Verification of AIRS boresight accuracy using coastline detection

The longitude and latitude of the centroids of the Atmospheric Infrared Sounder (AIRS) infrared spectrometer footprints are calculated by the Level 1a calibration software based on transformations of scan angles, instrument alignment angles relative to the Earth Observing System Aqua spacecraft, and the spacecraft ephemeris. The detection of coastline crossings is used to determine the accuracy of these coordinates. Tests using simulated AIRS data derived from real Moderate Resolution Imaging Spectroradiometer (MODIS) Terra satellite 10-/spl mu/m window data indicate that an accuracy of 1.7 km is easily achievable with modest amounts of data, such as should be available from AIRS by launch +90 days. This accuracy is a small fraction of the 13.5-km AIRS footprint and is consistent with the accuracy required by the Level 2 software. Preliminary results from actual AIRS data indicate that the algorithm works as predicted. For combined use of the AIRS 13.5-km footprints with MODIS 1-km footprints, accuracy of the order of 0.5 km is desirable. This accuracy may be achievable with several months of data, but depends on the accuracy of the reference map and whether a sufficient number of large clear homogeneous surface scenes can be found.     

一种新的MODIS 0级数据地理定位方法

MODIS 1B数据文件中包含有象素的地理定位信息,该地理定位信息的精确度越高,后期的处理及应用工作质量越高.通过对利用角度和向量关系迭代算法,不断修正MODIS 0级数据的地理经纬度的一种新的MODIS 0级数据地理定位方法进行研究,最终得到比使用传统方法更精确的地理定位信息.     

基于渐变方程的鱼眼图像校正方法

由于具有短焦距、大视场等特点,近年来鱼眼镜头被广泛应用于不同领域。但同时也存在鱼眼图像畸变较严重的问题,针对这一问题,提出了一种基于渐变方程和经纬映射模型的图像校正算法。利用经纬映射模型导出鱼眼图像中的相互关系,提取校正前后坐标的关系方程,然后引入渐变方程,实现图像校正。实验结果表明,该算法具有良好的校正效果。     

Optical thickness of tropical cirrus clouds derived from the MODIS 0.66and 1.375-/spl mu/m channels

In this paper, we introduce a method to retrieve the optical thickness of tropical cirrus clouds using the isolated visible cirrus reflectance (without atmospheric and surface effects). The isolated cirrus reflectance is inferred from level 1b calibrated 0.66- and 1.375-/spl mu/m Moderate Resolution Imaging Spectroradiometer (MODIS) data. We created an optical properties database and optical thickness lookup library using previously calculated single-scattering data in conjunction with the discrete ordinates radiative transfer (DISORT) code. An algorithm was constructed based on this lookup library to infer the optical thickness of tropical cirrus clouds for each pixel in a MODIS image. We demonstrate the applicability of this algorithm using several independent MODIS images from the Terra satellite. The present method is complimentary to the MODIS operational cloud retrieval algorithm for the case of cirrus clouds.     

Computing Coastal Ocean Surface Currents From Infrared and Ocean Color Satellite Imagery

Many previous studies have demonstrated the viability of estimating advective ocean surface currents from sequential infrared satellite imagery using the maximum cross-correlation (MCC) technique when applied to 1.1-km-resolution Advanced Very High Resolution Radiometer (AVHRR) thermal infrared imagery. Applied only to infrared imagery, cloud cover and undesirable viewing conditions (gaps in satellite data and edge-of-scan distortions) limit the spatial and temporal coverage of the resulting velocity fields. In addition, MCC currents are limited to those represented by the displacements of thermal surface patterns, and hence, isothermal flow is not detected by the MCC method. The possibility of supplementing MCC currents derived from thermal AVHRR imagery was examined, with currents calculated from 1.1-km-resolution Moderate Resolution Imaging Spectroradiometer (MODIS) and Sea-viewing Wide Field-of-view Sensor (SeaWiFS) ocean color imagery, which often have spatial patterns complementary to the thermal infrared patterns. Statistical comparisons are carried out between yearlong collections of thermal and ocean color derived MCC velocities for the central California Current. It is found that the image surface patterns and resulting MCC velocities complement one another to reduce the effects of poor viewing conditions and isothermal flow. The two velocity products are found to agree quite well with a mean correlation of 0.74, a mean rms difference of 7.4 cm/s, and a mean bias less than 2 cm/s which is considerably smaller than the established absolute error of the MCC method. Merging the thermal and ocean color MCC velocity fields increases the spatial coverage by approximately 25% for this specific case study     

