The estimation of actual daily evapotranspiration (ET) at field scale from satellite imagery poses a challenge for water management due to the spatio-temporal limitations of the current sensors operating in the thermal infrared. This study introduces a methodology that aims to address these issues by estimating ET and crop coefficients (Kc) once per week. Intermediate days are gap-filled, from these calibrated Kc and reference daily evapotranspiration (ETo) values. Weekly ET estimates were obtained using recent advances in Sentinel-3, Sentinel-2 and Landsat satellite data fusion as inputs in a Two Source Energy Balance (TSEB) model. This research was carried out in a semiarid region in southeastern Spain. An experiment was conducted in a drip-irrigated almond orchard between June 5 and July 25, 2023. The resulting ET was evaluated using data from an eddy-covariance tower. An overall bias of 1.0 mm•day-1and a root mean square error of 1.5 mm•day-1were revealed. These results underscore the feasibility of the proposed method to monitor almond crop evapotranspiration at the field scale on a daily basis while minimizing computational workload.

A precise and spatialized estimation of environmental demand is necessary for agriculture's best use of water resources since, in conjunction with crop coefficient maps (Kc), it enables crop irrigation requirements to be determined. In this way, the National Center for Environmental Prediction (NCEP) global weather prediction system (GFS) enables estimation of the environmental demand or reference evapotranspiration (ETo) based on forecasts of the meteorological variables used in its calculation. Though its comparison with measurements reveals an overestimation mostly because of the spatial resolution of the model, the global model examined in this work exhibits a strong fit with the model supplied by the Spanish State Meteorological Agency (AEMET).

The objective of this work is to evaluate the performance of two classic energy balance models, METRIC (one-source) and STSEB (two-sources), in the operational monitoring of the actual evapotranspiration (ETa) in almond orchards located in the semiarid regions of southeastern Spain through Landsat 8 images. In situ energy flux measurements were available in two experimental sites. This work shows the results of the analysis of a set of Landsat 8 images from the period 2019-2020. On a daily scale, the ETaresults show an average error of less than 1.0 mm d-1in both models, with an underestimation of 0.2 mm d-1for STSEB and an overestimation of 0.5 mm d-1for METRIC. The combined use of Landsat 8 and 9, represents a significant advance in the ability to detect situations of water stress at the field scale, and to quantify it in a distributed manner within an image.

Surface solar irradiance (SSI) is a crucial component of the radiation budget at the surface, which governs water and energy exchanges with the atmosphere. Good estimates of SSI at regional-to-global scales are needed for modeling land surface processes, climate and weather predictions, or management of solar power plants. This article presents the adaptation of the Mesoscale Atmospheric Global Irradiance Code (MAGIC)-Heliosat method used by the Climate Monitoring Satellite Application Facility (CM-SAF) for Meteosat Second Generation Spinning Enhanced Visible and Infrared Imager (MSG/SEVIRI) to the Multifunction Transport Satellite 2 (MTSAT-2)/Imager and Himawari-8/Advanced Himawari Imager (AHI) sensors managed by the Japanese Meteorological Agency. The method allows providing estimates of global horizontal irradiance (GHI) and direct normal irradiance (DNI) over the Asian Pacific coast and Oceania. These estimates were evaluated by comparison to ground data measured at six baseline surface radiation network (BSRN) stations during years 2014 and 2016. The results showed that GHI can be determined with an accuracy of$-5\,\,\text{W}\cdot \text{m}^{-2}$, a precision of 160$\text{W}\cdot \text{m}^{-2}$, and a relative absolute error of 30% in an hourly basis. They improved to an accuracy of$-5\,\,\text{W}\cdot \text{m}^{-2}$($-5\,\,\text{W}\cdot \text{m}^{-2}$), a precision of 70$\text{W}\cdot \text{m}^{-2}$(40$\text{W}\cdot \text{m}^{-2}$), and a relative error of 10% (7%) in daily (monthly) estimates. The results for DNI showed an accuracy of$-45\,\,\text{W}\cdot \text{m}^{-2}$and a precision of 330$\text{W}\cdot \text{m}^{-2}$, which represent a relative absolute error of 38%. These results improved for longer time steps, with an accuracy of +15$\text{W}\cdot \text{m}^{-2}$(+30$\text{W}\cdot \text{m}^{-2}$), a precision of 150$\text{W}\cdot \text{m}^{-2}$(130$\text{W}\cdot \text{m}^{-2}$), and a relative error of 35% (20%) in daily (monthly) estimations.

