Energy Balance Index Research Articles

Estimating Evapotranspiration Using Coupled Remote Sensing and Three SEB Models in an Arid Region

Τhree surface energy balance (SEB) models were used to estimate ET based on remote sensing (RS) techniques: surface energy balance algorithm for land (SEBAL), mapping evapotranspiration at a high-resolution with internalized calibration (METRIC™), and simplified surface energy balance index (S-SEBI). The study was conducted on the Todhia Arable Farm (TAF), located in the Riyadh province of the Kingdom of Saudi Arabia. Both RS data and meteorological data were used. RS data were obtained by processing 20 Landsat 8 satellite images for 2014 focused on alfalfa cultivation. The estimated ET was validated by comparing it with the measured ET using an eddy covariance (EC) device. The ET values estimated by the SEBAL, METRIC, and S-SEBI models were 1.7–18.9 mm·d−1 with a mean of 8.3 mm·d−1, 2.8–22.4 mm·d−1 with a mean of 11.0 mm·d−1, and 6.5–21.6 mm·d−1 with a mean of 15.7 mm·d−1, respectively. The highest ET values were observed in cultivated areas, particularly during the periods of increased irrigation. The comparison between the estimated and measured ET indicated that the METRIC model exhibited the best performance, with a root-mean-square deviation (RMSD) of 1.7 mm·d−1. The S-SEBI model was less accurate, followed by the SEBAL model, with an RMSD of 2.9 and 3.1 mm·d−1, respectively. The METRIC model exhibited moderate ET results as well and is considered to be the most suitable ET SEB model for arid regions.

The effect of mild sleep deprivation on diet and eating behaviour in children: protocol for the Daily Rest, Eating, and Activity Monitoring (DREAM) randomized cross-over trial

BackgroundAlthough insufficient sleep has emerged as a strong, independent risk factor for obesity in children, the mechanisms by which insufficient sleep leads to weight gain are uncertain. Observational research suggests that being tired influences what children eat more than how active they are, but only experimental research can determine causality. Few experimental studies have been undertaken to determine how reductions in sleep duration might affect indices of energy balance in children including food choice, appetite regulation, and sedentary time. The primary aim of this study is to objectively determine whether mild sleep deprivation increases energy intake in the absence of hunger.MethodsThe Daily, Rest, Eating, and Activity Monitoring (DREAM) study is a randomized controlled trial investigating how mild sleep deprivation influences eating behaviour and activity patterns in children using a counterbalanced, cross-over design. One hundred and ten children aged 8–12 years, with normal reported sleep duration of 8–11 h per night will undergo 2 weeks of sleep manipulation; seven nights of sleep restriction by going to bed 1 hr later than usual, and seven nights of sleep extension going to bed 1 hr earlier than usual, separated by a washout week. During each experimental week, 24-h movement behaviours (sleep, physical activity, sedentary behaviour) will be measured via actigraphy; dietary intake and context of eating by multiple 24-h recalls and wearable camera images; and eating behaviours via objective and subjective methods. At the end of each experimental week a feeding experiment will determine energy intake from eating in the absence of hunger. Differences between sleep conditions will be determined to estimate the effects of reducing sleep duration by 1–2 h per night.DiscussionDetermining how insufficient sleep predisposes children to weight gain should provide much-needed information for improving interventions for the effective prevention of obesity, thereby decreasing long-term morbidity and healthcare burden.Trial registrationAustralian New Zealand Clinical Trials Registry ACTRN12618001671257. Registered 10 October 2018.

