NOAA/AVHRR数据的雪盖信息提取与复合

在对NOAA/AVHRR数据特征与雪冰波谱特性分析的基础上，对各种提取雪盖信息的方法进行了比较，指出了各种方法的优劣，认为在实时的雪灾监浏与评估系统中，直方图分割的方法快速有效。另一方面，通过雪盖影像与GIS中各种矢量图形的复合配准实验，指出宜先对AVHRR影像进行点位计算，然后利用控制点、进行精校正，所产生的图像才能达到与矢量图形的准确配准。     

New radiance-based method for AVHRR thermal channel nonlinearity corrections

Thermal channels 4 and 5 of the Advanced Very High Resolution Radiometer (AVHRR) on National Oceanic and Atmospheric Administration (NOAA) polar-orbiting satellites have an onboard calibration process that provides data from which incoming scene radiance is linearly related to AVHRR count output. However, prelaunch calibration tests show that the radiance is more accurately modelled as a quadratic in count value and that the actual quadratic fit depends upon the operating temperature of the AVHRR itself. NOAA has developed a new method to provide prelaunch information to operational data users that is both concise and accurate. It corrects the linear radiance estimate instead of correcting equivalent blackbody temperature values. The nonlinear correction to the linear radiance estimate is provided by a single quadratic equation, independent of the AVHRR temperature. The new method was first applied to the NOAA-14 AVHRR. When corrected radiance estimates for the NOAA-14 AVHRR are compared to precise prelaunch data, the rms difference is 0.14K in channel 4 and 0.08K in channel 5, in temperature units. At present, users of NOAA-14 AVHRR 1b data have to apply the radiance correction themselves, but for the NOAA-15 and future AVHRRs the necessary information is included in the expanded 1b datastream. Direct readout High Resolution Picture Transmission (HRPT) users have to implement the correction process themselves for NOAA-14, NOAA-15 and future AVHRRs.     

Statistical models of fragmented land cover and the effect of coarse spatial resolution on the estimation of area with satellite sensor imagery

The feasibility of correcting for errors in apparent extent of land cover types on coarse spatial resolution satellite imagery was analysed using a modelling approach. The size distributions for small burn scars mapped with two Landsat Multi-spectral Scanner (MSS) images and ponds mapped with an ERS-1 synthetic aperture radar (SAR) image were measured using geographical information system (GIS) software. Regression analysis showed that these size distributions could be modelled with two types of statistical distributions a power distribution and an exponential distribution. A comparison of the size distributions of small burn scars as observed with the Landsat MSS imagery to the distribution observed with National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) imagery indicated that distortions due to the coarse spatial resolution of AVHRR caused overestimation of the burn area. This bias was primarily caused by detection in two or three AVHRR pixels of burns whose actual size was on the order of a single AVHRR pixel. Knowledge of the type of the actual size distribution of small fragments in a scene and the causes of distortion may lead to methods for correcting area estimates involving models of the size distribution observed with coarse imagery and requiring little or no recourse to fine scale data.     

Mapping sub-pixel proportional land cover with AVHRR imagery

A problem with NOAA AVHRR imagery is that the intrinsic scale of spatial variation in land cover in the U.K. is usually finer than the scale of sampling imposed by the image pixels. The result is that most NOAA AVHRR pixels contain a mixture of land cover types (sub-pixel mixing). Three techniques for mapping the sub-pixel proportions of land cover classes in the New Forest, U.K. were compared: (i) artificial neural networks (ANN); (ii) mixture modelling; and (iii) fuzzy c -means classification. NOAA AVHRR imagery and SPOT HRV imagery, both for 28 June 1994, were obtained. The SPOT HRV images were classified using the maximum likelihood method, and used to derive the 'known' sub-pixel proportions of each land cover class for each NOAA AVHRR pixel. These data were then used to evaluate the predictions made (using the three techniques and the NOAA AVHRR imagery) in terms of the amount of information provided, the accuracy with which that information is provided, and the ease of implementation. The ANN was the most accurate technique, but its successful implementation depended on accurate co-registration and the availability of a training data set. Supervised fuzzy c -means classification was slightly more accurate than mixture modelling.     

