Development and evaluation of SST algorithms for GOES-R ABI using MSG SEVIRI as a proxy

Cross-evaluation of sea surface temperature (SST) algorithms was undertaken using split-window channels of Meteosat Second Generation Spinning Enhanced Visible and Infrared Imager (SEVIRI) as a proxy for the Geostationary Operational Environmental Satellites-R (GOES-R) Advanced Baseline Imager (ABI). The goal of the study was to select the algorithm which provides the highest and the most uniform SST accuracy within the area observed by the geostationary sensor. The previously established algorithms, such as Non-Linear Regression (NLR) and Optimal Estimation (OE) were implemented along with two new algorithms, Incremental Regression (IncR) and Corrected Non-Linear Regression (CNLR), developed within preparations for the GOES-R ABI mission. OE, IncR and CNLR adopt the first guesses for SST and brightness temperatures (BT) and retrieve deviations of SST from the first guess (increments). OE retrieves SST increments with inversion of the radiative transfer model, whereas CNLR and IncR use regression equations. The difference between CNLR and IncR is that CNLR uses NLR coefficients, whereas IncR implies optimization of coefficients specifically for incremental formulation. Accuracy and precision of SST retrievals were evaluated by comparison with drifting buoys. The major observations from this study are as follows: 1) all algorithms adopting first guesses for SST and BTs are capable of improving SST accuracy and precision over NLR; and 2) IncR delivers the highest overall SST precision and the most uniform distributions of regional SST accuracy and precision. This paper also addresses implementation and validation issues such as bias correction in simulated BTs; preserving sensitivity of incremental SST retrievals to true SST variations; and selection of criteria for optimization and validation of incremental algorithms.     

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.     

Air temperature estimation with MSG-SEVIRI data: Calibration and validation of the TVX algorithm for the Iberian Peninsula

Air temperature can be estimated from remote sensing by combining information in thermal infrared and optical wavelengths. The empirical TVX algorithm is based on an estimated linear relationship between observed Land Surface Temperature (LST) and a Spectral Vegetation Index (NDVI). Air temperature is assumed to be equal to the LST corresponding to the effective full vegetation cover, and is found by extrapolating the line to a maximum value of NDVI_max. The algorithm has been tested and reported in the literature previously. However, the effect of vegetation types and climates and the potential variation in NDVI of the effective full cover has not been subject for investigation. The present study proposes a novel methodology to estimate NDVI_max that uses observed air temperature to calibrate the NDVI_max for each vegetation type. To assess the validity of this methodology, we have compared the accuracy of estimates using the new NDVI_max and the previous NDVI_max that have been proposed in literature with MSG-SEVIRI images in Spain during the year 2005. In addition, a spatio-temporal assessment of residuals has been performed to evaluate the accuracy of retrievals in terms of daily and seasonal variation, land cover, landscape heterogeneity and topography. Results showed that the new calibrated NDVI_max perform well, with a Mean Absolute Error ranging between 2.8 °C and 4 °C. In addition, vegetation-specific NDVI_max improve the accuracy compared with a unique NDVI_max.     

Modelling the effect of optical thickness on diurnal cycles of land surface temperature

Fitting a model to diurnal temperature cycles (DTC) of the land surface yields a set of parameters which summarizes the surface's thermal dynamics and is more informative and representative of its thermal characteristics than the individual land surface temperatures (LST). Modelling DTC is also useful for temporal compositing and for cloud screening. However, an earlier version of the DTC model presented in this article did not capture the smooth and gradual increase of LST around sunrise: this shortcoming is addressed here. Starting from the energy balance equation of the surface, a DTC model is developed which accounts for atmospheric attenuation of solar irradiation, the land surface's main heating source. By including total optical thickness (TOT), the new model reproduces the shape of morning rise of LST better, is able to “squash” DTC temporally, and more accurately reproduces the natural variability of DTC width and slope. Three different formulations of relative optical air mass are given and a Levenberg-Marquardt minimisation scheme is used to fit the DTC model to time series of LST, which are obtained from ground based thermal infrared (TIR) data measured at permanent validation stations near “Gobabeb Training and Research Centre”, Namibia, and near the town of Evora, Portugal. For turbid atmospheres (transparency 40%) the new model reduces the average deviation between modelled and measured LST by a factor of around three. Statistical analyses of 154 DTCs collected at the two validation sites show that the new DTC model consistently outperforms the earlier version of the model.     

