A constellation of high-resolution thermal infrared (TIR) missions is expected to be launched in the upcoming years. Land surface temperature (LST), as a key parameter retrieved from TIR observations, constrains the variations in energy and water exchanges in the surface-atmosphere continuum. The widely used temperature and emissivity separation (TES) algorithm stands as a promising candidate for LST retrieval from these future missions due to the availability of$\ge 3$TIR bands. To explore the possibilities of further refinements of TES, a revisit of the TES algorithm in terms of the error propagation from different sources is necessary. Until now, the respective uncertainties introduced by the three modules in TES (i.e., the normalized emissivity method (NEM), the ratio algorithm (RATIO), and the maximum minimum difference (MMD) module) remain unclear. In addition, the controversy over the performances of TES on gray and nongray bodies is still unresolved. To address these research gaps, a comprehensive simulation analysis was conducted for the ECOsystem and Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) sensor to quantify the independent impact of each error source in TES on LST retrieval accuracy, including: 1) sensor measurement noise; 2) atmospheric correction errors; 3) the NEM and RATIO modules; and 4) the MMD module. The respective responses of gray and nongray bodies to these factors were also compared based on the simulation dataset. Furthermore, the influence of the calibration scale of the minimum emissivity ($\varepsilon _{\min }$)–MMD relationship (i.e., cavity effect within vegetation canopies) was evaluated using the ECOSTRESS observations at 11 vegetated ground sites. The simulation analyses revealed that the error in atmospheric correction is the dominant impact factor significantly affecting the performances of all the other modules in TES, followed by the deviation from the regressed relationship in the MMD module. The measurement noise has minor impacts when it is well-controlled (e.g., NEdT$\le 0.1$K), and uncertainties caused by the NEM and RATIO modules are negligible. The performance discrepancy of TES over gray and nongray bodies is insignificant under low measurement noise, a condition anticipated to be met by the majority of current and future sensors. Regarding the calibration scale, the benefit of cavity effect correction is not evident according to the evaluation using the ground measurements. Based on the analyses, it is recommended that more efforts should be put into refining the atmospheric correction module and improving the fitting of emissivity samples to the$\varepsilon _{\min }$–MMD curve. In contrast, the expected benefits of refining the NEM and RATIO modules appear minimal.

Estimating evapotranspiration (ET) beyond the local or point scale is essential for many water-related studies. By exploiting the relationship between surface biophysical parameters and thermal emission, continuous ET at such larger spatial scales can be obtained. In this study, we applied the EVASPA tool, which provides an ensemble of ET estimates, over southern France. This was done using MODIS data, including Land Surface Temperature, NDVI, and albedo, resulting in 243 ET estimates. Initial evaluations using in-situ flux data yielded reasonable results even when a simple average was used, with a broad absolute and performance range between the member estimates being observed. Additionally, our uncertainty analyses indicated that ensemble-based contextual modelling can provide sufficient spread for enhanced flux simulations. As EVASPA is intended for operational use, this work aims to guide the establishment of an optimal weighting criteria for the members to improve ET estimates.

The European ECOSTRESS Hub (EEH) funded by European Space Agency targets at generating land surface temperature (LST), evapotranspiration (ET) and gross primary productivity (GPP) from the high-resolution ECOSTRESS observations. In Phase 1 (2020-2022), EEH LST obtained using the split-window and temperature and emissivity separation algorithms achieved good accuracy with an overall RMSE around 2 K. Evaluation of three ET estimates using different models, namely the Surface Energy Balance System (SEBS), Two Source Energy Balance (TSEB) parametric models, and the non-parametric Surface Temperature Initiated Closure (STIC) model, indicated that STIC ET had the highest accuracy (RMSE of ~70 W m-2). In Phase 2 (2023-2026), the surface energy balance will be coupled with photosynthesis through canopy-stomatal conductance. Overall, EEH is promising to advance the science of terrestrial ecosystem processes and facilitate the preparation for the future high-resolution thermal missions.

