Juan C. Jiménez-Muñoz

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.

Land Surface Temperature Retrieval Methods From Landsat-8 Thermal Infrared Sensor Data

The importance of land surface temperature (LST) retrieved from high to medium spatial resolution remote sensing data for many environmental studies, particularly the applications related to water resources management over agricultural sites, was a key factor for the final decision of including a thermal infrared (TIR) instrument on board the Landsat Data Continuity Mission or Landsat-8. This new TIR sensor (TIRS) includes two TIR bands in the atmospheric window between 10 and 12 μm, thus allowing the application of split-window (SW) algorithms in addition to single-channel (SC) algorithms or direct inversions of the radiative transfer equation used in previous sensors on board the Landsat platforms, with only one TIR band. In this letter, we propose SC and SW algorithms to be applied to Landsat-8 TIRS data for LST retrieval. Algorithms were tested with simulated data obtained from forward simulations using atmospheric profile databases and emissivity spectra extracted from spectral libraries. Results show mean errors typically below 1.5 K for both SC and SW algorithms, with slightly better results for the SW algorithm than for the SC algorithm with increasing atmospheric water vapor contents.

Record-breaking warming and extreme drought in the Amazon rainforest during the course of El Niño 2015-2016.

The El Niño-Southern Oscillation (ENSO) is the main driver of interannual climate extremes in Amazonia and other tropical regions. The current 2015/2016 EN event was expected to be as strong as the EN of the century in 1997/98, with extreme heat and drought over most of Amazonian rainforests. Here we show that this protracted EN event, combined with the regional warming trend, was associated with unprecedented warming and a larger extent of extreme drought in Amazonia compared to the earlier strong EN events in 1982/83 and 1997/98. Typical EN-like drought conditions were observed only in eastern Amazonia, whilst in western Amazonia there was an unusual wetting. We attribute this wet-dry dipole to the location of the maximum sea surface warming on the Central equatorial Pacific. The impacts of this climate extreme on the rainforest ecosystems remain to be documented and are likely to be different to previous strong EN events.

Comparison Between Fractional Vegetation Cover Retrievals from Vegetation Indices and Spectral Mixture Analysis: Case Study of PROBA/CHRIS Data Over an Agricultural Area

In this paper we compare two different methodologies for Fractional Vegetation Cover (FVC) retrieval from Compact High Resolution Imaging Spectrometer (CHRIS) data onboard the European Space Agency (ESA) Project for On-Board Autonomy (PROBA) platform. The first methodology is based on empirical approaches using Vegetation Indices (VIs), in particular the Normalized Difference Vegetation Index (NDVI) and the Variable Atmospherically Resistant Index (VARI). The second methodology is based on the Spectral Mixture Analysis (SMA) technique, in which a Linear Spectral Unmixing model has been considered in order to retrieve the abundance of the different constituent materials within pixel elements, called Endmembers (EMs). These EMs were extracted from the image using three different methods: i) manual extraction using a land cover map, ii) Pixel Purity Index (PPI) and iii) Automated Morphological Endmember Extraction (AMEE). The different methodologies for FVC retrieval were applied to one PROBA/CHRIS image acquired over an agricultural area in Spain, and they were calibrated and tested against in situ measurements of FVC estimated with hemispherical photographs. The results obtained from VIs show that VARI correlates better with FVC than NDVI does, with standard errors of estimation of less than 8% in the case of VARI and less than 13% in the case of NDVI when calibrated using the in situ measurements. The results obtained from the SMA-LSU technique show Root Mean Square Errors (RMSE) below 12% when EMs are extracted from the AMEE method and around 9% when extracted from the PPI method. A RMSE value below 9% was obtained for manual extraction of EMs using a land cover use map.

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.

A Single-Channel Algorithm for Land-Surface Temperature Retrieval From ASTER Data

This letter presents an adaptation to Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data of the generalized single-channel (SC) algorithm developed by JimE¿nez-MuN¿oz and Sobrino, also adapted to the Landsat thermal-infrared (TIR) channel (band 6) later by JimE¿nez-MuN¿oz The SC algorithm relies on the concept of atmospheric functions (AFs), which are dependent on atmospheric transmissivity, upwelling, and downwelling atmospheric radiances. These AFs are fitted versus the atmospheric water-vapor content for operational purposes, despite the fact that other computation options are also possible. The SC algorithm has been adapted to ASTER TIR bands 13 (10.659 ¿m) and 14 (11.289 ¿m), located in the typical split-window region (10.5-12 ¿m), where transmission through the atmosphere is higher and surface emissivity variations are lower in comparison with the ones in the 8-9.4 ¿m spectral region. Land-surface temperature retrieved with the SC algorithm has been tested over five different samples (including vegetated plots and bare soil) in an agricultural area using one single image. The comparison with ground-truth data provided a bias near to zero and standard deviations of around 2 K, with bands 13 and 14 providing similar results.

Feasibility of Retrieving Land-Surface Temperature From ASTER TIR Bands Using Two-Channel Algorithms: A Case Study of Agricultural Areas

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) provides the user community with standard products of land-surface temperature (LST) and emissivity using the temperature and emissivity separation (TES) algorithm. This letter analyzes the feasibility of using two-channel (TC) algorithms for LST retrieval from ASTER data, which could be considered as an alternative or complementary procedure to the TES algorithm. TC algorithms have been developed for all the ASTER thermal infrared bands combinations, and they have been applied to six ASTER images acquired over an agricultural area of Spain in 2000, 2001, and 2004. LST values obtained with TC algorithms were compared with the TES product. In addition, the TC algorithms were tested using simulated data and ground-based measurements collected coincident with the ASTER acquisition in 2004. The results show that TC algorithms provide similar accuracies than the TES algorithm (~1.5 K), with the main advantage that the atmospheric correction is included in the algorithm itself

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.