Studies on land surface temperature over heterogeneous areas using AVHRR data

The study addresses the use of the split-window method in tropical regions for estimation of surface temperature over heterogeneous surfaces from satellite sensor data. An attempt has been made to derive emissivity in the thermal channels using the NDVI in conjunction with fractional vegetation cover at pixel level. The estimated surface temperature values are compared with the in situ data over the region and are found to be within error limits of +/- 1.8°C. The utility of fractional vegetation cover in controlling surface temperature has been studied for the selected features over the area. The results suggest the utility of emissivity estimated from the NDVI in land surface temperature estimation.     

Estimating surface air temperatures,from Meteosat land surface temperatures,using an empirical solar zenith angle model

Temperature values derived from Meteosat are an indication of emitted long-wave radiation, and are not a true indication of ambient air temperature. The authors believe that Solar Zenith Angle (SZA) can be used as a proxy for solar energy reaching the ground surface, and its subsequent effects upon the land surface temperature detected by Meteosat. Raw satellite temperatures often overestimate the actual screen temperature during the day, and underestimate at night. By using a statistical model which relates Meteosat and WMO screen temperature deviations, and SZA values, it has been possible to generate a correction algorithm which minimizes these differences. The algorithm generates a new proxy value, being a simulated ambient (screen) air temperature. The algorithms achieve an accuracy of within 3 C for over 70% of the Meteosat temperatures processed. The operational use of this algorithm requires only the raw Meteosat temperature value, and the SZA. Such temperature corrections are useful for a wide range of environmental monitoring applications. An example is in the field of vector-borne disease modelling which requires proxies for temperature across large regions, and where more conventional meteorological stations are inadequate.     

Characterizing land surface anisotropy from AVHRR data at a global scale using high performance computing

We used the multi-temporal ten-day composite data from the Advanced Very High Resolution Radiometer (AVHRR) for the years 1983 to 1986 to retrieve the Bidirectional Reflectance Distribution Function (BRDF) using high performance computing techniques. Three different models are used: a simple linear model, a semi-empirical iterative model and a temporal model. The objectives of this study were to compare the performance of different BRDF models at a global scale, assess the computational requirements and optimize the algorithm implementation using high performance computational techniques, and to determine if there is any coherent spatial structure in the coefficients of different BRDF models corresponding to different land cover types. The standard error between model computed reflectances and the input data was used to quantify the performance of the models. Even though the iterative model is computationally more expensive (158 minutes) than either the simple linear model (15 minutes) or the temporal model (16 minutes), the results from all the three models were very similar when the BRDF was estimated at discrete time periods. If the BRDF models were applied without dividing the input data into discrete time intervals, then the temporal model gave better results than the other two. All the models were run on an IBM SP2 parallel machine with 16 CPUs. Most of the mountainous and snow covered areas in high latitudes had null values since the cloud screening algorithm used in the Pathfinder processing performed poorly in distinguishing between snow and clouds. The BRDF coefficients of the iterative model and the Fourier coefficients of the temporal model showed a strong spatial structure corresponding to known variations in land cover.     

Land surface air temperature mapping using TOVS and AVHRR

Surface air temperature is an important variable in land surface hydrological studies. This paper evaluates the ability of satellites to map air temperature across large land surface areas. Algorithms recently have been developed that derive surface air temperature using observations from the TOVS (TIROS Operational Vertical Sounder) suite of instruments and also from the AVHRR (Advanced Very High Resolution Radiometer), which have flown on the NOAA operational sun synchronous satellites TIROS-N NOAA-14. In this study we evaluate TOVS soundings from NOAA-10 (nominal local time of overpass 7:30 a.m./p.m.) and data from AVHRR aboard NOAA-9 (nominal local time 2:30 a.m./p.m.). Instantaneous estimates from the AVHRR and TOVS were compared with the hourly ground observations collected from 26 meteorological stations in the Red River-Arkansas River basin for a 3-month period from May to July 1987. Detailed comparisons between the satellite and ground estimates of surface air temperatures are reported and the feasibility of estimating the diurnal variation is explored. The comparisons are interpreted in the geographical context, i.e. land cover and topography, and in the seasonal context, i.e. early and midsummer. The results show that the average bias over the 3-month period compared with ground-based observations is approximately 2°C or less for the three times of day with TOVS having lower biases than AVHRR. Knowledge of these error estimates will greatly benefit use of satellite data in hydrological modelling.     

