Estimation of subpixel land surface temperature using an endmember index based technique: A case examination on ASTER and MODIS temperature products over a heterogeneous area

Land surface temperature (LST) is a key parameter in numerous environmental studies. Surface heterogeneity induces uncertainty in estimating subpixel temperature. To take an advantage of simultaneous, multi-resolution observations at coincident nadirs by the Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) and the MODerate-resolution Imaging Spectroradiometer (MODIS), LST products from the two sensors were examined for a portion of suburb area in Beijing, China. We selected Soil-Adjusted Vegetation Index (SAVI), Normalized Multi-band Drought Index (NMDI), Normalized Difference Built-up Index (NDBI) and Normalized Difference Water Index (NDWI) as representative remote sensing indices for four land cover types (vegetation, bare soil, impervious and water area), respectively. By using support vector machines, the overall classification accuracy of the four land cover types with inputs of the four remote sensing indices, extracted from ASTER visible near infrared (VNIR) bands and shortwave infrared (SWIR) bands, reached 97.66%, and Kappa coefficient was 0.9632. In order to lower the subpixel temperature estimation error caused by re-sampling of remote sensing data, a disaggregation method for subpixel temperature using the remote sensing endmember index based technique (DisEMI) was established in this study. Firstly, the area ratios and statistical information of endmember remote sensing indices were calculated from ASTER VNIR/SWIR data at 990 m and 90 m resolutions, respectively. Secondly, the relationship between the 990 m resolution MODIS LST and the corresponding input parameters (area ratios and endmember indices at the 990 m resolution) was trained by a genetic algorithm and self-organizing feature map artificial neural network (GA-SOFM-ANN). Finally, the trained models were employed to estimate the 90 m resolution subpixel temperature with inputs of area ratios and endmember indices at the 90 m resolution. ASTER LST product was used for verifying the estimated subpixel temperature, and the verified results indicate that the estimated temperature distribution was basically consistent with that of ASTER LST product. A better agreement was found between temperatures derived by our proposed method (DisEMI) and the ASTER 90 m data (R² = 0.709 and RMSE = 2.702 K).     

利用温度植被干旱指数反演三峡库区土壤水分

土壤水分是监测土地退化的一个重要指标,是气候、水文、生态、农业等领域的主要参数,在地表与大气界面的水分和能量交换中起重要作用。传统的监测土壤水分的方法只能得到单点的数据,很难获得大范围地区的土壤湿度。遥感能够快速方便地获取大区域的地表信息,因此使用遥感监测土壤水分意义重大。主要利用了温度指标干旱指数对三峡库区进行土壤水分反演及其验证。利用TM6波段的亮温方程,计算得出地表温度（Ts）,以TM3、TM4波段计算得出归一化植被指数（NDVI）;把Ts和NDVI作为基本参数,根据Ts-NDVI特征空间的形状,取中间范围的NDVI,拟合干湿边方程,确定干湿边参数;根据温度植被干旱指数（TVDI）进行土壤湿度等级划分。结果表明,利用TVDI可以很好地反演出地表的土壤湿度信息。     

Estimating surface radiation fluxes in the Arctic from TOVS brightness temperatures

A new method for estimating downwelling shortwave and longwave radiation fluxes in the Arctic from TOVS brightness temperatures has been developed. The method employs a neural network to bypass computationally intensive inverse and forward radiative transfer calculations. Results from two drifting ice camps (CEAREX, LeadEx) and from one coastal station show that downwelling fluxes can be estimated with r.m.s. errors of 20Wm-2 for longwave radiation and 35Wm-2for shortwave radiation. Mean errors are less than 4 Wm-2 and are well within the bounds required for many climate process studies.     

