Land surface emissivity (LSE) can reflect the unique spectral characteristics of land surfaces, which holds significant research value and promising applications. However, due to the limitations of spaceborne hyperspectral thermal infrared (TIR) sensors, there is currently a lack of high spatial resolution LSE imagery. Therefore, this study proposes a methodology based on machine learning and mathematical statistical approaches to extend multispectral emissivity values to hyperspectral LSE spectrums. Furthermore, the Spectral Angle Mapper (SAM) method was used for target identification. The results indicated that the proposed method can estimate hyperspectral LSE curves in high accuracy. Moreover, the target ground class can be identified accurately by matching the spectral angle with the measured LSE curve. This has crucial implications for applications such as land cover classification, mineral identification, geological exploration and soil property research.

The urban thermal environment has attracted increasing attention, especially under the background of frequent extreme heat events like heatwaves. They not only cause a decline in thermal comfort, but also contribute to further energy shortages, posing a threat to the sustainable development of the planet. Valencia, a Mediterranean coastal city which is severely affected by heat waves was selected as the study area. According to the Local climate zone (LCZ) classification results, four typical courtyards of different LCZ types were chosen for simulation in ENVI-met. The diurnal air temperature (Ta), Mean radiant temperature (Tmrt) and Predicted mean vote (PMV) and their relationships with Sky view factor (SVF) were explored to assess the thermal comfort level during a heat-wave day. In addition, a comparative analysis of four LCZ categories was conducted. The high-rise courtyard has worse thermal comfort at night, especially in compact high-rise (LCZ 1).

In this study we present a model to forecast wheat yield based on the evolution of the Difference Vegetation Index (DVI) and the Growing Degree Days (GDD), presented in Franch et al. (2015), but adapted to Franch et al. (2019) model. Additionally, we explore how the Land Surface Temperature (LST) can be included into the model and if this parameter adds any value to the model when combined with the optical information. This study is applied to MODIS data at 1km resolution to monitor the national and state level yield of winter wheat in the United States and Ukraine from 2001 to 2019.

In this study we present a methodology to develop a global winter wheat crop calendar based on the existing crop calendar products from FAO and GEOGLAM Crop Monitor in the framework of the WorldCereal project. It is based on integrating both datasets by building on the accuracy from Crop Monitor and the spatial resolution from the Food and Agriculture Organization of the United Nations (FAO). Additionally, given the global extent of WorldCereal and the gaps that both products present at global scale, we simulated the crop calendars in those areas not covered by any of the products. To do so, we integrated a Regression-Kriging model considering as training data the calendars derived from both products and based on the latitude, height and distance to the coast (DTC).

In this work we present results of an early assessment of the performance of the Land Surface Temperature (LST) product retrieved from the Sea and Land Surface Temperature Radiometer (SLSTR) on board the Sentinel-3 satellite (S3/SLSTR) over the Amazon basin. Results are validated from comparison of S3/SLSTR retrievals against in situ measurements of surface temperature collected over one instrumented site in the Peruvian Amazon. The validation exercise was performed on the standard S3/SLSTR Level-2 LST product as well as on a dedicated LST split-window algorithm with an explicit dependence on surface emissivity. Surface emissivity maps obtained from the high spatial resolution of S2/MSI are also presented over the test area.

The SEVIRI (Spinning Enhanced Visible and Infra Red Imager) instrument onboard MSG (Meteosat Second Generation) satellite series provides valuable data for the observation of our planet. We describe here the processing chain implemented at the Global Change Unit of the University of Valencia to provide information such as vegetation index, temperatures of both land and sea, synthetic quicklooks for an easy interpretation of the data as well as fire hotspots. Vegetation index and temperature data are available for download from a dedicated portal updated every 3 hours with the most recent processed data. Additionally, a web page displays this information for a non scientific public in near real time, with updated data every 15 minutes. Parameters available from this webpage will be validated in a near future, and extended to drought and vegetation condition index, as well as vegetation and temperature anomalies.

