Comparing SMMR and AVHRR data for drought monitoring

Abstract

Coincident Scanning Microwave Multi-channel Radiometer 37 GHz and Advanced Very High Resolution Radiometer normalized difference vegetation index satellite data have been compared from drought-affected regions of sub-Saharan Africa and northeastern Brazil for the time period of 1980–1985. Although the two satellite data types can be highly correlated, differences between them were found for the Sahel zone in 1985 and for northeastern Brazil from 1984–1985. These findings suggest that scattering or surface roughness contributions may be greater than previously assumed for the 37 GHz microwave data. A programme of field measurements should be undertaken to increase our understanding of natural vegetation at 37 GHz and higher microwave frequencies.     

A comparison of SMMR and AVHRR data for continental land cover characterization

Abstract

Images using reflected visible and near-infrared data and images using passive microwave data were compared in terms of their usefulness for characterizing land-cover types in South America and Africa. The former images are of the normalized difference vegetation index (NDVI) subsampled to approximately 15-20 km resolution in the NOAA global vegetation index product. The latter images of the microwave polarization difference temperature (MPDT) are derived from the difference between horizontally and vertically polarized radiation in the 37 GHz band. Results of maximum-likelihood classifications applied to multi-temporal data sets indicate that, overall, the NDVI data sets are substantially better than the MPDT data sets for land-cover characterization. However, the greater sensitivity of the MPDT data in semi-arid areas results in their superior performance for some classes in these areas. The combined use of MPDT and NDVI data sets show clear synergistic benefits in using the two data sets. However, the evidence suggests that for most cover types, increasing the temporal frequency of the NDVI images is more advantageous than incorporating MPDT data sets.     

Monitoring agricultural drought for arid and humid regions using multi-sensor remote sensing data

While existing remote sensing-based drought indices have characterized drought conditions in arid regions successfully, their use in humid regions is limited. We propose a new remote sensing-based drought index, the Scaled Drought Condition Index (SDCI), for agricultural drought monitoring in both arid and humid regions using multi-sensor data. This index combines the land surface temperature (LST) data and the Normalized Difference Vegetation Index (NDVI) data from Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, and precipitation data from Tropical Rainfall Measuring Mission (TRMM) satellite. Each variable was scaled from 0 to 1 to discriminate the effect of drought from normal conditions, and then combined with the selected weights. When tested against in-situ Palmer Drought Severity Index (PDSI), Palmer's Z-Index (Z-Index), 3-month Standardized Precipitation Index (SPI), and 6-month SPI data during a ten-year (2000-2009) period, SDCI performed better than existing indices such as NDVI and Vegetation Health Index (VHI) in the arid region of Arizona and New Mexico as well as in the humid region of North Carolina and South Carolina. The year-to-year changes and spatial distributions of SDCI over both arid and humid regions generally agreed to the changes documented by the United States Drought Monitor (USDM) maps.     

Satellite monitoring of ice conditions in polar regions

Data obtained by Russian and foreign polar-orbital satellites for remote sensing (RS) of the Earth is used for monitoring the ice cover of the polar regions. State Research Center of Space Hydrometeorology “Planeta” (SRC “Planeta”) and the Institute of Computational Mathematics and Mathematical Geophysics (CMMGI), have developed methods and technologies for processing the satellite data. Russian and foreign satellites (active and developing) including the satellite Arktika are described in the present paper. Procedures and techniques for monitoring the ice cover and examples of satellite data related to Arctic and Antarctic territories are given below.     

Remote sensing of cloud cover in the Arctic region from AVHRR data during the ARTIST experiment

In this paper we present a cloud detection algorithm developed for the Arctic region using Advanced Very High Resolution Radiometer (AVHRR) data. Our approach is a simplified version of the Ebert method to discriminate between clouds, ice and open water in the Arctic Sea. The algorithm is tuned to work on an AVHRR scene typical of the winter to spring transition period. The algorithm has been applied to 1 month (154 scenes) of NOAA-14 AVHRR images (from 16 March to 15 April 1998) covering the region of the Arctic Sea near the Svalbard Islands. The cloud detection results are analysed using various check procedures. The algorithm's pixel classification performance was verified by a satellite image expert. The misclassified pixels were digitalized on the image and counted by the expert in order to quantify the algorithm's accuracy. The cloud classification results are quite accurate: 70% of the images (109) have an error less than 5% and only 11% of the image results have an error greater than 10%. The method's performance is also tested against independent cloud and ice observations obtained, respectively, from the Ny-Ålesund meteorological base and from the Special Sensor Microwave/Imager (SSM/I) dataset. The comparison with these independent sources of data confirms the algorithm's good performance.     