Prelaunch algorithm and data format for the Level 1 calibrationproducts for the EOS-AM1 Moderate Resolution Imaging Spectroradiometer(MODIS)

The Moderate Resolution Imaging Spectroradiometer (MODIS) radiometric calibration product (Level 1B) is described for the thermal emissive and reflective solar bands. A band-integrated radiance is produced for all measurements. A reflectance factor product is also produced for the reflected solar band measurements. Specific sensor design characteristics are identified to assist in understanding how the calibration algorithm software product is designed. The product file format is summarized, and the location for the current file format is provided     

MODIS imagery geometric precision correction based on longitude and latitude information

Moderate resolution imaging spectrum-radiometer (MODIS) imagery is an important remote sensing source, whose goal is to research and forecast the earth changes. In order to provide high precision imagery for MODIS application, geometric correction is one of the most important steps in the image preprocessing. The principle and procedure of geometric correction are introduced in this paper. The appropriate selection method of ground control points (GCPs) is very crucial for geometric correction. Because there are much useful latitude and longitude information in MODIS data, these geographic data are used as GCP to realize geometric precision correction. Through selecting the proper map projection, the original image is transformed from original image coordinate system to map coordinate system. Using the polynomial transformation and the inverse distance weighted interpolation, the corrected imagery can be obtained at last. Experimental results indicate that the quality and precision of the corrected imagery can meet the requirements.     

Key characteristics of MODIS data products

Forty science products totaling 600 GB of storage volume per day will be produced from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS). Eighty-five percent of this data volume is in products that are in the instrument's scan geometry (processing Levels 1 and 2) that are not Earth located. Before ordering MODIS data products, users should consider processing level, data formats, product size, and the unique characteristics of MODIS products. Given the data volumes associated with the MODIS Levels 1 and 2 products, the resources required to process them and the issues associated with the scanning geometry of the instrument, users are encouraged to order data products that are Earth located. These include Level 3 products, which are produced on fixed global grids and Level 2G products, in which observations and their Earth location have been stored in bins of the MODIS global grids     

Dual-Satellite Cloud Product Generation Using Temporally Updated Canonical Coordinate Features

State-of-the-art cloud products are typically generated using scientific polar orbiting satellites such as the Moderate Resolution Imaging Spectroradiometer (MODIS). However, they do not allow for observation of the same region at a regular temporal frequency, rendering them ineffectual for nowcasting problems. Operational satellites such as Meteosat-8 SEVIRI, in contrast, are geostationary and provide continual data at a regular temporal frequency over a much larger region. MODIS-like cloud products cannot be directly generated from operational satellites as they typically have a smaller number of spectral bands and different wavelengths and spatial resolution. This paper applies the canonical coordinate decomposition method to estimate scientific cloud products using imagery from operational satellites. Using the proposed method features of the Meteosat-8 imagery data that are maximally coherent with the data from the MODIS are generated. These features are temporally updated at times and locations where MODIS data are unavailable using the alternating block power method. A subset of the canonical coordinates of Meteosat-8 SEVIRI is then used to create MODIS-like cloud products using several neural networks. The quality of the generated cloud products and their temporal consistency have been demonstrated on several data sets from July 2004. A benchmarking with an independent Meteosat-8-based algorithm is also provided, which shows the promise of our approach in generating MODIS-like cloud products     

