我国干旱具有普遍性、连续性、季节性、地域性等特征，旱灾不仅直接影响我国的粮食安全，还进一步影响水循环及其伴生水环境、水生态和水沙过程。变化环境下，我国干旱问题显得尤为突出，表现在发生频率不断增加、受旱范围不断扩大、影响领域扩展、灾害损失加重，如何应对变化环境下的干旱，已成为我国实现和谐发展过程中亟待解决的关键问题之一。为有效综合应对我国的干旱问题，本文从三个时间层面上探讨了干旱的应对措施；从长期宏观发展战略层次上，构建与水资源承载能力相适应的经济社会发展模式（重点调整种植结构和工业产业结构），可从根本上减少干旱危害；在中尺度时段上，优化水资源调配体系是干旱综合应对的重点；在短尺度时段上，需制定有效应急预案，保障应急水源。

<p>总结了目前广泛应用的气象监测模型和基于遥感数据的干旱监测模型, 将目前的遥感监测方法分为植被状态监测方法、微波土壤水分监测方法、热红外遥感监测方法和基于能量平衡的遥感监测方法进行综述, 深入分析了基于遥感数据的监测方法的特点、适用条件和存在的问题。通过综述基于多源数据的干旱综合监测模型, 对未来干旱监测方法的发展方向进行研究和探讨, 指出集成多源数据的干旱综合监测模型是解决复杂的干旱监测问题的新方法。</p>

土壤含水量是监测旱情墒情的关键参量, 近年来在利用高光谱遥感数据监测土壤含水量方面, 国内外进行了大量的研究。 文章首先在分析利用不同波段监测土壤含水量的原理及优缺点基础上, 指出高光谱遥感监测的独特优势和问题。 并以此为出发点, 从机理上归纳了土壤含水量对土壤反射率的整体影响, 以及对不同波段响应的差异。 在此基础上, 从物理机理和统计方法两个方面, 总结了土壤含水量与土壤反射率的关系。 并分析和评价了各模型及统计方法中的关键问题和优缺点。 以往研究土壤含水量与土壤反射率关系的实验方法中往往存在一些问题, 文章也一一指出并提出了解决方案。 同时, 探讨了高光谱在消除植被影响, 更好地反演土壤含水量方面的可行性。 最后对未来的研究方向进行了展望

土壤水分的遥感监测在农业干旱和水资源管理方面具有重要的意义。 利用MODIS反射率和温度产品来获取地表昼夜温差(ΔTs)和宽波段反照率(Albedo), 构建ΔTs-Albedo光谱特征空间, 提出温差-反照率干旱指数(temperature difference albedo drought index, TDADI)用来监测土壤水分, 并利用宁夏实测0～10 cm平均土壤含水量数据验证该指数的精度, 结果表明: 它们之间的相关性较好, R2变化范围为0.36～0.52。 TDADI与TADI相比, 该指数具有更高的土壤水分监测精度。 然而, 由于数据获取的局限性, 只采用了宁夏平原数据对TDADI进行验证, 在其他区域仍需要进一步验证。

面对多种多样的农业旱情遥感监测指数，如何进行选取是目前遥感指数应用所面临的主要难题。该文以MODIS产品为遥感数据源，比较分析了13种典型的农业干旱遥感监测指数，建立了农业干旱遥感监测指数的分类体系，阐述了不同指数类型的适用范围。结果表明，多种遥感干旱指数对农业干旱的描述并非完全一致。不同指数利用不同的地表特征变化来描述农业干旱程度，是造成这种不一致的主要原因。据此，研究将典型农业干旱遥感监测指数分为4大类：土壤水分变化类、冠层温度变化类、植被水分变化类和作物形态及绿度变化类。其中第1类指数比较适宜于农业旱情预警及土壤干旱型农业旱情的监测，这类指数中修正的垂直干旱指数MPDI可以较好地反映表层土壤水分的变化，并适宜于时序变化监测。第2类指数不仅适宜于旱情预警，更适宜于旱情监测，这类指数中推荐选择基于LAI-LST特征空间的温度植被干旱指数TVDI；第3、4类指数，较适宜于农业旱灾的预警以及灾后评估，该文为农业干旱遥感监测指数的选取提供参考。

