Modelling surface energy fluxes over maize using a two-source patch model and radiometric soil and canopy temperature observations

Models estimating surface energy fluxes over partial canopy cover with thermal remote sensing must account for significant differences between the radiometric temperatures and turbulent exchange rates associated with the soil and canopy components of the thermal pixel scene. Recent progress in separating soil and canopy temperatures from dual angle composite radiometric temperature measurements has encouraged the development of two-source (soil and canopy) approaches to estimating surface energy fluxes given observations of component soil and canopy temperatures. A Simplified Two-Source Energy Balance (STSEB) model has been developed using a “patch” treatment of the surface flux sources, which does not allow interaction between the soil and vegetation canopy components. A simple algorithm to predict the net radiation partitioning between the soil and vegetation is introduced as part of the STSEB patch modelling scheme. The feasibility of the STSEB approach under a full range in fractional vegetation cover conditions is explored using data collected over a maize (corn) crop in Beltsville Maryland, USA during the 2004 summer growing season. Measurements of soil and canopy component temperatures as well as the effective composite temperature were collected over the course of the growing season from crop emergence to cob development. Comparison with tower flux measurements yielded root-mean-square-difference values between 15 and 50 W m^− 2 for the retrieval of the net radiation, soil, sensible and latent heat fluxes. A detailed sensitivity analysis of the STSEB approach to typical uncertainties in the required inputs was also conducted indicating greatest model sensitivity to soil and canopy temperature uncertainties with relative errors reaching ∼ 30% in latent heat flux estimates. With algorithms proposed to infer component temperatures from bi-angular satellite observations, the STSEB model has the capability of being applied operationally.     

Effects of vegetation canopy structure on remotely sensed canopy temperatures

Remote sensing of vegetation temperatures is a promising technique for inferring plant water stress and yield on a large spatial scale. The effects of vegetation canopy structure on thermal infrared sensor response need to be understood before vegetation surface temperatures of canopies with low percentages of ground cover can be accurately inferred. The response of a sensor is a function of vegetation geometric structure, the vertical surface temperature distribution of the canopy components, and sensor view angle. Large deviations between the nadir sensor effective radiant temperature (ERT) and vegetation ERT for a soybean canopy were observed throughout the growing season. The nadir sensor ERT of a soybean canopy with 35% ground cover deviated from the vegetation ERT by as much as 11°C during the mid-day. These deviations were quantitatively explained as a function of canopy structure and soil temperature. Remote sensing techniques which uniquely determine the vegetation canopy temperature(s) from the sensor response need to be studied.     

Soil water and plant canopy effects on remotely measured surface temperatures

Abstract.

Thermal infrared remote sensing of diurnal crop canopy temperature variations represents a possible method for determining the availability of soil water to plants. This study was performed to assess the effects of soil water and crop canopy on apparent temperatures observed by means of remote sensors, and to determine the impact of these effects on remote soil water monitoring. Airborne thermal scanner and apparent reflectance data (one date) and ground PRT-5 data (three dates) were collected primarily over barley and other small grain canopies. Plant heights, cover, and available soil water for four layers in the top 20 cm were determined. Analysis of the data showed a close inverse linear relationship between the available water and the day minus night temperature difference δT, for thick barley canopies (plant cover above 90 per cent) only. The use of apparent reflectance values in the visible region did not improve available soil water regression equations substantially. These results suggest that the available water or plant stress could only be accurately determined for thick canopies, and that the reflectance data could probably be used to identify such canopies but would not improve regression estimates of soil water from remote sensing data.     

Managing heat rejected from air conditioning systems to save energy and improve the microclimates of residential buildings

Simulations are conducted to investigate the influence and improvement potential of air conditioning heat rejection management of residential buildings on microclimate and energy use. The microclimate and building energy use are simulated on a typical high-rise building in Taipei, Taiwan, on a summer night. Heat rejection from the air conditioners is estimated with a building energy program, EnergyPlus, and a computational fluid dynamics (CFD) program, Windperfect, is used to analyze how heat rejection affects the outside thermal environment. Results show that heat rejection from air conditioners worsens the thermal environment below the urban canopy, thus increasing building energy use. Three countermeasure cases of heat rejection management, which consider the type of cooling system and its installation position, are proposed in this study. The average air temperature increase around the buildings caused by heat rejection was analyzed by transferring the simulation results of the building energy program to the CFD model on an hourly basis. Results show that the air temperature next to the building envelope and the air temperature around air conditioners decrease and that there is a reduction in electricity consumption by the air conditioners when a split-type air conditioner is installed on each floor or on every third floor. A reduction in the ambient air temperature below the urban canopy can be obtained by placing a cooling tower on the roof of the building.     

