Land surface temperature retrieval from LANDSAT TM 5

In this paper, three methods to retrieve the land surface temperature (LST) from thermal infrared data supplied by band 6 of the Thematic Mapper (TM) sensor onboard the Landsat 5 satellite are compared. The first of them lies on the estimation of the land surface temperature from the radiative transfer equation using in situ radiosounding data. The others two are the mono-window algorithm developed by Qin et al. [International Journal of Remote Sensing 22 (2001) 3719] and the single-channel algorithm developed by Jiménez-Muñoz and Sobrino [Journal of Geophysical Research 108 (2003)]. The land surface emissivity (LSE) values needed in order to apply these methods have been estimated from a methodology that uses the visible and near infrared bands. Finally, we present a comparison between the LST measured in situ and the retrieved by the algorithms over an agricultural region of Spain (La Plana de Requena-Utiel). The results show a root mean square deviation (rmsd) of 0.009 for emissivity and lower than 1 K for land surface temperature when the Jiménez-Muñoz algorithm is used.     

An analysis of GOES and NOAA derived land surface temperatures estimated over a boreal forest

Precise estimates of land surface temperature (LST) are important for land monitoring investigations. This study compares LST values calculated using different satellite platforms (Geostationary Operational Environmental Satellite-Imager and National Oceanographic and Atmospheric Administration-Advanced Very High Resolution Radiometer) and five different split window algorithms. The analysis includes (1) a fitting test with the reference dataset, (2) a comparison of differences between algorithms, and (3) an inter-sensor comparison. Considering the hypothesis of the Temperature/Vegetation Index (TVX) technique, the reference dataset was made with air temperature measured over dense canopy having maximum Normalized Difference Vegetation Index (NDVI). The first and second analyses show that algorithms used by Becker and Li and Ulivieri et al. have smaller estimation errors (less than 2.3 K) than the other algorithms, for example, best-fit linear regression. Although these algorithms show a good agreement in the paired algorithms analysis, the final analysis presents a considerable difference in the root mean square error between Imager and AVHRR (1.7 K for the Ulivieri et al. algorithm and 5.3 K for the Becker and Li algorithm). Finally we considered that the Ulivieri et al. method is more stable for both satellites.     

Land surface temperature retrieval from MSG1-SEVIRI data

We have developed a physical-based split-window Land Surface Temperature (LST) algorithm for retrieving the surface temperature from SEVIRI/MSG1 (Spinning Enhanced Visible and Infrared Imager/Meteosat Second Generation1) data in two thermal infrared bands (IR 10.8 and IR 12.0). The proposed algorithm takes into account the SEVIRI angular dependence. The numerical values of the split-window coefficients have been obtained from a statistical regression method, using synthetic data. The look-up tables for atmospheric transmission, path radiance, and downward thermal irradiance are calculated with the MODTRAN3 code. The new LST algorithm has been tested with simulated SEVIRI/MSG1 data over a wide range of atmospheric and surface conditions. Comprehensive sensitivity and error analyses have been undertaken to evaluate the performance of the proposed LST algorithm and its dependence on surface properties, the ranges of atmospheric conditions and surface temperatures, and on the noise-equivalent temperature difference. The results show that the algorithm is capable of producing LST with a standard deviation lower than 1.5 K for viewing zenith angles lower than 50°. Since MSG1 is becoming fully operational in 2004, the proposed algorithm will allow MSG1 users to obtain surface temperatures immediately.     

Properties of ERS-1/2 coherence in the Siberian boreal forest and implications for stem volume retrieval

Properties of multi-temporal ERS-1/2 tandem coherence in boreal forests and retrieval accuracy of forest stem volume have been investigated mostly for small, managed forest areas. The clear seasonal trends and the high accuracy of the retrieval are therefore valid for specific types of forest and question is if these findings extend to large areas with different forest types in a similar manner. Using multi-temporal ERS-1/2 coherence data and extensive sets of inventory data at stand level at seven forest compartments in Central Siberia we confirm that the trend of coherence as a function of stem volume is mainly driven by the environmental conditions at acquisition. In addition, we have now found that the variability of the coherence for a given stem volume are due to spatial variations of the environmental conditions, strong topography (slope > 10°), small stand size (< 3-4 ha) and low relative stocking (< 50%). Further deviations can be related to errors in the ground data. Stem volume retrieval behaves consistently under stable winter frozen conditions. For stands larger than 3-4 ha and relative stocking of at least 50%, a relative RMSE of 20-25% can be considered the effective retrieval error achievable in Siberian boreal forest. Combined with previous experience from managed test forests in Sweden and Finland, C-band ERS-1/2 tandem coherence observations acquired under stable winter conditions with a snow cover and an at least moderate breeze can be considered so far the most suitable spaceborne remote sensing observable for the estimation of forest stem volume in homogeneous forest stands throughout the boreal zone.     