针对小麦株型对LAI反演精度的制约, 利用地面实测的多角度数据, 提出采用热点指数反演高精度的不同株型小麦LAI。 通过分析京411紧凑型和中优9507披散型两种株型小麦在红光（670 nm）和近红外波段（800和860 nm）的二向反射光谱特征, 借鉴热暗点指数HDS和归一化热暗点信息指数NDHD, 构建了改进的归一化热暗点指数MNDHD和热暗点比值指数HDRI两个新热点指数。 将HDS, NDHD, MNDHD和HDRI与NDVI, SR和EVI相乘得到的热点组合指数用于不同株型小麦LAI的反演。 分析得出对紧凑型小麦京411, 由860 nm近红外波段的NDVI与MNDHD和HDRI的组合指数反演的LAI精度分别为0.943 1和0.909 2； 对披散型小麦中优9507, 由800 nm近红外波段的SR与HDRI和MNDHD的组合指数获得的LAI反演精度分别为0.964 8和0.895 6。 表明基于冠层的热点效应反演不同株型小麦LAI的方法可行, 多角度遥感数据在作物结构参数提取方面比常规观测数据更具优势。

以陕西省关中平原为研究区域,应用点扩散函数、混合像素面积权重法和中值像素变异权重法将基于Landsat卫星遥感数据反演的分辨率为30 m的条件植被温度指数(VTCI)干旱监测结果上推至930 m的干旱监测结果,并与Aqua MODIS数据反演的分辨率为930 m的VTCI干旱监测结果进行对比分析,以期为两种空间尺度的干旱监测结果的综合应用提供技术支持。以MODIS数据反演的VTCI为参考,应用相关系数、均方根误差、半变异函数的估计值和图像纹理特征等对尺度上推的VTCI进行评价。结果表明,点扩散函数和混合像素面积权重法的尺度上推效果均较好,而中值像素变异权重法的尺度上推效果较差,说明点扩散函数和混合像素面积权重法均适用于研究区域VTCI干旱监测结果的尺度转换,且点扩散函数的数据处理过程更为简单。典型样点VTCI的尺度上推结果表明,空间异质性越小,尺度上推的结果越好。

Annual, inter-annual and long-term trends in time series derived from remote sensing can be used to distinguish between natural land cover variability and land cover change. However, the utility of using NDVI-derived phenology to detect change is often limited by poor quality data resulting from atmospheric and other effects. Here, we present a curve fitting methodology useful for time series of remotely sensed data that is minimally affected by atmospheric and sensor effects and requires neither spatial nor temporal averaging. A two-step technique is employed: first, a harmonic approach models the average annual phenology; second, a spline-based approach models inter-annual phenology. The principal attributes of the time series (e.g., amplitude, timing of onset of greenness, intrinsic smoothness or roughness) are captured while the effects of data drop-outs and gaps are minimized. A recursive, least squares approach captures the upper envelope of NDVI values by upweighting data values above an average annual curve. We test this methodology on several land cover types in the western U.S., and find that onset of greenness in an average year varied by less than 8days within land cover types, indicating that the curve fit is consistent within similar systems. Between 1990 and 2002, temporal variability in onset of greenness was between 17 and 35days depending on the land cover type, indicating that the inter-annual curve fit captures substantial inter-annual variability. Employing this curve fitting procedure enhances our ability to measure inter-annual phenology and could lead to better understanding of local and regional land cover trends.