Crop yield estimation by satellite remote sensing

Two methods for estimating the yield of different crops in Hungary from satellite remote sensing data are presented. The steps of preprocessing the remote sensing data (for geometric, radiometric, atmospheric and cloud scattering correction) are described. In the first method developed for field level estimation, reference crop fields were selected by using Landsat Thematic Mapper (TM) data for classification. A new vegetation index (General Yield Unified Reference Index (GYURI)) was deduced using a fitted double-Gaussian curve to the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) data during the vegetation period. The correlation between GYURI and the field level yield data for corn for three years was R ²=0.75. The county-average yield data showed higher correlation (R ²=0.93). A significant distortion from the model gave information of the possible stress of the field. The second method presented uses only NOAA AVHRR and officially reported county-level yield data. The county-level yield data and the deduced vegetation index, GYURRI, were investigated for eight different crops for eight years. The obtained correlation was high (R ²=84.6–87.2). The developed robust method proved to be stable and accurate for operational use for county-, region- and country-level yield estimation. The method is simple and inexpensive for application in developing countries, too.     

低空间分辨率多源遥感数据的空间一致性分析和相对几何校正

多源低空间分辨率遥感数据在空间上的一致性对于其在全球变化研究中的集成使用有非常重要的意义。对此,以公认几何精度较高的MODIS数据为基准,对NOAA/AVHRR、FY-3/VIRR、FY-3/MERSI、FY-2/VISSR这4类国内外常用的低空间分辨率传感器的L1B数据进行了一系列相对几何精度评价和多项式相对几何校正的实验。相对几何精度评价的结果表明:MODIS数据与这4类L1B数据在几何精度上的偏差都比较大。在此基础上,选取少量均匀分布的控制点并采用不同阶数的多项式几何校正模型对多源数据进行空间一致性校正。校正结果表明:低阶的多项式几何校正模型就能对各种待校正数据的几何精度有显著的提升,使其与基准数据在空间上达到一致,满足全球变化研究对低分辨率多源遥感数据在空间一致性上的需求。     

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.     

Estimating bidirectional angles in NOAA AVHRR images

In studies concerning the surface bidirectional reflectance distribution function (BRDF) and thermal-infrared multiangular emissions, Sun-sensor geometry must be known. This Letter provides a potential and simple method for NOAA Advanced Very High Resolution Radiometer (AVHRR) users to estimate the imaging configuration of each pixel in a geometrically corrected image. Our formulas were tested with example AVHRR data and their precision was shown to be comparatively high with a maximum error of either the satellite zenith or azimuth angle less than 4°. The standard deviation for the zenith is 2.07° and azimuth is 2.47°.     

Retrieval of temporal profiles of reflectances from simulated and real NOAA-AVHRR data over heterogeneous landscapes

To carry out functioning and dynamic vegetation studies, a temporal analysis is needed. So far, only data provided by the National Oceanic and Atmospheric Administration (NOAA) satellites with Advanced Very High Resolution Radiometer (AVHRR) sensors offer the required temporal resolution, but their spatial resolution is coarse (1.1 km). But, in many situations, the vegetation cover is heterogeneous and the 1.1 km AVHRR pixel contains several types of land use radiometrically different and is, in fact, a mixed pixel. Thus, the reflectance and consequently deduced parameters (NDVI, LAI, etc.) measured by AVHRR is actually average value and does not represent a value for each vegetation class present in the pixel. The objective is to extract the reflectance of each vegetation class from the mixed pixel using NOAA-AVHRR data and SPOT-HRV data simultaneously which give the proportions of each type of vegetation inside the mixed pixel through a classification map. The paper presents a method for radiometrically unmixing coarse resolution signals through the inversion of linear mixture modelling on heterogeneous regions of natural vegetation (Bidi-Bahn) in Burkina-Faso and in Niger (Hapex site). In a first step, simulated coarse resolution data (NOAA-AVHRR) obtained from the degradation of SPOT images are used to assess the method. In a second step, real NOAA-AVHRR data are used and some elements of validation are given by comparing the results to airborne reflectance measurements.     