Evolution in the Copernicus Space Component (CSC) is foreseen in the mid-2020s to meet priority Copernicus user needs not addressed by the existing infrastructure, and/or to reinforce services by monitoring capability in the thematic domains of CO2, polar, and agriculture/forestry. This evolution will be synergetic with the enhanced continuity of services for the next generation of CSC. The “High Spatio-Temporal Resolution Land Surface Temperature Monitoring (LSTM) Mission”, identified as one of the CSC Expansion High Priority Candidate Missions (HPCM), currently undergoes an ESA preparatory phase (phase A/B1) study to establish mission feasibility. The LSTM mission shall provide enhanced measurements of land surface temperature with a focus responding to user requirements related to agricultural monitoring.

The monitoring of the water cycle at the Earth surface which tightly interacts with the climate change processes as well as a number of practical applications (agriculture, soil and water quality assessment, irrigation and water resource management, etc…) requires surface temperature measurements at local scale. Such is the goal of the Indian-French high spatio-temporal TRISHNA mission (Thermal infraRed Imaging Satellite for High-resolution Natural resource Assessment). The scientific objectives of the mission and research work conducted to consolidate the mission specifications are presented. Progress in modelling of surface fluxes is then discussed. The main specifications of the mission such as the revisit, the spatial resolution, the overpass time, the spectral bands and the orbit are analyzed and justified. The resulting baseline of the mission is given.

Landsat 8 (L8) satellite was launched on February 11, 2013 with two thermal bands located in the atmospheric window between 10-12 μm. Continuous monitoring of the Thermal Infrared Sensor (TIRS) onboard of L8 was performed over two Spanish test sites - Barrax and Doñana - in order to contribute to the quality of TIRS data. In this work, a Vicarious Calibration (VC) of the TIRS bands was performed between years 2013-2016 in order to assess the new Stray Light (SL) data correction. The results of VC show us that band 10 and 11 provide accurate results (bias near to zero, and precision around 0.8 K) which is an improvement - especially for band 11 - in comparison to preprocessed SL data. This little better performance has direct influence on Split Window (SW) algorithm, lowering its standard deviation into 0.3 K, as is shown in this work.

Due to problems in the thermal infrared sensor on-board the Landsat-8 satellite, Landsat-7 (L7) can be an interesting alternative source of thermal data because it is the only source of well-calibrated, free, high-resolution data. To contribute to the quality of thermal data, a vicarious calibration (VC) of the enhanced thematic mapper instrument and a validation of the single-channel general equation and the water vapor approach algorithm in conjunction with an inversion of the radiative transfer equation (RTE) have been performed during 2013-2015 over two Spanish test sites. For this purpose, three global atmospheric profile data sets were used to better characterize the error due to atmospheric correction: 1) MODIS atmospheric product MOD07 version 5; 2) MOD07 version 6; and 3) national center for environmental prediction (NCEP) reanalysis data. The VC results show a negligible bias in our test sites while the land surface temperature algorithms validation present a root mean square error (RMSE) of 1.5 to 3 K, with the best performance for NCEP atmospheric profiles in combination with the inversion of the RTE (RMSE below 1.5 K).

Chlorophyll-a concentration ([Chl-a]) and Lake Surface Temperature (LST) were retrieved in Lake Titicaca (Peru-Bolivia) using MODIS and Landsat-8 images. The lake was chosen as a case-study for evaluating the feasibility of Landsat-8 images for [Chl-a] and LST monitoring in oligotrophic and mesotrophic water bodies. The big size of the lake and its spatial and temporal variability, allowed the comparison of MODIS and Landsat-8 products for a wide range of [Chl-a] and LST. The atmospheric correction of the images was facilitated by the very high altitude of the lake. MODIS images were processed with standard ocean color algorithms whereas for Landsat-8, specific algorithms were tested and validated The results show that Landsat-8 is capable of retrieving [Chl-a] and LST with an accuracy comparable to that of MODIS and with a finer spatial resolution, revealing surface patterns in greater detail. The combined use of both sensors allows monitoring the eutrophication and temperature trends of Lake Titicaca, which is a water body of the highest ecological interest, increasingly affected by human activities in its watershed and very sensitive to climate changes.