Combining the triangle method with thermal inertia to estimate regional evapotranspiration — Applied to MSG-SEVIRI data in the Senegal River basin

Spatially distributed estimates of evaporative fraction and actual evapotranspiration are pursued using a simple remote sensing technique based on a remotely sensed vegetation index (NDVI) and diurnal changes in land surface temperature. The technique, known as the triangle method, is improved by utilizing the high temporal resolution of the geostationary MSG-SEVIRI sensor. With 15 min acquisition intervals, the MSG-SEVIRI data allow for a precise estimation of the morning rise in land surface temperature which is a strong proxy for total daytime sensible heat fluxes. Combining the diurnal change in surface temperature, dT_s with an interpretation of the triangular shaped dT_s − NDVI space allows for a direct estimation of evaporative fraction. The mean daytime energy available for evapotranspiration (R_n − G) is estimated using several remote sensors and limited ancillary data. Finally regional estimates of actual evapotranspiration are made by combining evaporative fraction and available energy estimates. The estimated evaporative fraction (EF) and actual evapotranspiration (ET) for the Senegal River basin have been validated against field observations for the rainy season 2005. The validation results showed low biases and RMSE and R² of 0.13 [−] and 0.63 for EF and RMSE of 41.45 W m^− 2 and R² of 0.66 for ET.     

Evaluation of MODIS land surface temperature data to estimate air temperature in different ecosystems over Africa

The estimation of near surface air temperature (Ta) is useful for a wide range of applications such as agriculture, climate related diseases and climate change studies. Air temperature is commonly obtained from synoptic measurements in weather stations. In Africa, the spatial distribution of weather stations is often limited and the dissemination of temperature data is variable, therefore limiting their use for real-time applications. Compensation for this paucity of information may be obtained by using satellite-based methods. However, the derivation of near surface air temperature (Ta), from the land surface temperature (Ts) derived from satellite is far from straight forward. Some studies have tried to derive maximum Ta from satellites through regression analysis but the accuracy obtained is quite variable according to the study. The main objective of this study was to explore the possibility of retrieving high-resolution Ta data from the Moderate Resolution Imaging Spectroradiometer (MODIS) Ts products over different ecosystems in Africa. First, comparisons between night MODIS Ts data with minimum Ta showed that MODIS nighttime products provide a good estimation of minimum Ta over different ecosystems (with (ΔTs − Ta) centered at 0 °C, a mean absolute error (MAE) = 1.73 °C and a standard deviation = 2.4 °C). Secondly, comparisons between day MODIS Ts data with maximum Ta showed that (ΔTs − Ta) strongly varies according to the seasonality, the ecosystems, the solar radiation, and cloud-cover. Two factors proposed in the literature to retrieve maximum Ta from Ts, i.e. the Normalized Difference Vegetation Index (NDVI) and the Solar Zenith Angle (SZA), were analyzed. No strong relationship between (ΔTs − Ta) and (i) NDVI and (ii) SZA was observed, therefore requiring further research on robust methods to retrieve maximum Ta.     

Analysing NDVI for the African continent using the geostationary meteosat second generation SEVIRI sensor

This study presents first results on Normalized Difference Vegetation Index (NDVI), from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) sensor onboard the geostationary satellite Meteosat Second Generation (MSG) covering the African continent. With a temporal resolution of 15 min MSG offers complementary information for NDVI monitoring compared to vegetation monitoring based on polar orbiting satellites. The improved temporal resolution has potential implications for accurate NDVI assessment of the African continent; e.g. the increased amount of available scenes are expected to help overcome problems related to cloud cover which makes the MSG data particularly well suited for early warning systems. Time series of 2004 MSG NDVI was compared to MODIS (Moderate Resolution Imaging Spectroradiometer) Terra and Aqua NDVI for the Dahra site in the Senegalese Sahel, West Africa. It was found that NDVI was available for 82 days with multiple cloud free acquisitions per day during the growing season as compared to 47 days with information from either MODIS Terra or Aqua for that particular site. Differences in MSG SEVIRI and MODIS BRDF on a seasonal scale were found to influence the time series of NDVI for the test site; MSG NDVI being higher than MODIS in July-August and lower in October-November. Preliminary composite analysis suggests that the period of compositing to produce continent scale cloud free products can be reduced to ∼5 days using MSG NDVI as compared to polar orbiting data. With the availability of diurnal reflectance information the significance of differences between the red and near-infrared wavelengths due to anisotropy become evident, causing diurnal variations in observed NDVI. Diurnal MSG NDVI was compared to in situ measured MSG NDVI at the test site in Senegal and the same “bowl-shaped” diurnal curve was found for a medium dense cover of annual grasses. The range in observed NDVI and time of diurnal minimum was different due to different viewing geometry. Daily minimum of in situ measured NDVI was around solar noon whereas minimum MSG NDVI occurs one hour prior to noon due to the test site location 12° west of the satellite sensor. Diurnal variation in observed NDVI was studied for a number of pixels characterized by different sensor view zenith angles and vegetation types. This analysis illustrated the diurnal NDVI dependency of illumination conditions, view angle and vegetation intensity and pinpoints the importance of proper BRDF modeling to produce daily values of MSG NDVI normalized for acquisition time, which will be the subject of a forthcoming paper.     