Efficiency Research of Planted Rice Area Estimation From NOAA一AVHRR Data in Taihu Domain

运用NOAA-AVHRR资料估算水稻种植面积，是遥感应用领域中一个新的研究方向，结合国家“八五”攻关项目“太湖地区遥感话产”的要求，在太湖地区进行了初步的尝试:(1)根据估产精度要求和NOAA一AVHRR资料校正精度，探讨了运用NOAA一AVHRR资料估产所需的最小区域范围。(2)针对太湖地区的具休地理环境设计了提取水稻种植曲积的技术方案，并在试验区取得了初步成果。     

Improvements in the global biospheric record from the Advanced Very High Resolution Radiometer (AVHRR)

Results are provided of a project to derive improved products from the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) data record for land investigations. As part of this project, a prototype AVHRR processing system has been developed. This paper describes the different components of this system, which include radiometric in-flight vicarious calibration for the visible and near infrared channels, geometric correction and atmospheric correction as pre-processing steps. The processed data are then stored in a new intermediate data format, which enables flexible compositing approaches. The system generates surface reflectance and vegetation index products as well as new higher order products of reflectance at 3.75 mum and active fires. A comparison of a significant sample of data with widely used precursor AVHRR products is presented to evaluate the processing chain and the improvements it provides.     

Irrigated area determination by NOAA-Landsat upscaling techniques,Zayandeh River Basin,Isfahan, Iran

The processing and analysis of a set of NOAA-14/AVHRR images, obtained from the Satellite Active Archive (SAA) for 1995 and 1999, is discussed. The NDVI values were calculated using channel 1 and channel 2 reflection values after standard radiometric and geometric corrections. The average Normalized Difference Vegetation Index (NDVI) of the main irrigation systems in the Zayandeh Rud Basin, Isfahan, Iran, was then determined for each image. This made it possible to analyse the temporal evolution of these NDVI values for the individual irrigated areas during 1999. The National Oceanic and Atmospheric Administration (NOAA) NDVI values can, in principle, also be used for the determination of the size of the actual irrigated areas of the principal systems. However, calibration of the method proved necessary, because in most NOAA pixels the vegetation is only a fraction of the pixel area. Through the use of Geographic Information System (GIS) operations on a Landsat 7 image (1 August 1999), the actual irrigated areas in Borkhar, Abshar Left and Right, Nekouabad Left and Right, were determined for that particular date. Five NOAA images were then selected for the period July-August 1999. This made it possible to establish regression relations linking NOAA NDVI values to the size of the net irrigated systems, which were then applied to the 1995 dataset.     

Temporal stability of some global NDVI products derived from NOAA/AVHRR GVI

The Normalized DilTerence Vegetation Index (NDVI) derived from NOAA's Advanced Very High Resolution Radiometer (AVHRR) has been widely used in monitoring continental and global vegetation distribution and dynamics, drought severity and location, and environmental deterioration. Since 1982, NOAA has produced the Weekly Global Vegetation Index (GVI) product from AVHRR. The analyses of the GVI product have revealed many problems due to the simplified radiometric correction involved in the processing. Those limitations have inspired several elTorts to reprocess the NOAA GVI data sets to produce an improved representation of global NDVI patterns. In this paper, the quality of three Global NDVI products resulting from very simple to rather sophisticated reprocessing was examined by using a global approach. In general, the quality of data improves with increasing sophistication of radiometric correction. However, this study reveals some significant errors common in all three products assessed. The problems include a systematic annual increase in values computed from a single satellite and jumps between consecutive satellites. These errors are large enough to alTect results of the long term time-series analyses. This pattern suggests an additional radiometric distortion in NOAA/ AVHRR data. It is found that the values computed from data of the first year after satellite launch are roughly the same statistically for NOAA satellites. Thus, the discontinuity ofNDVls between satellites appears to be mainly caused by the systematic drift. Therefore, data collected in the first year of satellite launch might be considered as a baseline for correcting the systematic errors. By comparing NDVI from the first year of satellites in space, it is also found that NDVI increases at higher latitude and decreases or keeps constant at lower latitude. This change of NDVI with time might signal the change of global climate.     