Investigating the Behaviour of Human Thermal Indices under Divergent Atmospheric Conditions: A Sensitivity Analysis Approach

This paper aims to analyse and conclude about the behaviour of the most commonly used human thermal comfort indices under a variety of atmospheric conditions in order to provide further information about their appropriateness. Utilising Generalized Additive Models (GAMs), this article examines the indices’ sensitivity when exposed to diverse classified atmospheric conditions. Concentrated upon analysing commonly used human thermal indices, two Statistical/Algebraic indices (Thermohygrometric Index (THI) and HUMIDEX (HUM)), and four Energy Balance Model indices (Physiologically Equivalent Temperature (PET), modified PET (mPET), Universal Thermal Climate Index (UTCI), and Perceived Temperature (PT)) were selected. The results of the study are twofold, the identification of (1) index sensitivity to parameters’ variation, and change rates, resultant of different atmospheric conditions; and, (2) the overall pertinence of each of the indices for local thermal comfort evaluation. The results indicate that the thermohygrometric indices cannot follow and present the thermal conditions’ variations. On the other hand, UTCI is very sensitive under low radiation condition, and PET/mPET present higher sensitivity when the weather is dominated by high radiation and air temperature. PT index provides the lower sensitive among the human energy balance indices, but this is adequately sensitive to describe the thermal comfort environment.

Development and application of a spatially distributed snowmelt runoff model for limited data condition

Two main approaches are used to calculate snowmelt, namely, energy balance (physically based) and temperature index (conceptual). Energy balance requires comprehensive data to solve physical equations. In this study, a distributed conceptual model, “spatially distributed snowmelt runoff model”, based on modified temperature index approach was developed to estimate snow-related parameters like snow density and degree day factor in a distributed manner over space and time to meet the hydrological demands of data-scarce snow-dominated mountainous catchment. The model needs only daily temperature, precipitation, snow cover, and albedo as basic inputs. It gives pixel-wise output of snow density, snow depth, snow water equivalent, degree day, net radiation, snowmelt depth, rain-induced runoff, snowmelt runoff, actual evaporation, and infiltration, while routing the flow up to the catchment outlet to estimate discharge as time series along with contribution breakup between snowmelt and rain-induced runoff. The developed model was tested for a small-sized watershed Nuranang (58 km2) as well as for a medium-sized watershed Mago basin (844 km2) both located in the Tawang district of Arunachal Pradesh, India, and was successfully calibrated for 2004–2005 and validated for 2008–2009 for Nuranang watershed. In the case of Mago basin, a single year (2007) was used for calibration and validation was also performed for a single year (2009) as well due to the unavailability of data. Performance of the model was found satisfactory for both the basins with modelling efficiency (ME) greater than 0.6 and coefficient of residual mass (CRM) in between − 0.2 and 0.2 for both calibration and validation years in these data-scarce eastern Himalayan watersheds. Considering the satisfactory performance of the model in both the small- and medium-sized watersheds, the model can be very useful for any un-instrumented mountainous basins for pixel-wise snowmelt runoff computation and gridded output generation using degree day approach, where observed snow density data is not available.

SENSITIVITY OF FOUR CONTEXTUAL REMOTE SENSING BASED SURFACE ENERGY BALANCE MODELS TO SPATIAL DOMAIN

Abstract. It remains unclear how the selection of a spatial domain affects the accuracy of evapotranspiration (ET) estimates from contextual remote sensing based surface energy balance (SEB) models, particularly at large spatial scales. We thus tested the effect of spatial domain on four widely implemented contextual remote sensing based SEB models: Surface Energy Balance Algorithm for Land (SEBAL), Mapping ET with Internalized Calibration (METRIC), Simplified Surface Energy Balance Index (S-SEBI), and Triangular ET models. We applied these models on 44 near cloud-free Moderate Resolution Imaging Spectroradiometer (MODIS) thermal images across all of India from 2004 to 2006. Four spatial domains were considered: all of India, agro-ecological regions, 300 km × 300 km grids, and 600 km × 600 km grids and we compared hourly ET estimates from the models against observed ET data at four Bowen Ratio sites in India. Model performance varied across all models and spatial domains. ET values of neighboring pixels across spatial domains formed sharp edges along the boundaries of agro-ecological regions, 300 km × 300 km grids, and 600 km × 600 km grids suggesting that all ET models are highly sensitive to the selection of spatial domain. No single spatial domain was found to be optimal for all models and hence potential uncertainties associated with the selection of spatial domain should be taken into consideration when implementing these models at regional scales. The results from this study provide guidance for future regional-scale implementation of ET models and potential approaches to overcome these challenges.