SEVIRI PrePro: A novel software tool for the pre-processing of SEVIRI geostationary orbit EO data products

The Spinning Enhanced Visible and Infrared Imager (SEVIRI) is a geostationary orbit multispectral sensor on-board the Meteosat second Generation (MSG) platform, acquiring Earth Observation (EO) data over Earth's land surface from the optical to infrared parts of electromagnetic spectrum every 15 min. From the sensor a series of operational products are also provided to the user's community at no cost via EUMETSAT or LSA SAF portals.Herein, an open access stand-alone software product developed in Java programming language is presented for automating key pre-processing steps to all the SEVIRI operationally distributed products. The software tool, named Seviri PrePro, makes use of present day multi-core processors and is able to process very large datasets in a short time period, making it appropriate as well for use in a High Performance Computing (HPC) environment. The practical usefulness of the toolkit is also demonstrated herein using as a case study the SEVIRI evapotranspiration (ET) product.The development of SEVIRI PrePro is of significant importance to the SEVIRI users' community and is also very timely given that, to our knowledge, no similar software tool is freely distributed at present. Its use is anticipated to make a significant contribution to a large number of practical applications requiring use of SEVIRI data, including but not limited, weather forecasting and global climate monitoring at a range of geographical scales.     

Assessment of MODIS sun-sensor geometry variations effect on observed NDVI using MSG SEVIRI geostationary data

The quality of Earth observation (EO) based vegetation monitoring has improved during recent years, which can be attributed to the enhanced sensor design of new satellites such as MODIS (Moderate Resolution Imaging Spectroradiometer) on Terra and Aqua. It is however expected that sun-sensor geometry variations will have a more visible impact on the Normalized Difference Vegetation Index (NDVI) from MODIS compared to earlier data sources, since noise related to atmosphere and sensor calibration is substantially reduced in the MODIS data stream. For this reason, the effect of varying MODIS viewing geometry on red, near-infrared (NIR) and NDVI needs to be quantified. Data from the geostationary MSG (Meteosat Second Generation) SEVIRI (Spinning Enhanced Visible and Infrared Imager) sensor is well suited for this purpose due to the fixed position of the sensor, the spectral resolution, including a red and NIR band, and the high temporal resolution (15 min) of data, enabling MSG data to be used as a reference for estimating MODIS surface reflectance and NDVI variations caused by varying sun-sensor geometry. The study was performed on data covering West Africa for periods of lowest possible cloud cover for three consecutive years (2004–2006). An analysis covering the entire range of NDVI revealed day-to-day variations in observed MODIS NDVI of 50–60% for medium dense vegetation (NDVI ≈ 0.5) caused by variations in MODIS view zenith angles (VZAs) between nadir and the high forward-scatter view direction. Statistical analysis on red, NIR and NDVI from MODIS and MSG SEVIRI for three transects (characterized by different vegetation densities) showed that both MODIS red and NIR reflectances are highly dependant on MODIS VZA and relative azimuth angle (RAA), due to the anisotropic behaviour of red and NIR reflectances. The anisotropic reflectance in the red and NIR band was to some degree minimized by the ratioing properties of NDVI. The minimization by the NDVI normalization is very dependent on the vegetation density however, since the degree of anisotropy in red and NIR reflectances depends on the amount of vegetation present. MODIS VZA and RAA effects on NDVI were highest for medium dense vegetation (NDVI ≈ 0.5–0.6). The VZA and RAA effects were less for sparsely vegetated areas (NDVI ≈ 0.3–0.35) and the smallest effect on NDVI was found for dense vegetation (NDVI ≈ 0.7). These results have implications for the end users' interpretation of NDVI, and challenge the expediency of the MODIS NDVI compositing technique, which should be refined to distinguish between forward- and backward-scatter viewing direction by taking RAA into account.     