Ocean Color Satellites Show Extensive Lines of Floating Sargassum in the Gulf of Mexico

We present satellite imagery that is interpreted as showing extensive lines of floating Sargassum in the western Gulf of Mexico in the summer of 2005. In spite of frequent reports of floating weed covering extended areas in different parts of the world's ocean, this appears to be the first observation of Sargassum from space. Satellite observations were made with the Medium Resolution Imaging Spectrometer (MERIS) on the Envisat satellite launched by the European Space Agency, and subsequently with the Moderate Resolution Imaging Spectroradiometer (MODIS) launched on both the Terra and Aqua satellites by the National Aeronautics and Space Administration. Both instruments cover wide swaths, providing near-daily images. Both have optical spectral bands in the range 670 to 750 nm, which detect the chlorophyll red-edge characteristic of land and marine vegetation, but only MERIS has a band at 709 nm, which was critical to the initial discovery. The combined satellite data from both sensors show the seasonal cycle of weed density in different areas of the Gulf. A wider ranging study is now needed to map its occurrence in other areas, including the Sargasso Sea (named for the weed, but not so far covered in our survey). The satellite observations suggest that Sargassum biomass is greater than previously estimated, and hence plays a more important part in oceanic productivity     

A technique for detecting burn scars using MODIS data

The Moderate Resolution Imaging Spectroradiometer (MODIS) instruments onboard the National Aeronautics and Space Administration Terra and Aqua spacecrafts have several visible and near-infrared (NIR) channels with resolutions of 250, 500, and 1 km for remote sensing of land surfaces and atmosphere. The MODIS data directly broadcasted to ground receiving stations can have many practical applications, including the rapid assessment of fires and burned areas. In this paper, we describe an empirical technique for remote sensing of burn scars using a single dataset of MODIS NIR channels centered near 1.24 and 2.13 /spl mu/m. These channels are sensitive to changes in the surface properties induced by the fire and are not obscured by smoke. Therefore, they allow remote sensing of burn scars in the presence of smoke. Detection of burn scars from single MODIS images, without the need of data from previous days, is very useful for near real-time burn scar recognition in operational direct broadcasting systems. The technique is applied to MODIS data acquired over the western U.S. during the summer fire season, the southeastern part of Canada during the summer and spring seasons, and the southeastern part of Australia. The burnt areas estimated from MODIS data are consistent with those estimated from the high spatial resolution Landsat 7 imaging data.     

基于5G MR实现Massive MIMO权值智能寻优的技术方案研究

首先基于5G NSA的技术特点,提出了从NSA MR获取高精度经纬度信息的技术方案.进一步地,提出了基于MR经纬度信息的Massive MIMO权值智能寻优技术方案,利用智能化权值选择算法在Massive MIMO权值空间中搜索最优解;考虑到现有系统算力的限制,还提出了寻求局部最优解的简化算法,提高了算法的实用性和推广...     

On the blending of the Landsat and MODIS surface reflectance: predicting daily Landsat surface reflectance

The 16-day revisit cycle of Landsat has long limited its use for studying global biophysical processes, which evolve rapidly during the growing season. In cloudy areas of the Earth, the problem is compounded, and researchers are fortunate to get two to three clear images per year. At the same time, the coarse resolution of sensors such as the Advanced Very High Resolution Radiometer and Moderate Resolution Imaging Spectroradiometer (MODIS) limits the sensors' ability to quantify biophysical processes in heterogeneous landscapes. In this paper, the authors present a new spatial and temporal adaptive reflectance fusion model (STARFM) algorithm to blend Landsat and MODIS surface reflectance. Using this approach, high-frequency temporal information from MODIS and high-resolution spatial information from Landsat can be blended for applications that require high resolution in both time and space. The MODIS daily 500-m surface reflectance and the 16-day repeat cycle Landsat Enhanced Thematic Mapper Plus (ETM+) 30-m surface reflectance are used to produce a synthetic "daily" surface reflectance product at ETM+ spatial resolution. The authors present results both with simulated (model) data and actual Landsat/MODIS acquisitions. In general, the STARFM accurately predicts surface reflectance at an effective resolution close to that of the ETM+. However, the performance depends on the characteristic patch size of the landscape and degrades somewhat when used on extremely heterogeneous fine-grained landscapes.     