A comparative sensitivity analysis of human thermal comfort indices with generalized additive models

This paper presents a comparative sensitivity analysis of six of the widely used human thermal comfort indices. The analysis consists of the evaluation of the effect of indices ‘input parameters’ variation and change rate on the output of human energy balance and simple thermohygrometric indices. For the implementation of the sensitivity analysis, the generalized additive model’s methodology is applied on a long period and high temporal resolution dataset from Athens, Greece. The results indicate that the proposed methodology of generalized additive models is adequate for such an analysis. Moreover, this research revealed the differences in index behaviour. The thermohygrometric indices (i.e. Thermohygrometric Index and HUMIDEX) exhibit a clearly deferent sensitivity pattern in comparison to the human energy balance indices (i.e. physiologically equivalent temperature (PET), perceived temperature (PT), modified physiologically equivalent temperature (mPET) and Universal Thermal Comfort Index (UTCI)), and they are incapable to handle the complexity of the atmospheric stimuli on human thermal perception. On the other hand, human energy balance indices can follow the input parameters fluctuations but with different grades of sensitivity. PET and mPET present a moderate and gradual sensitivity both in terms of the input variation and input change rate. PT is the less sensitive index among the human energy balance investigated, but it is able to follow efficiently the input parameters variation during the measurements period. Moreover, UTCI is the most sensitive among all the selected indices for low values (and low change rate) of the input parameters but for high input parameter values (except the wind speed), UTCI exhibits a low sensitivity in comparison to the other human energy balance indices. In terms of sensitivity, the most influential input parameter is global radiation, and the less influential is vapour pressure.

Basin scale rainfall-evapotranspiration dynamics in a tropical semiarid environment during dry and wet years

In semiarid regions the occurrence of alternating long drought and heavy rainfall periods directly impacts water availability, affecting human water supply, agriculture development and the provision of ecosystem services. Because of that, research on the water input and output fluxes at the basin scale is of paramount importance. In this sense, rainfall-evapotranspiration (ET) dynamics play a critical role in water, soil and vegetation interactions, in hydrometeorological modelling and in the energy fluxes dynamics of semiarid regions. Therefore, the objective of this study was to quantify daily ET during a wet year and a dry year in a watershed located in the Brazilian Semiarid, by using remote sensing data and formulations based on the Simplified Surface Energy Balance Index (S-SEBI) and the Simplified Surface Energy Balance (SSEB) algorithms. Land surface temperature, albedo and NDVI data from MODIS sensor and solar radiation data from weather stations located in the basin were used. Rainfall analysis indicated 2009 and 2012 as being representatives of anomalously wet and dry years respectively, which were selected for the quantification of ET. The proposed algorithm was adjusted and verified with data from a flux tower equipped with eddy covariance system. Daily remote sensing ET estimates showed good agreement with observed values (RMSE = 0.79 mm.d−1) and the annual ET relative error was of 7.7% (35.4 mm.year−1). Results showed that the native vegetation can delay its dormant state for five months during wet years. During the wet year, ET differences between land cover classes were less noticeable due to soil saturation and the urgency of vegetated surfaces to meet their physiological needs. In dry year, however, differences were more evident, with bare soil presenting lower ET rates and vegetation classes showing higher ET values.