Estimation of instantaneous air temperature using remote sensing data

In arid areas, the variation of air temperature can be considerable, so instantaneous air temperature (T_ai) estimation is needed in different environmental researches. In this research, two different remote sensing data are used for estimating T_ai for clear sky days in 2009 in Fars Province, Iran, including atmospheric temperature profile and land surface temperature (LST) data from Moderate Resolution Imaging Spectroradiometer. The T_ai from a number of surface weather sites is used to judge the best T_ai estimation. Stations’ elevation, latitude, and land cover type are considered to show their effect on T_ai estimation. The estimated T_ai evaluation focuses on daily and seasonal timescales in the daytime and night time separately. Both LST and vertical temperature profile data produced relatively high coefficient of determination values and small root mean square error value for T_ai estimation, especially during the night time. Land cover and elevation vary the error values in T_ai estimation more, when LST data is used. In comparison atmospheric temperature profile indicates a smaller error in T_ai estimation in spring and summer and in urban land cover type, while using LST data presents a better result in fall and winter especially at night time.     

基于用户数据的空调负荷与气温关系研究

为有效管理高峰时段的空调负荷,配合政府部门做好电力需求侧管理工作提供理论依据.本文对2018年-2019年武汉市电网空调负荷与气温关系进行了全面的分析,利用回归分析方法进行了近两年空调负荷与日最值温度的相关性分析,给出了温度每改变1℃时空调负荷的变化幅度,着重分析了第二、三产业和居民中空调负荷与气温的关系.其中,首先提...     

Improving the spatio-temporal distribution of surface solar radiation data by merging ground and satellite measurements

Appropriate information on solar resources is very important for a variety of technological areas, such as: agriculture, meteorology, forestry engineering, water resources and in particular in the designing and sizing of solar energy systems. However, the availability of observed solar radiation measurements has proven to be spatially and temporally inadequate for many applications. In this paper we propose to merge the global solar radiation measurements from the Royal Meteorological Institute of Belgium solar measurements network with the operationally derived surface incoming global short-wave radiation products from Meteosat Second Generation satellites imageries to improve the spatio-temporal resolution of the surface global solar radiation data over Belgium. We evaluate several merging methods with various degrees of complexity (from mean field bias correction to geostatistical merging techniques) together with interpolated ground measurements and satellite-derived values only. The performance of the different methods is assessed by leave-one-out cross-validation.     

Accuracy assessment of land surface temperature retrievals from MSG2-SEVIRI data

The accuracy of the Land Surface Temperature (LST) product generated operationally by the EUMETSAT Land Surface Analysis Satellite Applications Facility (LSA SAF) from the data registered by the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board the geostationary METEOSAT Second Generation 2 (MSG2, Meteosat 9) satellite was assessed on two test sites in Eastern Spain: a homogeneous, fully vegetated rice field and a high-plain, homogeneous area of shrubland. The LSA SAF LSTs were compared with ground LST measurements in the conventional temperature-based (T-based) method. We also validated the LSA SAF LST product by using an alternative radiance-based (R-based) method, with ground LSTs calculated from MSG-SEVIRI channel 9 brightness temperatures (at 10.8 μm) through radiative transfer simulations using atmospheric temperature and water vapor profiles together with surface emissivity data. Two lakes were also used for validation with the R-based method. Although the LSA SAF LST algorithm works mostly within the uncertainty expectation of ± 2 K, both validation methods showed significant biases for the LSA SAF LST product, up to 1.5 K in some cases. These biases, with the LSA SAF LST product overestimating reference values, were also observed in previous studies. Nevertheless, the present work points out that the biases are related to the land surface emissivities used in the operational generation of the product. The use of more appropriate emissivity values for the test sites in the LSA SAF LST algorithm led to better results by decreasing the biases by 0.7 K for the shrubland validation site. Furthermore, we proposed and checked an alternative algorithm: a quadratic split-window equation, based on a physical split-window model that has been widely proved for other sensors, with angular-dependent coefficients suitable for the MSG coverage area. The T-based validation results for this algorithm showed LST uncertainties (robust root-mean-squared-errors) from 0.2 K to 0.5 K lower than for the LSA SAF LST algorithm after the emissivity replacement. Nevertheless, the proposed algorithm accuracies were significantly better than those obtained for the current LSA SAF LST product, with an average accuracy difference of 0.6 K.     