This work aims to improve the accuracy in Land Surface Temperature (LST) from single-channel thermal sensors by providing spatialized maps of transmittance, upwelling and downwelling atmospheric radiances required in the radiative transfer equation. Two different techniques are introduced for the estimation of pixel-by-pixel atmospheric parameters, focusing on the correction of Landsat Thermal Infrared (TIR) data. First technique is based on the linearization of the atmospheric parameters with the total column water vapor (W), extracted from the MOD05 product, whereas a second technique uses the Single Band Atmospheric Correction (SBAC) tool. Ground-measured values of LST in an agricultural area in central Spain, covering a variety of surface conditions, were used for a local assessment. Very similar estimation errors <;2 K were obtained using both, linearization with W and SBAC techniques applied to a set of 12 Landsat 7 images. W values ranged 0.6-4 cm in this work. Wetter conditions might deteriorate the performance of the linearization technique. A preliminary analysis of the operational spatialization of this pixel-by-pixel atmospheric correction is also included.

The thermal infrared hemispherical downwelling irradiance (HDI) emitted by the atmosphere and surrounding elements contributes through reflection to the signal measured over an observed surface by remote sensing. This irradiance must be estimated in order to obtain accurate values of land-surface temperature (LST). There are some fast methods to measure the HDI with a single measurement pointing to the sky at a specified viewing direction, but these methods require completely cloud-free or cloudy skies, and they do not account for the radiative contribution of surrounding elements. Another method is the use of a diffuse reflectance panel (usually, a rough gold-coated surface) with near-Lambertian behavior. This method considers the radiative contribution of surrounding elements and can be used under any sky condition. A third possibility is the use of atmospheric profiles and a radiative transfer code (RTC) in order to simulate the atmospheric signal and to calculate the HDI by integration. This study compares the HDI estimations with these approaches, using measurements made on four different days with a completely clear sky and two days with a partially cloudy sky. The measurements were made with a four-channel CIMEL Electronique radiometer working in the 8-14-μm spectral range. The HDI was also estimated by means of National Centers for Environmental Prediction atmospheric profiles introduced in the MODTRAN RTC. Additionally, the measurements were made at two different places with very different environments to quantify the effect of the contributing surroundings. Results showed that, for a clear-sky day with a minimal contribution of the surroundings, all methods differed from each other between 5% and 11%, depending on the spectral range, and any of them could be used to estimate HDI in these conditions. However, in the case of making surface measurements in an area with significant surrounding elements (buildings, trees, etc.), HDI values retrieved from the panel present an increase of +3 W·m-2·μm-1compared with the other methods; this increase, if ignored, implies to make an error in LST ranging from +0.5°C to +1.5°C, depending on the spectral range and on surface emissivity and temperature. Comparison under heterogeneous skies with changing cloud coverage showed also large differences between the use of panel and the other methods, reaching a maximum difference of +4.6 W·m-2·μm-1, which implies to make an error on LST of +2.2°C. In these cases, the use of the diffuse reflectance panel is proposed, since it is the unique way to capture the contribution of the surroundings and also to adequately measure HDI for sky changing conditions.

Ground-based measurements of land-surface temperature (LST) performed in a homogeneous site of rice crops close to Valencia, Spain, were used for the validation of the calibration and the atmospheric correction of the Landsat-7 Enhanced Thematic Mapper Plus (ETM+) thermal band. Atmospheric radiosondes were launched at the test site around the satellite overpasses. Field-emissivity measurements of the near-full-vegetated rice crops were also performed. Seven concurrences of Landsat-7 and ground data were obtained in July and August 2004-2007. The ground measurements were used with the MODTRAN-4 radiative transfer model to simulate at-sensor radiances and brightness temperatures, which were compared with the calibrated ETM+ observations over the test site. For the cases analyzed here, the differences between the simulated and ETM+ brightness temperatures show an average bias of 0.6 K and a rootmean-square difference (rmsd) of ±0.8 K. The ground-based measurements were also used for the validation of LSTs derived from ETM+ at-sensor radiances with atmospheric correction calculated from the following: 1) the local-radiosonde profiles and 2) the operational atmospheric-correction tool available at http://atmcorr.gsfc.nasa.gov. For the first case, the differences between the ground and satellite LSTs ranged from -0.6 to 1.4 K, with a mean bias of 0.7 K and an rmsd = ±1.0 K. For the second case, the differences ranged between -1.8 and 1.3 K, with a zero average bias and an rmsd = ±1.1 K. Although the validation cases are few and limited to one land cover at morning and summer, results show the good LST accuracy that can be achieved with ETM+ thermal data.