A non-regression-coefficients method of sea surface temperature retrieval from space

For several years now NOAA/NESDIS have derived an operational global sea surface temperature (SST) product from the AVHRR instrument on the NOAA satellites. This is done using the MCSST and CPSST algorithms which contain coefficients that are determined from a regression analysis of satellite data against in situ surface data. The current algorithms are used to provide global SST data without taking into account the latitude, climate or location of the satellite data, although the CPSST coefficients do have a weak dependence on the satellite brightness temperatures. Because of this global application the current SST algorithms have inherent errors due to local climate influences. In this paper a new SST algorithm is developed that does not rely on regression analysis to derive its coefficients. By using the spatial variation of the brightness temperatures in a small area (50 km by 50 km) it is possible to derive the appropriate coefficients to use in the algorithm. The SST field can thus be derived at any location without need for prior determination of the algorithm coefficients. In a simulation study, data from twenty-five radiosonde ascents-arc use with an atmospheric transmission model to derive a range of atmospheric transmittances and satellite brightness temperatures. Coincident AVHRR data and ship data are used to assess the accuracy of the new algorithm. The various dependencies of the terms in the SST algorithm are investigated. As with the MCSST and CPSST algorithms, the new method has largest errors when applied in situations of abnormal atmospheric structure. The improvement over the MCSST product may initially be only marginal, but with the advent of the more precise data from the Along Track Scanning Radiometer (ATSR) a more accurate global SST product may be possible.     

Improvements in land surface temperature retrieval based on atmospheric water vapour content and atmospheric temperature

Three methods are currently used to retrieve land surface temperatures (LSTs) from thermal infrared data supplied by the Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) sensors: the radiative transfer equation, mono-window, and generalized single-channel algorithms. Most retrieval results obtained using these three methods have an average error of more than 1 K. But if the regional mean atmospheric water vapour content and temperature are supplied by in situ radiosounding observations, the mono-window algorithm is able to provide better results, with a mean error of 0.5 K. However, there are no in situ radiosounding data for most regions. This article provides an improved method to retrieve LST from Landsat TM and ETM+ data using atmospheric water vapour content and atmospheric temperature, which can be obtained from remote-sensing data. The atmospheric water vapour content at the pixel scale was first calculated from Moderate Resolution Imaging Spectroradiometer (MODIS) data. The emissivities of various land covers and uses were then defined by Landsat TM or ETM+ data. In addition, the temperature–vegetation index method was applied to map area-wide instantaneous near-surface air temperatures. The parameters of mean atmospheric water vapour content and temperature and land surface emissivity were finally inputted to the mono-window algorithm to improve the LST retrieval precision. Our results indicate that this improved mono-window algorithm gave a significantly better retrieval of the estimated LST than that using the standard mono-window algorithm, not only in dry and elevated mountain regions but also in humid regions, as shown by the bias, standard deviation (σ), and root mean square deviation (RMSD). In Madoi County, the improved mono-window algorithm validated against the LST values measured in situ produced a bias and RMSD of –0.63 K and 0.91 K, respectively, compared with the mono-window algorithm’s bias and RMSD of –1.08 K and 1.27 K. Validated against the radiance-based method, the improved algorithm shows bias and RMSD values of –1.08 K and 1.27 K, respectively, compared with the initial algorithm’s bias and RMSD –1.65 K and 1.75 K. Additionally, the improved mono-window algorithm also appeared to be more accurate than the mono-window algorithm, with lower error values when validated against in situ measurement and the radiance-based method in the validation area in Zhangye City, Gansu Province, China. Remarkable LST accuracy improvements are shown by the improved mono-window algorithm, with better agreement not only with the in situ measurements but also with the simulated LSTs in the two validation areas, indicating the soundness and suitability of this method.     