High-resolution thermal infrared (TIR) remote sensing has a wide range of applications. In this paper, we describe the different applications and requirements identified in a literature review and during a consultation meeting with researcher experts in different fields. As a result, more than 30 applications were identified within three different fields: 1) land and solid Earth; 2) health and hazards; and 3) security and surveillance. A complete set of requirements (spatial, temporal, and radiometric resolution, algorithms used, and supporting data, among others) for each application is also provided. The results presented in this paper provide useful information to enhance the importance of high-resolution TIR data for civil applications and may serve as a reference document for future TIR mission concepts.

The Meteosat Second Generation-Spinning Enhanced Visible and Infrared Imager (MSG-SEVIRI) instrument provides observations of half the globe every 15 min, at low spatial resolution. These data are an invaluable tool to observe daily to yearly cycle of land surface temperature (LST), as well as for various early warning systems. However, advanced algorithms for LST estimation requires a previous estimation of the water vapor (WV) column above the observed pixel, for which no instantaneous retrieval methods are yet available, and therefore hinders their implementation in a near-real-time processing chain for MSG-SEVIRI data. This work analyzes three different formulations for such WV retrieval, which are compared to independent WV estimates obtained from radiosoundings. The best suited algorithm is then selected for WV estimation and compared with the results obtained with a previous noninstantaneous algorithm [23]. This comparison shows that, in spite of retrieval errors higher than the ones reported in the literature, the estimated WV compares relatively well with in situ data, while allowing for an instantaneous estimation of WV column (every 15 min). Error propagation analysis and direct comparison show that the observed increase in WV estimation error has a negligible influence on LST retrieval. Therefore, this algorithm is well suited to be implemented in a near-real-time processing chain for MSG-SEVIRI 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.

In this paper, we will evaluate the Vermote et al. method, hereafter referred to as VJB, in comparison to the MCD43 MODerate Resolution Imaging Spectroradiometer (MODIS) product, focusing on the white sky albedo parameter. We also present and study three different methods based on the VJB assumption, the 4param, 5param Rsqr, and 5param Vsqr. We use daily MODIS Climate Modeling Grid data both from Terra and Aqua platforms from 2002 to 2011 for all the pixels over Europe. We obtain an overall root-mean-square error of 5% when using the VJB method and 6.1%, 5.1%, and 5.3% for the 4param, 5param Rsqr, and 5param Vsqr methods, respectively. The main differences between the methods are located in areas where only few cloud-free snow-free samples were available that correspond mainly to mountainous areas during the winter. We finally conclude that the VJB method has an equivalent performance in deriving the white sky albedo results to the MODIS product with the advantage of daily temporal resolution. Additionally, we propose the 5param Rsqr method as an alternative to the VJB method due to its decreased data processing time.

In this paper, we analyze the feasibility of applying the temperature and emissivity separation (TES) algorithm to thermal-infrared data acquired with three bands of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the Meteosat Second Generation platform (SEVTES). The performance of the SEVTES algorithm was tested using data simulated over different atmospheric conditions and surface emissivities, with errors around 1.5% for emissivity and 1.5 K for temperature when atmospheric correction is accurate enough. In contrast, errors on land-leaving radiances higher than 2% or uncertainties on total atmospheric water vapor amount higher than 5% lead to errors on emissivity higher than 2% and errors on land surface temperature higher than 3 K, especially when the atmospheric absorption is overestimated. SEVIRI data acquired in August 2011 were also used to validate SEVTES emissivities against in situ measurements collected in five different homogeneous areas over Africa. Values were also intercompared to Moderate Resolution Imaging Spectroradiometer (MODIS)-derived and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)-derived emissivities and to the LSA SAF emissivity product. Results show that SEVTES-derived emissivity values are consistent with MODIS-TES and ASTER-TES retrievals and that SEVTES also improves the retrievals included in LSA SAF and MOD11Cx v5 products. When compared to laboratory measurements, accuracies of around 1%-2% were obtained, although occasional inaccuracies (2%-3%) were also found in some cases at band 8.7 μm. The results presented in this paper show the potential SEVTES has for improving the LSA SAF product over arid and semiarid areas.