Transect method for Antarctic cloud property retrieval using AVHRR data

For studies of Antarctic climate change, the Advanced Very High Resolution Radiometer (AVHRR) offers a time series spanning more than two decades, with numerous overpasses per day from converging polar orbits, and with radiometrically calibrated thermal infrared channels. However, over the Antarctic Plateau, standard multispectral application of AVHRR data for cloud optical property retrieval with individual pixels is problematic due to poor scene contrasts and measurement uncertainties. We present a method that takes advantage of rapid changes in radiances at well-defined cloud boundaries. We examine a transect of AVHRR-measured radiances in the three thermal infrared channels across a boundary between cloudy and cloud-free parts of the image. Using scatter diagrams, made from the data along this transect, of the brightness temperature differences between channels 3 and 4, and channels 4 and 5, it is possible to fit families of radiative transfer solutions to the data to estimate cloud effective temperature, thermodynamic phase, and effective particle radius. The major approximation with this method is that along such a transect, cloud water path has considerable spatial variability, while effective radius, phase, and cloud temperature have much less variability. To illustrate this method, two AVHRR images centred about the South Pole are analysed. The two images are chosen based on their differing contrasts in brightness temperature between clear and cloud-filled pixels, to demonstrate that our method can work with varying cloud top heights. In one image the data are consistent with radiative transfer simulations using ice cloud. In the other, the data are inconsistent with ice cloud and are well simulated with supercooled liquid water cloud at 241.5 K. This method therefore has potential for climatological investigation of the radiatively important phase transition in the extremely cold and pristine Antarctic environment.     

Cloud track winds in the polar regions from sequences of AVHRR images

Abstract

A new method is described for obtaining information on the high latitude upper level wind field through the production of cloud motion vectors from sequences of images from polar orbiting satellites. The vectors are produced using sets of three calibrated, 11 μm AVHRR images of the polar regions from successive orbits of the TIROS-N series satellites. Two schemes have been developed for the generation of vectors based on manual and automatic techniques coupled with manual quality control. Case studies have shown that, provided the images are accurately registered and the cloud tracers are carefully selected, useful vectors may be derived.     

Correction of atmospheric effects of satellite remote sensing data (Landsat MSS-NOAA AVHRR) for surface canopy investigations

Abstract

A new correction method for atmospheric effects in Landsat-MSS and NOAA AVHRR data is presented which uses only the remotely-sensed multispectral data. The method is based on a new quasi-single-variable radiative transfer model, and as a first step we assumed that the surface is covered by vegetation. For Landsat-MSS data the method was developed for the tasseled cap indices using known empirical relationships among them. For NOAA AVHRR data ‘ cap-like’ indices and the average reflectance of the average canopy in the visible band known from Landsat-MSS data were used. The method was used in yield forecasting project in north-eastern part of Hungary and provided a significant enhancement in the quality of remotely sensed data.     

A multi-spectral non-local method for retrieval of boundary layer cloud properties from optical remote sensing data

A multi-spectral non-local (MSN) method is developed for advanced retrieval of boundary layer cloud properties from remote sensing data, as an alternative to the independent pixel approximation (IPA) method. The non-local method uses data at both the target pixel and neighboring pixels to retrieve cloud properties such as pixel-averaged cloud optical thickness and effective droplet radius. Radiance data to be observed from space were simulated by a three-dimensional (3D) radiation model and a stochastic boundary layer cloud model with two-dimensional (horizontal and vertical) variability in cloud liquid water and effective radius. An adiabatic assumption is used for each cloud column to model the geometrical thickness and vertical profiles of cloud liquid water content and effective droplet radius, neglecting drizzle and cloud brokenness for simplicity. The dependence of radiative smoothing and roughening on horizontal scale, optical thickness and single scattering albedo are investigated. Then, retrieval methods using 250-m horizontal resolution data onboard new generation satellites are discussed. The regression model for the MSN method was trained based on datasets from numerical simulations. The training was performed with respect to various domain averages of optical thickness and effective radius, because smoothing and roughening effects are strongly dependent on the two variables. Retrieval accuracy is discussed here with datasets independent of those used in the training, towards assessing the generality of the technique. It is demonstrated that retrieval accuracy of cloud optical thickness, which is often retrieved from single-spectral visible-wavelength data, is improved the most using neighboring pixel data and secondly using multi-spectral data, and ideally with both. When the IPA retrieval method is applied to optical thickness and effective radius, the root-mean-square relative errors can be 15-90%, depending on solar and view directions. In contrast, the MSN method has errors of 4-10%, which is smaller than IPA by a factor of 2-10. It is also suggested that the accuracy of the MSN method is insensitive to some assumptions in the inhomogeneous cloud input data used to train the regression model.     