Characterising groundwater use by vegetation using a surface energy balance model and satellite observations of land surface temperature

This study presents a novel ‘model-data’ approach to detect groundwater-dependent vegetation (GDV), through differences in modelled and observed land surface temperatures (LST) in space and time. Vegetation groundwater use is inferred where modelled LST exceeds observed LST by more than a threshold determined from consideration of systematic and random errors in model and observations. Modelled LST was derived from a surface energy balance model and LST observations were obtained from Terra-MODIS thermal imagery. The model-data approach, applied in the Condamine River Catchment, Queensland, Australia, identified GDV coincident to existing mapping. GDV were found to use groundwater up to 48% of the time and for as many as 56 consecutive days. Under driest of conditions, groundwater was estimated to contribute up to 0.2 mm h⁻¹ to total ET for GDV. The ability to both detect the location and water-use dynamics of GDV is a significant advancement on previous remote-sensing GDV methods.     

Spatial-temporal dynamics of land surface temperature in relation to fractional vegetation cover and land use/cover in the Tabriz urban area, Iran

Rapid changes of land use and land cover (LULC) in urban areas have become a major environmental concern due to environmental impacts, such as the reduction of green spaces and development of urban heat islands (UHI). Monitoring and management plans are required to solve this problem effectively. The Tabriz metropolitan area in Iran, selected as a case study for this research, is an example of a fast growing city. Multi-temporal images acquired by Landsat 4, 5 TM and Landsat 7 ETM+ sensors on 30 June 1989, 18 August 1998, and 2 August 2001 respectively, were corrected for radiometric and geometric errors, and processed to extract LULC classes and land surface temperature (LST). The relationship between temporal dynamics of LST and LULC was then examined. The temperature vegetation index (TVX) space was constructed in order to study the temporal variability of thermal data and vegetation cover. Temporal trajectory of pixels in the TVX space showed that most changes due to urbanization were observable as the pixels migrated from the low temperature-dense vegetation condition to the high temperature-sparse vegetation condition in the TVX space. The uncertainty analysis revealed that the trajectory analysis in the TVX space involved a class-dependant noise component. This emphasized the need for multiple LULC control points in the TVX space. In addition, this research suggests that the use of multi-temporal satellite data together with the examination of changes in the TVX space is effective and useful in urban LULC change monitoring and analysis of urban surface temperature conditions as long as the uncertainty is addressed.     

Interpretation of the relationship between skin temperature and vegetation fraction: Effect of subpixel soil temperature variability

Both land surface/skin temperature and vegetation indices data provided routinely and globally by NASA MODIS sensors at 1‐km grid resolution represent an important piece of information assimilated into various environmental applications/models. Previous studies based on these and similar remotely data sets and on two‐component pixel representation (accounting for pixel‐aggregated vegetation and bare soil temperatures only) have shown a rather strong linear relationship between the pixel's skin temperature and the vegetation index/fraction. Deviations (Δ₀) from this relationship are frequently used for soil moisture content estimates at a pixel scale. As the two‐component pixel model does not account for subpixel heterogeneity (associated, for example, with bare soil temperature variability within the pixel), its role in controlling a magnitude of Δ₀ has been examined. A simple tri‐component pixel model describing vegetation and wet and dry bare soil temperatures was suggested to analyse an impact of this heterogeneity on Δ₀ estimates. This model was considered to provide a ‘true’ estimate of Δ₀ as compared with Δ₀ evaluated from the two‐component pixel model. A comparison between the models shows that a substantial underestimation of Δ₀ was likely to occur at a level of individual pixels when the two‐component approach was applied for interpretation of the observed relationship between the skin temperature and the vegetation index. Depending on the fraction of pixel occupied by the dry soil, this underestimation might be as much as 100%.     

Estimating subpixel fire sizes and temperatures from ASTER using multiple endmember spectral mixture analysis

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) shortwave infrared subsystem can acquire images of active fires during daytime and night-time from a polar orbit, providing useful data on fire properties at a nominal spatial resolution of 30 m. Binary fire/no-fire counts of ASTER pixels have also been useful in evaluating the performance of widely-used fire products from the Moderate-Resolution Imaging Spectroradiometer (MODIS), which have a nominal spatial resolution of 1 km. However, the ASTER fire pixels are actually mixed pixels that can contain flaming, smouldering and non-burning components, and ASTER fire pixel counts provide no information about the sizes or temperatures of these subpixel components. This paper uses multiple endmember spectral mixture analysis (MESMA) to estimate subpixel fire sizes and temperatures from a night-time ASTER image of a fire in California, USA, demonstrating new methods that can provide information on fires not available from other sources. As a fire's size and its temperature exert strong influences on its gas and aerosol emissions, ecological impact and spreading rates, these MESMA estimates from ASTER imagery could contribute valuable new information towards monitoring, forecasting and understanding the behaviour and impacts of many fires worldwide.     