Validation of land surface temperature derived from MSG/SEVIRI with in situ measurements at Gobabeb,Namibia

Land surface temperature (LST) derived from Meteosat Second Generation/?Spinning-Enhanced Visible and Infrared Imager MSG/SEVIRI data is an operational product of the Land Surface Analysis Satellite Applications Facility (LSA SAF). The LST has a temporal resolution of 15 minutes, a sampling distance of 3 km at nadir, and a targeted accuracy of better than 2 K. Gobabeb (Namibia) is one of Karlsruhe Institute of Technology's (KIT's) four dedicated stations for LST validation. In March 2010, a field survey was performed to characterize the Gobabeb site more closely. SAF LST and in situ LST obtained over a period of 3 days from additional measurements with a telescopic mast on the Namib gravel plains were in good agreement with each other (bias 1.0 K). For the same period, the bias between SAF LST and Gobabeb main station LST was even smaller (0.4 K). A mobile measurement system was set up by fixing the telescopic mast to a four-wheel drive. Around solar noon, LST from in situ measurements along a 40 km track and LST from Gobabeb main station had a bias of 0.4 K and a standard deviation of 1.2 K, which means that in situ LSTs at Gobabeb main station are representative for large parts of the gravel plains. Exploiting this relationship, 2 years of LST from MSG/SEVIRI were compared with in situ LST from Gobabeb main station. The magnitude of the monthly biases between the two data sets was generally less than 1.0 K and root mean square errors were below 1.5 K. Furthermore, the bias appears to exhibit a seasonality, which could be accounted for in future validation work.     

Estimation of air temperature from MODIS data in east China

Air temperature, T _a, with high spatial and temporal resolution is desired for global change, agricultural disaster, land surface studies, and modelling applications. A statistical algorithm for Moderate Resolution Imaging Spectroradiometer (MODIS) data is developed for daytime T _a retrievals over east China at a resolution of 0.05° × 0.05°. The approach first applies a statistical regression of the first guess, i.e. the preliminary estimate, of T _a to MODIS 11 μm and 12 μm brightness temperature (T _11μm and T _12μm) and site data (longitude, latitude and altitude) for east China. Then the first guess of T _a is further corrected with a series of bias equations for different latitude zones in east China. Further quantitative validation with measured T _a using 335 synoptic weather stations for the whole of 2006 indicates that the algorithm performs well with overall statistics of R = 0.96, RMSE = 3.23°C, and bias = ?0.09°C. 75% of the estimated T _a is within 3°C of the actual T _a and 92% of the estimated T _a is within 5°C of the actual T _a. This bias correction algorithm can be applied to other geostationary and sun-synchronous satellite instruments for T _a retrieval.     

Estimation of urban air pollution

The problem of real-time estimation of air pollutant concentrations in an urban atmosphere based on concentration measurements made intermittently at a set of monitoring stations is considered. A square-root distributed parameter filter for a general class of dynamic urban air pollution models is developed. The filter is tested by application to a hypothetical urban area, and the effect of the number of monitoring stations on the estimated concentrations is studied.     

The vegetation cycle in West Africa from AVHRR-NDVI data: Horizons of predictability versus spatial scales

The predictability of the vegetation cycle is analyzed as a function of the spatial scale over West Africa during the period 1982-2004. The NDVI-AVHRR satellite data time series are spatially aggregated over windows covering a range of sizes from 8 × 8 km² to 1024 × 1024 km². The times series are then embedded in a low-dimensional pseudo-phase space using a system of time delayed coordinates. The correlation dimension (D_c) and entropy of the underlying vegetation dynamics, as well as the noise level (σ) are extracted from a nonlinear analysis of the time series. The horizon of predictability (H_P) of the vegetation cycle defined as the time interval required for an n% RMS error on the vegetation state to double (i.e. reach 2n% RMS) is estimated from the entropy production. Compared to full resolution, the intermediate scales of aggregation (in the range of 64 × 64 km² to 256 × 256 km²) provide times series with a slightly improved signal to noise ratio, longer horizon of predictability (about 2 to 5 decades) and preserve the most salient spatial patterns of the vegetation cycle. Insights on the best aggregation scale and on the expected vegetation cycle predictability over West Africa are provided by a set of maps of the correlation dimension (D_c), the horizon of predictability (H_P) and the level of noise (σ).     

