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.

In this study, we perform land use classification (LUC) using sequences of multispectral Sentinel-2 images taken over multiple years. It evaluates the convolutional neural network (CNN) applied in successive years after the training one because in many cases the availability of field data for updating the model involves a notable delay and the identification of irrigated crops must be near real-time to be properly managed. The strategy consists of interpolating data to the same dates every year with a format of 2D-fingerprints to make their patterns explicit and aligned to use a CNN to capture them. It reaches a 76%-80% accuracy in classifying pixels among 11 classes across different years being trained only using one year. The main interest is focused on identifying irrigated fields, reaching over 90% F1-Score in most classes. Consequently, this model is useful for monitoring crops and aids experts in the generation of data for decision-making.

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.

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 use of multispectral sensors onboard of unmanned vehicles is nowadays a common practice in precision agriculture. Their radiometric behavior is an important issue to ensure comparable information along space and time. A calibration of a Sequoia® camera is conducted in terms of reflectance and NDVI, taking benefit from a wheat cover field work. The output reflectance values in the green and red channel show linear behavior in the range of 0.00 to 0.45, with a saturation over this value. The red edge and NIR channels show linear behavior in the whole reflectance range, with better performance in the NIR than in the red-edge channel. The NDVI has a good agreement with field measurements, near the 1:1 line, showing the highest variability for the lower values due to heterogeneity of soil brightness linked to variations in soil moisture.

Land surface temperature (LST) is a key magnitude in many exchange processes between the surface and the atmosphere. LST measurement from satellites provides an efficient way to monitor its change across wide areas on Earth, an essential issue being LST validation using in situ measurements to assess its accuracy and precision. Presently, there are two widely used methodologies: temperature measurements made by wideband radiometers observing the land surface with a given viewing angle and a limited field of view, and measurements provided by total radiation pyrgeometers with a nearly hemispheric field of view. Although both measurements are correlated, they are not equivalent; thus, it is relevant to establish their differences when they are used as ground reference for the thermal infrared sensors. Both methodologies have been compared over a homogeneous grass and a heterogeneous vineyard under different atmospheric conditions. The results show good correspondence in the first case, with differences +0.3±1.0 K, while in the second one the discrepancies can be significant (-1.7±1.1 K) affecting the reliability on the results of the validation of satellite-borne sensors.

A field campaign was carried out to evaluate the Soil Moisture (SM) MIR_SMUDP2 product (v5.51) generated from the data of the Microwave Imaging Radiometer using Aperture Synthesis (MIRAS) aboard the Soil Moisture and Ocean Salinity (SMOS) mission. The study area was the Pampean Region of Argentina, which was selected because it is a vast area of flatlands containing quite homogeneous rain-fed croplands, which are considered SMOS nominal land uses and hardly affected by radio-frequency interference contamination. Transects of ground handheld SM measurements were performed using ThetaProbe ML2x probes within four Icosahedral Snyder Equal Area Earth (ISEA) grid nodes, where permanent SM stations are located. The campaign results showed a negative bias of -0.02 m3m-3between concurrent SMOS data and ground SM measurements, which means a slight SMOS underestimation, and a standard deviation of ±0.06 m3m-3. Additionally, a good correlation was obtained between the handheld SM measurements taken during the campaign and the permanent SM station data within a node, which pointed out that the station data could be used as reference data to evaluate the SMOS product over a longer temporal period. SMOS-retrieved data were also compared with station mean SM values from 2012 to 2014. A general SMOS underestimation of -0.05 m3m-3was observed, with a standard deviation of ±0.04 m3m-3, which yields an uncertainty of ±0.07 m3m-3for the SMOS product. Although the random error meets the SMOS mission's goal of ±0.04 m3m-3, the product overall uncertainty is higher than that due to the significant dry bias, which is also found in other regions of the world.

An accurate estimate of land surface temperature, which is a key parameter in surface energy balance models, requires knowledge of surface emissivity. Emissivity dependence on soil water content has been already reported and modeled under controlled conditions at the laboratory. This paper completes and extends that previous work by providing emissivity measurements under field conditions without elimination of impurities, local heterogeneities, or soil cracks appearing in the drying process. The multispectral radiometer CE312-2, with five narrow bands and a broad band in the 8-13-μm range, was used, and surface emissivity values were determined through a temperature-emissivity separation algorithm. A bare soil plot of 10 ×17 m2was selected for this study in the framework of a camelina 2010 experiment. This experiment was carried out during March and April 2010 at The University of Arizona Maricopa Agricultural Center in central Arizona, USA. The soil plot was flood irrigated every two to three days and left to dry. Field emissivity measurements were collected under cloud-free skies, around noon, for different values of soil water content. Soil samples were collected to estimate the soil moisture (SM) using the gravimetric method. An overall increase of emissivity with SM was obtained in all channels. However, when wetted soils subsequently dried, the final minimum emissivity was greater than the initial minimum emissivity. This hysteresis could be due to cavity effects produced by soil cracks not originally present. Thus, the deterioration of soil surface tends to reduce the emissivity spectral contrast. Soil-specific and general relationships obtained by Mira et al. were tested and compared with the field measurements. Field emissivities agree within 2% with the modeled values for all bands under noncracked surface conditions, whereas differences reach 5% in the 8-9- μm range when cracks are present.

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.

In this work, we present a micro-meteorological approach for estimating surface energy fluxes that can be operationally used together with satellite images to monitor surface energy fluxes at a regional scale. In particular we will focus on the retrieval of daily evapotranspiration. The feasibility of the model is explored at a local scale using data collected over a maize crop in Beltsville, Maryland, USA, and a boreal forest in Sodankyla, Finland. Comparison of the results with ground measurements shows errors between plusmn15 and plusmn50 W m-2for the retrieval of net radiation, soil heat flux, and sensible and latent heat fluxes in both sites. A methodology to apply the model to Landsat imagery is presented. Finally, we show its application to three different Landsat scenes covering the whole Basilicata region (Southern Italy). Maps of daily evapotranspiration are generated. Results are compared with ground measurements. Accuracy close to plusmn30 W m-2is obtained.

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.

A new formulation for the canonical basis multiplication in the finite fields GF(2/sup m/) based on the use of a triangular basis and on the decomposition of a product matrix is presented. From this algorithm, a new method for multiplication (named transpositional) applicable to general irreducible polynomials is deduced. The transpositional method is based on the computation of 1-cycles and 2-cycles given by a permutation defined by the coordinate of the product to be computed and by the cardinality of the field GF(2/sup m/). The obtained cycles define groups corresponding to subexpressions that can be shared among the different product coordinates. This new multiplication method is applied to five types of irreducible trinomials. These polynomials have been widely studied due to their low-complexity implementations. The theoretical complexity analysis of the corresponding bit-parallel multipliers shows that the space complexities of our multipliers match the best results known to date for similar canonical GF(2/sup m/) multipliers. The most important new result is the reduction, in two of the five studied trinomials, of the time complexity with respect to the best known results.