Remotely sensed land-surface energy fluxes at sub-field scale in heterogeneous agricultural landscape and coniferous plantation

In this study we evaluate a methodology for disaggregating land surface energy fluxes estimated with the Two-Source Energy Balance (TSEB)-based Dual-Temperature Difference (DTD) model which uses day and night polar orbiting satellite observations of land surface temperature (LST) as a remotely sensed input. The DTD model is run with MODIS input data at a spatial resolution of around 1 km while the disaggregation uses Landsat observations to produce fluxes at a nominal spatial resolution of 30 m. The higher-resolution modelled fluxes can be directly compared against eddy covariance (EC)-based flux tower measurements to ensure more accurate model validation and also provide a better visualization of the fluxes' spatial patterns in heterogeneous areas allowing for development of, for example, more efficient irrigation practices. The disaggregation technique is evaluated in an area covered by the Danish Hydrological Observatory (HOBE), in the west of the Jutland peninsula, and the modelled fluxes are compared against measurements from two flux towers: the first one in a heterogeneous agricultural landscape and the second one in a homogeneous conifer plantation. The results indicate that the coarse-resolution DTD fluxes disaggregated at Landsat scale have greatly improved accuracy as compared to high-resolution fluxes derived directly with Landsat data without the disaggregation. At the agricultural site the disaggregated fluxes display small bias and very high correlation (r ≈ 0.95) with EC-based measurements, while at the plantation site the results are encouraging but still with significant errors. In addition, we introduce a~modification to the DTD model by replacing the "parallel" configuration of the resistances to sensible heat exchange by the "series" configuration. The latter takes into account the in-canopy air temperature and substantially improves the accuracy of the DTD model

Press hardening of alternative materials: conventional high- strength steels

he increase in strength of new high strength steels(HHS) and advanced high strength steels (AHHS) has led toforming issues, such as high springback, low formability, increase of forming forces and tool wear. These problems increase thecosts of manufacturing and maintainingstamping tools in the automotive industry. The aim of this research was to analyse the advantages of applyingthe press-hardening process toconventional HSS and AHSS steel to increase their formability and therefore reduce thenumber of forming steps and productioncosts. With this aimin mind, the press-hardening process was used to manufacturean industrialcomponent using four different automotive steelgrades: dual phase (DP),complex phase (CP), transformation-induced plasticity (TRIP) and martensitic (MS) grade.Springback measurements werecarried out, together with ananalysis of the obtained final mechanical properties and microstructures. The results showed that the formability of all thematerials increased. The mechanical properties of theCP800and TRIP700 materials were maintained or even improved, whereas those of the MS1200 and HCT980Xmaterials were significantly reduced. Weconclude thatpress hardening is a suitable manufacturing processforCP800 and TRIP700components

Seasonal variation in grass water content estimated from proximal sensing and MODIS time series in a Mediterranean Fluxnet site