Effects of orbital drift on land surface temperature measured by AVHRR thermal sensors

The NOAA series of meteorological satellites that carry the Advanced Very High Resolution Radiometer (AVHRR) suffer from orbital drift so that during each satellite's duty period the overpass time occurs later in the day. Replacement satellites restore the overpass time temporarily, but then it gradually decays. The goals of this paper are to document the effects of variable observation time owing to orbital drift on brightness temperatures (BT) and land surface temperature (LST) calculated from them in the NOAA/NASA Pathfinder AVHRR Land (PAL) data set and to consider possible corrections for the resulting trends and discontinuities in the PAL BT data. The drift effects were found to be greater for bare ground than for vegetated land cover classes, however, significant effects were found for most vegetated classes. The magnitude of the orbital drift effect for most global cover types was at least as large as the other errors that affect LST measurement. A simple empirical correction for observation time based on solar zenith angle (SZA) was used to correct the PAL BT time series following Gutman [Int. J. Remote Sens. 20 (1999a) 3407]. The correction from this method was compared with that predicted by a physically based model and was found to differ in the early part of each satellite's duty cycle. Finally, the impacts of correction on the effective observation time are analyzed and the simple statistical correction was found to suffer from greater variability than has hitherto been recognized. A modification to the statistical correction to adjust the effective observation time is described.     

On the monitoring of land surface temperatures with the NOAA/AVHRR: Removing the effect of satellite orbit drift

To study the inter-annual variability of land surface temperature with NOAA Advanced Very High Resolution Radiometer (AVHRR) data, one must account for changes in the observed radiances due to changes in the observation time caused by satellite orbit drift (SOD). This study proposes a simple method to remove the SOD component from the AVHRR thermal IR. Spurious trends in these data should be corrected for to prevent their misidentification as real trends in the Earth's climate system and to infer more reliable conclusions from the inter-annual land surface variability studies, such as monitoring droughts. The proposed correction requires information on the observation solar zenith angle and normalized difference vegetation index for the region of interest.     

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。     

Development of a daily long term record of NOAA-14 AVHRR land surface temperature over Africa

We developed a new 6-year daily, daytime and nighttime, NOAA-14 AVHRR based land surface temperature (LST) dataset over continental Africa for the period 1995 through 2000. The processing chain was developed within the Global Inventory Modeling and Mapping System (GIMMS) at NASA's Goddard Space Flight Center. This paper describes the processing methodology used to convert the Global Area Coverage Level-1b data into LST and collateral data layers, such as sun and view geometries, cloud mask, local time of observation, and latitude and longitude. We used the Ulivieri et al. [Ulivieri, C., M.M. Castronuovo, R. Francioni, and A. Cardillo (1994), A split window algorithm for estimating land surface temperature from satellites, Adv. Space Research, 14(3):59-65.] split window algorithm to determine LST values. This algorithm requires as input values of surface emissivity in AVHRR channels 4 and 5. Thus, we developed continental maps of emissivity using an ensemble approach that combines laboratory emissivity spectra, MODIS-derived maps of herbaceous and woody fractional cover, and the UNESCO FAO soil map. A preliminary evaluation of the resulting LST product over a savanna woodland in South Africa showed a bias of < 0.3 K and an uncertainty of < 1.3 K for daytime retrievals (< 2.5 K for night). More extensive validation is required before statistically significant uncertainties can be determined. The LST production chain described here could be adapted for any wide field of view sensor (e.g., MODIS, VIIRS), and the LST product may be suitable for monitoring spatial and temporal temperature trends, or as input to many process models (e.g., hydrological, ecosystem).     