Spatial and temporal variations of summer surface temperatures of high-arctic tundra on Svalbard — Implications for MODIS LST based permafrost monitoring

The ground surface temperature is one of the key parameters that determine the thermal regime of permafrost soils in arctic regions. Due to remoteness of most permafrost areas, monitoring of the land surface temperature (LST) through remote sensing is desirable. However, suitable satellite platforms such as MODIS provide spatial resolutions that cannot resolve the considerable small-scale heterogeneity of the surface conditions characteristic for many permafrost areas. This study investigates the spatial variability of summer surface temperatures of high-arctic tundra on Svalbard, Norway. A thermal imaging system mounted on a mast facilitates continuous monitoring of approximately 100 × 100 m of tundra with a wide variability of different surface covers and soil moisture conditions over the entire summer season from the snow melt until fall. The net radiation is found to be a control parameter for the differences in surface temperature between wet and dry areas. Under clear-sky conditions in July, the differences in surface temperature between wet and dry areas reach up to 10 K. The spatial differences reduce strongly in weekly averages of the surface temperature, which are relevant for the soil temperature evolution of deeper layers. Nevertheless, a considerable variability remains, with maximum differences between wet and dry areas of 3 to 4 K. Furthermore, the pattern of snow patches and snow-free areas during snow melt in July causes even greater differences of more than 10 K in the weekly averages. Towards the end of the summer season, the differences in surface temperature gradually diminish. Due to the pronounced spatial variability in July, the accumulated degree-day totals of the snow-free period can differ by more than 60% throughout the study area. The terrestrial observations from the thermal imaging system are compared to measurements of the land surface temperature from the MODIS sensor. During periods with frequent clear-sky conditions and thus a high density of satellite data, weekly averages calculated from the thermal imaging system and from MODIS LST agree within less than 2 K. Larger deviations occur when prolonged cloudy periods prevent satellite measurements. Furthermore, the employed MODIS L2 LST data set contains a number of strongly biased measurements, which suggest an admixing of cloud top temperatures.We conclude that a reliable gap filling procedure to moderate the impact of prolonged cloudy periods would be of high value for a future LST-based permafrost monitoring scheme. The occurrence of sustained subpixel variability of the summer surface temperature is a complicating factor, whose impact needs to be assessed further in conjunction with other spatially variable parameters such as the snow cover and soil properties.     

A vegetation index based technique for spatial sharpening of thermal imagery

High spatial resolution (∼ 100 m) thermal infrared band imagery has utility in a variety of applications in environmental monitoring. However, currently such data have limited availability and only at low temporal resolution, while coarser resolution thermal data (∼ 1000 m) are routinely available, but not as useful for identifying environmental features for many landscapes. An algorithm for sharpening thermal imagery (TsHARP) to higher resolutions typically associated with the shorter wavebands (visible and near-infrared) used to compute vegetation indices is examined over an extensive corn/soybean production area in central Iowa during a period of rapid crop growth. This algorithm is based on the assumption that a unique relationship between radiometric surface temperature (T_R) relationship and vegetation index (VI) exists at multiple resolutions. Four different methods for defining a VI − T_R basis function for sharpening were examined, and an optimal form involving a transformation to fractional vegetation cover was identified. The accuracy of the high-resolution temperature retrieval was evaluated using aircraft and Landsat thermal imagery, aggregated to simulate native and target resolutions associated with Landsat, MODIS, and GOES short- and longwave datasets. Applying TsHARP to simulated MODIS thermal maps at 1-km resolution and sharpening down to ∼ 250 m (MODIS VI resolution) yielded root-mean-square errors (RMSE) of 0.67-1.35 °C compared to the ‘observed’ temperature fields, directly aggregated to 250 m. Sharpening simulated Landsat thermal maps (60 and 120 m) to Landsat VI resolution (30 m) yielded errors of 1.8-2.4 °C, while sharpening simulated GOES thermal maps from 5 km to 1 km and 250 m yielded RMSEs of 0.98 and 1.97, respectively. These results demonstrate the potential for improving the spatial resolution of thermal-band satellite imagery over this type of rainfed agricultural region. By combining GOES thermal data with shortwave VI data from polar orbiters, thermal imagery with 250-m spatial resolution and 15-min temporal resolution can be generated with reasonable accuracy. Further research is required to examine the performance of TsHARP over regions with different climatic and land-use characteristics at local and regional scales.     