This study evaluates three different metrics of water content of an herbaceous cover in a Mediterranean wooded grassland (dehesa) ecosystem. Fuel moisture content (FMC), equivalent water thickness (EWT) and canopy water content (CWC) were estimated from proximal sensing and MODIS satellite imagery. Dry matter (Dm) and leaf area index (LAI) connect the three metrics and were also analyzed. Metrics were derived from field sampling of grass cover within a 500 m MODIS pixel. Hand-held hyperspectral measurements and MODIS images were simultaneously acquired and predictive empirical models were parametrized. Two methods of estimating FMC and CWC using different field protocols were tested in order to evaluate the consistency of the metrics and the relationships with the predictive empirical models. In addition, radiative transfer models (RTM) were used to produce estimates of CWC and FMC, which were compared with the empirical ones. <br><br> Results revealed that, for all metrics spatial variability was significantly lower than temporal. Thus we concluded that experimental design should prioritize sampling frequency rather than sample size. Dm variability was high which demonstrates that a constant annual Dm value should not be used to predict EWT from FMC as other previous studies did. Relative root mean square error (RRMSE) evaluated the performance of nine spectral indices to compute each variable. Visible Atmospherically Resistant Index (VARI) provided the lowest explicative power in all cases. For proximal sensing, Global Environment Monitoring Index (GEMI) showed higher statistical relationships both for FMC (RRMSE = 34.5 %) and EWT (RRMSE = 27.43 %) while Normalized Difference Infrared Index (NDII) and Global Vegetation Monitoring Index (GVMI) for CWC (RRMSE = 30.27 % and 31.58 % respectively). When MODIS data were used, results showed an increase in <i>R</i>² and Enhanced Vegetation Index (EVI) as the best predictor for FMC (RRMSE = 33.81 %) and CWC (RRMSE = 27.56 %) and GEMI for EWT (RRMSE = 24.6 %). Differences in the viewing geometry of the platforms can explain these differences as the portion of vegetation observed by MODIS is larger than when using proximal sensing including the spectral response from scattered trees and its shadows. CWC was better predicted than the other two water content metrics, probably because CWC depends on LAI, that shows a notable seasonal variation in this ecosystem. Strong statistical relationship was found between empirical models using indices sensible to chlorophyll activity (NDVI or EVI which are not directly related to water content) due to the close relationship between LAI, water content and chlorophyll activity in grassland cover, which is not true for other types of vegetation such as forest or shrubs. The empirical methods tested outperformed FMC and CWC products based on radiative transfer model inversion

Seasonal variation in grass water content estimated from proximal sensing and MODIS time series in a Mediterranean Fluxnet site

This study evaluates three different metrics of water content of an herbaceous cover in a Mediterranean wooded grassland (dehesa) ecosystem. Fuel moisture content (FMC), equivalent water thickness (EWT) and canopy water content (CWC) were estimated from proximal sensing and MODIS satellite imagery. Dry matter (Dm) and leaf area index (LAI) connect the three metrics and were also analyzed. Metrics were derived from field sampling of grass cover within a 500 m MODIS pixel. Hand-held hyperspectral measurements and MODIS images were simultaneously acquired and predictive empirical models were parametrized. Two methods of estimating FMC and CWC using different field protocols were tested in order to evaluate the consistency of the metrics and the relationships with the predictive empirical models. In addition, radiative transfer models (RTM) were used to produce estimates of CWC and FMC, which were compared with the empirical ones. Results revealed that, for all metrics spatial variability was significantly lower than temporal. Thus we concluded that experimental design should prioritize sampling frequency rather than sample size. Dm variability was high which demonstrates that a constant annual Dm value should not be used to predict EWT from FMC as other previous studies did. Relative root mean square error (RRMSE) evaluated the performance of nine spectral indices to compute each variable. Visible Atmospherically Resistant Index (VARI) provided the lowest explicative power in all cases. For proximal sensing, Global Environment Monitoring Index (GEMI) showed higher statistical relationships both for FMC (RRMSE = 34.5 %) and EWT (RRMSE = 27.43 %) while Normalized Difference Infrared Index (NDII) and Global Vegetation Monitoring Index (GVMI) for CWC (RRMSE = 30.27 % and 31.58 % respectively). When MODIS data were used, results showed an increase in R2 and Enhanced Vegetation Index (EVI) as the best predictor for FMC (RRMSE = 33.81 %) and CWC (RRMSE = 27.56 %) and GEMI for EWT (RRMSE = 24.6 %). Differences in the viewing geometry of the platforms can explain these differences as the portion of vegetation observed by MODIS is larger than when using proximal sensing including the spectral response from scattered trees and its shadows. CWC was better predicted than the other two water content metrics, probably because CWC depends on LAI, that shows a notable seasonal variation in this ecosystem. Strong statistical relationship was found between empirical models using indices sensible to chlorophyll activity (NDVI or EVI which are not directly related to water content) due to the close relationship between LAI, water content and chlorophyll activity in grassland cover, which is not true for other types of vegetation such as forest or shrubs. The empirical methods tested outperformed FMC and CWC products based on radiative transfer model inversion