Preliminary applications of a land surface temperature retrieval method to IASI and AIRS data

Land surface temperature (LST) is one of the key state variables for many applications. This article aims to apply our previously developed LST retrieval method to infrared atmospheric sounding interferometer (IASI) and atmospheric infrared sounder (AIRS) data. On the basis of the opposite characteristics of the atmospheric spectral absorption and surface spectral emissivity, a ‘downwelling radiance residual index’ (DRRI) has been recalled and improved to obtain LST and emissivity. To construct an efficient DRRI, an automatic channel selection procedure has been proposed, and 11 groups of channels have been selected within the range 800–1000 cm^?1. The DRRI has been tested with IASI and AIRS data. For the IASI data, the radiosonde data have been used to correct for atmospheric effects and to retrieve LST, while the atmospheric profiles retrieved from AIRS data have been used to perform the atmospheric corrections and subsequently to estimate LST from AIRS data. The differences between IASI- and Moderate Resolution Imaging Spectroradiometer (MODIS)-derived LSTs are no more than 2 K, while the differences between AIRS- and MODIS-derived LSTs are less than 5 K. Even though an exceptionally problematic value occurred (–12.89 K), the overall differences between AIRS-estimated LST and the AIRS L2 LST product are no more than 5 K. Although the IASI-derived LST is more accurate than the AIRS-derived one, the convenient retrieval of AIRS atmospheric profile made this method more applicable. Limitations and uncertainties in retrieving LST using the DRRI method are also discussed.     

Assessment of multi-resolution and multi-sensor data for urban surface temperature retrieval

Data from three thermal sensors with different spatial resolution were assessed for urban surface temperature retrieval over the Yokohama City, Japan. The sensors are Thermal Airborne Broadband Imager (TABI), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and MODerate resolution Imaging Spectroradiometer (MODIS). Two algorithms were developed for land surface temperature (LST) retrieval from TABI image and ASTER thermal infrared (TIR) channels 13 and 14. In addition, ASTER LST and MODIS LST products were also collected. All the LST images were assessed by analyzing the relationship between LST and normalized difference vegetation index (NDVI) and by spatial distributions of LST profiles, derived from typical transects over the LST images. In this study, a strong negative relationship between LST and NDVI has been demonstrated although the degree of correlation between NDVI and LST varies slightly among the different LST images. Cross-validation among the LST images retrieved from the three thermal sensors of different spatial resolutions indicates that the LST images retrieved from the 2 channel ASTER data and a single band TABI thermal image using our developed algorithms are reliable. The LST images retrieved from the three sensors should have different potential to urban environmental studies. The MODIS thermal sensor can be used for the synoptic overview of an urban area and for studying urban thermal environment. The ASTER, with its TIR subsystem of 90-m resolution, allows for a more accurate determination of thermal patterns and properties of urban land use/land cover types, and hence, a more accurate determination of the LST. In consideration of the high heterogeneity of urban environment, the TABI thermal image, with a high spatial resolution of 2 m, can be used for rendering and assessing complex urban thermal patterns and detailed distribution of LST at the individual house level more accurately.     

Sea and lake surface temperature retrieval from Landsat thermal data in Northern Germany

Landsat thermal data are employed to derive lake and sea surface temperatures. The limitations of this approach are obvious, since the calculation of surface temperatures based solely on image data requires at least two thermal bands to compensate the atmospheric influence which is mainly caused by water vapour absorption. However, the 1 km spatial resolution of currently available multi‐band thermal satellite sensors (NOAA‐AVHRR, MODIS) is often not appropriate for lake and coastal zone applications. Therefore, it is worthwhile investigating the accuracy which can be obtained with single‐band thermal data using radiosonde information of the atmospheric water vapour column from meteorological stations in the study area. In addition, standard atmospheres from the MODTRAN code were considered that are based on seasonal climatologic values of water vapour, e.g. mid‐latitude summer, mid‐latitude winter, etc.