Modelling surface energy fluxes over maize using a two-source patch model and radiometric soil and canopy temperature observations

Models estimating surface energy fluxes over partial canopy cover with thermal remote sensing must account for significant differences between the radiometric temperatures and turbulent exchange rates associated with the soil and canopy components of the thermal pixel scene. Recent progress in separating soil and canopy temperatures from dual angle composite radiometric temperature measurements has encouraged the development of two-source (soil and canopy) approaches to estimating surface energy fluxes given observations of component soil and canopy temperatures. A Simplified Two-Source Energy Balance (STSEB) model has been developed using a “patch” treatment of the surface flux sources, which does not allow interaction between the soil and vegetation canopy components. A simple algorithm to predict the net radiation partitioning between the soil and vegetation is introduced as part of the STSEB patch modelling scheme. The feasibility of the STSEB approach under a full range in fractional vegetation cover conditions is explored using data collected over a maize (corn) crop in Beltsville Maryland, USA during the 2004 summer growing season. Measurements of soil and canopy component temperatures as well as the effective composite temperature were collected over the course of the growing season from crop emergence to cob development. Comparison with tower flux measurements yielded root-mean-square-difference values between 15 and 50 W m^− 2 for the retrieval of the net radiation, soil, sensible and latent heat fluxes. A detailed sensitivity analysis of the STSEB approach to typical uncertainties in the required inputs was also conducted indicating greatest model sensitivity to soil and canopy temperature uncertainties with relative errors reaching ∼ 30% in latent heat flux estimates. With algorithms proposed to infer component temperatures from bi-angular satellite observations, the STSEB model has the capability of being applied operationally.     

Intercalibration of vegetation indices from different sensor systems

Spectroradiometric measurements were made over a range of crop canopy densities, soil backgrounds and foliage colour. The reflected spectral radiances were convoluted with the spectral response functions of a range of satellite instruments to simulate their responses. When Normalised Difference Vegetation Indices (NDVI) from the different instruments were compared, they varied by a few percent, but the values were strongly linearly related, allowing vegetation indices from one instrument to be intercalibrated against another. A table of conversion coefficents is presented for AVHRR, ATSR-2, Landsat MSS, TM and ETM+, SPOT-2 and SPOT-4 HRV, IRS, IKONOS, SEAWIFS, MISR, MODIS, POLDER, Quickbird and MERIS (see Appendix A for glossary of acronyms). The same set of coefficients was found to apply, within the margin of error of the analysis, for the Soil Adjusted Vegetation Index SAVI. The relationships for SPOT vs. TM and for ATSR-2 vs. AVHRR were directly validated by comparison of atmospherically corrected image data. The results indicate that vegetation indices can be interconverted to a precision of 1-2%. This result offers improved opportunities for monitoring crops through the growing season and the prospects of better continuity of long-term monitoring of vegetation responses to environmental change.     

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.     