The assessment of heat radiation

Approximately 900 climatic chamber experiments were performed with 16 male subjects to study the thermal strain at climates including increased heat radiation. Based on the reactions of heart rate, rectal temperature and sweat rate, a heat stress index was developed for the assessment of climates with effective heat radiation intensities up to 1400 W m⁻². The index considers different combinations of dry air temperature (5–55°C), globe temperature (25–76°C), mean radiant temperature (25–160°C), air velocity (0.5–2.0 m s⁻¹), clothing, physical work load and directions of radiation and air flow.

The index integrates combinations of the variables producing the same degree of thermal strain into a single value. This value indicates the temperature of the physiologically equivalent climate in which air and radiant temperature are equal. It can be determined from a simple formula or from correspondent graphs.

In comparison, the international recommended heat stress indices are less capable to evaluate heat radiation correctly. The incorporation of the new partial index into the used indices may improve substantially their physiological validity in the assessment of climates with radiant heat stress.

Relevance to industry

The goal of this paper is to provide an improved assessment of thermal stress in working environments in which heat radiation is an important heat stress factor.     

Application of MODIS derived parameters for regional crop yield assessment

NOAA AVHRR has been used extensively for monitoring vegetation condition and changes across the United States. Integration of crop growth models with MODIS imagery at 250 m resolution from the Terra Satellite potentially offers an opportunity for operational assessment of the crop condition and yield at both field and regional scales. The primary objective of this research was to evaluate the quality of the MODIS 250 m resolution data for retrieval of crop biophysical parameters that could be integrated in crop yield simulation models. A secondary objective was evaluating the potential use of MODIS 250 m resolution data for crop classification. A field study (24 fields) was conducted during the 2000 crop season in McLean County, Illinois, in the U.S. Midwest to evaluate the applicability of the MODIS 8-day, 250 m resolution composite imagery (version 4) for operational assessment of crop condition and yields. Ground-based canopy and leaf reflectance and leaf area index (LAI) measurements were used to calibrate a radiative transfer model to create a look up table (LUT) that was used to simulate LAI. The seasonal trend of MODIS derived LAI was used to find crop model parameters by adjusting the LAI simulated from the climate-based crop yield model. Other intermediate products such as crop phenological events were adjusted from the LAI seasonal profile. Corn (Zea mays L.) and soybean (Glycine max (L.) Merr.) yield simulations were conducted on a 1.6 × 1.6 km² spatial resolution grid and the results integrated to the county level. The results were within 10% of county yields reported by the USDA National Agricultural Statistics Service (NASS).     

Thermal sensation of the body as influenced by the thermal microclimate in a face mask

Subjective and physiological responses were obtained from six subjects wearing a face mask while exercising (220 W m ^?2) for 15 min on a bicycle ergometer. Different combinations of ambient air temperatures (7, 16 and 25°C) and mask air temperatures (22,27 and 33°C) were studied with two different air humidities inside the mask (61 and 86% relative humidity (RH)). Control experiments were performed without the mask at the same ambient temperatures. Skin temperatures, heart rates and skin wettedness were monitored during exercise. The subjects’ thermal sensations, sensations of sweating and skin wettedness and their thermal preferences were assessed at the end of the exercise period. Whole body thermal sensation was primarily determined by the ambient air temperature, but was also significantly influenced by the mask air temperature. This could only partly be explained by the change in respiratory heat loss. Other possible avenues of influence are discussed.     

The influence of heated or cooled seats on the acceptable ambient temperature range

In 11 climate chamber experiments at air temperatures ranging from 15 to 45°C, a total of 24 subjects, dressed in appropriate clothing for entering a vehicle at these temperatures, were each exposed to four different seat temperatures, ranging from cool to warm. In one simulated summer series, subjects were preconditioned to be too hot, while in other series they were preconditioned to be thermally neutral. They reported their thermal sensations, overall thermal acceptability and comfort on visual analogue scales at regular intervals. Instantaneous heat flow to the seat was measured continuously. At each ambient room temperature, the percentage dissatisfied was found to be a second-order polynomial function of local heat flow. Zero heat flow was preferred at an air temperature of 22°C and the heat flow that minimized the percentage dissatisfied was found to be a single linear function of air temperature in all conditions. The analysis indicates that providing optimal seat temperature would extend the conventional 80% acceptable range of air temperature for drivers and passengers in vehicle cabins by 9.3°C downwards and by 6.4°C upwards.     