APPLICATION OF SIMPLIFIED SURFACE ENERGY BALANCE INDEX (S-SEBI) FOR CROP EVAPOTRANSPIRATION USING LANDSAT 8

Abstract. Assessment and monitoring of crop water requirement (CWR) or crop evapotranspiration (ETc) over a large spatial scale is the critical component for irrigation and drought management. Due to growing competition and increasing shortage of water, careful utilization of water in irrigation is essential. The usage of water for irrigation/agriculture is a top priority for countries like Pakistan, where the GDP mostly based on agriculture, and its scarcity may affect the crop production. Remote sensing techniques can be used to estimate crop water requirement or crop evapotranspiration which can help in efficient irrigation. Simplified-surface energy balance index (SSEBI) model is used to estimate evapotranspiration (ET) of wheat during 2015–16 growing period in Tando Adam, Sindh. Landsat-8 satellite data for the corresponding years were used. With the help of National Agromet Centre report chart of Crop coefficient (Kc) the CWR, ETc of all phonological stages were estimated. Results indicated that maximum ET and maximum CWR were found in the third leaf to tillering stage with a value of 0.75 and 0.89 respectively. This study will help in managing and monitoring of ET spatial distribution over irrigated crops which results in better irrigation scheduling and water consumption.

Hydrological response of two high altitude Swiss catchments to energy balance and temperature index melt schemes

Comparison of energy Balance (EB) and temperature Index (TI) snow/ice melt methods for glacio-hydrological modeling in the Alps has been the subject of numerous studies; however, the debate on which method is preferable under which conditions and for which application is still ongoing. In this study, both melting routines were tested within the complex numerical framework of the fully distributed model (Alpine3D) to analyze the differences. We included two high-altitude Swiss alpine glaciered catchments, which represent two distinct types of glaciers, namely cirque and valley glaciers, with different weather conditions, topography, and data type. In this experiment, hydrological discharge in the Damma catchment was overestimated by the model with EB approach, which demonstrated Nash-Sutcliffe efficiency (NSE) of 0.61 mainly due to the overestimation of wind speed and longwave radiation. On the other hand, model with TI approach obtained a high NSE of 0.93, as this simple conceptual model does not rely on the abovementioned variables. For the Arolla catchment, better results were observed for both EB and TI melt schemes, with slight underestimation in modeled runoff as shown by NSEs of 0.89 and 0.77, respectively. The very satisfactory performance of EB model at the Arolla catchment is due to the use of input data sets from local weather stations that are more representative of the real catchment conditions. It was observed that the EB melt model performs better if driven with correct meteorological data sets from catchment-representative stations and that the calibration procedure of TI-melt model significantly affects its accuracy.

Clinical Utility of Plasma Fructosamine Concentration as a Hypoglycemic Biomarker during Early Lactation in Dairy Cattle.

BackgroundPlasma fructosamine concentration ([FRA]) is a widely used long term hyperglycemic biomarker in humans and dogs, but its clinical usefulness as a hypoglycemic biomarker in dairy cattle is uncertain.ObjectivesTo evaluate the relationship between plasma [FRA] and glucose concentration ([gluc]) as well as indices of energy balance during early lactation in dairy cattle, and to characterize the influence of plasma total protein concentration ([TP]) and albumin concentration ([albumin]) on [FRA].AnimalsConvenience sample comprising 103 periparturient Holstein–Friesian cattle.MethodsPlasma [gluc], [TP], [albumin], and other clinicopathologic indices of energy status were determined periodically from Day 4 postpartum. Body condition score (BCS) was assessed, and backfat thickness (BFT) and longissimus dorsi muscle thickness (LDT) were measured ultrasonographically. Plasma [FRA] was measured at approximately 28 days postpartum. Associations between plasma [FRA] and study variables were evaluated using Spearman's rho and stepwise forward linear regression. Statistical significance was declared at P < 0.05.ResultsA positive association was detected between plasma [FRA] and mean plasma [gluc] from Days 4–28 postpartum (r s = +0.36, P < 0.001), and between plasma [FRA] and LDT (r s = +0.28, P = 0.007), BCS (r s = +0.23, P = 0.029), and BFT (r s = +0.21, P = 0.043). Multivariable regression identified a positive association between plasma [FRA] and mean plasma [gluc] and [albumin] from Days 4–28 postpartum. Correcting plasma [FRA] for [albumin] improved the association (r s = +0.46, P < 0.001) between plasma [FRA] and mean plasma [gluc].Conclusions and Clinical ImportancePlasma [FRA] does not provide a clinically useful method for quantifying the magnitude of hypoglycemia or negative energy balance in dairy cows during early lactation.