Estimation of land surface temperature-vegetation abundance relationship for urban heat island studies

Remote sensing of urban heat islands (UHIs) has traditionally used the Normalized Difference Vegetation Index (NDVI) as the indicator of vegetation abundance to estimate the land surface temperature (LST)-vegetation relationship. This study investigates the applicability of vegetation fraction derived from a spectral mixture model as an alternative indicator of vegetation abundance. This is based on examination of a Landsat Enhanced Thematic Mapper Plus (ETM+) image of Indianapolis City, IN, USA, acquired on June 22, 2002. The transformed ETM+ image was unmixed into three fraction images (green vegetation, dry soil, and shade) with a constrained least-square solution. These fraction images were then used for land cover classification based on a hybrid classification procedure that combined maximum likelihood and decision tree algorithms. Results demonstrate that LST possessed a slightly stronger negative correlation with the unmixed vegetation fraction than with NDVI for all land cover types across the spatial resolution (30 to 960 m). Correlations reached their strongest at the 120-m resolution, which is believed to be the operational scale of LST, NDVI, and vegetation fraction images. Fractal analysis of image texture shows that the complexity of these images increased initially with pixel aggregation and peaked around 120 m, but decreased with further aggregation. The spatial variability of texture in LST was positively correlated with those in NDVI and in vegetation fraction. The interplay between thermal and vegetation dynamics in the context of different land cover types leads to the variations in spectral radiance and texture in LST. These variations are also present in the other imagery, and are responsible for the spatial patterns of urban heat islands. It is suggested that the areal measure of vegetation abundance by unmixed vegetation fraction has a more direct correspondence with the radiative, thermal, and moisture properties of the Earth's surface that determine LST.     

On the need to observe vegetation canopies in the near-infrared to estimate visible light absorption

This paper examines the rationale for and implications of using a near-infrared band to estimate the absorption of visible light by vegetation canopies. The benefits of using near-infrared observations have already been documented extensively in the literature, notably in the context of applications based on vegetation indices. These include, for instance, a degree of normalization with respect to undesirable perturbing factors. Our intent here is twofold: provide the theoretical basis to justify using measurements outside the main absorption band of vegetation for the purpose of retrieving canopy properties, and uncover the implications of doing so. On the basis of simple radiation transfer considerations, we conclude that near-infrared observations are critical to ensure the accurate retrieval of absorption estimates in the visible domain, and that observations within the absorption band help control the perturbing effect of the soil background.The analytical approach implemented here is conceptually similar to a scale analysis which permits us assessing the most significant contributions to the absorption and scattering processes in the vast majority of geophysical situations. Our final conclusions derived from a series of intermediate steps that need to be performed first. To this end, we illustrate in Section 2 the fact that a suitably-defined one-dimensional radiation transfer model can always be setup to represent accurately the reflected, transmitted and absorbed fraction of vertical fluxes in any vegetation volume at medium spatial resolutions (100 m or lower), and this irrespective of the local variability exhibited by the canopy attributes. This finding is exploited throughout the paper to show that 1) measurements performed in the near-infrared band are needed to ensure a large dynamic range in albedo for dense canopy conditions, by contrast to the visible domain, 2) measurements in the visible domain are effective to remove the contribution due to the background below vegetation for low to intermediate LAI conditions. This is made possible thanks to the soil line concept and the spectral invariance of the interception process, and 3) the estimation of visible light absorption in a canopy on the basis of combinations of spectral bands (as implemented in traditional vegetation indices) hinges on spectral correlations between variables, most notably those controlling the absorbing and scattering properties of the soil and leaves. A series of implications and consequences is drawn from our analysis and, in particular, the suggestion to adopt modern interpretation techniques, superseding the commonly used vegetation index approaches. These advances allow us to improve on current approaches, in particular by lifting some of the hypotheses associated with approaches based on combinations of spectral bands.     

Ground measurements for the validation of land surface temperatures derived from AATSR and MODIS data