Emphysema presence, severity, and distribution has little impact on the clinical presentation of a cohort of patients with mild to moderate COPD

Phenotypic characterization of patients with COPD may have potential prognostic and therapeutic implications. Available information on the relationship between emphysema and the clinical presentation in patients with COPD is limited to advanced stages of the disease. The objective of this study was to describe emphysema presence, severity, and distribution and its impact on clinical presentation of patients with mild to moderate COPD. METHODS: One hundred fifteen patients with COPD underwent clinical and chest CT scan evaluation for the presence, severity, and distribution of emphysema. Patients with and without emphysema and with different forms of emphysema distribution (upper/lower/core/peel) were compared. The impact of emphysema severity and distribution on clinical presentation was determined. RESULTS: Fifty percent of the patients had mild homogeneously distributed emphysema (1.84; 0.76%-4.77%). Upper and core zones had the more severe degree of emphysema. Patients with emphysema were older, more frequently men, and had lower FEV(1)%, higher total lung capacity percentage, and lower diffusing capacity of the lung for carbon monoxide. No differences were found between the clinical or physiologic parameters of the different emphysema distributions. CONCLUSIONS: In patients with mild to moderate COPD, although the presence of emphysema has an impact on physiologic presentation, its severity and distribution seem to have little impact on clinical presentation

Local validation of MODIS sensor sea surface temperature on western Mediterranean shallow waters

Revista oficial de la Asociación Española de Teledetección[EN] The sea surface temperature (SST) estimated from MODIS Aqua products (daytime and nighttime 11 μm and night 4 μm) has been correlated with field data taken at three depths (15, 50, 100 cm) in a Western Mediterranean coastal area. The comparison has allowed us to analyze the uncertainty in the estimation of this parameter in coastal waters using low spatial resolution satellite images. The results show that the daytime SST_11 μm product obtains fittest statistical values: RMSE (root mean square error) and r2 (Pearson’s correlation coefficient) of 1°C and 0.96, respectively, for 50 cm depth.[ES] La temperatura superficial del mar (SST) estimada a partir de los productos 11μm diurnos y nocturnos y 4μm nocturnos del sensor MODIS (Moderate Resolution Imaging Spectroradiometer) a bordo de la plataforma Aqua, han sido comparados con datos medidos in situ a tres profundidades diferentes (15, 50 y 100 cm) en una zona costera del Mediterráneo Occidental. Esta comparación ha permitido analizar la incertidumbre que existe en la estimación de este parámetro en aguas someras y próximas a la costa mediante imágenes de satélite de baja resolución espacial. Los resultados obtenidos demuestran que el producto diurno SST_11 μm, obtiene los estadísticos RMSE (error cuadrático medio) y r2 (coeficiente de correlación de Pearson) más ajustados con valores de 1ºC y 0.96, respectivamente, para la profundidad 50 cm.Los autores agradecen el soporte económico de LIFE+08NAT-CUBOMED (http://www.cubomed.eu) para la adquisición de datos in situ, a la NASA (http://www.nasa.gov) que ha facilitado las imágenes de forma gratuita, y al programa JAE-TEC del CSIC que ha financiado el contrato de Elia Durá. Nuestro agradecimiento a los voluntarios de CUBOMED, especialmente a Neus Figueras, Leticia Vázquez, Vicente Bernabeu y Felipe Escolano, por su valioso trabajo. Agradecemos finalmente al Portet de Dénia (www.elportetdedenia.es), al Real Club Náutico de Dénia (www.cndenia.es) y a la Fundación Baleària (www.balearia.com) por su apoyo.Durá, E.; Mendiguren, G.; Martín, M.; Acevedo-Dudley, M.; Bosch-Bolmar, M.; Fuentes, V.; Bordehore, C. (2014). Validación local de la temperatura superficial del mar del sensor MODIS en aguas someras del Mediterráneo occidental. Revista de Teledetección. (41):59-69. doi:10.4995/raet.2014.2314.SWORD59694

