Crop Surface Temperatures Research Articles

MONTPEL: A multi-component Penman-Monteith energy balance model

Mechanistic modelling is gradually replacing empiricism in crop models, focusing on leaf-level physiological processes. This shift necessitates simulating crop surface temperature at infra-canopy sub-daily scales but many crop models still rely on empirical formulations for canopy temperature estimation, typically on a daily basis. We developed MONTPEL, a multi-component Penman-Monteith model that allows simulating the crop energy balance with flexible canopy representations (“BigLeaf” vs. “Layered”, “Lumped” vs. “Sunlit-Shaded”) and accounts for atmospheric stability conditions. We analyzed the model behavior, sensitivity and accuracy, using measurements from four wheat (Triticum aestivum L.) experiments conducted under varying pedoclimatic and water stress conditions. Measurements included hourly energy balance terms (total net radiation, soil heat flux, sensible and latent energy fluxes), hourly temperature of the canopy surface or of leaves at different depths inside the canopy, and sunlit and shaded leaf temperatures around solar noon at different dates. MONTPEL reproduced the measured energy balance terms with a root mean square error (RMSE) between 21 and 87 Wm-2 and a coefficient of determination (R²) exceeding 0.65. The model's accuracy in simulating canopy temperature, with RMSE ≤ 2.2 °C and R² ≥ 0.92, remained consistent regardless of measurement scale. Adjusting the aerodynamic resistance for atmospheric stability minimized simulated canopy temperature errors, notably in semi-arid conditions. Crop latent energy flux and temperature were most sensitive to the maximal stomatal conductance (gs,max) parameter. However, using a single gs,max value across the simulated experiments yielded satisfactory results, suggesting a weak sensitivity to the temporal and site-to-site variability of gs,max. Distinguishing sunlit from shaded canopy fractions systematically resulted in lower latent energy fluxes compared to “Lumped” canopy representation results. Analysis identified limitations in the multi-component approach, particularly an unrealistic uniform temperature shift across leaf layers when soil surface temperature changes.

Influence of the biochar application on the thermal properties of soddy-podzolic soil and on the energy balance fluxes of spring wheat in the Leningrad region under various soil moisture conditions

The article presents the results of a field experiment to assess the effect of pre-sowing application of the biochar on the thermal properties of the arable horizon of soddy-podzolic sandy loam soil, on the energy balance components, on the crop surface temperature and on the leaves temperature of spring wheat (variety “Daria”) under various conditions of soil moisture in 2022. The experiment took place at the Menkovo Experimental Station of the Agrophysical Research Institute, located in the Gatchinsky District of the Leningrad Region. The experiment included the plot with the biochar application at the dose of 21.9 t ha-1 and the control plot. The soil thermal properties were measured by the heat pulse method. The components of the energy balance were determined using agrometeorological measurements, radiation balance measurements, crop surface temperature, and phenological measurements. The crop surface temperature was measured by a non-contact method using pyrometers. The soil moisture conditions and available water for wheat were characterized by volumetric soil moisture and evapotranspiration. The volumetric soil moisture was measured using a capacitive soil moisture sensor. The evapotranspiration was determined using the residual term of the energy balance equation through the latent heat flux. According to the results of field experiments, a significant effect (p < 0.05) of the biochar application on the soil thermal properties was found, however, under different moisture conditions, the effect was multidirectional. At zero soil moisture, the biochar application reduced thermal conductivity by 29.7%, reduced volumetric heat capacity by 18.5%, reduced diffusivity by 13.7%, and reduced thermal inertia by 24.3%. Under the conditions of field capacity, the biochar application increased thermal conductivity by 9.4%, reduced volumetric heat capacity by 2.6%, increased diffusivity by 12.3%, and increased thermal inertia by 3.2%. The biochar application significantly (p < 0.05) increased the turbulent heat flux – by 35.5%, which is due to an increase in the crop surface temperature (by 6.4%). Resulting from the decrease in soil evaporation, the biochar application reduced the latent heat flux by 17.0%, and the evapotranspiration by 13.9%. Leaf temperature is related to transpiration. Transpiration can increase when biochar is applied on light-textured soils due to an increase in soil water capacity. The biochar application did not result in significant changes of leaf temperature. The study results are confirmed by numerous articles of both foreign and Russian researchers.