An experimental site was set up in a large, flat and homogeneous area of rice crops for the validation of satellite derived land surface temperature (LST). Experimental campaigns were held in the summers of 2002-2004, when rice crops show full vegetation cover. LSTs were measured radiometrically along transects covering an area of 1 km². A total number of four thermal radiometers were used, which were calibrated and inter-compared through the campaigns. Radiometric temperatures were corrected for emissivity effects using field emissivity and downwelling sky radiance measurements. A database of ground-based LSTs corresponding to morning, cloud-free overpasses of Envisat/Advanced Along-Track Scanning Radiometer (AATSR) and Terra/Moderate Resolution Imaging Spectroradiometer (MODIS) is presented. Ground LSTs ranged from 25 to 32 °C, with uncertainties between ± 0.5 and ± 0.9 °C. The largest part of these uncertainties was due to the spatial variability of surface temperature. The database was used for the validation of LSTs derived from the operational AATSR and MODIS split-window algorithms, which are currently used to generate the LST product in the L2 level data. A quadratic, emissivity dependent split-window equation applicable to both AATSR and MODIS data was checked as well. Although the number of cases analyzed is limited (five concurrences for AATSR and eleven for MODIS), it can be concluded that the split-window algorithms work well, provided that the characteristics of the area are adequately prescribed, either through the classification of the land cover type and the vegetation cover, or with the surface emissivity. In this case, the AATSR LSTs yielded an average error or bias of − 0.9 °C (ground minus algorithm), with a standard deviation of 0.9 °C. The MODIS LST product agreed well with the ground LSTs, with differences comparable or smaller than the uncertainties of the ground measurements for most of the days (bias of + 0.1 °C and standard deviation of 0.6 °C, for cloud-free cases and viewing angles smaller than 60°). The quadratic split-window algorithm resulted in small average errors (+ 0.3 °C for AATSR and 0.0 °C for MODIS), with differences not exceeding ± 1.0 °C for most of the days (standard deviation of 0.9 °C for AATSR and 0.5 °C for MODIS).     

Analysis of teleconnections between AVHRR-based sea surface temperature and vegetation productivity in the semi-arid Sahel

Vegetation productivity across the Sahel is known to be affected by a variety of global sea surface temperature (SST) patterns. Often climate indices are used to relate Sahelian vegetation variability to large-scale ocean-atmosphere phenomena. However, previous research findings reporting on the Sahelian vegetation response to climate indices have been inconsistent and contradictory, which could partly be caused by the variations in spatial extent/definitions of climate indices and size of the region studied. The aim of this study was to analyze the linkage between climate indices, pixel-wise spatio-temporal patterns of global sea surface temperature and the Sahelian vegetation dynamics for 1982-2007. We stratified the Sahel into five subregions to account for the longitudinal variability in rainfall. We found significant correlations between climate indices and the Normalized Difference Vegetation Index (NDVI) in the Sahel, however with different magnitudes in terms of strength for the western, central and eastern Sahel. Also the correlations based on NDVI and global SST anomalies revealed the same East-West gradient, with a stronger association for the western than the eastern Sahel. Warmer than average SSTs throughout the Mediterranean basin seem to be associated with enhanced greenness over the central Sahel whereas colder than average SSTs in the Pacific and warmer than average SSTs in the eastern Atlantic were related to increased greenness in the most western Sahel. Accordingly, we achieved high correlations for SSTs of oceanic basins which are geographically associated to the climate indices yet by far not always these patterns were coherent. The detected SST-NDVI patterns could provide the basis to develop new means for improved forecasts in particular of the western Sahelian vegetation productivity.     

Relationship between satellite-derived land surface temperatures, arctic vegetation types, and NDVI

Arctic vegetation distribution is largely controlled by climate, particularly summer temperatures. Summer temperatures have been increasing in the Arctic and this trend is expected to continue. Arctic vegetation has been shown to change in response to increases in summer temperatures, which in turn affects arctic fauna, human communities and industries. An understanding of the relationship of existing plant communities to temperature is important in order to monitor change effectively. In addition, variation along existing climate gradients can help predict where and how vegetation changes may occur as climate warming continues. In this study we described the spatial relationship between satellite-derived land surface temperature (LST), circumpolar arctic vegetation, and normalized difference vegetation index (NDVI). LST, mapped as summer warmth index (SWI), accurately portrayed temperature gradients due to latitude, elevation and distance from the coast. The SWI maps also reflected NDVI patterns, though NDVI patterns were more complex due to the effects of lakes, different substrates and different-aged glacial surfaces. We found that for the whole Arctic, a 5 °C increase in SWI along the climate gradient corresponded to an increase in NDVI of approximately 0.07. This result supports and is of similar magnitude as temporal studies showing increases of arctic NDVI corresponding to increases in growing season temperatures over the length of the satellite record. The strongest positive relationship between NDVI and SWI occurred in partially vegetated and graminoid vegetation types. Recently deglaciated areas, areas with many water bodies, carbonate soil areas, and high mountains had lower NDVI values than predicted by SWI. Plant growth in these areas was limited by substrate factors as well as temperature, and thus is likely to respond less to climate warming than other areas.     