Temporal compositing of daily optical satellite data has become an accepted methodology for obtaining frequent images of large areas for studies of the land surface. However, such composite data sets sometimes contain large “artifacts” (i.e., errors due to sources unrelated to the land surface itself). The goal of this study was to develop a series of operations and algorithms that would identify and remove as many of these errors as possible. The specific objective was to obtain, every ten days, surface reflectance in advanced very high resolution radiometer (AVHRR) channels 1 and 2 and normalized difference vegetation index (NDVI)—all referenced to a constant viewing geometry—and surface temperature. The processing steps of the resulting methodology, dubbed “ABC3” for atmospheric, bidirectional, and contamination corrections of the Canada Centre for Remote Sensing (CCRS), includes atmospheric corrections; the identification of pixels “contaminated” by clouds, snow (including subpixel), and shadows from optically thick clouds; bidirectional reflectance and thermal emissivity corrections; and the replacement of the contaminated pixels in the composites through interpolation. The resulting procedures yield surface reflectance and NDVI fully corrected for bidirectional effects; a version of NDVI corrected for solar zenith only; and surface temperature corrected for atmospheric and surface emissivity effects. An evaluation of the resulting data set shows that the procedures provide significantly improved data products compared with the raw composites, but they do not approximate a single-date image sufficiently closely, especially for AVHRR channel 1, whose reflectance values are generally low. This is attributed to the limitations of the input data and knowledge of atmospheric (and partly bidirectional) characteristics applicable to each composite pixel.

遥感时间序列数据(MODIS,NOAA/AVHRR,SPOT/VEGETATION等)在植被生长监测、物候信息提取、土地利用类型监测等诸多领域得到了广泛应用,是生产研究的重要数据源之一.由于传感器、云层大气等影响,遥感时间序列数据存在着严重的噪声,应用前必须进行序列滤波重建工作.综述现有各类滤波重建方法,对研究中广为采用的3类主要方法(基于最小二乘的非对称高斯函数拟合、SavitZky-Golay滤波、基于离散傅里叶的系列分析方法)集中阐述其理论基础、应用步骤和优缺点.总结当前遥感时间序列滤波重建方法需要进一步改进之处.

The Best Index Slope Extraction (BISE) is proposed as an alternative to the Maximum Value Composite (MVC) as a way of reducing noise in Normalized Difference Vegetation Index (NDVI) time-series from Advanced Very High Resolution Radiometer (AVHRR) data. The BISE method retains more valuable elements of an NDVI profile than the MVC. NDVI profiles prepared using the BISE method show strong differences in the development characteristics of various West African vegetation types.

The Pathfinder AVHRR Land (PAL) dataset is a consistently processed 13-year time series derived from AVHRR global area coverage (GAC) data. We found significant cloud contamination and other noise present in the PAL dataset that diminished its utility. This Letter describes a simple and successful attempt to reduce these effects. The filtered dataset is available from the authors.

The Spanish surface fishery operates mainly during the summer season in the waters of the Bay of Biscay. Sea surface temperature (SST) data recovered from satellite images are being used to improve the operational efficiency of fishing vessels (e.g. reduce search time and increase catch rate) and to improve the understanding of the variations in catch distribution and rate needed to properly manage fisheries. The images used for retrieval of SST often present gaps due to the existence of clouds or satellite malfunction periods. The data gaps can totally or partially affect the area of interest. Within this study, an application of a technique for the reconstruction of missing data called DINEOF (data interpolating empirical orthogonal functions) is analysed, with the aim of testing its applicability in operational SST retrieval during summer months. In this case study, the Bay of Biscay is used as the target area. Three months of SST Moderate Resolution Imaging Spectroradiometer (MODIS) images, ranging from 1 May 2006 to 31 July 2006, were used. The main objective of this work is to test the overall performance of this technique, under potential operational use for the support of the fleet during the summer fishing season. The study is designed to analyse the sensitivity of the results of this technique to several details of the methodology used in the reconstruction of SST, such as the number of empirical orthogonal functions (EOFs) retained, the handling of the seasonal cycle or the length (number of images) of the SST database used. The results are tested against independent SST data from International Comprehensive Ocean tmosphere Data Set (ICOADS) ship reports and standing buoys and estimations of the error of the reconstructed SST fields are given. Conclusions show that over this area three months of data are enough for efficient SST reconstruction, which yields four EOFs as the optimal number needed for this case study. An extended EOF experiment with SST and SST with a lag of one day was carried out to analyse whether the autocorrelation of the SST data allows better performance in the SST reconstruction, although the experiment did not improve the results. The validation studies show that the reconstructed SSTs can be trusted, even when the amount of missing data is very high. The mean absolute deviation maps show that the error is greatest near to the coast and mainly in the upwelling areas close to the French and north-western Spanish coasts.

