Leonardo F. Peres

Emissivity maps to retrieve land-surface temperature from MSG/SEVIRI

Retrieval of land-surface temperature (LST) using data from the METEOSAT Second Generation-1 (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI) requires adequate estimates of land-surface emissivity (LSE). In this context, LSE maps for SEVIRI channels IR3.9, IR8.7, IR10.8, and IR12.0 were developed based on the vegetation cover method. A broadband LSE map (3-14 /spl mu/m) was also developed for estimating longwave surface fluxes that may prove to be useful in both energy balance and climate modeling studies. LSE is estimated from conventional static land-cover classifications, LSE spectral data for each land cover, and fractional vegetation cover (FVC) information. Both International Geosphere-Biosphere Program (IGBP) Data and Information System (DIS) and Moderate Resolution Imaging Spectrometer (MODIS) MOD12Q1 land-cover products were used to build the LSE maps. Data on LSE were obtained from the Johns Hopkins University and Jet Propulsion Laboratory spectral libraries included in the Advanced Spaceborne Thermal Emission and Reflection Radiometer spectral library, as well as from the MODIS University of California-Santa Barbara spectral library. FVC data for each pixel were derived based on the normalized differential vegetation index. Depending on land cover, the LSE errors for channels IR3.9 and IR8.7 spatially vary from /spl plusmn/0.6% to /spl plusmn/24% and /spl plusmn/0.1% to /spl plusmn/33%, respectively, whereas the broadband spectrum errors lie between /spl plusmn/0.3% and /spl plusmn/7%. In the case of channels IR10.8 and IR12.0, 73% of the land surfaces within the MSG disk present relative errors less than /spl plusmn/1.5%, and almost all (26%) of the remaining areas have relative errors of /spl plusmn/2.0%. Developed LSE maps provide a first estimate of the ranges of LSE in SEVIRI channels for each surface type, and obtained results may be used to assess the sensitivity of algorithms where an a priori knowledge of LSE is required.

Thermal Land Surface Emissivity Retrieved From SEVIRI/Meteosat

The methodologies used by the Satellite Application Facility on Land Surface Analysis (Land SAF) for retrieving emissivity are presented here. In the first approach, i.e., the vegetation cover method (VCM), the land surface emissivity (EM) is computed for Spinning Enhanced Visible and Infrared Imager (SEVIRI) infrared channels and for the 3- to 14- range using information on the pixel fraction of vegetation cover (FVC). The VCM uses a lookup table, which takes into account the channels spectral response function, and laboratory reflectance spectra for different materials. The accuracy of the VCM depends on the reliability of FVC and the land cover classification. The EM for SEVIRI split-window channels is primarily used as an internal product by Land SAF for land surface temperature (LST) estimations. However, sensitivity studies show that LST often fails to meet the required accuracy of 2 K over desert and semiarid regions, where the VCM is unable to model the EM spatial variability, which is mostly associated with soil composition. Moreover, it is also over such areas where the atmosphere is generally dry that the impact of EM uncertainties on LST is largest. A second approach to determine the EM for SEVIRI split-window channels is currently being tested. This methodology allows the simultaneous retrieval of LST and channel EMs with the assumption that the latter remain constant. The channel EMs are then averaged over a 22-day period to filter out the noise in the retrievals. A first analysis of the maps obtained for an area within Northern Africa shows spatial patterns with features also present in the surface albedo.

Urban climate and clues of heat island events in the metropolitan area of Rio de Janeiro

This paper aims to map the thermal field in the metropolitan region of Rio de Janeiro (MARJ) considering the atmospheric characteristics and the land use that contribute to understanding the urban heat island. Three thermal maps are defined through the use of Landsat5-TM satellite images for three winter events chosen for the decades of 1980, 1990, and 2000, respectively. The results reveal a concentration of warmer cores in urban central areas as well as some local warmer areas in suburban region. Sites with lower temperatures correspond to vegetated areas which are away from the central part of the MARJ, including points of suburban areas. This work emphasizes the importance of the combined analysis of surface temperature with land use and atmospheric conditions, depicting a distinct pattern of heat islands for tropical climate.