利用NDVI估算云覆盖地区的植被表面温度研究

干旱监测等实际应用都需要全面掌握地表温度(LST)的空间分布,而云覆盖是这种应用的重要阻碍。试图根据地表温度变化与地表植被之间的相互关系,研究遥感影像中云覆盖区域植被表面温度的估算方法。由于植被的蒸腾作用,植被茂密程度对其表面温度的空间分布有较大影响。这种影响不仅在晴朗无云区域存在,同样适用于云覆盖区域。因此,首先分析云覆盖区域周边无云植被像元的LST与植被指数NDVI之间的关系,建立方程式,然后再利用NDVI在短时间内相对稳定的特点用另一幅图像来获取云覆盖区域的NDVI值,最后根据NDVI与LST之间的关系估计云覆盖植被像元的表面温度。将这一方法应用到山东省聊城市的Landsat ETM+图像,结果表明：当云覆盖范围≤2 000个像元(约1.72 km²)时,通过NDVI来估计云覆盖区域植被表面温度的平均绝对误差<0.7 ℃,均方根误差<1.2 ℃。为了验证其实用性,又将该方法应用于安徽省蚌埠地区的TM图像,云覆盖范围在300个像元以下时,平均绝对误差小于0.1 ℃。因此,可以认为,当云覆盖范围不是很大时,利用NDVI估算云覆盖地区的植被表面温度,具有一定的可行性。     

Development of a virtual active fire product for Africa through a synthesis of geostationary and polar orbiting satellite data

We explore the ability to enhance landscape fire detection and characterization by constructing a ‘virtual’ fire product from a synthesis of geostationary and polar orbiting satellite data. Active fire pixels detected by the Spinning Enhanced Visible and Infrared Imager (SEVIRI) and the Moderate Resolution Imaging Spectroradiometer (MODIS) were spatially and temporally collated across Africa between February 2004 and January 2005. Coincident fire pixels detected by SEVIRI and MODIS were used to populate an empirical database of frequency density (f-D) distributions of fire radiative power (FRP). Frequency density distributions of FRP measured by SEVIRI at 5.0° grid cell resolution and 15-minute temporal resolution were then cross referenced in the database to a set of counterpart f-D distributions of FRP measured by MODIS. This procedure resulted in the first generation of a ‘virtual’ fire product that exhibits the full continental coverage and high temporal resolution of SEVIRI whilst quantifying fire pixel counts and FRP with accuracies approaching those of MODIS. Diurnal cycles extracted from the virtual fire product indicate that SEVIRI measures a greater proportion of the active fire pixels and FRP potentially detectable by MODIS during the day due to the increased prevalence and stronger radiant contribution of highly energetic fire pixels. On a daily basis (sample size n = 365) the peak magnitude in the diurnal cycle of the virtual FRP occurred within the same 15-minute timeslot as in the native SEVIRI fire product. Continental-scale ignition and extinction events, however, were detected on average 44 min earlier (standard deviation s.d. = 40 min) and 137 min later (s.d. = 92 min), respectively. It is anticipated that the methodology developed here can be used to cross-calibrate active fire products between a variety of different satellite platforms.     

The effects of air temperature on office workers’ well-being, workload and productivity-evaluated with subjective ratings

Productivity bears a close relationship to the indoor environmental quality (IEQ), but how to evaluate office worker’s productivity remains to be a challenge for ergonomists. In this study, the effect of indoor air temperature (17 °C, 21 °C, and 28 °C) on productivity was investigated with 21 volunteered participants in the laboratory experiment. Participants performed computerized neurobehavioral tests during exposure in the lab; their physiological parameters including heart rate variation (HRV) and electroencephalograph (EEG) were also measured. Several subjective rating scales were used to tap participant’s emotion, well-being, motivation and the workload imposed by tasks. It was found that the warm discomfort negatively affected participants’ well-being and increased the ratio of low frequency (LF) to high frequency (HF) of HRV. In the moderately uncomfortable environment, the workload imposed by tasks increased and participants had to exert more effort to maintain their performance and they also had lower motivation to do work. The results indicate that thermal discomfort caused by high or low air temperature had negative influence on office workers’ productivity and the subjective rating scales were useful supplements of neurobehavioral performance measures when evaluating the effects of IEQ on productivity.