Inversion of the surface-energy-budget (SEB) for evapotranspiration using directional temperatures is susceptible to thermal-radiation-directionality. Here, we apply the SPARSE/SPARSE4 schemes for directionality analyses using ‘synthetic references’ from SCOPE: experiments from sparsely-vegetated/water-stressed to fully-vegetated/sufficiently-watered canopies. The SCOPE simulations were then used to invert/evaluate the aforementioned SEB methods. Since, oblique-versus-nadir-based retrievals should ideally be equivalent, analyses to check the angular retrieval consistency were performed. Within sparsely/densely vegetated surfaces, where anisotropy is usually low due to uniformity, little oblique-nadir retrieval inconsistencies were observed. Relatively larger inconsistencies were however observed at intermediate LAIs. With regard to other characteristics, the incoming-radiation (particularly around noon) appeared to be the main driver of the observed inconsistencies, with wind speed and vegetation water status also contributing to some of the mismatches. Influences from the soil water status were mostly in sparsely-vegetated surface experiments. SPARSE4, which discriminates illuminated from shaded elements, was nonetheless observed to reduce the retrieval inconsistencies in most of the scenarios (especially with increasing view and canopy-cover).

In preparation of the micro-bolometer-based MIcro Satellite for Thermal Infrared GRound surface Imaging (MISTIGRI) mission, we study the error budget of the Temperature-Emissivity Separation (TES) method using several spectral configurations that differ in channel numbers, locations, and widths. The error budget quantifies the contribution of 1) the TES underlying assumption about emissivity spectral contrast, 2) the errors on atmospheric corrections, and 3) the instrumental noise. When dealing with atmospheric corrections, we consider errors in atmospheric temperature, water vapor content, and concentrations of CO2and O3. To that end, we design an end-to-end simulator of MISTIGRI measurements in order to simulate the radiative and biophysical quantities involved in the data processing. We conduct numerous simulations over a wide range of realistic setups that include cavity effect, i.e., radiance trapping within vegetation canopy. In the case of micro-bolometer-based sensing, the current study highlights that atmospheric and instrumental noises have similar impacts on the TES retrievals, with resulting errors twice as large as those due to the TES intrinsic assumption about spectral contrast, where the latter contributes to the TES error budget within the [0.005–0.009] interval for emissivity, and within the [0.3–0.4 K] interval for land surface temperature (LST). Also, we show that retrieval performance of surface temperature is very similar across all considered MISTIGRI spectral configurations, with RMSE variation within 0.2 K. Eventually, our study permits us to select a 4-channels spectral configuration as the most suited for the MISTIGRI instrument, notably because it enables a moderately better capture of the emissivity contrast than a 3-channels one.

We propose an original approach to optimize the Thermal infraRed Imaging Satellite for High-resolution Natural resource Assessment (TRISHNA) instrument spectral configuration for the split-window (SW) method. First, we consider as input of end-to-end simulations an emissivity data set that accounts for cavity effect within vegetation canopy. Second, we propose a bidimensional approach where both locations of TRISHNA SW channels, namely$\lambda _{c}^{\text {TIR3}}$and$\lambda _{c}^{\text {TIR4}}$, can slide within predefined spectral intervals. We report a large sensitivity to channel positions, with variations of root mean square error (RMSE) on retrieved land surface temperature (LST) up to 3 K. Our bidimensional approach shows that this sensitivity is consistent with the underlying assumptions of the SW method. Indeed, two regions are observed in the$(\lambda _{c}^{\text {TIR3}},\,\lambda _{c}^{\text {TIR4}})$space: 1) an unfavorable region corresponding to$\lambda _{c}^{\text {TIR3}}\leq 10.0~\mu \text{m}$, where large RMSE values are ascribed to large differences between emissivities in both SW channels, and 2) a favorable region corresponding to$\lambda _{c}^{\text {TIR3}}\geq 10.3~\mu \text{m}$, where differences between emissivities in both SW channels are small and RMSE values are driven by the differences between atmospheric transmittance in both SW channels. Overall, it is necessary to better account for the difference in surface emissivities between the two SW channels, whereas disregarding the cavity effect within vegetation canopy is not critical. Eventually, our bidimensional approach permits to define an optimal position for$\lambda _{c}^{\text {TIR3}}$at$10.6~\mu \text{m}$, which induces larger robustness to uncertainties on channel positions. By applying our study on two structurally different SW formulations and addressing impacts of uncertainties on land surface emissivity (LSE) and atmospheric water vapor content (AWVC), we show that these results can be generalized to other SW formulations.