The temperature and emissivity separation (TES) algorithm is used to retrieve the land surface emissivity (LSE) and land surface temperature (LST) values from multispectral thermal infrared sensors. In this paper, we analyze the performance of this methodology over urban areas, which are characterized by a large number of different surface materials, a variability in the lowest layer of the atmospheric profiles, and a 3-D structure. These specificities induce errors in the LSE and LST retrieval, which should be quantified. With this aim, the efficiency of the TES algorithm over urban materials, the atmospheric correction, and the impact of the 3-D architecture of urban scenes are analyzed. The method is based on the use of a 3-D radiative transfer tool, TITAN, for modeling all of the radiative components of the signal registered by a sensor. From the sensor radiance, an atmosphere compensation process is applied, followed by a TES methodology that considers the observed scene to be a flat surface. Finally, the retrieved LSE and LST are compared with the original parameters. Results show the following: First, the TES algorithm used reproduces the LSE (LST) of urban materials within a root-mean-square error (rmse) of 0.017 (0.9 K). Second, 20% of uncertainty in the water vapor content of the total atmosphere introduces an rmse of 0.005 (0.4 K) for the LSE (LST) product. Third, in a standard case, the 3-D structure of an urban canyon leads to an rmse of 0.005 (0.2 K) for the LSE (LST) retrieval of the asphalt at the bottom of the scene.

Thermal infrared satellite observations of the Earth's surface are key components in estimating the surface skin temperature over global land areas. This work presents validation methodologies to estimate the quantitative uncertainty in Land Surface Temperature (LST) product derived from the Visible Infrared Imager Radiometer Suite (VIIRS) onboard Suomi National Polar-orbiting Partnership (NPP) using ground-based measurements currently made operationally at many field and weather stations around the world. Over heterogeneous surfaces in terms of surface types or biophysical properties (e.g., vegetation density, emissivity), the validation protocol accounts for land surface spatial variability around the ground station. Over sparse vegetation canopies, the methodology accounts for viewing directional effects and sun configuration when validating VIIRS LST products.

In a global warming scenario there is an increase interest in examining climate trends at specific biomes such as the tropical rainforest biome. The Amazonian region is the largest carbon sink, so changes in this area are expected to have a direct impact over the climate change. In this work we analyse the temporal evolution (trends and anomalies) of different land parameters from 2001 to 2010 over the Amazonian forest. For this purpose, we used Moderate Resolution Imaging Spectroradiometer (MODIS) Land Products at 0.05° latitude/longitude Climate Modeling Grid (CMG), namely, combined Terra/Aqua 16-day Albedo (MCD43C3), Terra monthly Land Surface Temperature & Emissivity (MOD11C3), Terra monthly Vegetation Indices (MOD13C2), and combined Terra/Aqua yearly Land Cover product (MCD12C1. Results show an anomalous widespread warming over the study area, and also a decadal decrease in vegetation indices (EVI and NDVI). Mean value of surface albedo do not provide a significant trend, but some regions show an anomalous increase.

Many studies have focused on land surface phenology as a means to characterize global climate. The Spinning Enhanced Visible Infra-Red Imager (SEVIRI) sensor onboard Meteosat Second Generation (MSG) geostationary satellite can also contribute to this task thanks to its adequate spatial and temporal resolutions. Here, four years of MSG-SEVIRI Normalized Difference Vegetation Index (NDVI) daily time series have been retrieved, which were then gap-filled with the help of an algorithm based on the iterative Interpolation for Data Reconstruction [Julien and Sobrino, 2010]. Finally, phenological parameters have been retrieved from the reconstructed time series, and compared with independent MODIS (Moderate resolution Imaging Spectrometer) data, showing differences for specific land covers although the stability of the retrieved phenophases over the year is surprisingly good for MSG data. This approach can be applied to other geostationary satellites worldwide to obtain quick remotely sensed estimates of vegetation phenology at global scale.