The influence of heated or cooled seats on the acceptable ambient temperature range

In 11 climate chamber experiments at air temperatures ranging from 15 to 45 degrees C, a total of 24 subjects, dressed in appropriate clothing for entering a vehicle at these temperatures, were each exposed to four different seat temperatures, ranging from cool to warm. In one simulated summer series, subjects were preconditioned to be too hot, while in other series they were preconditioned to be thermally neutral. They reported their thermal sensations, overall thermal acceptability and comfort on visual analogue scales at regular intervals. Instantaneous heat flow to the seat was measured continuously. At each ambient room temperature, the percentage dissatisfied was found to be a second-order polynomial function of local heat flow. Zero heat flow was preferred at an air temperature of 22 degrees C and the heat flow that minimized the percentage dissatisfied was found to be a single linear function of air temperature in all conditions. The analysis indicates that providing optimal seat temperature would extend the conventional 80% acceptable range of air temperature for drivers and passengers in vehicle cabins by 9.3 degrees C downwards and by 6.4 degrees C upwards.     

Modelling the impact of drought and heat stress on common bean with two different photosynthesis model approaches

Extreme temperature and drought stress are major environmental factors limiting agriculture worldwide. A comprehensive understanding of plant behavior under different environmental conditions can be gained through experiments and through the application of biophysical crop models. This study presents a field experiment conducted with bean exposed to heat and drought stress. Based on an experimental data collection a crop model was set up, calibrated and validated. Hereby, the two different photosynthesis model approaches already implemented in the model, a simple empirical (the Goudriaan and van Laar or GvL model) and a biochemical photosynthesis model approach (the Farquhar-Ball-Collatz or FBC model), were tested. Both photosynthesis model approaches performed adequately under no stress conditions. Under heat stress conditions, yield was underestimated by both models. However, the FBC model performed better than the simpler photosynthesis model approach of the GvL model. The FBC crop model was able to predict the soil water dynamics, the plant growth and the stomatal conductance.     

The influence of meteorological conditions and topographic parameters on the beech forest microclimate of Simbruini Mountains,central Italy

The relation between vegetation surface temperature and remotely sensed spectral vegetation indices has been examined by a number of authors. The observed linear decrease in surface temperature with the increase in vegetation cover density has generally been explained in terms of the increase in latent heat flux associated with greater amounts of transpirationally active vegetation. However, these investigations have initially concentrated in spatially uniform crop or pasture targets on level terrain, excluding more complex forested environments with variable Sun-sensor-surface geometry. In irregular terrains, the vegetation surface temperature may be strongly influenced by topographic parameters, such as altitude and insulation angle, so that the actual forest microclimate is often difficult to evaluate. Moreover, in the thermal regime, the emission of radiative flux within the canopy element is very tightly coupled to the environment through driving mechanisms such as meteorological conditions. In fact, the allocation of absorbed solar radiation into sensible heat flux and latent heat flux is dominated by the availability of water at the Earth's surface and thus by precipitations and air temperature conditions. In this paper, which uses remotely sensed inputs of surface temperature and vegetation fractional cover, the effects of topographic parameters and vegetation cover density on surface temperature of vegetation are investigated based on Landsat 5 satellite images obtained in the daytime of two clear summer days with different antecedent meteorological conditions. For both scenes analysed, results indicate that altitude as well as the orientation of the surface relative to the Sun were the most important factors controlling surface temperatures of beech forests of Simbruini Mountains, in central Italy.     