Aerosol properties over bright-reflecting source regions

Retrieving aerosol properties from satellite remote sensing over a bright surface is a challenging problem in the research of atmospheric and land applications. In this paper we propose a new approach to retrieve aerosol properties over surfaces such as arid, semiarid, and urban areas, where the surface reflectance is usually very bright in the red part of visible spectrum and in the near infrared, but is much darker in the blue spectral region (i.e., wavelength <500 nm). In order to infer atmospheric properties from these data, a global surface reflectance database of 0.1/spl deg/ latitude by 0.1/spl deg/ longitude resolution was constructed over bright surfaces for visible wavelengths using the minimum reflectivity technique (e.g., finding the clearest scene during each season for a given location). The aerosol optical thickness and aerosol type are then determined simultaneously in the algorithm using lookup tables to match the satellite observed spectral radiances. Examples of aerosol optical thickness derived using this algorithm over the Sahara Desert and Arabian Peninsula reveal various dust sources, which are important contributors to airborne dust transported over long distances. Comparisons of the satellite inferred aerosol optical thickness and the values from ground-based Aerosol Robotic Network (AERONET) sun/sky radiometer measurements indicate good agreement (i.e., within 30%) over the sites in Nigeria and Saudi Arabia. This new algorithm, when applied to Moderate Resolution Imaging Spectroradiometer (MODIS), Sea-viewing Wide Field of view Sensor (SeaWiFS), and Global Imager (GLI) satellite data, will provide high spatial resolution (/spl sim/1 km) global information of aerosol optical thickness over bright surfaces on a daily basis.     

Validation of the MODIS bidirectional reflectance distribution function and albedo retrievals using combined observations from the aqua and terra platforms

We evaluate the performance of the MODerate Resolution Imaging Spectroradiometer (MODIS) Bidirectional Reflectance Distribution Function (BRDF)/Albedo algorithm using observations from MODIS instruments aboard NASA's Terra (EOS AM-1) and Aqua (EOS PM-1) platforms. This "combined" albedo product is evaluated against continuous field measurements from SURFace RADiation Budget Network (SURFRAD) and Atmospheric Radiation Measurement Southern Great Plains (ARM/SGP) stations, and through an internal analysis of the product's quality assurance (QA) fields. The combined product is compared to the initial MODIS albedo product, which used observations from the Terra satellite only. During the spring and summer months, the combined product showed a slight improvement over the original Terra-only albedo product, with a root mean square error (RMSE) of 0.0130 and a bias of about -0.02. As with the Terra-only product, accuracy drops during the fall and winter months at some sites. Jin et al. found that increased heterogeneity of validation sites during the fall and winter months is partially responsible for this drop in accuracy. The additional data provided by the Aqua platform changes high-quality albedo estimations only slightly, which underscores the stability of the MODIS algorithm. The most significant benefit of the combined product is a near 50% decrease in lower quality backup algorithm retrievals for the entire globe. A decrease in backup algorithm retrievals improves the overall accuracy of the MODIS albedo product, as it reduces algorithm reliance upon an a priori determination of the underlying surface anisotropy that is not entirely data derived.     