Airborne multispectral imagers have been used in validation campaigns in order to acquire very high spatial resolution data as a benchmark for current or future satellite data. Imagery acquired with such sensors implies specific data processing in relation to view-angle-dependent atmospheric correction and removal or minimization of stripping-based noise. It is necessary to appropriately perform this processing in order to benefit from reference imageries of surface temperature (T) and emissivity (ε) maps retrieved from thermal infrared data. In particular, ε images generated fromT/ε separation algorithms show undesirable noise that jeopardizes their photointerpretation. This letter addresses the following: 1) the removal of view-angle-dependent atmospheric effects by using ratio techniques for deriving atmospheric water vapor content in a pixel-by-pixel basis and atmospheric radiative transfer simulations to construct lookup tables (LUTs) and 2) the removal of image stripping using maximum/minimum noise fraction (MNF) transforms. For this purpose, imagery acquired with the Airborne Hyperspectral Scanner (AHS) sensor has been used. Results show that angular effects in the atmospheric correction can be addressed from AHS-derived water vapor content and LUTs, whereas due to the AHS noise specific characteristics, the MNF transform only removed part of the noise.

A simple approach for correcting for the effect of vegetation in the estimation of the surface soil moisture (wS) from L-band passive microwave observations is presented in this study. The approach is based on semi-empirical relationships between soil moisture and the polarized reflectivity including the effect of the vegetation optical depth which is parameterized as a function of the normalized vegetation difference index (NDVI). The method was tested against in situ measurements collected over a grass site from 2004 to 2007 (SMOSREX experiment). Two polarizations (horizontal/vertical) and five incidence angles (20°, 30°, 40°, 50°, and 60°) were considered in the analysis. The bestwSestimations were obtained when using both polarizations at an angle of 40°. The average accuracy in the soil moisture retrievals was found to be approximately 0.06 m3/m3, improving the estimations by 0.02 m3/m3 with respect to the case in which the vegetation effect is not considered. The results indicate that information on vegetation (through a vegetation index such as NDVI) is useful for the estimation of soil moisture through the semi-empirical regressions.

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.

The main purpose of this letter is to give an operational algorithm for retrieving the land surface temperature (LST) using the Spinning Enhanced Visible and Infrared Imager data onboard the Meteosat Second Generation (MSG2) satellite. The algorithm is a split-window method using the two thermal infrared channels IR10.8 and IR12.0. The MODTRAN 4.0 code was used to obtain numerical coefficients of the algorithm proposed. The results show that for viewing angles lower than 50degthe algorithm is capable of producing LST with a standard deviation of 0.7 K and a root-mean-square error (rmse) of 1.3 K. The algorithm has been applied to a series of MSG2 images obtained from an MSG antenna system installed at the imaging processing laboratory (IPL) in the University of Valencia, Valencia, Spain. The LST product has been evaluated using theinsitudata from the European Space Agency (ESA) field campaign named CarboEurope, FLEx and Sentinel-2 (CEFLES2) carried out in 2007 in Bordeaux (France), and using the official LST MODIS product and another algorithm to retrieve LST from the MODIS data. This evaluation has been applied over different surfaces and under different viewing angles. The results show an rmse of 1.9 K for theinsitudata, 1.5 K for the official LST MODIS product, and 0.7 K compared with that of the MODIS LST algorithm.

This paper presents a revision, an update, and an extension of the generalized single-channel (SC) algorithm developed by JimÉnez-MuÑoz 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$\hbox{g} \cdot \hbox{cm}^{-2}$. For values higher than 3$\hbox{g} \cdot \hbox{cm}^{-2}$, 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 measuredin situat the sensor overpass time. The comparison with this “ground-truth” map provided an rmse of 1.5 K.

