types. The best results are obtained when all three data sets are used. Furthermore, some other combinations of data are tried, and their ability to discriminate between the different land cover types is quantified

Surface pigments,algal biomass profiles,and potential production of the euphotic layer:Relationships reinvestigated in view of remote-sensing applications

Maps of surface chlorophyllous pigment (Chl a + Pheo a) are currently produced from ocean color sensors. Transforming such maps into maps of primary production can be reliably done only by using light-production models in conjuction with additional information about the column-integrated pigment content and its vertical distribution. As a preliminary effort in this direction. $\ticksim 4,000$ vertical profiles pigment (Chl a + Pheo a) determined only in oceanic Case 1 waters have been statistically analyzed. They were scaled according to dimensionless depths (actual depth divided by the depth of the euphotic layer, $Z_e$) and expressed as dimensionless concentrations (actual concentration divided by the mean concentration within the euphotic layer). The depth $Z_e$ generally unknown, was computed with a previously develop bio-optical model. Highly sifnificant relationships were found allowing $\langle C \rangle_tot$, the pigment content of the euphotic layer, to be inferred from the surface concentration, $\bar C_pd$, observed within the layer of one penetration depth. According to their $\bar C_pd$ values (ranging from $0.01 to > 10 mg m^-3$), we categorized the profiles into seven trophic situations and computed a mean vertical profile for each. Between a quasi-uniform profile in eutrophic waters and a profile with a strong deep maximum in oligotrophic waters, the shape evolves rather regularly. The wellmixed cold waters, essentially in the Antarctic zone, have been separately examined. On average, their profiles are featureless, without deep maxima, whatever their trophic state. Averaged values their profiles are featureless, without deep maxima, whatever their trophic state. Averaged values their profiles are featureless, without deep maxima, whatever their trophic state. Averaged values of $ρ$, the ratio of Chl a tp (Chl a + Pheo a), have also been obtained for each trophic category. The energy stored by photosynthesizing algae, once normalized with respect to the integrated chlorophyll biomass $\langle C \rangle _tot $ is proportional to the available photosythetic energy at the surface via a parameter $ψ∗$ which is the cross-section for photosynthesis per unit of areal chlorophyll. By tanking advantage of the relative stability of $ψ∗.$ we can compute primary production from ocean color data acquired from space. For such a computation, inputs are the irradiance field at the ocean surface, the "surface" pigment from which $\langle C \rangle _tot$ can be derived, the mean $ρ value pertinent to the trophic situation as depicted by the $\bar C_pd or $\langle C \rangle _tot$ values, and the cross-section $ψ∗$. Instead of a contant $ψ∗.$ value, the mean profiles can be used; they allow the climatological field of the $ψ∗.$ parameter to be adjusted through the parallel use of a spectral light-production model.

A data-model fusion approach for upscaling gross ecosystem productivity to the landscape scale based on remote sensing and flux footprint modelling

Chinese Academy of Sciences ; National Science Foundation of China [41071059]; National Key Technology R&D Program of China [2008BAK50B06-02]; National Basic Research Program of China [2010CB950900, 2010CB950704]; Natural Sciences and Engineering Research Council of Canada

Research on extracting method of micro-scale remote sensing information combination and application in coastal zone

973 Project of China [2006CB701305]; "863" Project of China [2009AA12Z148]; National Natural Science Foundation of China [40971224]