Land Use Classification: A Surface Energy Balance and Vegetation Index Application to Map and Monitor Irrigated Lands

Irrigated agriculture consumes the largest share of available fresh water, and awareness of the spatial distribution and application rates is paramount to a functional and sustainable communal consumptive water use. This remote sensing study leverages surface energy balance fluxes and vegetation indices to classify and map the spatial distribution of irrigated and non-irrigated croplands. The purpose is to introduce a classification scheme applicable across a wide variation in regional climate and inter-growing seasonal precipitation. The rationale for climate and inter-growing seasonal adaptability is founded in the derivation and calibration of the scheme based on the wettest growing season. Therefore, the scheme becomes a more efficient classifier during normal and dry growing seasons. Using empirical distribution functions, two indices are derived from evapotranspiration fluxes and vegetation indices to contrast and classify irrigated croplands from non-irrigated. The synergy of the two indices increases the classification proficiency by adding another classifying layer which re-characterizes misclassified croplands by the base index. The scheme was applied to a region with wide climate variation and to multiple years of growing seasons. The results presented, in cross validation with groundtruth, show an accurate and consistent approach to classify irrigation with overall accuracy of 92.1%, applicable from humid to semi-arid climate, and from dry to normal and wet growing seasons.

Utility of remote sensing-based surface energy balance models to track water stress in rain-fed switchgrass under dry and wet conditions

The ability of remote sensing-based surface energy balance (SEB) models to track water stress in rain-fed switchgrass (Panicum virgatum L.) has not been explored yet. In this paper, the theoretical framework of crop water stress index (CWSI; 0 = extremely wet or no water stress condition and 1 = extremely dry or no transpiration) was utilized to estimate CWSI in rain-fed switchgrass using Landsat-derived evapotranspiration (ET) from five remote sensing based single-source SEB models, namely Surface Energy Balance Algorithm for Land (SEBAL), Mapping ET with Internalized Calibration (METRIC), Surface Energy Balance System (SEBS), Simplified Surface Energy Balance Index (S-SEBI), and Operational Simplified Surface Energy Balance (SSEBop). CWSI estimates from the five SEB models and a simple regression model that used normalized difference vegetation index (NDVI), near-surface temperature difference, and measured soil moisture (SM) as covariates were compared with those derived from eddy covariance measured ET (CWSIEC) for the 32 Landsat image acquisition dates during the 2011 (dry) and 2013 (wet) growing seasons. Results indicate that most SEB models can predict CWSI reasonably well. For example, the root mean square error (RMSE) ranged from 0.14 (SEBAL) to 0.29 (SSEBop) and the coefficient of determination (R2) ranged from 0.25 (SSEBop) to 0.72 (SEBAL), justifying the added complexity in CWSI modeling as compared to results from the simple regression model (R2 = 0.55, RMSE = 0.16). All SEB models underestimated CWSI in the dry year but the estimates from SEBAL and S-SEBI were within 7% of the mean CWSIEC and explained over 60% of variations in CWSIEC. In the wet year, S-SEBI mostly overestimated CWSI (around 28%), while estimates from METRIC, SEBAL, SEBS, and SSEBop were within 8% of the mean CWSIEC. Overall, SEBAL was the most robust model under all conditions followed by METRIC, whose performance was slightly worse and better than SEBAL in dry and wet years, respectively. Underestimation of CWSI under extremely dry soil conditions and the substantial role of SM in the regression model suggest that integration of SM in SEB models could improve their performances under dry conditions. These insights will provide useful guidance on the broader applicability of SEB models for mapping water stresses in switchgrass under varying geographical and meteorological conditions.