Physical and statistical approaches for cloud identification using Meteosat Second Generation-Spinning Enhanced Visible and Infrared Imager Data

In this paper a cloud detection algorithm applied to the MSG-SEVIRI (Meteosat Second Generation-Spinning Enhanced Visible and Infrared Imager) data is described. In order to obtain a good performance in cloud detection, physical, statistical and temporal approaches have been used. In the statistical algorithm, the spectral and textural features of the MSG-SEVIRI images have been used as input, while, in the physical tests, a set of dynamic thresholds has been used. The physical algorithm does not use real time ancillary data— such as sea surface temperature map and NWP temperature and humidity profiles. A further test is applied to that pixels having low confidence to be clear or cloudy. This test takes advantage of the best MSG-SEVIRI temporal resolution and it applies the K-Nearest Neighbour classifier to the spectral and textural features calculated in “temporal” boxes 3 × 3 pixels, defined “temporal” because their elements belong to three subsequent MSG-SEVIRI images. The MACSP (cloud MAsk Coupling of Statistical and Physical methods) algorithm has been validated against the MODIS cloud mask and compared with CPR (Cloud Profiling Radar) and SAFNWC cloud masks. The outcomes show that the MACSP detects 91.8% of the total number of the pixels used for validation against MODIS cloud mask correctly, while the SAFNWC cloud mask detects 89.2% of them correctly. In particular, the MACSP classifies as cloudy 8.8% of the pixels classified by the MODIS cloud mask as clear, while the SAFNWC cloud mask classifies as cloudy 12.1% of them. The MACSP detects 91.2% of the cloudy CPR pixels and 90.8% of the cloud-free CPR pixels, considered for comparison, correctly. On the other hand, the SAFNWC and CPR cloud masks agree in the detection of 90.7% of the cloudy pixels and of 90.2% of the cloud-free pixels.     

基于ITS模型的短波宽带信道建模与仿真

短波宽带信道的研究是设计短波宽带通信系统的基础,从上个世纪80年代以来短波宽带信道的探测和建模得到了极大发展,美国ITS组织提出的短波宽带信道模型突破了传统Watterson模型有效带宽仅有12kHz的限制,其有效带宽可达1MHz.为了评估短波宽带通信系统性能,提出一种基于ITS模型的短波宽带信道仿真的实现方法,并利用散射函数对信道传输特性进行了仿真.通过引入平滑滤波算法获得散射函数等高线,对平均延时、延时扩展、多普勒频移、多普勒扩展以及多普勒频移随延时变化的特性进行了定量分析,证明了仿真信道的有效性.     

Mapping sediment-laden sea ice in the Arctic using AVHRR remote-sensing data: Atmospheric correction and determination of reflectances as a function of ice type and sediment load

Exploiting the fact that the spectral characteristics of light backscattered from sediment-laden ice differ substantially from those of clean ice and that sediment tends to accumulate at the ice surface during the first melt season, remote-sensing techniques provide a valuable tool for mapping the extent of particle-laden ice in the Arctic basin and assessing its particulate loading. This study considers two fundamental problems that still need to be addressed in order to make full use of satellite observations for this type of assessment: (i) the effects of the atmosphere on surface reflectances derived from radiances measured by the satellite sensor need to be quantified and ultimately corrected for, and (ii) the spectral reflectance of the ice surface as a function of particle loading and sub-pixel distribution needs to be determined in order to derive quantitative estimates from the at-sensor satellite signal. Here, spectral albedos have been computed for different ice surfaces of variable sediment load with a radiative transfer model for sea ice coupled with an optical model for particulates included in sea ice. In a second step, the role of the atmosphere in modulating the surface reflectance signal is assessed with the aid of an atmospheric radiative transfer model applied to a “standard” Arctic atmosphere and surface boundary conditions as prescribed by the sea ice radiative transfer model. A series of sensitivity studies helps assess differences between top-of-the-atmosphere and true surface reflectance and has been utilized to derive a look-up table for atmospheric correction of Advanced Very High Resolution Radiometer (AVHRR) data over sediment-laden sea ice surfaces. In particular, the effects of solar elevation, viewing geometry, and atmospheric properties are considered. The atmospheric corrections are necessary for certain geometries and surface types. Large discrepancies between raw and corrected data are particularly evident in the derived coverage of clean ice and ice with small sediment loading.     