Sea surface temperature simulation and forecast

Unconventional computing of sea surface temperature (SST) was once featured by NASA as a unique merger of science and art. Our approach led to a discovery that just one geographical point could be sufficient to track global anomalies of SST based on El Niño Southern Oscillation (ENSO). Such single point in the Pacific Ocean off of the island of Isabella in the Galapagos Islands was named the Galapagos indicator. Now we show that a single point in the Baltic Sea off of the coast of Göteborg could be also sufficient to track ENSO. We propose to name it the Baltic indicator. We also demonstrate that two crisis falls of oil price in 2008 and 2014 followed just after the local maximums of Baltic indicator. However, Baltic and Galapagos indicators do not show any evident trend in settling the global warming from the beginning of this century.     

海面温度栅格图的锋面提取与矢量化

提出一种海面温度栅格图的锋面提取方法。针对海洋表层温度(SST)锋面强度分布不均匀的特点,利用低通滤波对表温梯度图像进行平滑。再利用迭代法确定出梯度图像的分割阈值,将图像分割成目标与背景两部分。通过数学形态学中图像细化的方法,提取海洋温度锋面的骨架并对细小分枝进行修剪。经矢量化生成锋面线后,利用抹角法对折线进行光滑。最后以西太平洋为例,给出了一个表温锋面提取的实例,表明利用此方法进行海表温度锋面的提取是可行与有效的。     

Assessment of the impacts of surface topography, off-nadir pointing and vegetation structure on vegetation lidar waveforms using an extended geometric optical and radiative transfer model

In order to prioritize the measurement requirements and accuracies of the two new lidar missions, a physical model is required for a fundamental understanding of the impact of surface topography, footprint size and off-nadir pointing on vegetation lidar waveforms and vegetation height retrieval. In this study, we extended a well developed Geometric Optical and Radiative Transfer (GORT) vegetation lidar model to take into account for the impacts of surface topography and off-nadir pointing on vegetation lidar waveforms and vegetation height retrieval and applied this extended model to assess the aforementioned impacts on vegetation lidar waveforms and height retrieval.Model simulation shows that surface topography and off-nadir pointing angle stretch waveforms and the stretching effect magnifies with footprint size, slope and off-nadir pointing angle. For an off-nadir pointing laser penetrating vegetation over a slope terrain, the waveform is either stretched or compressed based on the relative angle. The stretching effect also results in a disappearing ground peak return when slope or off-nadir pointing angle is larger than the “critical slope angle”, which is closely related to various vegetation structures and footprint size. Model simulation indicates that waveform shapes are affected by surface topography, off-nadir pointing angle and vegetation structure and it is difficult to remove topography effects from waveform extent based only on the shapes of waveform without knowing any surface topography information.Height error without correction of surface topography and off-nadir pointing angle is the smallest when the laser beams at the toward-slope direction and the largest from the opposite direction. Further simulation reveals within 20° of slope and off-nadir pointing angle, given the canopy height as roughly 25 m and the footprint size as 25 m, the error for vegetation height (RH100) ranges from − 2 m to greater than 12 m, and the error for the height at the medium energy return (RH50) from − 1 m to 4 m. The RH100 error caused by unknown surface topography and without correction of off-nadir pointing effect can be explained by an analytical formula as a function of vegetation height, surface topography, off-nadir pointing angle and footprint size as a first order approximation. RH50 is not much affected by topography, off-nadir pointing and footprint size. This forward model simulation can provide scientific guidance on prioritizing future lidar mission measurement requirements and accuracies.