For sustainable irrigation water management as well as ecosystem health monitoring, it is important to provide an estimate of evapotranspiration components, i.e. transpiration and soil evaporation. To do so, Thermal InfraRed data can be used with dual-source surface energy balance models, because they solve separate energy budgets for the soil and the vegetation. But those models rely on specific assumptions on raw levels of plant water stress to get both components (evaporation and transpiration) out of a single source of information, namely the surface temperature. Additional information from remote sensing data is thus required. This works evaluates the ability of the SPARSE dual-source energy balance model to compute not only total evapotranspiration, but also water stress and transpiration/evaporation components, using either the sole surface temperature as a remote sensing driver, or a combination of surface temperature and soil moisture level derived from microwave data.

Land surface emissivity is required for deriving surface temperature from thermal infrared radiances. When using single-channel or two-channel thermal infrared sensors, information on emissivity may be derived from spectral reflectance measurements through regression models. In this study, we present relationships derived over bare soils for Landsat 7 - ETM+ sensor. Reflectances in ETM+ channels were obtained from soil spectra (between 0.4 and 13 μm) extracted from the ASTER spectral library and the dataset acquired by Lesaignoux et al. (2013). The best relations were obtained between reflectances in the mid-infrared channels (ETM5 and ETM7) and the thermal infrared channel (ETM6) with correlation coefficients of 0.63 and 0.72 respectively. The relations were mostly generated by the variations of soil reflectances due to changes in soil moisture. Correlations were lower when considering the variations due to soil type.

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.

Evapotranspiration (ET) can be mapped using thermal infrared and spectral reflectance data. Various ET models have been developed but there was no competitive evaluation of them over a large range of situations. Ensemble model averaging is a tool that can be used for deriving ET from multi-model simulations. In this study, we used bayesian model averaging, which consists in weighting each model according to their performances when deriving the ensemble average. It was applied to the monitoring of ET over a saltmarsh scrub area in South France from MODIS data. ET monitoring was improved ( RMSE=0.57 mm d-1) when using a weighted averaging procedure as compared to the performances of a simple average or to the performances of each individual model.

The SAIL-Thermique model was developed to simulate land surface emissivity. It is adapted from the original SAIL model. A specific experiment was set-up over a soybean canopy for evaluating spectral emissivity simulations. Multispectral data were obtained thanks to a CIMEL CE 312-2 radiometer and emissivities calculated using the TES method. Comparison of multispectral emissivity simulations and measurements at various leaf area index levels were comparable in terms of emissivity values and spectral behavior.

Satellite Sentinel-2 offers a global coverage of the Earth at the frequency of a few days with pixel size ranging from 10 to 20 meters. Such spatio-temporal resolution fosters an advanced research in agriculture. Accounting for BRDF (Bidirectional Reflectance Distribution Function) information is required both for target monitoring and surface albedo estimate. BRDF sampling being limited from HR (High Resolution) sensors, the added-value of the BRDF 300m from PROBA-V instrument is assessed. Results are shown for the seasonal cycles 2016 and 2017 using parameterized and detailed radiation transfer models. The validation is carried on for anchor ICOS (Integrated Carbon Observation System) stations located on the French territory.

Correction of the directional effects affecting satellite measurements in thermal infrared (TIR) require simple methods robust enough to be easily integrated into data processing chains. The ability of a simple parametric model based on an analytic formulation of the hot spot to estimate directional TIR anisotropy is tested. For this purpose, the necessary data are provided using the deterministic model SCOPE (Soil Canopy Observations, Photochemistry and Energy fluxes, Van der Tol et al. 2009) as a data generator. SCOPE is first validated against experimental energy fluxes and directional temperatures measured over winter wheat and pine stands. Then, the sensitivity of anisotropy to several parameters (leaf area index, water stress, wind speed and hot spot parameter) is illustrated. Finally the parameters of the simplified model are derived by inverting SCOPE simulated data. The comparison of directional anisotropy for zenith viewing angles lower than 35° reveals absolute error lower than 0.3K.