Application of S-SEBI model for crop evapotranspiration using Landsat-8 data over parts of North India

Estimation and monitoring of crop evapotranspiration (ETc) or consumptive water use over large-area holds the key to irrigation management plans and regional drought preparedness. The objective of this study was to estimate ETc by applying the simplified-surface energy balance index (S-SEBI) model to Landsat-8 data for the 2014–2015 period in parts of North India. An average ETc was estimated 2.72 and 2.47 in mm day−1 with 0.22, 0.18 standard deviation and 0.11, 0.07 standard error for Kharif and Rabi crops, respectively. On validation part, a close relationship was observed between S-SEBI derived and scintillometer observed evaporative fraction with 0.85 correlation coefficient and 0.86 agreement index. The statistical analysis also endorses the results accuracy and reliability with 0.026 and 0.602, relative root-mean square errors and model efficiency for wheat crop, respectively. The study showed that normalized difference vegetation index and LST are closely related and serve as a proxy for qualitative representation of ETc.

Performance of five surface energy balance models for estimating daily evapotranspiration in high biomass sorghum

Robust evapotranspiration (ET) models are required to predict water usage in a variety of terrestrial ecosystems under different geographical and agrometeorological conditions. As a result, several remote sensing-based surface energy balance (SEB) models have been developed to estimate ET over large regions. However, comparison of the performance of several SEB models at the same site is limited. In addition, none of the SEB models have been evaluated for their ability to predict ET in rain-fed high biomass sorghum grown for biofuel production. In this paper, we evaluated the performance of five widely used single-source SEB models, namely Surface Energy Balance Algorithm for Land (SEBAL), Mapping ET with Internalized Calibration (METRIC), Surface Energy Balance System (SEBS), Simplified Surface Energy Balance Index (S-SEBI), and operational Simplified Surface Energy Balance (SSEBop), for estimating ET over a high biomass sorghum field during the 2012 and 2013 growing seasons. The predicted ET values were compared against eddy covariance (EC) measured ET (ETEC) for 19 cloud-free Landsat image. In general, S-SEBI, SEBAL, and SEBS performed reasonably well for the study period, while METRIC and SSEBop performed poorly. All SEB models substantially overestimated ET under extremely dry conditions as they underestimated sensible heat (H) and overestimated latent heat (LE) fluxes under dry conditions during the partitioning of available energy. METRIC, SEBAL, and SEBS overestimated LE regardless of wet or dry periods. Consequently, predicted seasonal cumulative ET by METRIC, SEBAL, and SEBS were higher than seasonal cumulative ETEC in both seasons. In contrast, S-SEBI and SSEBop substantially underestimated ET under too wet conditions, and predicted seasonal cumulative ET by S-SEBI and SSEBop were lower than seasonal cumulative ETEC in the relatively wetter 2013 growing season. Our results indicate the necessity of inclusion of soil moisture or plant water stress component in SEB models for the improvement of their performance, especially under too dry or wet environments.

Water Consumption of Agriculture and Natural Ecosystems along the Ili River in China and Kazakhstan

The Ili River is a transboundary river shared by China, upstream, and Kazakhstan, downstream. The Ili is the main water supplier to Lake Balkhash, the largest lake in Central Asia after desiccation of the Aral Sea. Agreements over water allocation have not been concluded between China and Kazakhstan. This paper investigated water consumption of agriculture and riparian ecosystems in the Ili river basin, to provide information for further debate on water allocation, through the Simplified Surface Energy Balance Index (S-SEBI) approach using Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images. The overall water consumption in the Ili river basin was 14.3 km3/a in 2000, 17.2 km3/a in 2005, and 15 km3/a in 2014. In 2000, China and Kazakhstan consumed 38% and 62% of the water, respectively. By 2014, the relative share of China’s water consumption increased to 43%. In China, 80% of the water consumption is due to agriculture. High runoff during the past 10 years enabled increasing water consumption in China and sufficient water supply to agriculture and riparian ecosystems in Kazakhstan. When runoff of the Ili River decreases, as expected for most rivers in Central Asia, then irrigation efficiency has to be further increased in China, and irrigation systems in Kazakhstan have to be restored and modernized in order to reduce water consumption and protect Lake Balkhash and the riparian ecosystems.