Monitoring yield and fruit quality parameters in open-canopy tree crops under water stress. Implications for ASTER

Work on water stress detection at tree and orchard levels using a high-spatial airborne thermal sensor is presented, showing its connection with yield and some fruit quality indicators in olive and peach commercial orchards under different irrigation regimes. Two airborne campaigns were conducted with the Airborne Hyperspectral Scanner (AHS) over olive and peach orchards located in Córdoba, southern Spain. The AHS sensor was flown at three different times on 25 July 2004 and 16 July 2005, collecting 2 m spatial resolution imagery in 80 spectral bands in the 0.43-12.5 μm spectral range. Thermal bands were assessed for the retrieval of land surface temperature using the split-window algorithm and TES (Temperature-Emissivity-Separation) method, separating pure crowns from shadows and sunlit soil pixels using the reflectance bands. Stem water potential and stomatal conductance were measured on selected trees at the time of airborne flights over the orchards. Tree fruit yield and quality parameters such as oil, weight and water content (for the olive trees), and fruit volume and weight (for the peach trees) were obtained at harvest and through laboratory analysis. Relationships between airborne-estimated crown temperature minus air temperature and stem water potential yielded r² = 0.5 (12:30 GMT) at the olive tree level, and r² = 0.81 (9:00 GMT) at the treatment level in peach trees. These results demonstrate that water stress can be detected at the crown level even under the usual water management conditions of commercial orchards. Regressions of yield and fruit quality against remote sensing estimates of crown temperature as an indicator of water stress, yielded r² = 0.95 (olive fruit water content) and r² = 0.94 (peach fruit mean diameter). These results suggest that high-spatial remote sensing thermal imagery has potential as an indicator of some fruit quality parameters for crop field segmentation and irrigation management purposes. A simulation study using ASTER spectral bands and aggregated pixels for stress detection as a function of irrigation level was conducted in order to study the applicability of medium resolution thermal sensors for the global monitoring of open-canopy tree crops. The determination coefficients obtained between the ASTER-simulated canopy temperature minus air temperature and stem water potential yielded r² = 0.58 (12:30 GMT) for olive trees, suggesting the potential for extrapolating these methods to ASTER satellite for global monitoring of open tree canopies.     

Using multidirectional thermography to characterize water status of cotton

A pertinent interpretation of thermal infrared (TIR) information to characterize crop water status requires at least to consider the fraction of crop cover. Even if the crop cover is known, such an interpretation remains difficult and the current issues to be overcome in the field of TIR remote sensing applications stands on bare soil effects. An experiment was conducted during summer 1999 in Montpellier (France) on a row-cotton crop in order to acquire a data set relating thermal and optical multidirectional measurements to crop structure and water status. The crop was monitored all along its development. Three plots were delimited: a reference plot with no water limitation and two plots without water supply respectively at flowering and cutout stage. On three dates, directional TIR and optical images were acquired both on the reference plot and on the one with limited water supply. Directional averaged temperatures (Ts) and Normalized Difference Vegetation Index (NDVI) values showed a strong dependence on canopy gap fraction. Ts appeared particularly influenced by directional sunlit soil fraction variability, depending on both sun/sensor angle configuration, crop structure and water status. Leaves at different levels in the canopy (with different ages and spectral properties) could be observed by the sensor, but the impact of the sunlit/shaded leaves ratio on directional temperature measurements was weak in comparison to soil effects.The different directional influence of sunlit soil fractions on Ts and NDVI values explains in a large part the limits encountered by water stress indices approaches, aiming at relating linearly such variables, when applied to partially covering crops. Such results provide an exhaustive experiment-based biophysical analysis of very high resolution multidirectional TIR signal. They point out further ways of investigations to be explored in the field of water stress indices improvement or performing. This comes as a preamble of an experiment-based analysis of the limits and opportunities of water stress indices methods, complemented with a 3D model-based analysis that allows confirmation and extrapolation of the results to larger ranges of crop characteristics and directional configurations.     

Grain Yield Prediction: Extending the Stress-Degree-Day Approach to Accommodate Climatic Variability

Combination of the stress-degree-day concept of plant water stress assessment with the growing degree-day concept of plant phenological development leads to a new approach to the prediction of grain crop yields that accounts for climatic effects of light, temperature, and water. The model requires knowledge of the dates of plant emergence and heading, maximum and minimum air temperatures through the growing season, and midafternoon canopy-air temperature differentials from the time of heading to physiological maturity. Its utility is illustrated by development of the predictive relationship for a specific cultivar of wheat grown at Phoenix, Arizona, and a test of its predictions for the same cultivar grown at Davis, California. Both final yield and the time of cessation of crop growth are adequately predicted.     