Untangling irrigation effects on maize water and heat stress alleviation using satellite data

Abstract. Irrigation has important implications for sustaining global food production by enabling crop water demand to be met even under dry conditions. Added water also cools crop plants through transpiration; irrigation might thus play an important role in a warmer climate by simultaneously moderating water and high temperature stresses. Here we used satellite-derived evapotranspiration estimates, land surface temperature (LST) measurements, and crop phenological stage information from Nebraska maize to quantify how irrigation relieves both water and temperature stresses. Unlike air temperature metrics, satellite-derived LST revealed a significant irrigation-induced cooling effect, especially during the grain filling period (GFP) of crop growth. This cooling appeared to extend the maize growing season, especially for GFP, likely due to the stronger temperature sensitivity of phenological development during this stage. Our analysis also revealed that irrigation not only reduced water and temperature stress but also weakened the response of yield to these stresses. Specifically, temperature stress was significantly weakened for reproductive processes in irrigated maize. Attribution analysis further suggested that water and high temperature stress alleviation was responsible for 65±10 % and 35±5.3 % of the irrigation yield benefit, respectively. Our study underlines the relative importance of high temperature stress alleviation in yield improvement and the necessity of simulating crop surface temperature to better quantify heat stress effects in crop yield models. Finally, considering the potentially strong interaction between water and heat stress, future research on irrigation benefits should explore the interaction effects between heat and drought alleviation.

Detection of the 3D temperature characteristics of maize under water stress using thermal and RGB-D cameras

Global warming and water resource shortage greatly influence the crop growth and negatively affect the crop yield. Breeding water–stress resistant crop varieties is one of the effective ways to handle this problem. The surface temperatures of the crop are essential for assessing their water stress resistance. Thermal imaging has been widely used to acquire the crop surface temperatures. However, most studies focus on 2D measurement. A 3D thermal imaging system is designed, and a method is developed by using the thermal and RGB-D data to acquire 3D thermal information of maize under water stress conditions. First, thermal and RGB-D cameras were used to collect the thermal and color images, and depth data of maize at the jointing stage and the thermal and color images were processed to extract the edge images of maize. Second, the KAZE feature was selected to register the edge images. Testing results showed that the KAZE feature has better performance than the SURF and BRISK features in the thermal and color image registration of maize. On the basis of the thermal and color image registration, the depth data of maize were assigned with temperature values. Third, the depth data of maize were further processed with denoising, maize extraction, amplification, smoothing, and temperature correction steps to improve the data qualities. Finally, the crop water stress index and canopy–air temperature difference values of each point were calculated. The results demonstrated that the system and the proposed method can effectively detect the water stress characteristics of maize in 3D, which can be combined with the morphological traits of leaves to synthetically analyze the water stress resistance of the crop.

Field-Based Calibration of Unmanned Aerial Vehicle Thermal Infrared Imagery with Temperature-Controlled References.

Accurate and reliable calibration methods are required when applying unmanned aerial vehicle (UAV)-based thermal remote sensing in precision agriculture for crop stress monitoring, irrigation planning, and harvesting. The primary objective of this study was to improve the calibration accuracies of UAV-based thermal images using temperature-controlled ground references. Two temperature-controlled ground references were installed in the field to serve as high- and low-temperature references, approximately spanning the expected range of crop surface temperatures during the growing season. Our results showed that the proposed method using temperature-controlled references was able to reduce errors due to ambient conditions from 9.29 to 1.68 °C, when tested with validation panels. There was a significant improvement in crop temperature estimation from the thermal image mosaic, as the error reduced from 14.0 °C in the un-calibrated image to 1.01 °C in the calibrated image. Furthermore, a multiple linear regression model (R2 = 0.78; p-value < 0.001; relative RMSE = 2.42%) was established to quantify soil moisture content based on canopy surface temperature and soil type, using UAV-based thermal image data and soil electrical conductivity (ECa) data as the predictor variables.

Design, modeling and analysis of efficient multi-rack tray solar cabinet dryer coupled with north wall reflector