Retrieval and validation of land surface temperature (LST) from NOAA AVHRR thermal images of Gujarat,India

A study was undertaken to retrieve land (soil-vegetation complex) surface temperature (LST) over a 100 km 2 100 km area in Gujarat (India) using thermal bands (channel 4 and 5) and estimated emissivity from atmospherically corrected NDVI, derived from NOAA-14 AVHRR data. The LST values were compared with near synchronous soil and air temperature measurements over five sites in December and May 1997 during Land Surface Processes Experiment (LASPEX) in Gujarat, India. The estimated LST of a semi-arid mixed agricultural barren landscape at 10.00 GMT was found to vary from 302 to 305.6 K on 13 December 1997 (winter) and from 317.5 to 328.5 K at 08.30 GMT on 15 May 1997 (Summer). During December, the LST values were near midway between air temperature (AT) and soil surface temperature (ST) with mean bias of m 2.9 K and 7.0 K respectively. However, in May, the LST values were found to be closer to ST, which may be due to lower fractional vegetation cover and NDVI.     

Identification of land surface temperature and albedo trends in AVHRR Pathfinder data from 1982 to 2005 for northern Siberia

The arctic regions are highly vulnerable to climate change. Climate models predict an increase in global mean temperatures for the upcoming century. The arctic environment is subject to significant changes of the land surface. Especially the changes of vegetation pattern and the phenological cycle in the taiga–tundra transition area are of high importance in climate change research. This study focuses on time series and trend analysis of land surface temperature, albedo, snow water equivalent, and normalized difference vegetation index information in the time period of 1982–2005 for northern Siberia. The findings show strong dependencies between these parameters and their inter-annual dynamics, which indicate changes in vegetation growing period. We found a strong negative correlation between land surface temperature and albedo conditions for the beginning (60–90%) of the growing season for selected hot spot trend regions in northern Siberia.     

Determination of land surface temperature and its lapse rate in the Satluj River basin using NOAA data

Temperature lapse rate (TLR), an essential parameter for snowmelt runoff analysis, was determined for the Satluj River basin in the Western Himalayas. National Oceanic and Atmospheric Administration/Advanced Very High Resolution Radiometer (NOAA/AVHRR) data sets were used to determine the land surface temperature (LST) of the region using the split‐window algorithm proposed by Coll and Caselles (Journal of Geophysical Research, 1997, 102, pp. 16697–16713). The LST was correlated with the elevation values obtained from a US Geological Survey digital elevation model (USGS‐DEM) of the same area and the trend showed an inverse relationship between LST and elevation. The TLRs for the study area on 2 February, 1 March, 26 March, 16 October, 1 November and 20 November 2004 were in the range 0.6–0.74°C/100 m. The results obtained were compared with lapse rates determined using Moderate Resolution Imaging Spectroradiometer (MODIS) LST maps. TLR determination in the past was based on air temperature data available from meteorological stations that are sparsely located in rugged terrain such as the Himalayas. As these measurements were point data and had been measured manually, they may have led to erroneous results. Satellite data, however, provide continuous and potentially unbiased recording provided an accurate radiometric calibration and atmospheric correction can be achieved. A previous TLR calculation using air temperature from meteorological stations for the western Himalayan region was found to be 0.65°C/100 m. Air temperature and LST from NOAA‐AVHRR and MODIS‐Terra data were found to be in good agreement. This type of study will be useful for snowmelt runoff modelling studies for the Himalayan region.     