Evaluating the impact of adjusting surface temperature derived from Landsat 7 ETM+ in crop evapotranspiration assessment using high-resolution airborne data

Surface temperature (T_s) is an essential parameter in many land surface processes. When T_s is obtained from remotely sensed satellite data the consideration of atmospheric correction may be needed to obtain accurate surface temperature estimates. Most atmospheric correction methods adjust atmospheric transmissivity, path radiance and downward thermal radiation coefficients. Following a standardized atmospheric correction of Landsat 7 thermal data, some differences were found between these corrected data and surface temperature derived from very-high resolution airborne thermal data. Five different methods for determining atmospheric correction were evaluated comparing atmospherically corrected Landsat 7 data with airborne data for an area of olive orchards located at Southern Spain. When using standard default Landsat 7 calibration coefficients T_s differences between satellite and airborne observations ranged from 1 to 6 K, highlighting the need to perform more robust atmospheric correction. When applying the customized values for semi-arid temperate climate in Idaho, USA, and the values based on the National Centers for Environmental Prediction (NCEP) T_s differences ranged from 1 to 4 K, indicating that additional local calibration may be appropriate. Optimal coefficients were determined using the Generalized Reduced Gradient (GRG) approach, a nonlinear algorithm included in Solver tool, obtaining T_s differences around 1–3 K. In order to evaluate the impact of considering the proposed correction approaches, assessment of the evapotranspiration and crop coefficient values derived from the Mapping Evapotranspiration with Internalized Calibration (METRIC) energy balance model provided maximum errors of around 4%, indicating that the METRIC model does not require a robust atmospheric correction. However, the localized calibration approaches are proposed as useful alternatives when absolute land surface temperatures values are required, as in the case of the determination of crop water stress based on differences between canopy (T_c) and air temperature (T_air).     

Relation between vegetation canopy surface temperature and the Sun-surface geometry in a mountainous region of central Italy

Evapotranspiration is the dominant energy exchange process in dense vegetated environments with an adequate water supply. If water is available vegetation canopy temperatures do not respond immediately upon intercepting solar radiation because of the apportionment of absorbed solar radiation into sensible and latent heat. This lag in the thermal conditions of vegetation canopy following the incident solar flux can be even more complex after sunrise because the presence of dew on the foliage requires more available energy investment in evaporating water and less energy spent in warming the foliage. The aim of this Letter, which is based on remotely-sensed thermal data obtained from Landsat Thematic Mapper in the daytime of a clear summer day, is to investigate the relationship between canopy surface temperatures and the incident solar radiation for a forested montainous landscape of central Italy. Results show that, under the conditions of our experiment, a time lag of one hour considerably increases the linear relation between vegetation canopy temperature and local solar illumination angle.     

Estimation of total above-ground phytomass production using remotely sensed data

Remote sensing potentially offers a quick and nondestructive method for monitoring plant canopy condition and development. In this study, multispectral reflectance and thermal emittance data were used in conjunction with micrometeorological data in a simple model to estimate above-ground total dry phytomass production of several spring wheat canopies. The fraction of absorbed photosynthetic radiation (PAR) by plants was estimated from measurements of visible and near-infrared canopy reflectance. Canopy radiation temperature was used as a crop stress indicator in the model. Estimated above-ground phytomass values based on this model were strongly correlated with the measured phytomass values for a wide range of climate and plant-canopy conditions.     

Comparison of individual and combined ANN models for prediction of air and dew point temperature

Predicted air and dew point temperatures can be valuable in decision making in many areas including protecting crops from damage, avoiding heat stress on animals and humans, and in planning related to energy management. Current web-based artificial neural network (ANN) models on the Automated Environment Monitoring Network (AEMN) in Georgia predict hourly air and dew point temperature for twelve prediction horizons, using 24 models. The observed air temperature may approach the observed dew point temperature, but never goes below it. Current web based ANN models have prediction errors which, when the air and dew point temperatures are close, may cause air temperature to be predicted below the dew point temperature. Herein this error is referred to as a prediction anomaly. The goal of this research was to improve the prediction accuracy of existing air and dew point temperature ANN models by combining the two weather variables into a single ANN model for each prediction horizon. The objectives of this study were to reduce the mean absolute error (MAE) of prediction and to reduce the number of prediction anomalies. The combined models produced a reduction in the air temperature MAE for ten of twelve prediction horizons with an average reduction in MAE of 1.93 %. The combined models produced a reduction in the dew point temperature MAE for only six of twelve prediction horizons with essentially no average decrease in MAE. However, the combined models showed a marked reduction in prediction anomalies for all twelve prediction horizons with an average reduction of 34.1 %. The reduction in prediction anomalies ranged from 4.6 % at the one-hour horizon to 60.5 % at the eleven-hour horizon.