一种星载SAR图像的系统级几何校正技术

在星历参数和星载SAR空间几何模型的基础上，本文实现了星载SAR图像的系统级几何校正，给出了一种基于空间几何模型的星载SAR图像像素定位技术，说明了经纬网中任意点所在距离线被雷达波束中心照射的时刻的估算方法。最后利用仿真数据验证了这种几何校正方法的有效性。     

Detection of Sea Surface Temperature (SST) and Salinity Using Thermal Infrared Data of AVHRR and MODIS in the Gulf of Bohai Sea of China

This paper presents the detection of sea surface temperature (SST) and salinity in the Gulf of Bohai Sea of China using thermal infrared (TIR) data of Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS). Both AVHRR and MODIS imageries are evaluated as main data sources for monitoring SST as a measure of upwelling's dynamic. The relationship between SST and salinity in the area is also discussed during 1997-2000 derived from AVHRR data and then examined using MODIS data of 2000. The obtained results indicated that both AVHRR and MODIS are useful to detect SST and salinity in the study area.     

改进型ICA和SVM相结合的火山灰云遥感检测

针对独立分量分析(ICA)模型在火山灰云遥感检测中的不足,提出了一种改进型ICA即变分贝叶斯ICA (VBICA)和支持向量机(SVM)相结合的火山灰云遥感检测算法,实现了火山灰云信息的近似分离.实验结果表明,所提算法能够从中分辨率成像光谱仪(MODIS)遥感图像中检测出火山灰云目标信息,且总检测精度和Kappa系数分别达到了88.4％和0.801 1,取得了较好的检测效果.     

A physics-based algorithm for retrieving land-surface emissivityand temperature from EOS/MODIS data

The authors have developed a physics-based land-surface temperature (LST) algorithm for simultaneously retrieving surface band-averaged emissivities and temperatures from day/night pairs of MODIS (Moderate Resolution Imaging Spectroradiometer) data in seven thermal infrared bands. The set of 14 nonlinear equations in the algorithm is solved with the statistical recession method and the least-squares fit method. This new LST algorithm was tested with simulated MODIS data for 80 sets of hand-averaged emissivities calculated from published spectral data of terrestrial materials in wide ranges of atmospheric and surface temperature conditions. Comprehensive sensitivity and error analysis has been made to evaluate the performance of the new LST algorithm and its dependence on variations in surface emissivity and temperature, upon atmospheric conditions, as well as the noise-equivalent temperature difference (NEΔT) and calibration accuracy specifications of the MODIS Instrument. In cases with a systematic calibration error of 0.5%, the standard deviations of errors in retrieved surface daytime and nighttime temperatures fall between 0.4-0.5 K over a wide range of surface temperatures for mid-latitude summer conditions. The standard deviations of errors in retrieved emissivities in bands 31 and 32 (in the 10-12.5 μm IR spectral window region) are 0.009, and the maximum error in retrieved LST values falls between 2-3 K     

An algorithm using visible and 1.38-/spl mu/m channels to retrieve cirrus cloud reflectances from aircraft and satellite data

The Moderate Resolution Imaging Spectro-Radiometer (MODIS) on the Terra spacecraft has a channel near 1.38 /spl mu/m for remote sensing of high clouds from space. The implementation of this channel on MODIS was primarily based on previous analysis of hyperspectral imaging data collected with the Airborne Visible Infrared Imaging Spectrometer (AVIRIS). We describe an algorithm to retrieve cirrus bidirectional reflectance using channels near 0.66 and 1.38 /spl mu/m. It is shown that the apparent reflectance of the 1.38-/spl mu/m channel is essentially the bidirectional reflectance of cirrus clouds attenuated by the absorption of water vapor above cirrus clouds. A practical algorithm based on the scatterplot of 1.38-/spl mu/m channel apparent reflectance versus 0.66-/spl mu/m channel apparent reflectance has been developed to scale the effect of water vapor absorption so that the true cirrus reflectance in the visible spectral region can be obtained. To illustrate the applicability of the present algorithm, results for cirrus reflectance retrievals from AVIRIS and MODIS data are shown. The derived cirrus reflectance in the spectral region of 0.4-1 /spl mu/m can be used to remove cirrus contamination in a satellite image obtained at a visible channel. An example of such an application is shown. The spatially averaged cirrus reflectances derived from MODIS data can be used to establish global cirrus climatology, as is demonstrated by a sample global cirrus reflectance image.