Disaggregation of SMOS soil moisture over West Africa using the Temperature and Vegetation Dryness Index based on SEVIRI land surface parameters

The overarching objective of this study was to produce a disaggregated SMOS Soil Moisture (SM) product using land surface parameters from a geostationary satellite in a region covering a diverse range of ecosystem types. SEVIRI data at 15 minute temporal resolution were used to derive the Temperature and Vegetation Dryness Index (TVDI) that served as SM proxy within the disaggregation process. West Africa (3 N, 26 W; 28 N, 26 E) was selected as a case study as it presents both an important North-South climate gradient and a diverse range of ecosystem types. The main challenge was to set up a methodology applicable over a large area that overcomes the constraints of SMOS (low spatial resolution) and TVDI (requires similar atmospheric forcing and triangular shape formed when plotting morning rise temperature versus fraction of vegetation cover) in order to produce a 0.05 degree resolution disaggregated SMOS SM product at sub-continental scale. Consistent cloud cover appeared as one of the main constraints for deriving TVDI, especially during the rainy season and in the southern parts of the region and a large adjustment window (105x105 SEVIRI pixels) was therefore deemed necessary. Both the original and the disaggregated SMOS SM products described well the seasonal dynamics observed at six locations of in situ observations. However, there was an overestimation in both products for sites in the humid southern regions; most likely caused by the presence of forest. Both TVDI and the associated disaggregated SM product was found to be highly sensitive to algorithm input parameters; especially of conditions of high fraction of vegetation cover. Additionally, seasonal dynamics in TVDI did not follow the seasonal patters of SM. Still, its spatial heterogeneity was found to be a good proxy for disaggregating SMOS SM data; main river networks and spatial patterns of SM extremes (i.e. droughts and floods) not seen in the original SMOS SM product were revealed in the disaggregated SM product for a test case of July-September 2012. The disaggregation methodology thereby successfully increased the spatial resolution of SMOS SM, with potential application for local drought/flood monitoring of importance for the livelihood of the population of West Africa

Spatial pattern evaluation of a calibrated national hydrological model – a remote-sensing-based diagnostic approach

Distributed hydrological models are traditionally evaluated against discharge stations, emphasizing the temporal and neglecting the spatial component of a model. The present study widens the traditional paradigm by highlighting spatial patterns of evapotranspiration (ET), a key variable at the land–atmosphere interface, obtained from two different approaches at the national scale of Denmark. The first approach is based on a national water resources model (DK-model), using the MIKE-SHE model code, and the second approach utilizes a two-source energy balance model (TSEB) driven mainly by satellite remote sensing data. Ideally, the hydrological model simulation and remote-sensing-based approach should present similar spatial patterns and driving mechanisms of ET. However, the spatial comparison showed that the differences are significant and indicate insufficient spatial pattern performance of the hydrological model.The differences in spatial patterns can partly be explained by the fact that the hydrological model is configured to run in six domains that are calibrated independently from each other, as it is often the case for large-scale multi-basin calibrations. Furthermore, the model incorporates predefined temporal dynamics of leaf area index (LAI), root depth (RD) and crop coefficient (Kc) for each land cover type. This zonal approach of model parameterization ignores the spatiotemporal complexity of the natural system. To overcome this limitation, this study features a modified version of the DK-model in which LAI, RD and Kc are empirically derived using remote sensing data and detailed soil property maps in order to generate a higher degree of spatiotemporal variability and spatial consistency between the six domains. The effects of these changes are analyzed by using empirical orthogonal function (EOF) analysis to evaluate spatial patterns. The EOF analysis shows that including remote-sensing-derived LAI, RD and Kc in the distributed hydrological model adds spatial features found in the spatial pattern of remote-sensing-based ET