The impact of using non-coincident radiosoundings to remove atmosphere effect from thermal radiances is analyzed here. We considered 27 Landsat-7 ETM+ images acquired over a Mediterranean agricultural region, benefiting from nearby radiosoundings launched almost 2 hours later, and from the availability of a network of ground stations deployed over different types of ecosystems. We observed that, in the conditions of our images, surface temperature estimates slightly improved when considering one atmospheric profile interpolated to our particular date, time and location, in comparison with the use of non-coincident radiosoundings. However, it may imply an error up to ±2.5 K for brightness temperatures (in particular for very high temperatures and during summer when the atmosphere was warmer and the vapor pressure was higher), leading to important errors in the derivation of surface energy fluxes. The characterization of the lowest atmosphere layer appeared to be essential to improve the estimates of brightness temperatures.

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.

Among MODIS products (freely available to the scientific community from 2001), albedo data (MCD43B3) are 16 days composites at 1km spatial resolution, widely used for various applications in climate models, but which still remains difficult to validate. The objective of this study is to propose a method to validate these products with high spatial and temporal resolution data. 31 FORMOSAT-2 images acquired over a small region in the South-Eastern France at 8m for spatial resolution were aggregated at MODIS resolution using a Point Spread Function. The correlation coefficient resulting from comparisons between albedo MODIS and the 1-km FORMOSAT-2 albedos varied from 0.93 to 0.98, which show reasonably accurate results for this study area.

The method to derive surface temperature from top of canopy brightness temperature developed by Olioso (1995b) [20] is tested over the Avignon-Crau-Camargue area (France) using Landsat-7 ETM+ images. The difference between surface temperature and brightness temperature depends on surface emissivity, incident atmospheric radiation and the temperature itself. Differences up to 2 K were obtained for a surface emissivity of 0.97. It can increase up to 7 K when surface emissivity was 0.91. The surface temperature derived from Landsat data were in agreement with the ground measurements when using local calibration of the surface emissivity derivation method and a modification of the calculation of atmospheric radiation as compared to [20]. The impact of error in emissivity derivation was higher than the impact of errors in deriving atmospheric radiation.

A field platform dedicated to fluorescence measurements (INRA, Avignon, France) was used to monitor the fluorescence emission of a sorghum field during its growing period. The measurements were performed continuously at the canopy level, from seeding to maturity. A passive instrument based on three spectrometers was used to monitor the evolution of fluorescence fluxes and vegetation indexes such as Photochemical Reflectance Index (PRI) and Normalized Difference Vegetation Index (NDVI). Fluorescence fluxes were retrieved from radiances, using the filling-in of the atmospheric oxygen absorption bands, at 687 and 760 nm. In parallel, leaf fluorescence spectra, canopy height, and leaf chlorophyll contents were acquired during the growth. Both PRI and NDVI indexes varied with the development of the sorghum field: we observed that NDVI was more sensitive during the early stage of the growth. However, NDVI saturates before the PRI index. Fluorescence fluxes at 687 nm (Fs687) and 760 nm (Fs760) showed an overall increase: Fs687 increased more rapidly at the beginning of growth but trends to saturate while Fs760 still increase. During the growth, the Fs687/Fs760 ratio at the canopy level is found lower than at leaf level. At canopy level, the ratio decreased when the leaf chlorophyll content increases. A decrease was also observed at leaf level with a lower extend. This more important decrease of the fluorescence ratio at canopy level is attributed to a reabsorption of red fluorescence (Fs687) during its transfer through the canopy layers. In the context of forthcoming large-scale remote sensing application, the modification of the leaf level fluorescence emission by the structure of the canopy observed in this article is one of the major issues that must be addressed to interpret the fluorescence signal.

This letter describes a laboratory experiment where reflectance signatures of three plant species are measured at a leaf level in the 3-15-μm spectral domain. The leaf samples are progressively dried in order to analyze the behavior of their spectral signature according to the variations in their water content. Our first objective aims to underline leaf water content (LWC) impact on the spectral signatures. This work is a necessary step toward further studies dealing with interpretation of multispectral remote sensing data or estimation of water stress and energy budget. The drying process and measurement method are detailed. This letter deals with dry and fresh leaves (as found in literature) and considers intermediate water content levels as well. For intermediate LWC levels, our analysis outlines some important results: The spectral domain may be divided into two parts, namely, 3-5.5 and 5.5-15 μm, each corresponding to different impacts of LWC variation; both sides of a cherry tree leaf have not the same behavior according to the water content amount; and, in the 8-15-μm, the drying process impacts when the LWC becomes lower than a threshold value (around 30%).