The study area of this investigation comprises various lakes and coastal zones of the Baltic Sea in NE Germany. Landsat‐7 ETM+ imagery of nine acquisition dates was selected covering the time span from February to November 2000. Results of derived lake and sea surface temperatures were compared with in situ measurements and with an empirical model of the Deutscher Wetterdienst (Germany's National Meteorological Service, DWD). RMS deviations of 1.4 K were obtained for the satellite‐derived lake surface temperatures with respect to in situ measurements and 2.2 K with respect to the empirical DWD model. RMS deviations of 1.6 K were obtained with respect to in situ bulk temperatures in coastal zones of the Baltic Sea. This level of agreement can be considered as satisfactory given the principal constraints of this approach. A better accuracy can only be obtained with high spatial resolution (<100 m) multi‐band thermal instruments delivering imagery on an operational basis.     

Multi-temporal JERS SAR data in boreal forest biomass mapping

Multi-temporal JERS SAR data were studied for forest biomass mapping. The study site was located in South-eastern Finland in Ruokolahti. Pre-processing of JERS SAR data included ortho-rectification and radiometric normalization of topographic effects.In single-date regression analysis between backscatter amplitude and stem volume, summer scenes from July to October produced correlation coefficients (r) between 0.63 and 0.81. Backscatter level and the slope of the (linear) regression line were stable from scene to scene. Winter scenes acquired in very cold and dry winter conditions had a very low correlation. One winter scene acquired in conditions where snow is not completely frozen produced a correlation coefficient similar to summer scenes.Multivariate regression analysis with a 6-date JERS SAR dataset produced correlation coefficient of 0.85. A combined JERS-optical regression analysis improved the correlation coefficient to 0.89 and also alleviated the saturation, which affects both SAR and optical data.The stability of the regression results in summer scenes suggests that a simple constant model could be used in wide-area forest biomass mapping if accuracy requirements are low and if biomass estimates are aggregated to large areal units.     

Seasonal reflectance dynamics of common understory types in a northern European boreal forest

The influence of the seasonal cycle of boreal forest understory has been noticed in global remote sensing of vegetation, especially in remote sensing of biophysical properties (e.g. leaf area index) of the tree-layer in a forest. A general problem in the validation of operationally produced global biophysical vegetation products is the lack of ground reference data on the seasonal variability of different land surface types. Currently, little is known about the spectral properties of the understory layers of boreal forests, and even less is known about the seasonal dynamics of the spectra. In this paper, we report seasonal trajectories of understory reflectance spectra measured in a European boreal forest. Four study sites representing different forest fertility site types were selected from central Finland. The understory composition was recorded and its spectra measured with an ASD FieldSpec Hand-Held UV/VNIR Spectroradiometer ten times during the growing period (from May to September) in 2010. Our results show that the spectral differences between and within understory types are the largest at the peak of the growing season in early July whereas in the beginning and end of the growing season (i.e. early May and late September, respectively) the differences between the understory types are marginal. In general, the fertile sites had the brightest NIR spectra throughout the growing season whereas infertile types appeared darker in NIR. Our results also indicated that a mismatch in the seasonal development of understory and tree layers does not occur in boreal forests: the understory and tree layer vegetation develop at a similar pace in the spring (i.e. there are no or only few spring ephemerals present), and the forests with the strongest seasonal dynamics in tree canopy structure (LAI) have also the strongest dynamics in understory spectra.     

On the relationship between thermodynamic surface temperature and high-frequency (37 GHz) vertically polarized brightness temperature under semi-arid conditions

A method to estimate surface temperature from high-frequency microwave observations is presented. Microwave brightness temperature is a function of the emissivity and the physical temperature of the emitting layer, and therefore possesses a strong physical basis for the estimation of surface temperature. Field observations have shown that maximum and minimum daily air temperatures are strongly related to daytime (1200h) and night-time (2400h) surface temperature. Field measurements of surface temperature are also compared to METEOSAT thermal observations. Long-term daily maximum and minimum air temperatures are then used to derive datasets of daytime and night-time surface temperatures. The results indicate that 37 GHz vertical polarization brightness temperature provides a reasonable estimate of spatially averaged surface temperature. This approach could provide a useful tool for climate modelling, land surface processes investigations, and other energy balance applications by providing consistent and independent long-term estimates of daily global surface temperature.     