新型云区域检测算法

为了更加精确地检测出遥感图像中云区域的边界及细节信息,提出了将最小交叉熵和形态学相结合的方法来对遥感图像进行云区域检测。从遥感图像的灰度特征出发,通过最小交叉熵准则选取最优的阈值来检测图像中的云区域,再通过形态学的开运算,消除与云区域不相连或者被误判的小的光亮的地物信息,最后在彩色遥感图像上勾勒出云区域的边界。实验结果表明,该算法简单快速,能够很好地区分出云区域和下垫面,并且能够准确地对云区域边界细节信息做出判断。     

单幅遥感图像去除薄云算法的改进

针对基于小波阈值理论提出的云区阈值法存在的容易产生边界效应的问题,提出了一种改进的单幅图像去除薄云的新算法。对有云图像做多层小波分解,找到合适的分界层数,将小波系数分成高、低细节系数和近似系数3个部分。去云处理中仅对高层小波细节系数作同态滤波,而对低层小波细节系数和近似系数作简单的加权处理,最后将三部分系数重构得到去云的结果图像。将该方法与同态滤波法和云区阈值法进行了对比实验研究。实验结果表明,该方法不仅可以有效减少薄云雾的影响,而且可以更好地保留图像细节信息,同时防止了边缘效应的产生。     

基于贝叶斯网络的遥感云用户行为认证方法

针对遥感云服务平台中不可信用户的入侵现象，结合遥感云用户行为特点和贝叶斯网络算法设计了一种用户行为认证方案。该方案论述了遥感云平台用户的行为认证机制，并且根据用户行为特点建立了用户行为认证集，结合贝叶斯网络算法预测特点和用户行为属性建立了用于认证等级预测的贝叶斯网络模型，把该模型中分析得出的用户行为属性的权重信息应用到用户等级预测算法中，使该算法针对遥感云用户认证更安全准确，从而实现对用户行为认证等级的预测。仿真实例表明该方法能够准确识别出不可信用户，有效保证遥感云服务平台的安全性。     

Satellite remote sensing of rangelands in Botswana II. NOAA AVHRR and herbaceous vegetation

NOAA-7 Advanced Very High Resolution Radiometer (AVHRR) global-area coverage (GAC) data for the visible and near-infrared bands were used to investigate the relationship between the normalized difference vegetation index (NDVI) and the herbaceous vegetation in three representative rangeland types in eastern Botswana. Regressions between Landsat MSS band-7/band-5 ratios and field measurements of the cover of the live parts of herbaceous plants, above-ground biomass of live herbaceous plants and bare ground were used in conjunction with MSS data in order to interpolate the field data to 144 km² areas for comparison with blocks of nine AVHRR GAC pixels. NOAA NDVI data were formed into 10-day composites in order to remove cloud cover and extreme off-nadir viewing angles. Both individual NDVI composite data and multitemporal integrations throughout the period May 1983-April 1984 were compared with the field data.

In multiple linear regressions, the cover and biomass of live herbaceous plants and bare ground measurements accounted for 42, 56 and 19 per cent respectively of the variation in NDVI. When factors were included in I he regression models to specify the site and date of acquisition of the data, between 93 and 99 per cent of the variation in NDVI was accounted for. The total herbaceous biomass at the end of the season was positively related to integrated NDVI, up lo the maximum biomass observed in a 12km × 12km area (590kgha^?1)- These results give a different regression of herbaceous biomass values on integrated AVHRR NDVI to that reported by Tucker et at. (1985 b) for Senegalese grasslands. The effect of the higher cover of the tree canopy in Botswana on this relationship and on the detection of forage available to livestock is discussed.     

Data dynamics for remote data possession checking in cloud storage

In cloud storage, storage servers may not be fully trustworthy. Therefore, it is of great importance for users to check whether the data is kept intact. This is the goal of remote data possession checking (RDPC) schemes. In this paper, an RDPC scheme based on homomorphic hashing is proposed. To enable data dynamics, the Merkle hash tree is introduced to record the location for each data operation in the scheme. Data dynamics, including the most general forms of data operations such as block modification, insertion and deletion, are supported. Our scheme provides provable data possession and integrity protection. The security and performance analysis shows that the scheme is practical for real-world use.     

