Guillem Sòria

Land Surface Emissivity Retrieval From Different VNIR and TIR Sensors

This paper discusses the application and adaptation of two existing operational algorithms for land surface emissivity (epsiv) retrieval from different operational satellite/airborne sensors with bands in the visible and near-infrared (VNIR) and thermal IR (TIR) regions: (1) the temperature and emissivity separation algorithm, which retrieves epsiv only from TIR data and (2) the normalized-difference vegetation index thresholds method, in which epsiv is retrieved from VNIR data.

Revision of the Single-Channel Algorithm for Land Surface Temperature Retrieval From Landsat Thermal-Infrared Data

This paper presents a revision, an update, and an extension of the generalized single-channel (SC) algorithm developed by Jimenez-Munoz and Sobrino (2003), which was particularized to the thermal-infrared (TIR) channel (band 6) located in the Landsat-5 Thematic Mapper (TM) sensor. The SC algorithm relies on the concept of atmospheric functions (AFs) which are dependent on atmospheric transmissivity and upwelling and downwelling atmospheric radiances. These AFs are fitted versus the atmospheric water vapor content for operational purposes. In this paper, we present updated fits using MODTRAN 4 radiative transfer code, and we also extend the application of the SC algorithm to the TIR channel of the TM sensor onboard the Landsat-4 platform and the enhanced TM plus sensor onboard the Landsat-7 platform. Five different atmospheric sounding databases have been considered to create simulated data used for retrieving AFs and to test the algorithm. The test from independent simulated data provided root mean square error (rmse) values below 1 K in most cases when atmospheric water vapor content is lower than 2 g middotcm-2. For values higher than 3 g middotcm-2, errors are not acceptable, as what occurs with other SC algorithms. Results were also tested using a land surface temperature map obtained from one Landsat-5 image acquired over an agricultural area using inversion of the radiative transfer equation and the atmospheric profile measured in situ at the sensor overpass time. The comparison with this ldquoground-truthrdquo map provided an rmse of 1.5 K.

Single-channel and two-channel methods for land surface temperature retrieval from DAIS data and its application to the Barrax site

In this paper, a methodology using a single-channel and a two-channel method is presented to estimate the land surface temperature from the DAIS (Digital Airborne Imaging Spectrometer) thermal channels 74 (8.747 µm), 75 (9.648 µm), 76 (10.482 µm), 77 (11.266 µm), 78 (11.997 µm) and 79 (12.668 µm). The land surface temperature retrieved with both methods has been validated over the Barrax site (Albacete, Spain) in the framework of the DAISEX (Digital Airborne Imaging Spectrometer Experiment) field campaigns. Prior to the validation an analysis of the DAIS data quality has been performed in order to check the agreement between in situ data and the values extracted from the DAIS images supplied by the DLR (German Optoelectronic Institute). Suitable differences between in situ and DAIS data have been found. To solve this problem a linear re-calibration of the DAIS thermal channels has been applied using two ground calibration points (bare soil and water). For the land surface temperature retrieved, rms deviations of 0.96 K using a single-channel method and 1.46 K using a two-channel method with the DAIS thermal channels 77 and 78 have been obtained considering re-calibrated data.

Thermal remote sensing in the framework of the SEN2FLEX project: field measurements, airborne data and applications

A description of thermal radiometric field measurements carried out in the framework of the European project SENtinel‐2 and Fluorescence Experiment (SEN2FLEX) is presented. The field campaign was developed in the region of Barrax (Spain) during June and July 2005. The purpose of the thermal measurements was to retrieve biogeophysical parameters such as land surface emissivity (LSE) and temperature (LST) to validate airborne‐based methodologies and to characterize different surfaces. Thermal measurements were carried out using two multiband field radiometers and several broadband field radiometers, pointing at different targets. High‐resolution images acquired with the Airborne Hyperspectral Scanner (AHS) sensor were used to retrieve LST and LSE, applying the Temperature and Emissivity Separation (TES) algorithm as well as single‐channel (SC) and two‐channel (TC) methods. To this purpose, 10 AHS thermal infrared (TIR) bands (8–13 µm) were considered. LST and LSE estimations derived from AHS data were used to obtain heat fluxes and evapotranspiration (ET) as an application of thermal remote sensing in the context of agriculture and water management. To this end, an energy balance equation was solved using the evaporative fraction concept involved in the Simplified Surface Energy Balance Index (S‐SEBI) model. The test of the different algorithms and methods against ground‐based measurements showed root mean square errors (RMSE) lower than 1.8 K for temperature and lower than 1.1 mm/day for daily ET.

Temporal analysis of normalized difference vegetation index (NDVI) and land surface temperature (LST) parameters to detect changes in the Iberian land cover between 1981 and 2001

In past decades, the Iberian Peninsula has been shown to have suffered vegetation changes such as desertification and reforestation. Normalized difference vegetation index (NDVI) and land surface temperature (LST) parameters, estimated from data acquired by the Advanced Very High Resolution Radiometer (AVHRR) sensor onboard the National Oceanic and Atmospheric Administration (NOAA) satellite series, are particularly adapted to assess these changes. This work presents an application of the yearly land-cover dynamics (YLCD) methodology to analyse the behaviour of the vegetation, which consists of a combined multitemporal study of the NDVI and LST parameters on a yearly basis. Throughout the 1981–2001 period, trend analysis of the YLCD parameters emphasizes the areas that have endured the greatest changes in their vegetation. This result is corroborated by results from previous studies.