Predicting the spectral information of future land cover using machine learning

Application of machine learning models to study land-cover change is typically restricted to the change detection of categorical, i.e. classified, land-cover data. In this study, our aim is to extend the utility of such models to predict the spectral band information of satellite images. A Random Forests (RF)-based machine learning model is trained using topographic and historical climatic variables as inputs to predict the spectral band values of high-resolution satellite imagery across two large sites in the western United States, New Mexico (10,570 km²), and Washington (9400 km²). The model output is used to obtain a true colour photorealistic image and an image showing the normalized difference vegetation index values. We then use the trained model to explore what the land cover might look like for a climate change scenario during the 2061–2080 period. The RF model achieves high validation accuracy for both sites during the training phase, with the coefficient of determination (R²) = 0.79 for New Mexico site and R² = 0.73 for Washington site. For the climate change scenario, prominent land-cover changes are characterized by an increase in the vegetation cover at the New Mexico site and a decrease in the perennial snow cover at the Washington site. Our results suggest that direct prediction of spectral band information is highly beneficial due to the ability it provides for deriving ecologically relevant products, which can be used to analyse land-cover change scenarios from multiple perspectives.     

Forward and inverse modeling of land surface energy balance using surface temperature measurements

Land surface temperature measurements have been widely used to estimate surface energy balance. However, because land surface temperature and energy balance both depend on a complex suite of factors, precise estimation of surface energy exchanges using thermal remote sensing is difficult. In recent years, a variety of methods have been developed that overcome previous limitations and show substantial promise for robust estimation of surface fluxes from remote sensing. This paper reviews recent progress in this domain and describes a two-layer energy balance model designed for use with thermal remote sensing. An important aspect of the model is that it is specifically designed to account for the complex micrometeorology and thermal properties of land surfaces possessing a range of density in vegetation. Further, the physics underlying this model are complementary to the physics of land surface thermal remote sensing. Comparisons between field measurements and modeled fluxes show good agreement, which suggests that the model describes land surface energy balance processes with good realism. More importantly, these results reinforce the conclusions of other recent studies that have demonstrated the compatibility of two-layer energy balance models with remote sensing observations and, by extension, the viability of using thermal remote sensing to model surface energy balance.     

应用NOAA／AVHRR资料动态监测洪涝灾害的研究

以ＮＯＡＡ卫星为主要监测手段，对洪水、植被、土壤的光谱特征进行分析研究，提出了同时突出水体和植被的光谱分析思路。并根据客观分析需要，建立了洪涝灾情图像处理软件系统，对ＮＯＡＡ／ＡＶＨＲＲ图像资料进行投影、截取、放大、配准等一系列预处理。同时采用模糊非监督分类、比值、归一化植被指数方法对洪涝信息进行分析处理，确定受灾范围，量算受灾面积，划分受灾等级，提供灾情分布图，为防汛抗洪部门提供有力的科学依据。     

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.     

Validation of coastal sea and lake surface temperature measurements derived from NOAA/AVHRR data

An interactive validation monitoring system is being used at the NOAA/NESDIS to validate the sea surface temperature (SST) derived from the NOAA-12 and NOAA-14 polar orbiting satellite AVHRR sensors for the NOAA CoastWatch program. In 1997, we validated the SST in coastal regions of the Gulf of Mexico, Southeast US and Northeast US and the lake surface temperatures in the Great Lakes every other month. The in situ     

Atmospheric correction for the sea surface temperature using NOAA-7 AVHRR and METEOSAT-2 infrared data

The multi-spectral method for atmospheric corrections is investigated. The analysis shows that the non-linear effects of the atmospheric absorption can be accounted for by using three channels. The results clarify and lend theoretical support to previous works. A simple correction procedure is proposed. In the case where channel 3 of the NOAA AVHRR is not available or is contaminated, simultaneous METEOSAT infrared data can be used instead. The result tested with in situ measured temperatures shows a marked improvement when three channels are used. In this procedure, the detailed knowledge of the local atmospheric profiles is not required.