A new method for extracting seasonality information from time-series of satellite sensor data is presented. The method is based on nonlinear least squares fits of asymmetric Gaussian model functions to the time-series. The smooth model functions are then used for defining key seasonality parameters, such as the number of growing seasons, the beginning and end of the seasons, and the rates of growth and decline. The method is implemented in a computer program TIMESAT and tested on Advanced Very High Resolution Radiometer (AVHRR) normalized difference vegetation index (NDVI) data over Africa. Ancillary cloud data [clouds from AVHRR (CLAVR)] are used as estimates of the uncertainty levels of the data values. Being general in nature, the proposed method can be applied also to new types of satellite-derived time-series data.

Remote Sensing (RS) provides images over large areas such as provincial or country level. During the last 20 years, it plays a vital role for developing many complex applications. However, RS image includes data noises due to influence of haze or cloud especially in the rainy season. It is thus necessary to remove the noise to recover the real ground variations of information studied. Local Maximum Fitting (LMF) is a combined procedure, which helps to remove the noisy data. When dealing with sufficiently large and such complex processing with RS data, single computers time processing extends to unacceptable limits. Such as, to remove the noise from RS image with 146 bands, 38 rows and 37 columns which mean 146 X 38 X 37= 205276 pixels, the LMF procedure requires 26 minutes approximately. So, 1000 X 1000 Remote Sensing Image with 146 bands is required approximately two weeks. It is necessary to reduce the time constraint and make the LMF process executable in a suitable time limit. This study deals with the design and implementation of a distributed LMF procedure. Especially, inside the LMF procedure, a consecutive amount of pixels (pixels in a column) is processed for each row. This behavior is used in this study to make the LMF parallel. For processing the RS image (146 bands, 38 rows and 37 columns), the execution time reduces to 16.13 minutes by adding distributed computing to the program (37 columns distributed to 3 computers). Key words: LMF, MPI, remote sensing, distributed computing, cluster computers

借助图像处理软件ERDAS、地理信息系统软件ArcInfo以及空间统计分析软件ILWIS ,在对TM遥感影像进行分类的基础上 ,运用空间统计学理论以及Kriging插值技术 ,内插了影像真实信息“缺失”斑块的信息 ,插值结果通过了精度检验。为解译影像信息“缺失”区 ,提供了一种手段和方法。

完整的长时间序列遥感数据是大 尺度环境监测工作顺利完成的重要保证。本文选取2007年1月至2010年12月南海北部MODIS海表叶绿素a浓度月平均遥感影像为基础数据,利用地统 计的Kriging空间插值方法对月平均影像中的缺失数据进行插补,并从多个角度对插补结果进行分析和评价。结果表明:Kriging方法的插值结果能突 出研究海域海表叶绿素a浓度一定时期的特殊变化,在一定程度上也能体现研究海域海表叶绿素a浓度的时空变化规律;但是Kriging方法空间插值操作复 杂,精度较低,可变性估计不足,均方根误差平均值为0.459 8,标准化均方根误差的平均值为2.608 2。