In this letter, we provide a complete set of split-window coefficients that can be used to retrieve land surface temperature (LST) from thermal infrared sensors onboard the most popular remote-sensing satellites: ERS-ATSR2, ENVISAT-AATSR, Terra/Aqua-MODIS, NOAA series-AVHRR, METOP-AVHRR3, GOES series-IMAGER, and MSG1/MSG2-SEVIRI. The coefficients have been obtained by minimization from an extensive simulated database constructed from MODTRAN radiative transfer code calculations, emissivity spectra extracted from spectral libraries, and spectral response functions of the thermal bands considered. This letter also analyzes the magnitude of the error on the LST retrieval and the contribution to the error of the different uncertainties. Results are summarized in a lookup table useful for scientists interested on land surface retrievals at global scale, thereby facilitating and homogenizing the task of retrieving this parameter from different common sensors.

This paper presents a new method for NOAA's (National Ocean and Atmospheric Administration) orbital drift correction. This method is pixel-based, and in opposition with most methods previously developed, does not need explicit knowledge of land cover. This method is applied to AVHRR (Advanced Very High Resolution Radiometer) channel information, and relies only on the additional knowledge of solar zenithal angle (SZA) and acquisition date information. In a first step, anomalies in SZA and channel time series are retrieved, and screened out for anomalous values. Then, the part of the parameter anomaly which is explained by SZA anomaly is removed from the data, to estimate new parameter anomalies, and this iteratively until the influence of SZA anomalies is totally removed from the parameter data. This correction has been applied to bimonthly AVHRR data provided by the GIMMS group (Global Inventory Modeling and Mapping Studies), covering Africa from November 2000 to December 2006. NDVI and LST (land surface temperature) have been estimated from raw and corrected data, and averaged over homogeneous vegetation classes. Differences between raw and corrected averaged parameters show an improvement in the quality of the data. In order to validate this method, a whole week (10 to 17 July 2004) of METEOSAT SEVIRI (Spinning Enhanced Visible and InfraRed Imager) data have been used, from which LST have been estimated using a similar method to the one used to retrieve LST from AVHRR data. The comparison between both platforms at the same time of acquisition shows good concordance.

This paper discusses the application and adaptation of two existing operational algorithms for land surface emissivity ($\varepsilon$) 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$\varepsilon$only from TIR data and 2) the normalized-difference vegetation index thresholds method, in which$\varepsilon$is retrieved from VNIR data.

This work provides a description of the research conducted to assess if ASTER satellite data enable the detection of the water status in orchard tree crops. Summer and winter TERRA-ASTER images were acquired over a study area of Seville in southern Spain over a 6-year period. 1076 olive orchards were monitored in this area, obtaining field location, area, tree density, and irrigation status information. Surface temperature images were obtained using the TES method and 0.5 m resolution panchromatic ortho-rectified imagery collected over the entire area to obtain vegetation cover. A comparison study of the temperature difference between orchards under different irrigation schemes is presented. Results in summer ASTER images showed differences between irrigated and rainfed orchard fields of 2 K, decreasing the differences to 0.5 K in ASTER winter images due to the lack of water stress condition. This methodology could be useful to detect the crop irrigation stratus operationally from ASTER satellite thermal imagery, with potential implications for evapotranspiration studies in non- homogeneous canopies.

It has been demonstrated that the spectrum of fluorescence emission is dependent on leaf temperature, thus there is a need for thermal information in order to interpret fluorescence signals. Temperature is also related to transpiration and stomata closure, which affects CO2 uptake and fluorescence. Therefore temperature measurements help to confirm the trends observed in fluorescence measurements. While fluorescence is immediately and uniquely related to photosynthesis, temperature provides additional information about plant status and instantaneous energy/water fluxes between plants and the atmosphere. The objective of this paper is to demonstrate the role of surface temperature in the context of FLuorescence EXplorer (FLEX) mission. To this end a database of land surface emissivity and temperature obtained from thermal radiometric measurements carried out in the framework of the Sen2FLEX (SENtinel-2 and FLuorescence Experiment) campaign has been used. These data were acquired in the agricultural site of Barrax (Spain) in June and July of 2005 simultaneously with airborne imagery acquired with Airborne Hyperspectral Scanner (AHS) and Compact Airborne Spectrographic Imager (CASI) sensors and data from the airborne fluorescence measuring instrument (AIRFLEX). As a result of these studies we have identified the optimal band configuration for the FLEX mission, that allows the estimation of land surface temperature with an accuracy lower than 1.5 K. To this end single-channel, split-window, and Temperature Emissivity Separation algorithms have been compared using a database of simulated brightness temperatures.