An approach for assessing boreal forest conditions based on combined use of satellite SAR and multi-spectral data

The degree and spatial distribution of boreal forest ecosystem degradation in Russia are not well known. The objective of this study is to develop an interpretation basis for analysis of satellite remote sensing data using a set of indicators characterizing the ecological situation and the degree of industrial pollution. European Remote Sensing Satellite (ERS) Synthetic Aperture Radar (SAR) and Landsat Multi-Spectral Scanner (MSS) data are used in combination for this purpose, along with an exceptionally extensive in situ data set of ground measurements of spectral radiance of pine biocenose components, and the results of moss chemistry and bio-indicator studies from the ecologically stressed St Petersburg region. It is shown that ERS SAR images provide an assessment of forested area distribution and forest type classification. The main factors of variability in parameters such as Normalized Difference Vegetation Index (NDVI) that are most strongly related to in situ indicators reflecting the state of the forest are identified. A supervised classification of forest degradation was performed on the basis of the NDVI values from the Landsat images. The results obtained make it possible to specify the areas at a local level and perform regional assessments. The potential for multi-temporal ERS SAR and multi-spectral sensor observations to trace the dynamics of changes in forest ecosystems is evaluated.     

实时监测阴离子表面活性剂在石英表面的吸附

在 pH >2的水溶液中 ,石英表面带负电荷。当以Ca2 + 作为吸附桥时 ,阴离子表面活性剂能够吸附在石英表面上 ,以液隔电极式压电传感器实时监测该吸附过程中的质量变化 ,获得吸附动力学参数。     

Thinning-caused change in reflectance of ground vegetation in boreal forest

Ground reflectances were measured in the blue, green, red and near infrared (NIR) regions of the spectrum in a set of recently thinned pine- and spruce-dominated stands near Umea, Sweden. Compared with the untouched reference stands, the change in ground reflectance of the thinned stands was approximately linearly related to the thinning grade and to the coverage of the cutting waste left on the ground. Typically, thinning resulted in a reflectance increase in the red and decrease in the NIR band. The major effects of the presence of cutting waste on the ground reflectance can be simulated following a rather simple theoretical analysis. It appeared to be more difficult to quantitatively describe the effects on reflectance caused by the successional changes in the ground and field layer vegetation.     

Automated estimation of localized forest volume from large-scale aerial photographs and ancillary cartographic information in a boreal forest

The development of an automated method for obtaining locally reliable estimates of forest volume is demonstrated for a mixed-species boreal forest of the Lac St. Jean region of Quebec. The method relies on the ability of an algorithm based on local maxima to identify individual stems from a scanned aerial photograph under the assumption that the points of maximum light reflectance will be the highest points on individual trees. This information is linked via regression analysis to mean heights of dominant and co-dominant trees and ground-based forest inventory data to provide a statistical relationship with forest volume. It was demonstrated that, by using the method, the local uncertainty of volume estimates could be decreased by 61% relative to standard forest inventory procedures. The method is not applicable to young or disturbed stands. The greatest difficulty with the method is that sample plots used for validation must be locatable with absolute accuracy on the scanned aerial photographs something that is likely to be problematic in many forest conditions.     

Simulation of atmospheric profile retrieval from hyperspectral infrared data under cloudy conditions

In this paper, simulated space-based high spectral resolution AIRS (Atmospheric Infrared Sounder) infrared radiances with different cloud top heights and effective cloud fractions are used to demonstrate measurement sensitivity and atmospheric profile retrieval performance. Simulated cloudy retrievals of atmospheric temperature and moisture derived from the statistical eigenvector regression algorithm are analysed with different effective cloud fractions and different cloud heights. The results show that knowledge of cloud height is critical to sounding retrieval performance and the root mean square error of retrieved temperature and the mixed ratio of water vapour below the cloud top increases with effective cloud fraction. When there is 50 hPa error in the cloud height the retrieval accuracy of temperature and humidity decrease, compared with when the cloud height is known perfectly; the temperature retrieval is more sensitive to cloud height error than humidity retrieval. Collocated cloudy AIRS and the associated clear MODIS (Moderate Resolution Imaging Spectroradiometer) infrared observations within the AIRS field of view (FOV) are also used to demonstrate profile retrieval improvement below the cloud layer. It is demonstrated that using collocated clear MODIS multispectral imager data along with AIRS high spectral resolution infrared radiances can greatly improve the single FOV cloudy retrieval even under opaque cloudy conditions.