Footprint issues in scintillometry over heterogeneous landscapes

Scintillometry is widely recognized as a potential tool for obtaining spatially aggregated sensible heat fluxes at regional scales. Although many investigations have been made over contrasting component surfaces, few aggregation schemes consider footprint contributions. In this paper, an approach is presented to infer average sensible heat flux over a very heterogeneous landscape by using a large aperture scintillometer. The methodology is demonstrated on simulated data and tested on a time series of measurements obtained during the SPARC2004 experiment in Barrax, Spain. Results show that the two-dimensional footprint approach yields more accurate results of aggregated sensible heat flux than traditional methods

miRNAs in serum exosomes for differential diagnosis of brain metastases

Circulating miRNAs are increasingly studied and proposed as tumor markers with the aim of investigating their role in monitoring the response to therapy as well as the natural evolution of primary or secondary brain tumors. This study aimed to evaluate the modulation of the expression of three miRNAs, miR-21, miR-222 and miR-124-3p, in the serum exosomes of patients with high-grade gliomas (HGGs) and brain metastases (BMs) to verify their usefulness in the differential diagnosis of brain masses; then, it focused on their variations following the surgical and/or radiosurgical treatment of the BMs. A total of 105 patients with BMs from primary lung or breast cancer, or melanoma underwent neurosurgery or radiosurgery treatment, and 91 patients with HGGs were enrolled, along with 30 healthy controls. A significant increase in miR-21 expression in serum exosomes was observed in both HGGs and BMs compared with healthy controls; on the other hand, miR-124-3p was significantly decreased in BMs, and it was increased in HGGs. After the surgical or radiosurgical treatment of patients with BMs, a significant reduction in miR-21 was noted with both types of treatments. This study identified a signature of exosomal miRNAs that could be useful as a noninvasive complementary analysis both in the differential diagnosis of BMs from glial tumors and in providing information on tumor evolution over time

Observation and assessment of model retrievals of surface exchange components over a row canopy using directional thermal data

Land surface temperature is an essential climate variable that can serve as a proxy for detecting water deficiencies in croplands and wooded areas. Its measurement can however be influenced by anisotropic properties of surface targets leading to occurrence of directional effects on the signal. This may lead to an incorrect interpretation of thermal measurements. In this study, we perform model assessments and check the influence of thermal radiation directionality using data over a vineyard. To derive the overall directional surface temperatures, elemental values measured by individual cameras were aggregated according to the respective cover fractions/weights in viewing direction. Aggregated temperatures from the turbid model were compared to corresponding temperatures simulated by the 3D DART radiative transfer model. The reconstructed temperatures were then used in surface-energy-balance (SEB) simulations to assess the impact of the Sun-target-sensor geometry on retrievals. Here, the pseudo-isotropic Soil-Plant-Atmosphere-Remote-Sensing-of-Evapotranspiration (SPARSE) dual-source model together with the non-isotropic version (SPARSE4), were used. Both schemes were able to retrieve overall fluxes satisfactorily, confirming a previous study. However, the sensitivity (of flux and component temperature estimates) of the schemes to viewing direction was tested for the first time using reconstructed sets of directional thermal data to force the models. Degradation (relative to nadir) in flux retrieval cross-row was observed, with better consistency along rows. Overall, it was nevertheless shown that SPARSE4 is less influenced by the viewing direction of the temperature than SPARSE, particularly for strongly off-nadir viewing. Some directional/asymmetrical artefacts are however not well reproduced by the simple Radiative Transfer Methods (RTM), which can then manifest in and influence the subsequent thermal-infrared-driven SEB modelling.This work was supported by the ALTOS project (PRIMA 2018 - Section 2), with grants provided by ANR via the agreement n°ANR-18-PRIM-0011-02 as well as the CNES/TOSCA program for the TRISHNA project. First author acknowledges the financial support of his PhD from CNES and Région Occitanie. The field experiments were carried out in the context of the HiLiaise and ESA WineEO projects. Joan Boldu (proprietor) and David Tous (SafSampling) are also acknowledged for allowing/providing access to the site and other site related data. Nicolas Lauret’s help with preparation of the DART mock-ups is appreciated.info:eu-repo/semantics/publishedVersio

The SPARSE model for the prediction of water stress and evapotranspiration components from thermal infra-red data and its evaluation over irrigated and rainfed wheat