The purpose of this paper is the capacity of spectral reflectance, in the full optical domain 0.4 - 12 μm, to retrieve the Soil Moisture Content (SMC) of bare soils. For this, a new empirical soil model was performed from spectra lab measurements, and more precisely from soil's classification, to simulate spectral reflectance for a given SMC, and in inverse way to estimate SMC. In solar and thermal domain, simulated spectra are highly correlated with measured spectra, respectively with 98% and 85%. In inverse way, estimation of SMC from a spectral reflectance and a given soil classes, provides a RMSE of 3.2% in solar and 2.8% in thermal domain.

The purpose of this paper is to analyse the impact of surface soil moisture on spectral reflectance in the optical domain [0.4-15 μm]. This work is based on lab spectral reflectance measurements of many bare soils at different moisture contents. Firstly, a classification of bare soil samples is performed according to their spectral signatures: five classes are then defined. Secondly, the soil moisture content impact on spectral signatures is analysed. In the [0.4-15 μm] domain, measurements exhibit, for all the samples, a decreasing of the reflectance level with an increasing of moisture content. These measurements give information on absorption peaks related to soil mineral components like hydroxyl, carbonate, and quartz. Thus, analysis of our lab measurements indicates that soil moisture impact on spectral reflectance depends of studied spectral domain. These measurements may improve existing data bases, and will be used in a processing chain to estimate the soil moisture content (cases of bare soil and/or sparse vegetation) in the optical domain [0.4-12 μm] by using airborne hyperspectral imaging.

A SVAT calibration methodology based on the optimization of surface brightness temperature has been developed (Coudert et al., 2006a; 2006b, 2007) and validated at field scale in the framework of the Alpilles-ReSeDA ([1]; http://www.avignon.inra.fr/reseda/base/) experiment. This methodology has been extended at regional scale and applied to MSG/Meteosat8 thermal infrared data. MSG/SEVIRI instrument provides the Land Surface Temperature (LST) every 15 mn with a spatial resolution of about 3 km x 5 km over France. A model spatialization is proposed, based on a Geographic Information System in order to estimate the surface temperature at the MSG pixel scale from local SVAT simulation. The estimation of the surface water and energy fluxes and of the soil water content at a finer resolution are provided from the calibration methodology applied at the MSG pixel scale to the the spatialized model. These methods have been applied in the framework of the CITRAM experimental program in order to monitor soil water content and irrigation in agricultural zones. A comparison between methodologies is presented. The validation is done with local soil moisture measurements acquired in different instrumented fields over the region and with high spatial resolution surface temperature estimation from ASTER/TERRA satellite data.

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 objectives of this study were to explore quantitative relationships between remotely sensed signatures and ecosystem variables such as vegetation growth and CO/sub 2/ flux, and to investigate the potential of the synergy between remote sensing and process models. Results showed both thermal and optical remote sensing data could be effectively used for estimating dynamic change of important variables in agricultural ecosystems.

Soil-vegetation-atmosphere transfer models have been developed to simulate mass and energy exchanges between vegetation canopies, the soil, and the atmosphere. They may be used in conjunction with remote sensing data through inversion procedures. In this study, the inversions of two soil-vegetation-atmosphere transfer models are compared on the same data set. Hourly evolutions of stomatal conductance and evapotranspiration are retrieved from the midday measurement of thermal infrared brightness temperature. Seasonal evolution of evapotranspiration and midday stomatal conductance are monitored with a good accuracy with both models. However, the simpler model underestimates evapotranspiration because it does not include a realistic description of hourly evolution of stomatal conductance, and then underestimates morning and afternoon evapotranspiration. The other model gives a better description of hourly evolutions of stomatal conductance and evapotranspiration. This model gives also better estimates of hourly canopy photosynthesis. However, it requires more parameters and computer time than the simpler model, two unfavorable factors for inversion.