Validation of a temperature emissivity separation hybrid method from airborne hyperspectral scanner data and ground measurements in the SEN2FLEX field campaign

This paper presents an assessment of the performance of a hybrid method that allows a simultaneous retrieval of land‐surface temperature (LST) and emissivity (LSE) from remotely‐sensed data. The proposed method is based on a synergistic usage of the split‐window (SW) algorithm and the two‐temperature method (TTM) and combines the advantages of both procedures while mitigating their drawbacks. The method was implemented for thermal channels 76 (10.56 µm) and 78 (11.72 µm) of the Airborne Hyperspectral Scanner (AHS), which was flown over the Barrax test site (Albacete, Spain) in the second week of July 2005, within the framework of the Sentinel‐2 and Fluorescence Experiment (SEN2FLEX) field campaign. A set of radiometric measurements was performed in the thermal infrared region in coincidence with aircraft overpasses for different surface types, e.g. bare soil, water body, corn, wheat, grass. The hybrid method was tested and compared with a standard SW algorithm and the results obtained show that the hybrid method is able to provide better estimates of LST, with values of bias (RMSE) of the order of 0.8 K (1.9 K), i.e. about one third (one half) of the corresponding values of 2.7 K (3.4 K) that were obtained for bias (RMSE) when using the SW algorithm. These figures provide a sound indication that the developed hybrid method is particularly useful for surface and atmospheric conditions where SW algorithms cannot be accurately applied.

Improving two-temperature method retrievals based on a nonlinear optimization approach

The two-temperature method (TTM) is known to be sensitive to noise, and therefore, land-surface temperature (LST) and emissivity (LSE) retrievals based on TTM are in general not reliable when obtained by algebraic procedures. Accordingly, the added value of using TTM together with a nonlinear mathematical optimization approach is investigated, focusing on the effect that an increase in the temperature difference as well as in the number of observations might have on LST and LSE retrievals. TTM has provided values of LST and LSE with a bias (root mean square error) ranging from 0.1-0.4 K (2.1-2.8 K) and from 0.005-0.010 (0.040-0.055), respectively. Obtained results were almost the same for both well-determined and overdetermined cases, as well as for the considered temperature differences, suggesting that increasing the number of observations and the temperature difference does not lead to significant improvements on the results. On the other hand, it was found out that a greater temperature difference between the first and the last observation acts like a natural constraint by restricting the solutions to a narrower region. In this case, the estimated LST and LSE values do not strongly depend upon the initial guess, and therefore, the use of several initial guess vectors may be avoided, turning TTM computationally more efficient.

Inverse problems theory and application: analysis of the two-temperature method for land-surface temperature and emissivity estimation

The two-temperature method (TTM) allows the separation of land-surface temperature and land-surface emissivity information from radiance measurements, and therefore, the solution can be uniquely determined by the data. However, the inverse problem is still an ill-posed problem, since the solution does not depend continuously on the data. Accordingly, we have used some mathematical tools, which are suited for analyses of ill-posed problems in order to show TTM properties, evaluate it, and optimize its estimations. Related to this last point, we have shown that it is necessary to constrain the problem, either by defining a region of physically admissible solutions and/or by using regularization methods, in order to obtain stable results. Besides, the results may be improved by using TTM with systems that possess a high temporal resolution, as well as by acquiring observations near the maximum and minimum of the diurnal temperature range.

Synergistic use of the two-temperature and split-window methods for land-surface temperature retrieval

A strategy is presented with the aim of achieving an operational accuracy of 2.0 K in land-surface temperature (LST) from METEOSAT Second Generation (MSG)/Spinning Enhanced Visible and Infrared Imager (SEVIRI) data. The proposed method is based on a synergistic usage of the split-window (SW) and the two-temperature method (TTM) and consists in combining the use of a priori land-surface emissivity (LSE) estimates from emissivity maps with LST estimates obtained from SW method with the endeavour of defining narrower and more reliable ranges of admissible solutions before applying TTM. The method was tested for different surface types, according to SEVIRI spatial resolution, and atmospheric conditions occurring within the MSG disc. Performance of the method was best in the case of relatively dry atmospheres (water-vapour content less than 3 g cm−2), an important feature since in this case SW algorithms provide the worst results because of their sensitivity to uncertainties in surface emissivity. The hybrid method was also applied using real MSG/SEVIRI data and then validated with the Moderate resolution Imaging Spectroradiometer (MODIS)/Terra LST/LSE Monthly Global 0.05° geographic climate modeling grid (CMG) product (MOD11C3) generated by the day/night algorithm. The LST and LSE retrievals from the hybrid-method agree well (bias and root mean square error (RMSE) of −0.2 K and 1.4 K for LST, and around 0.003–0.02 and 0.009–0.02 for LSE) with the MOD11C3 product. These figures are also in conformity with the MOD11C3 performance at a semi-desert where LST (LSE) values is 1–1.7 K (0.017) higher (less) than the ground-based measurements.