Evapotranspiration is an important component of the water cycle, especially in semi-arid lands. A way to quantify the spatial distribution of evapotranspiration and water stress from remote-sensing data is to exploit the available surface temperature as a signature of the surface energy balance. Remotely sensed energy balance models enable one to estimate stress levels and, in turn, the water status of continental surfaces. Dual-source models are particularly useful since they allow derivation of a rough estimate of the water stress of the vegetation instead of that of a soil–vegetation composite. They either assume that the soil and the vegetation interact almost independently with the atmosphere (patch approach corresponding to a parallel resistance scheme) or are tightly coupled (layer approach corresponding to a series resistance scheme). The water status of both sources is solved simultaneously from a single surface temperature observation based on a realistic underlying assumption which states that, in most cases, the vegetation is unstressed, and that if the vegetation is stressed, evaporation is negligible. In the latter case, if the vegetation stress is not properly accounted for, the resulting evaporation will decrease to unrealistic levels (negative fluxes) in order to maintain the same total surface temperature. This work assesses the retrieval performances of total and component evapotranspiration as well as surface and plant water stress levels by (1) proposing a new dual-source model named Soil Plant Atmosphere and Remote Sensing Evapotranspiration (SPARSE) in two versions (parallel and series resistance networks) based on the TSEB (Two-Source Energy Balance model, Norman et al., 1995) model rationale as well as state-of-the-art formulations of turbulent and radiative exchange, (2) challenging the limits of the underlying hypothesis for those two versions through a synthetic retrieval test and (3) testing the water stress retrievals (vegetation water stress and moisture-limited soil evaporation) against in situ data over contrasted test sites (irrigated and rainfed wheat). We demonstrated with those two data sets that the SPARSE series model is more robust to component stress retrieval for this cover type, that its performance increases by using bounding relationships based on potential conditions (root mean square error lowered by up to 11 W m−2 from values of the order of 50–80 W m−2), and that soil evaporation retrieval is generally consistent with an independent estimate from observed soil moisture evolution

Thermal remote sensing from Airborne Hyperspectral Scanner data in the framework of the SPARC and SEN2FLEX projects: an overview

The AHS (Airborne Hyperspectral Scanner) instrument has 80 spectral bands covering the visible and near infrared (VNIR), short wave infrared (SWIR), mid infrared (MIR) and thermal infrared (TIR) spectral range. The instrument is operated by Instituto Nacional de Técnica Aerospacial (INTA), and it has been involved in several field campaigns since 2004. <br><br> This paper presents an overview of the work performed with the AHS thermal imagery provided in the framework of the SPARC and SEN2FLEX campaigns, carried out respectively in 2004 and 2005 over an agricultural area in Spain. The data collected in both campaigns allowed for the first time the development and testing of algorithms for land surface temperature and emissivity retrieval as well as the estimation of evapotranspiration from AHS data. Errors were found to be around 1.5 K for land surface temperature and 1 mm/day for evapotranspiration

Evapotranspiration and evaporation/transpiration partitioning with dual source energy balance models in agricultural lands

EvapoTranspiration (ET) is an important component of the water cycle, especially in semi-arid lands. Its quantification is crucial for a sustainable management of scarce water resources. A way to quantify ET is to exploit the available surface temperature data from remote sensing as a signature of the surface energy balance, including the latent heat flux. Remotely sensed energy balance models enable to estimate stress levels and, in turn, the water status of most continental surfaces. The evaporation and transpiration components of ET are also just as important in agricultural water management and ecosystem health monitoring. Single temperatures can be used with dual source energy balance models but rely on specific assumptions on raw levels of plant water stress to get both components out of a single source of information. Additional information from remote sensing data are thus required, either something specifically related to evaporation (such as surface water content) or transpiration (such as PRI or fluorescence). This works evaluates the SPARSE dual source energy balance model ability to compute not only total ET, but also water stress and transpiration/evaporation components. First, the theoretical limits of the ET component retrieval are assessed through a simulation experiment using both retrieval and prescribed modes of SPARSE with the sole surface temperature. A similar work is performed with an additional constraint, the topsoil surface soil moisture level, showing the significant improvement on the retrieval. Then, a flux dataset acquired over rainfed wheat is used to check the robustness of both stress levels and ET retrievals. In particular, retrieval of the evaporation and transpiration components is assessed in both conditions (forcing by the sole temperature or the combination of temperature and soil moisture). In our example, there is no significant difference in the performance of the total ET retrieval, since the evaporation rate retrieved from the sole surface temperature is already fairly close to the one we can reconstruct from observed surface soil moisture time series, but current work is underway to test it over other plots.</p