An innovative design of front loaded multi-rack tray (MRT) solar cabinet dryer is presented having optimally inclined reflective north wall (RNW) which utilizes reflected component of the solar radiation in addition to the beam radiation for efficient drying particularly in winter when conventional dryer does not perform well at higher latitudes (>30°N). A modified global solar radiation capture model coupled with RNW is presented to determine the effect of RNW on its performance. A thermal model is also developed for chamber air temperature (Tch) and crop surface temperature (Tc) with experimental validation and solved using Modified Euler’s method using FREEMAT software with experimental validation at Ludhiana climate (30.56°N) India. Use of RNW enhances the radiation capture by 37.58%, 31.57% and 23.24% at 30°, 40° and 50°N latitude respectively in winter. Daily average efficiency η(avg) with RNW was 5% and 4.35% higher under natural and forced convection modes respectively as compared to without using RNW. By using RNW, 4–7 °C and 2–5 °C higher Tch was observed as compared to when RNW was not used under natural and forced convection modes respectively. Total reduction in drying time for carrot drying was observed to be 20% and 15% under natural and forced convection modes respectively. Predicted and experimental values for Tch were successfully validated with root mean square error (RMSE) ranging between 2.6 and 4.9. Uncertainty was found to be 20.5 out of which 19.3 was internal and 1.2 external which indicates the accuracy of observed data to determine the performance of solar dryer.

Performance Evaluation and Drying Kinetics for Solar Drying of Hygroscopic Crops in Vacuum Tube Assisted Hybrid Dryer

Abstract The present experimentation work discloses drying of hygroscopic crops under the new concept of solar-assisted greenhouse type dryer integrated with evacuated tube water heating system to control and maintain the temperature of the greenhouse environment according to the regulated flowrate of heated water in the drying trays. The dryer consists of an evacuated tube solar collector, flow regulating device and drying bed with provision for the flow of heated water. The power supply for forced circulation of solar-heated water inside the copper tube as well as the greenhouse environment air is maintained by solar photovoltaic (PV) modules. The dryer is tested for drying two hygroscopic crops namely coriander and fenugreek. The drying performance of the hybrid system is evaluated in terms of mass reduction and its derived influence on moisture content and drying rate. The derived parameters are compared with the corresponding evaluations under open sun drying. The rise in the greenhouse environment temperature and the crop surface temperature at hourly intervals as compared to the ambient condition were used as parameters for the thermal performance of the dryer. The drying curve for change in mass shows complete drying time for coriander and fenugreek reduced by 3.5 and 2.5 h, respectively, for present sample sizes. The most suitable mathematical model was also regressed using matlab followed by the development of a neural network for more precise prediction of moisture ratio (MR) for present hybrid drying.

Irrigation scheduling using intervention of Geomatics tools—A case study of Khedli minor

Food is the basic and compulsory requirement of the human being. It is expected that demand for food crops will double during the next 50 years with limited land and water resources. Production of food requires water. Global estimates indicates that irrigated agriculture sector consumes about 85% of the available water. Further it is expected that in this sector will witness increase water consumption about 20% by the year 2025. The requirement of water increases as the production increases. Proper utilization of water resource is required which is known as scheduling of water for irrigation or irrigation scheduling. Studies carried out in the water deficit area indicated that proper irrigation scheduling may save water and energy up to 35%. The present trend of irrigation has caused 25% depletion in ground water reserve by 2010. Research shows that increasing trend in demand of water in irrigation may lead to total depletion of the ground water table within 50 years. More enhancements required in techniques of irrigation scheduling as the data associated with the estimation of scheduling of water are not sufficient and reliable. An attempt has been made to solve some of the existing problems of data insufficiency and non reliable data can better be solved by the help of remote sensing and GIS. An endeavor has been made to compute the three main parameters influencing the irrigation scheduling namely, crop coefficient, albedo and crop surface temperature through intervention of remote sensing. The approach suggested for the irrigation scheduling using remote sensing and GIS can save irrigation water by 12.5% as compared to the conventional or prevailing approach in study area.

Towards using a thermal infrared index combined with water balance modelling to monitor sugarcane irrigation in a tropical environment

Abstract In humid regions, the timing and quantity of a complementary irrigation regime is challenging because of the irregularity of rainfalls events. In this study, we tested the use of a thermal infrared derived empirical crop water stress index (CWSIe) as an in situ measurement of the water status of sugarcane, to better monitor the irrigation scheduling. To do this, we set up a 2-year experiment in Reunion Island, on a trial with plots under different water conditions (rainfed and irrigated). Crop surface temperature was measured daily with infrared radiometers (Apogee Instruments) installed above the canopy, and soil moisture and drainage measurements were used to derive the ratio between actual and maximum evapotranspiration (AET/MET) values that were then averaged on “hydrically homogeneous” time periods (between 7 and 25 days). Only the thermal data acquired on clear days and 1 h after noon in 2007 were used to define the empirical lower and upper baselines required for the calculation of empirical CWSI. The data set acquired in 2008 was used to test the robustness of the method as we used the upper and lower baselines defined in 2007 to calculate CWSIe. The linear regression between AET/MET and (1 − CWSIe) averaged on the same periods (values ranging between 0.4 and 1) showed a significant correlation for both experimental years (global R2 = 0.75 and RMSE = 0.12). This result indicates the effectiveness of the CWSIe to measure the water status of the sugarcane crop, even in humid conditions with a vapor pressure deficit (VPD) between 0.5 and 2.1. We conclude the study by discussing the complementarity of this remote water stress index (CWSIe) with OSIRI water balance modelling tool currently used in Reunion Island for monitoring sugarcane crop irrigation.