Comparing Landsat-7 ETM+ and ASTER Imageries to Estimate Daily Evapotranspiration Within a Mediterranean Vineyard Watershed

We compared the capabilities of Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imageries for mapping daily evapotranspiration (ET) within a Mediterranean vineyard watershed. We used Landsat and ASTER data simultaneously collected on four dates in 2007 and 2008, along with the simplified surface energy balance index (S-SEBI) model. We used previously ground-validated good quality ASTER estimates as reference, and we analyzed the differences with Landsat retrievals in light of the instrumental factors and methodology. Although Landsat and ASTER retrievals of S-SEBI inputs were different, estimates of daily ET from the two imageries were similar. This is ascribed to the S-SEBI spatial differencing in temperature, and opens the path for using historical Landsat time series over vineyards.

Evaluation of Methods for Aerodynamic Roughness Length Retrieval from Very High-Resolution Imaging LIDAR Observations over the Heihe Basin in China

The parameterization of heat transfer based on remote sensing data, and the Surface Energy Balance System (SEBS) scheme to retrieve turbulent heat fluxes, already proved to be very appropriate for estimating evapotranspiration (ET) over homogeneous land surfaces. However, the use of such a method over heterogeneous landscapes (e.g., semi-arid regions or agricultural land) becomes more difficult, since the principle of similarity theory is compromised by the presence of different heat sources at various heights. This study aims to propose and evaluate some models based on vegetation geometry partly developed by Colin and Faivre, to retrieve the surface aerodynamic roughness length for momentum transfer ( z 0 m ), which is a key parameter in the characterization of heat transfer. A new approach proposed by the authors consisted in the use of a Digital Surface Model (DSM) as boundary condition for experiments with a Computational Fluid Dynamics (CFD) model to reproduce 3D wind fields, and to invert them to retrieve a spatialized roughness parameter. Colin and Faivre also applied the geometrical Raupach’s approach for the same purpose. These two methods were evaluated against two empirical ones, widely used in Surface Energy Balance Index (SEBI) based algorithms (Moran; Brutsaert), and also against an alternate geometrical model proposed by Menenti and Ritchie. The investigation was carried out in the Yingke oasis (China) using very-high resolution remote sensing data (VNIR, TIR &amp; LIDAR), for a precise description of the land surface, and a fine evaluation of estimated heat fluxes based on in-situ measurements. A set of five numerical experiments was carried out to evaluate each roughness model. It appears that methods used in experiments 2 (based on Brutsaert) and 4 (based on Colin and Faivre) are the most accurate to estimate the aerodynamic roughness length, according to the estimated heat fluxes. However, the formulation used in experiment 2 allows to minimize errors in both latent and sensible heat flux, and to preserve a good partitioning. An additional evaluation of these two methods based on another k B − 1 parameterization could be necessary, given that the latter is not always compatible with the CFD-based retrieval method.

Influence of Energy Balance and Glycemic Index on Metabolic Endotoxemia in Healthy Men

Objective: Overfeeding with a high-fat and/or high-carbohydrate (CHO) diet is known to increase plasma concentrations of endotoxin (lipopolysaccharide [LPS]) that may lead to metabolic disturbances like insulin resistance. The impact of CHO quality (i.e., the glycemic index [GI]) independent of fat intake on metabolic endotoxemia remains unclear. In the present study, the effects of changes in energy balance and GI on plasma endotoxin were studied.Methods: Fifteen healthy young men overconsumed diets containing 65% CHO and 20% fat for 1 week (OF; +50% of energy requirement) followed by 3 weeks of caloric restriction (CR; −50% of energy requirement) and were then randomized to 2 weeks hypercaloric refeeding (RF, +50% of energy requirement) with either a low- or high-GI (40 vs 74) diet.Results: During OF, subjects gained 1.9 ± 0.7 kg body weight (+0.6 ± 0.8% fat mass) followed by a weight loss of 6.1 ± 0.8 kg (−2.0 ± 0.6% fat mass) and weight regain of 4.0 ± 0.6 kg (0.9 ± 0.8% fat mass). Fasting insulin and homeostasis model assessment–insulin resistance (HOMAIR) increased with OF and RF and decreased with CR, MatsudaISI decreased by 37% after RF (all p < 0.05). Endotoxin significantly increased by 30.8% with OF and by 24.7% with RF (both p < 0.05), whereas CR normalized endotoxin levels. No difference in endotoxin levels was observed between refeeding a hypercaloric high- or low-GI diet. Changes in endotoxin levels with RF were not related to changes in insulin sensitivity.Conclusion: A hypercaloric diet (OF and RF) increased plasma endotoxin irrespective of GI, whereas a negative energy balance did not reduce endotoxemia. Impaired insulin sensitivity with hypercaloric refeeding on a high-GI diet was not explained by metabolic endotoxemia.