Terra and Aqua MODIS inter‐comparison of three reflective solar bands using AVHRR onboard the NOAA‐KLM satellites

Cross‐sensor inter‐comparison is important to assess calibration quality and consistency and ensure continuity of observational datasets. This study conducts an inter‐comparison of Terra and Aqua MODIS (the MODerate Resolution Imaging Spectroradiometer) to examine the overall calibration consistency of the reflective solar bands. Observations obtained from AVHRR (the Advanced Very High Resolution Radiometer) onboard the NOAA‐KLM series of satellites are used as a transfer radiometer to examine three MODIS bands at 0.65 (visible), 0.85 (near‐IR) and 1.64 µm (far near‐IR) that match spectrally with AVHRR channels. Coincident events are sampled at a frequency of about once per month with each containing at least 3000 pixel‐by‐pixel matched data points. Multiple AVHRR sensors on‐board NOAA‐15 to 18 satellites are used to check the repeatability of the Terra/Aqua MODIS inter‐comparison results. The same approach applied in previous studies is used with defined criteria to generate coincident and co‐located near nadir MODIS and AVHRR pixel pairs matched in footprint. Terra and Aqua MODIS to AVHRR reflectance ratios are derived from matched pixel pairs with the same AVHRR used as a transfer radiometer. The ratio differences between Terra and Aqua MODIS/AVHRR give an indication of the calibration biases between the two MODIS instruments. Effects due to pixel footprint mismatch, band spectral differences and surface and atmospheric bi‐directional reflectance distributions (BRDFs) are discussed. Trending results from 2002 to 2006 show that Terra and Aqua MODIS reflectances agree with each other within 2% for the three reflective solar bands.     

An automatic system for AVHRR land surface product generation

Making products from the Advanced Very High Resolution Radiometer (AVHRR) on board the National Oceanic and Atmospheric Administration (NOAA) polar‐orbiting satellites can be time consuming and an automated technique for image processing is required to generate long time series of AVHRR imagery. This paper aims to describe the development of a system for fully‐automated AVHRR image processing, including radiometric calibration, precise geo‐registration and generating land‐surface products, such as vegetation indices, maximum value composites and cloud masks. Tests for crop monitoring purposes were carried out using High Resolution Picture Transmission (HRPT) images between October 2003 and April 2004. The region used to evaluate the system was the State of Paraná, one of the primary soybean producers in Brazil. Results have shown that for severely cloud‐filtered images, the system was effective in generating geometrically precise image products, with geolocation errors less than a pixel. The developed system can be operated with no human intervention and can be used as an important tool for NOAA‐AVHRR image users especially those who need to use long time series.     

The 1 km AVHRR global land data set: first stages in implementation

The global land 1 km data set project represents an international effort to acquire, archive, process, and distribute 1 km AVHRR data of the entire global land surface in order to meet the needs of the international science community. A network of 26 high resolution picture transmission (HRPT) stations, along with data recorded by the National Oceanic and Atmospheric Administration (NOAA), has been acquiring daily global land coverage since 1 April 1992. A data set of over 30000 AVHRR images has been archived and made available for distribution by the United States Geological Survey, EROS Data Center and the European Space Agency

Under the guidance of the International Geosphere Biosphere programme, processing standards for the AVHRR data have been developed for calibration, atmospheric correction, geometric registration, and the production of global 10-day maximum normalized difference vegetation index (NDVI) composites. The major uses of the composites are related to the study of surface vegetation cover. A prototype 10-day composite was produced for the period of 21–30 June 1992. Production of an 18-month time series of 10-day composites is underway.