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

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

The WATERMED project (2000-2004) is founded by the European Union (INCO-med project) and contributes to the international efforts in analysing efficiency in water use, in particular for the Mediterranean Basin countries. The general objective of the WATERMED project is to develop a comprehensive method for the study of the water use and the resistance to the drought of the natural and irrigated vegetation in the Mediterranean Basin, by means of a combined historical and current space-based remote sensing database, vegetation models and field measurements. The Mediterranean Basin was selected as the area of study thanks to its high environmental diversity. This area is clearly affected by the risk of the advance of the desert. Analysing multi-temporal data from the NOAA/NASA Pathfinder AVHRR land (PAL) dataset is taking place as part of the WATERMED project, and at the same time REANALYSIS data are used to provide mean monthly climatological data to compare with. The main objective of this study is to map, and monitor land-cover change in the Mediterranean Basin between 1981 and 2001. The study consists in combining both the information in the visible/near-infrared bands in terms of Normalised Difference Vegetation Index (NDVI) and in the thermal-infrared bands in terms of Land Surface Temperature (LST), together with climatic data given by the REANALYSIS. The space-temporal dynamics of these parameters have been sought by analysing seasonal and inter-annual variability. Finally, we analyse the evolution of LST and NDVI for the months of April and July by the use of the Land Cover Dynamic (VCLD) method and compare with the analysis of Reanalysis data.

Land surface temperature is an important key for environmental studies like energy balances and climate models. As is well-known, to retrieve land surface temperature from remotely sensed data, land surface emissivities are needed due to the nondeterministic nature of the temperature/emissivity separation from thermal infrared measurements: if thermal radiation is measured in N spectral bands, there will be N+1 unknowns (N emissivities and a single surface temperature). In this paper, two methods to estimate land surface emissivity from the Digital Airborne Imaging Spectrometer (DAIS) sensor are considered: the temperature/emissivity separation method (TES) developed by Gillespie et al. (1998), in which thermal infrared data are needed, and the NDVI thresholds method (NDVI/sup THM/) developed by Sobrino et al. (2001), in which visible and near-infrared data are also needed. Once the LSE has been estimated, LST can be retrieved using a single-channel or two-channel method using only one thermal channel or a combination of two thermal channels respectively. These methods usually use the atmospheric water vapor content as input data, sp a method based on the ratios between absorbent and transparent bands in the red and near-infrared region to estimate atmospheric water vapor is also presented. The final results obtained for the validation carried out over the Barrax test site (Albacete, Spain) in the framework of the DAISEX (DAIS experiment) campaigns supported by ESA show deviations of around 0.01 for LSE and deviations between 1 K and 1.5 K for LST.

The estimation of sea and land surface temperature using multi-angular algorithms with Advanced Along Track Scanning Radiometer (AATSR) data require an accurate knowledge of the angular variation of surface emissivity in the thermal infrared. A range of operative and accurate multiangle algorithms for estimating surface temperature has been developed using MODTRAN 3.5 simulations of the AATSR data at 11 and 12 /spl mu/m wavelengths, making use of the dual-angle viewing capability of the AATSR. To build the simulated database, emissivity values have been used. An experimental investigation has been made in order to determine the angular variation of the infrared emissivity as a well-known parameter to estimate ST in multiangular methods. The results show a general decrease of the emissivity with increasing viewing angles. Finally, the proposed algorithms have been applied to a series of satellite images acquired by the CSIRO in Australia, retrieving LST with a standard deviation less than 0.6 K if the satellite data are error free; in this case, the emissivity and temperature values necessary to retrieve the ST have been acquired from CSIRO in situ data.