面向遥感图像云分割问题的新型U-Net模型

目前,已知基于深度学习的云分割方法通常采用传统U型编解码结构的网络,该结构虽能有效利用编码端的空间位置信息,但整个网络参数过多、计算量大,同时其编码端仅采用简单卷积与下采样操作,无法有效获取高质量的上下文语义特征信息.针对上述情况提出一种新型的轻量级U-Net模型用于云分割问题.整个模型采用跳跃连接编码端浅层和中层信息的新U型编解码结构,并在其编码端嵌入由分组卷积与注意力机制组成的优化模块,同时构建上下语义融合连接,连接编码端与解码端相应的上下层.实验结果表明,在公共基准数据集38-Cloud上,该模型相比其他主流云分割网络在分割精度与模型参数等方面均能取得更优异的结果.     

A simple cloud masking approach using NOAA AVHRR daytime data for tropical areas

A simple method for cloud detection for AVHRR daytime data is presented and checked for equatorial/tropical areas based on a study area in northeastern Brazil. Five different cloud masking techniques based on visible and infrared spectral information for cloud detection are calibrated. The significant differences between the equatorial threshold obtained in this work and the midlatitude thresholds given by Saunders and Kriebel and by Thiermann and Ruprecht are compared and discussed. Results from the cloud masking algorithm are presented and comments are made about the problems related to the automatic cloud detection algorithm presented in this study.     

Automatic hierarchical clustering algorithm for remote sensing data

A histogram clustering algorithm is suggested, which builds the hierarchy of distributions better in cluster separability. The algorithm optimizes the average cluster separability choosing the system of the data subdomain quantization grid and allows a significant decrease in the number of clusters. Application of the algorithm for uncontrolled Earth’s surface classification by satellite spectral data is shown.     

An integrated data system for airborne remote sensing

This paper outlines the rationale and design for an enhanced Airborne Remote Sensing (ARS) facility, being implemented by the Natural Environment Research Council (NERC) in the U.K., to support environmental research and monitoring. It is based on the provision of a suite of optical remote sensing instruments, available for year-round deployment, and the development of an on-board integrated data acquisition, and ground-based data processing, system. Acquisition of the remotely sensed data will be synchronized with attitude/position information from a global positioning satellite (GPS) system to enable an accurate geometric rectification to be performed. A strategy has been devised for the endto-end ground processing of this data to enable the production of standard data products and distribution in a sensor-independent data format. The facility has been fully operational from Spring 1997 and is ready to support the needs of environmental scientists and organizations requiring high quality, and immediately usable, airborne remotely sensed data products.     

Fault tectonics of the Shillong plateau and adjoining regions,north-east India using remote sensing data

The Shillong plateau in north-east India shows innumerable fracture lineaments on satellite images. In fact the whole plateau is criss-crossed by fractures. The plateau has been subjected to extensive compressional forces mainly in N-S and E-W directions resulting from the collision of the Indian plate with the Tibetan and Burmese plates, respectively. This type of plate tectonism has been responsible for the formation of many significant faults, folds and other tectonic features in the Shillong plateau and adjoining regions. The well known E-W trending Dauki fault, N-S trending faults, NE-SW trending shear zone and other prominent lineaments and their associated structural features could be studied on the basis of remote sensing techniques. Folding in soft sediments along the Dauki fault reveals the compression direction. Directional compressional tectonism has been expressed through lateral movements along the NE-SW trending shear zone and N-S trending faults and folds in the area just south of the Hatlong thrust. It has been observed from the studies that the Dauki fault zone and the areas in Bangladesh show various tectonic features, which are mostly controlled by vertical movements. Seismicity of the Shillong plateau is quite scattered all over the area. The occurrence of several deep earthquakes (> 1OOkm) indicate deep tectonic activities in the upper mantle. Earthquakes have been found to occur near some faults and this is probably an indication of recent activities along these faults.     

遥感影像数据云存储平台的安全防护机制研究

针对基于Hadoop的遥感影像数据云存储平台存在的认证机制不完善、访问控制过于简单等安全问题,采用Kerberos认证结合令牌认证的身份认证机制,提出了基于用户属性与遥感影像数据属性的多维细粒度访问控制策略,达到了完善平台认证机制和增强平台访问控制的目的.实验结果证明,提出的身份认证机制和访问控制策略能有效增强遥感影像数据云存储平台的安全防护能力,且对平台性能影响很小.