     

Determination of land surface spectral reflectances using Meteosat and NOAAA/AVHRR Shortwave Channel Data

Abstract

A method to derive surface spectral reflectances from currently available Meteosat geostationary and NOAA/AVHRR polar orbiting satellite data is described. Broadband reflectance was derived from Meteosat measurements while NOAA/AVHRR vegetation index provided a spectral weighting which enabled the spectral reflectances on either side of 0-7 μm to be estimated. The method takes into account satellite calibrations, viewing geometry, and correction of some atmospheric effects. Conversion from narrow-band to broadband reflectances is discussed. The method was applied to a month of data to obtain the surface spectral reflectances of Africa which are compared with some data sets used by climate modellers, in order to assess them and to monitor their seasonal and interannual changes on a global scale.     

The estimation of atmospheric effects for SPOT using AVHRR channel-1 data

The aim of this paper is to estimate the effect of atmospheric contamination on SPOT satellite data for stereoscopic modelling by using data from the AVHRR instrument carried on the NOAA satellite series. This paper includes the development of an atmospheric correction algorithm for the visible spectral channel data from the AVHRR instrument, and an analysis of the atmospherically corrected AVHRR data from many successive days, bearing in mind that the SPOT data for stereoscopic modelling will be from two orbits which will be separated by several days.     

Quantitative dynamic flood monitoring with NOAA AVHRR

The analysis of the spatial extent and temporal pattern of flood inundation from remotely sensed imagery is of critical importance to flood mitigation. With a high frequency of global coverage, NOAA/AVHRR has the advantage of detecting flood dynamics during devastating floods. In this paper, we describe a systematic approach to flood monitoring using AVHRR data. Four critical issues for successful flood monitoring with AVHRR were identified: correct identification of water bodies, effective reduction of cloud contamination, accurate area estimation of flood extent, and dynamic monitoring of flood processes. In accordance with the spectral characteristics of water and land in AVHRR channels, a simple but effective water identification method was developed with the ability to reduce cloud influences. The area of flooded regions was calculated with the consideration of areal distortion due to map projection, and mixed pixels at water/land boundaries. Flood dynamics were analysed from flood distributions in both space and time. The maximum spatial extent of floods, generated by compiling the time series of flood maps, was informative about flood damages. We report a successful use of this approach to monitor the Huaihe river flood, a centennial devastating disaster occurred in the Huaihe river basin of China in the summer of 1991.     

Dynamics of ENSO drought events on Sabah rainforests observed by NOAA AVHRR

Drought, associated with the El Niño Southern Oscillation (ENSO), can have considerable impact on tropical rainforests. Concern over drought, particularly given the possibility of an increase in its occurrence and intensity, has fostered a desire for an increased understanding of drought events and their impact to inform the development of a drought monitoring system. This paper investigates the use of National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) data in a drought monitoring system for the rainforests of Sabah, Borneo. These rainforests are dynamic with respect to their coupling with ENSO processes and in their biophysical properties, and such dynamism may have implications for how NOAA AVHRR data may be used. In particular, this paper explores the transferability of relationships between a drought indicator (rainfall) and the response of the rainforest, as measured by four NOAA AVHRR variables (middle infrared reflectance; VI3; Ts/VI3 and Ts/NDVI), under particular site conditions. It was found that both spatial variability in forest biophysical properties and geographical variability in drought impact had implications for the transferability of relationships developed under local conditions across Sabah rainforests within a drought monitoring system. Suggestions are presented for how NOAA AVHRR data could be used, with a new drought monitoring index – the Ts/VI3 – recommended.