采用多元并扫方式(1 km分辨率10元并扫,500 m分辨率20元并扫,250 m分辨率40元并扫)的MODIS传感器,由于各探测单元在轨响应差异而引起的条带噪声对MODIS定量产品的反演计算精度造成一定影响.为了尽可能减少这种影响,提高MODIS定量产品反演精度,本文提出了应用插值法剔除MODIS数据条带噪声.研究首先分析了传统去条带噪声方法的弊端,根据条带噪声在MODIS影像中的分布规律,以及如何准确判断条带噪声所在的行,然后应用插值法去除了MODIS波段5和波段30影像的条带噪声,取得了满意的效果,最后对图像的去除效果与传统的矩匹配法和傅立叶变换法进行了比较,并用两种评价指标做出评价.结果表明,两种评价指标得出的结果一致,也正说明了插值法是一种很好的去除MODIS数据条带噪声的方法,同时用这种方法处理后的图像还能保留原有的大部分信息.

<p>为了提高MODIS_L1B含有条带噪声的波段5和波段27的影像质量,基于MODIS的扫描特性提出了一种通过扫描带行均最大值判断条带噪声行的方法。去除影像的噪声行时,在波段5的单行邻域插值法基础上,针对波段27提出了一种相邻多行插值方法。最后,通过对比原始数据和去条带噪声后数据的差值图、行均值图和数值分析来证实条带噪声处理效果。结果表明:该方法可以精确判断这两个波段影像的所有条带噪声行,且对条带噪声去除效果较好,去除条带过程简单且适用于复杂场景的光谱图像。</br></p>

针对遥感影像上的“缺失”区域现象,阐明了数字图像修复技术的原理和方法,分析了遥感影像缺失区域恢复的特点和难点,并提出了基于GIS辅助信息的缺失数据恢复思想,提高了图像修复的质量。

图像条带是成像光谱仪采用多元并扫方式获取遥感数据而引起的新问题之一.本文对图像条带的成因进行了初步阐述,并简要介绍了国内外现有图像条带处理的方法,着重介绍了归一化经验分布函数匹配方法工作原理,并用该方法对&lt;神舟三号&gt;成像光谱仪图像条带消除进行了试验.结果表明:归一化经验分布函数匹配方法对&lt;神舟三号&gt;中分辨率成像光谱仪图像条带消除是非常有效的,图像质量明显得到改善,对以后同类仪器的资料处理具有很好的借鉴作用.

本文通过数位模拟和实测资料分析，表明用最优插值法测量区域降水量的测量精度和变分法相当，而最优插值法对雨量计的分布和密度要求较低，计算处理也简单。

研究黄淮海平原旱灾监测中作物缺水指数模型实现的方法 ,特别是以与遥感图象相同的分辨率来计算作物缺水指数采取的对气象数据的处理 ,以及在 GIS的支持下 ,对矢量格式的地形图及遥感图象的不同处理 ,以实现图象、图形、数据的一体化。从而得到所需的研究区旱情分布图等不同形式的结果。实时监测表明 :本方法精度高 ,可靠性强 ,基本上达到了准确、实时监测干旱的目的。

根据常规最优插值算法,在引入时间关联的基础上,详细推导了动态最优插值法的具体同化表达形式,利用这些公式,可以处理常见的在海洋观测数据同化中存在的时间错位问题.在陆架海模式HAM-SOM基础上,以7月份的渤海海表温度为例,验证了同化公式的可行性,并得到了较好的同化结果.验证结果表明:该方案能够兼顾观测数据与背景数据双方的特性,同化后的结果更加接近实际情况.

Vegetation condition albedo drought index (VCADI); surface moisture; spectral feature space; drought estimation

对宁夏境内分布面积较大、对农业和生态环境有重大影响的12种主要土壤类型特征、分布区域做了实地调查,并对各类土壤剖面特征做了研究,土壤剖面特征指标包括土壤质地、颜色、结构,土壤中的根系状况及新生体和（或者）嵌入体等。研究将进一步深化人们对宁夏境内土壤的性质和认识,可为工农业生产、土壤改良以及土壤调查工作提供参考资料,同时对于揭示自然与人类活动在土壤变化过程中的作用具有重要意义。