Emissivity mapping over urban areas using a classification-based approach: Application to the Dual-use European Security IR Experiment (DESIREX)

Abstract In this work a methodology to provide an emissivity map of an urban area is presented. The methodology is applied to the city of Madrid (Spain) using data provided by the Airborne Hyperspectral Scanner (AHS) in 2008. From the data a classification map with twelve different urban materials was created. Each material was then characterized by a different emissivity, whose values were obtained from the application of the TES algorithm to in situ measurements and values extracted from the ASTER spectral library. This new emissivity map could be used as a basis for determining the temperature of the city and to understand the urban heat island effect in terms of spatial distribution and size.

Evaluation of the surface urban heat island effect in the city of Madrid by thermal remote sensing

The surface urban heat island (SUHI) effect is defined as the increased surface temperatures in urban areas in contrast to cooler surrounding rural areas. In this article, the evaluation of the SUHI effect in the city of Madrid (Spain) from thermal infrared (TIR) remote-sensing data is presented. The data were obtained from the framework of the Dual-use European Security IR Experiment (DESIREX) campaign that was carried out during June and July 2008 in Madrid. The campaign combined the collection of airborne hyperspectral and in situ measurements. Thirty spectral and spatial high-resolution images were acquired with the Airborne Hyperspectral Scanner (AHS) sensor in a 11, 21, and 4 h UTC scheme. The imagery was used to retrieve the SUHI effect by applying the temperature and emissivity separation (TES) algorithm. The results show a nocturnal SUHI effect with a highest value of 5 K. This maximum value agrees within 1 K with the highest value of the urban heat island (UHI) observed using air temperature data (AT). During the daytime, this situation is reversed and the city becomes a negative heat island.

Phenology Estimation From Meteosat Second Generation Data

Many studies have focused on land surface phenology, for example as a means to characterize both water and carbon cycles for climate model inputs. However, the Spinning Enhanced Visible Infra-Red Imager (SEVIRI) sensor onboard Meteosat Second Generation (MSG) geostationary satellite has never been used for this goal. Here, five years of MSG-SEVIRI data have been processed to retrieve Normalized Difference Vegetation Index (NDVI) daily time series. Due to existing gaps as well as atmospheric and cloud contamination in the time series, an algorithm based on the iterative Interpolation for Data Reconstruction (IDR) has been developed and applied to SEVIRI NDVI time series, from which phenological parameters have been retrieved. The modified IDR (M-IDR) algorithm shows results of a similar quality to the original method, while dealing more efficiently with increased temporal resolution. The retrieved phenological phases were then analyzed and compared with an independent MODIS (Moderate resolution Imaging Spectrometer) dataset. Comparison of SEVIRI and MODIS-derived phenology with a pan-European ground phenology record shows a high accuracy of the SEVIRI-retrieved green-up and brown-down dates (within days) for most of the selected European validation sites, while differences with MODIS product are higher although this can be explained by differences in methodology. This confirms the potential of MSG data for phenological studies, with the advantage of a quicker availability of the data.

Evidence of Low Land Surface Thermal Infrared Emissivity in the Presence of Dry Vegetation

Land surface emissivity in the thermal infrared usually increases when the vegetation amount increases, reaching values that are larger than 0.98. During an experiment in Morocco over dry barley crops, it was found that emissivity may be significantly lower than 0.98 at full cover and that in some situations, it might decrease with increasing amount of vegetation, which was unexpected. Older data acquired in Barrax, Spain, over senescent barley also exhibited emissivity values lower than 0.98. The decrease of emissivity was also observed by means of simulations done with our land surface emissivity model developed earlier. The main reason for such behavior might be found in low leaf emissivity due to leaf dryness. This letter also stresses that knowledge on leaf and canopy emissivities and on their variation as a function of water content is still very limited

A simplified method for estimating the total water vapor content over sea surfaces using NOAA-AVHRR channels 4 and 5

A simplified method for estimating the total amount of atmospheric water vapor, W, over sea surfaces using NOAA-AVHRR Channels 4 and 5 is presented. This study has been carried out using simulated AVHRR data at 11 and 12 /spl mu/m (with MODTRAN 3.5 code and the TIGR database) and AVHRR, PODAAC, and AVISO databases provided by the Louis Pasteur University (Strasbourg-France), NASA-NOAA, and Meteo France, respectively. The method is named linear atmosphere-surface temperature relationship (LASTR). It is based on a linear relationship between the effective atmospheric temperature in AVHRR Channel 4 and sea surface temperature. The LASTR method was compared with the linear split-window relationship (LSWR), which is based on a linear regression between W and the difference of brightness temperature measured in the same channels (/spl Delta/T=T4-TS). The results demonstrate the advantage of the LASTR method, which is capable of estimating W from NOAA-14 afternoon passes with a bias accuracy of 0.5 g cm/sup -2/ and a standard deviation of 0.3 g cm/sup -2/, compared with the W obtained by the AVISO database. In turn, a global bias accuracy of 0.1 g cm/sup -2/ and a standard deviation within 0.6 g cm/sup -2/ have been obtained in comparison with the W included in the PODAAC database derived from the special sensor microwave/imager (SSM/I) instrument.