Thermal infrared (TIR) emissivities of soils with different textures were measured for several soil moisture (SM) contents under controlled conditions using the Box method and a high-precision multichannel TIR radiometer. The results showed a common increase of emissivity with SM at water contents lower than the field capacity. However, this dependence is negligible for higher water contents. The highest emissivity variations were observed in sandy soils, particularly in the 8-9-μm range due to water adhering to soil grains and decreasing the reflectance in the 8-9-μm quartz doublet region. Thus, in order to model the emissivity dependence on soil water content, different approaches were studied according to the a priori soil information. Soil-specific relationships were provided for each soil texture and different spectral bands between 8 and 13 μm, with determination coefficients up to 0.99, and standard estimation errors in emissivity lower than ± 0.014. When considering a general relationship for all soil types, standard estimation errors up to ±0.03 were obtained. However, if other soil properties (i.e., organic matter, quartz, and carbonate contents) were considered, along with soil water content, the general relationship predicted TIR emissivities with a standard estimation error of less than ±0.008. Furthermore, the study showed the possibility of retrieving SM from TIR emissivities with a standard estimation error of about ±0.08 m3. m-3.

Land Surface Temperature (LST) monthly maps are necessary in climatic studies and remote sensing is a key tool used to obtain these maps. For this reason, we used the LST product of AATSR on board Envisat. This product uses a split-window algorithm which depends on vertical column water vapour content (W) and viewing angle. The algorithm proposed in Galve et al. [1] was also used, which is based on the Coll and Caselles [2] split-window model and depends explicitly on the emissivity and W. This algorithm was tested with concurrent ground measurements in the Valencia validation site, yielding an error of + 0.5 K. With both algorithms we perform LST maps over Europe with a spatial resolution of 0.05ousing the emissivities given in the monthly average of the MODIS MOD11_8D product. Regarding W, we obtain it through the NCEP global Tropospheric Analysis product. In order to evaluate the quality of the maps we resampled these to a 1° pixel size for comparison with the LST from the NCEP Global Tropospheric Analyses product. The most similar algorithm to NCEP LST is Galve et al. [1] with a difference lower than ± 1.2 K. Regarding the maps obtained from the AATSR LST product, the difference was close to ± 1.7 K.

Emissivity is one of the main factors to take into account when studying processes that take place in the Earth surface by using radiance measurements in the thermal infrared, such as surface energy balance, land surface temperature (LST) retrieval, classification of different types of surface, etc. For this reason it is necessary to study the factors that can influence the emissivity. The present work evaluates one of these factors: the variation of the emissivity with the zenithal observation angle over bare soils, specifically the variation of the relative emissivity calculated from measurements of radiances, almost simultaneous, at nadir (0o) and at a certain angle (¿). The measurements of radiance were taken with the aid of a straightforward goniometric system that allows the measurement from nadir observation to 70o(at 10oincrements) for a fixed azimuthal angle. The results show a significant decrease of emissivity with observation angle, which is especially accentuated in the case of sandy soils with high quartz content.

The land surface temperature (LST) product of the Advanced Along-Track Scanning Radiometer (AATSR) was validated with ground measurements at the following two thermally homogeneous sites: Lake Tahoe, CA/NV, USA, and a large rice field close to Valencia, Spain. The AATSR LST product is based on the split-window technique using the 11- and 12- mum channels. The algorithm coefficients are provided for 13 different land-cover classes plus one lake class (index i). Coefficients are weighted by the vegetation-cover fraction (f). In the operational implementation of the algorithm, i and f are assigned from a global classification and monthly fractional vegetation-cover maps with spatial resolutions of 0.5deg times 0.5deg. Since the validation sites are smaller than this, they are misclassified in the LST product and treated incorrectly despite the fact that the higher resolution AATSR data easily resolve the sites. Due to this problem, the coefficients for the correct cover types were manually applied to the AATSR standard brightness temperature at sensor product to obtain the LST for the sites assuming they had been correctly classified. The comparison between the ground-measured and the AATSR-derived LSTs showed an excellent agreement for both sites, with nearly zero average biases and standard deviations les 0.5degC. In order to produce accurate and precise estimates of LST, it is necessary that the land-cover classification is revised and provided at the same resolution as the AATSR data, i.e., 1 km rather than the 0.5deg resolution auxiliary data currently used in the LST product.