Retrieving Middle-Infrared Reflectance Using Physical and Empirical Approaches: Implications for Burned Area Monitoring

A systematic comparison is carried out between retrieved values of middle-infrared (MIR) reflectance by means of the complete radiative transfer equation (RTE) and the simplified algorithm proposed by Kaufman and Remer in 1994 (KR94). The added value to be expected when using RTE is assessed both within and beyond the region where KR94 produces usable estimates of MIR reflectance, paying special attention to their application for discriminating burned areas (BAs) in tropical environments, where KR94 is the most common approach. For large values of land surface temperature (LST) and solar zenith angle (SZA), the retrieval of MIR reflectance based either on RTE or KR94 is an ill-posed problem, i.e., small perturbations due to sensor noise and uncertainties in atmospheric profiles and LST may induce large errors in the retrieved values. It is found that the RTE approach leads to better estimates in virtually all cases, with the exception of high values of LST and SZA, where results from KR94 are also not usable. Impacts on BA discrimination were finally evaluated using Moderate Resolution Imaging Spectroradiometer imagery showing a large fire event in southern Brazil. Synthetic values were generated, assuming a hot tropical environment, and MIR reflectance was retrieved using the two approaches. Whereas retrieved values of MIR reflectance via KR94 did not allow an effective discrimination between burned and unburned areas, those obtained via RTE have shown to be usable for BA monitoring, opening good perspectives for successful applications in hot tropical environments.

A Fire-Risk-Breakdown System for Electrical Power Lines in the North of Brazil

AbstractAnthropogenic or spontaneous fires (hotspots) are the main causes of unexpected breakdowns of electrical power lines in the northern region of Brazil. This research has tested, adapted, and implemented a preoperational system aiming to prevent electrical breakdowns for 382 km of electrical transmission lines in the state of Maranhao. The breakdown electrical fire risk is based on a combination of three variables: 1) the fire risk index, 2) the remotely sensed hotspot presence in the vicinity of electrical power lines, and 3) the vegetation stage. These variables are converted into Boolean variables, and their combination will classify the electrical fire risk as extreme, high, medium, low, or null. In regard to the system input variables, the fire risk index carries the highest representativeness in composition value of the breakdown electrical fire risk. Therefore, the results of two fire risk indices, calculated on the basis of the (a) Monte Alegre and (b) Angstrom methods, are presented and dis...

Land-Surface Emissivity Retrieval in MSG–SEVIRI TIR Channels Using MODIS Data

A procedure is presented that allows using information from the MODerate resolution Imaging Spectroradiometer (MODIS) sensor to improve the quality of emissivity maps for the Meteosat Second Generation/Spinning Enhanced Visible and Infrared Imager (SEVIRI) currently in use as input to a generalized split window (SW) algorithm for land-surface temperature (LST) retrievals in the operational chain of the Satellite Application Facility on Land Surface Analysis (LSA SAF). Information from MODIS is incorporated by means of linear regression models expressing emissivity in SEVIRI thermal-infrared channels as a linear combination of emissivities in MODIS bands. The linear models are applied to the MODIS emissivity product MOD11C3, and a comparison is performed with the operational LSA-SAF product. Special attention is devoted to the semiarid and arid regions of North Africa where emissivity is highly variable. When compared with the new emissivity maps, the LSA-SAF product displays more uniform emissivity values over these regions, leading to higher retrievals for all channels (bias around 0.03) except for IR3.9 (bias from -0.05 to -0.08). The root-mean-square error (RMSE) varies from 0.06 to 0.09 (0.02 to 0.03) for IR3.9 (IR10.8 and IR12.0) and is about 0.06 for IR8.7. The impact on LST is assessed by comparing the retrievals from a SW algorithm using as input the following: 1) the SEVIRI emissivity LSA-SAF product and 2) SEVIRI emissivity maps from MOD11C3. The uncertainty in the LSA-SAF emissivity product results into LST values with bias ranging from -0.4 to -1.0 K and RMSE around 1.6 K. The new emissivity maps based on MODIS data may be an alternative to the standard LSA-SAF emissivity product over semiarid and arid areas, which cover 26% of the land surfaces within the SEVIRI full disk.