Effect of observation scale on remote sensing based estimates of evapotranspiration in a semi-arid row cropped orchard environment

Understanding in detail the spatial distribution of evapotranspiration (ET) in row cropped fruit production areas with diverse water requirements is vital for monitoring water use and efficient irrigation scheduling. Spatially distributed ET for these environments can be estimated using remote sensing (RS). However, the computation of RS based ET under such conditions is complicated because of the complex parameterizations that are required to derive ET for the mixed pixels comprising of bare soil and well-watered plants typical of row cropped areas. Also, the parameterization of these processes is not scale invariant, owing to change in the percentage of vegetation cover in the mixed pixels across remote sensing observation scales. In this study, our main objectives were (1) to isolate and evaluate the effect of varying spatial scales (comparable to canopy sizes and larger) of the remote sensing data on ET estimates; and (2) provide an operational method for estimating remote sensing based ET for row cropped conditions. ET was computed using an empirical technique (S-SEBI: Simplified-Surface Energy Balance Index Algorithm) for almond and pistachio orchards from remote sensing imagery collected at a scale comparable to the canopy sizes of the trees (5.8 and 7.2 m) and a scale that was much larger than the canopy size (120 m) using the MASTER and Landsat sensors, respectively. In order to account for the effect of mixed pixels, a Normalized Difference Vegetation Index based correction factor was applied to the derived ET values and the results averaged for different fields were validated with Penman–Monteith based ET estimates. It was found that the corrected mean ET estimates at 120 m were in agreement with the Penman–Monteith based ET estimates (RMSEaverage = 0.12 mm/h), whereas they were underestimated at the finer resolutions. Our results indicated that a remote sensing pixel resolution comparable to the row spacing and smaller and comparable to the canopy size overestimated the land surface temperature and consequently, underestimated ET when using operational models that do not account for vegetation and soil temperature separately. The results of the application of the NDVI correction factor indicates that good spatial estimates of crop ET can be made for crops growing in orchards using simple ET models that require minimal data and freely available Landsat imagery. These findings are very encouraging for the regular monitoring of crop health and effective management of irrigation water in highly water stressed agricultural environments.

Prévalence À L’obésité Par La Consommation Modérée D’huile De Palme Chez Le Rat

Palm oil is a vegetable oil extracted from the pulp of the palm oil fruit. It comes in crude or refined form. It is an oil which caused controversy due to lack of enough information on its use. Our study has shown that moderate consumption of palm oil, that do not exceed 15% of food content, over 28 days does not cause a significant increase in body mass in young rats. Similarly, the lack of significance in the indices of food intake, digestibility, protein efficiency and energy balance, among control and experimental groups of the rats confirmed that palm oil is good for nutrition when its consumption is moderate. However, our study showed that moderate consumption of palm oil does not affect the total cholesterol, HDL-cholesterol and LDL-cholesterol levels in young. While, moderate consumption of crude palm showed a significant increase in triglyceride rates but refined palm oil does not increase significantly triglyceride rates. Finally, our study showed that moderate consumption of palm oil does not cause a massive accumulation of the oil in the liver. This leads to say that palm oil is not the matter, but the way of consuming palm oil that could be a risk of metabolic syndromes.