In this study, two different methods for retrieving the Land Surface Temperature (LST) from Terra/Moderate Resolution Imaging Spectroradiometer (MODIS) and Envisat/Advanced Along Track Scanning Radiometer (AATSR) data are compared against a database of ground measured LSTs. These are the split-window (SW) and the single-channel (SC) methods. The SW method expresses LST as a combination of the brightness temperatures in the 11 iquestm and 12 iquestm channels with coefficients that can have local or global validity, depending on the way they are obtained. SC methods are based on the atmospheric radiative transfer equation. To solve this equation, convenient atmospheric temperature and water vapor profiles are required as inputs of a radiative transfer model. In this work we used three different sources of atmospheric profiles: local radiosoundings (LR) reanalysis model output (RM), and satellite-based profiles (SB). Results show that the SW method produces more accurate LST retrievals (plusmn0.5 K) than the SC method with the three profile sources used, of which the RM source yielded the best results (plusmn1.0 K).

A database of global, cloud-free, and atmospheric radiosounding profiles was compiled with the aim of simulating radiometric measurements from satellite-borne sensors in the thermal infrared. The objective of the simulated data is to generate split-window (SW) and dual-angle (DA) algorithms for the retrieval of land surface temperature (LST) from Terra/Moderate Resolution Imaging Spectroradiometer (MODIS) and Envisat/Advanced Along Track Scanning Radiometer (AATSR) data. The database contains 382 radiosounding profiles acquired over land, with nearly uniform distribution of precipitable water between 0.02 and 5.5 cm. Radiative transfer calculations were performed with the MODTRAN 4 code for six viewing angles between 0deg and 60deg. The resulting radiance spectra were convoluted with the response filter functions of MODIS bands 31 and 32 and AATSR channels at 11 and 12 mum. By using the simulation database, the SW algorithms adapted for MODIS and AATSR data and the DA algorithms for AATSR data were developed. Both types of algorithms are quadratic in the brightness temperature difference and depend explicitly on the land surface emissivity. The SW and DA algorithms were validated with actual ground measurements of LST collected concurrently to MODIS and AATSR observations in a site located close to the city of Valencia, Spain, in a large, flat, and thermally homogeneous area of rice crops. The results obtained have no bias and a standard deviation around plusmn0.5 K for the SW algorithms at nadir for both sensors. The SW algorithm used in the forward view results in a bias of 0.6 K and a standard deviation of plusmn0.8 K. The worst results are obtained in the other algorithms with a bias close to -1.0 K and a standard deviation close to plusmn1.1 K in the case of the DA algorithms.

A database of global, cloud-free, atmospheric radiosounding profiles was compiled with the aim of simulating radiometric measurements from satellite-borne sensors in the thermal infrared. The objective of the simulation is to generate split-window (SW) and dual-angle (DA) algorithms for the retrieval of land surface temperature (LST) from Terra/Moderate Resolution Imaging Spectroradiometer (MODIS) and Envisat/advanced along track scanning radiometer (AATSR) data. The database contains 382 radiosonde profiles acquired over land, with nearly-uniform distribution of precipitable water between 0 and 5.5 cm. Radiative transfer calculations were performed with the MODTRAN 4 code. Different viewing angles were considered in the simulation, taking into account the features of each sensor. The viewing capability of AATSR, with near simultaneous observations first at a forward angle (55deg from nadir) and then close to nadir, allows the implementation of DA algorithms. Using the simulation database, SW algorithms adapted for MODIS and AATSR data, and DA algorithms for AATSR data were developed. Both types of algorithms are quadratic in the brightness temperature difference, and depend explicitly on the land surface emissivity. A sensitivity analysis of all algorithms was made to obtain estimation of the algorithm errors. Furthermore the SW and DA algorithms developed from the simulation database were validated with actual ground measurements of LST collected, concurrently to MODIS and AATSR observations, in a site located close to the city of Valencia, Spain, in a large, flat and thermally homogeneous area of rice crops, where field campaigns were held during the summers of 2002-2006. Operational LST algorithms of each sensor were also validated in order to compare with the algorithms generated.