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.

A study has been carried out using simulated NOAA/advanced very high resolution radiometer (AVHRR) data at 11 and 12 /spl mu/m (with LOWTRAN-7, MODTRAN 2.0, and the TIGR database), AVHRR images of the Iberian Peninsula and the Palma de Mallorca Island, radiosonde observations at seven meteorological stations, and the AVISO database provided by Meteo France to describe, compare, and analyze two different approaches for estimating the total atmospheric water vapor content (W) over land surfaces from AVHRR data. These two techniques are: 1) the split-window covariance-variance ratio (SWCVR), based on a quadratic relationship between W and the ratio of the spatial covariance and variance of brightness temperatures measured in channels 4 (T/sub 4/) and 5 (T/sub 5/) of AVHRR in subsets of N neighboring pixels and 2) the linear split-window relationship (LSWR), based on a linear regression between W and the difference of brightness temperatures measured in the same channels (/spl Delta/T=T/sub 4/-T/sub 5/). The results demonstrate the advantage of the SWCVR technique for regions with a certain level of thermal heterogeneity (standard deviation of T/sub 4/ in the subset >0.5 K), which is capable of estimating W from NOAA-14 afternoon and night passes over the Iberian Peninsula with a standard deviation of 0.5 (g cm/sup -2/), whereas the LSWR technique predicts the atmospheric water vapor with a standard deviation from 1.3-1.5 (g cm/sup -2/). Finally a water vapor image of the entire Iberian Peninsula constructed by applying the SWCVR to NOAA-14 data is presented.

Land surface temperature (LST) retrievals obtained from NOAA Advanced Very High Resolution Radiometer (AVHRR) are of considerable importance for climatic research. However, the accurate evaluation of LST from space has been severely limited because of the difficulty in separating atmospheric from surface effects as the surface cannot be modeled as a black-body radiator. With this goal in mind, a novel extension of the split-window technique is presented in which the atmospheric contribution to the radiance measured by the satellite is investigated by the ratioing of covariance and variance of the brightness temperatures measured in channels 4 and 5 of AVHRR/2. Furthermore, the contribution of emissivity is evaluated from coefficients that depend on the spectral emissivities in both thermal channels. Using a wide range of simulations from an atmospheric radiative transfer model it is shown that the proposed algorithm provides an estimate of LST, to within 0.4 K if the spectral surface emissivity is known, which is better than that given by the currently used split-window algorithms for LST determination. Also the limitations on algorithm accuracy are discussed considering different values of noise equivalent temperature. Finally the authors present the preliminary results obtained using the proposed method from AVHRR data over a semi-arid region-of Northwestern Victoria in Australia provided by CSIRO, and a mountainous region of Northeast of France acquired in the frame of Regio Klimat Projekt.>

Sea surface temperature (SST) algorithms for NOAA AVHRR data can determine SST with rms values of 0.7 K on a global basis. However, this figure is not compatible with the high accuracy of 0.3 K required by climate studies. Biases in the SST product, arising when the factors that increase the optical path-length (absorbents concentration in the atmosphere or viewing angles) are large, cause problems in the use of the split-window formulation for climate monitoring. The reason is that the split-window coefficients currently used are not adequate to cover for all the atmospheric variability. To show this, simulations of channels 4 and 5 of AVHRR/2 of NOAA-11 using a radiative transfer model have been made. The range of atmospheric conditions and surface temperatures introduced in the simulation covers the variability of these parameters on a worldwide scale. From these data, the authors present new split-window coefficients that take into account the atmospheric variability through the ratio of the channel transmittances, or else through the total water vapor content along the path. They also show, using simulated and actual data, that the proposed split-window algorithm has a real global character and represents an improvement over the conventional algorithms.>