Combining Remotely Sensed Data and Ground-Based Radiometers to Estimate Crop Cover and Surface Temperatures at Daily Time Steps

Estimation of evapotranspiration (ET) is important for monitoring crop water stress and for developing decision support systems for irrigation scheduling. Techniques to estimate ET have been available for many years, while more recently remote sensing data have extended ET into a spatially distributed context. However, remote sensing data cannot be easily used in decision systems if they are not available frequently. For many crops ET estimates are needed at intervals of a week or less, but unfortunately due to cost, weather, and sensor availability constraints, high resolution ( <100 m ) remote sensing data are usually available no more frequently than 2 weeks. Since resolution of this problem is unlikely to occur soon, a modeling approach has been developed to extrapolate remotely sensed inputs needed to estimate ET. The approach accomplishes this by combining time-series observations from ground-based radiometers and meteorological instruments with episodic visible, near infrared, and thermal infrared ...

Crop surface temperature estimation in irrigated command areas using MODIS satellite data

Crop surface temperature (CST) is an important parameter to monitor crop status. Satellite data in thermal region provide an opportunity to estimate CST over large regions at frequent intervals. In the present study, various split‐window algorithms are employed to estimate CST over rice areas in irrigation projects of Krishna basin, South India using multi‐resolution MODIS satellite data. NDVI is used to approximate the mean pixel emissivity, by taking known values for emissivity and NDVI for pure vegetation and bare soil pixels. Diurnal ground measurements are made to evaluate satellite‐derived CST. CST values obtained using the Sobrino method have been found to be closer to the ground‐measured values compared with other algorithms, as it takes into account view angle, atmospheric transmittance, and water vapour corrections. It has been observed that the error in estimating CST is comparatively lower for well‐grown crops.

On the utilization of ENVISAT AATSR data for geological/hydrological applications

ENVISAT AATSR data over the Indian subcontinent have been procured from ESA and preprocessed with DESCW and BEAM 2.2 software for extraction of our area of interest. Two intensive test sites have been chosen, including a part of the Himalayan region and the Sundarban Delta. Calibration of AATSR thermal IR data for surface temperature modelling has been attempted over the Sundarban Delta region. Later, the surface temperature maps have been utilized to generate the momentary and daily evapotranspiration patterns over the area of interest which have been further used for broad land use/crop classifications. A simplified model suggested by Soer [Estimation of regional evapotranspiration and soil moisture conditions using remotely sensed crop surface temperatures, Remote Sensing of Environment 9 (1980) 27–45] has been used to estimate evapotranspiration over the area of interest. The results match with the earlier observation in this region. Further processing of AATSR data has been performed over the Himalayan region to extract two minor faults across the main thrust belts.

Crop Canopy Characterization in Irrigation Command Areas Using MODIS Satellite Data

The study aims at crop canopy characterisation in irrigation command areas using multi‐resolution MODIS satellite data. A regional model was developed between the ground measured Leaf Area Index (LAI) and MODIS derived NDVI over rice areas in irrigation projects of Krishna basin, South India. The model was used to estimate spatial variation of LAI from MODIS data and for further estimation of Crop Resistance Factor (CRF) defined as a function of crop height. NDVI was used to approximate the mean pixel emissivity, by taking known emissivity of pure vegetation and bare soil pixels. Crop Surface Temperature (CST) was estimated using MODIS thermal data in split window algorithms over rice areas. Evapo‐Transpiration (ET) was estimated using MODIS derived Albedo and net long wave radiation in surface energy balance equation. Ground measured crop parameters like crop height, CST and LAI were used to evaluate satellite derived crop parameters. Satellite derived LAI and spectral indices were found to be highly correlated with CST and ET.

Using directional TIR measurements and 3D simulations to assess the limitations and opportunities of water stress indices

Multidirectional remotely sensed optical and thermal images acquired within a row cotton crop in Montpellier (France) were used to test the opportunities and limitations of an existing water stress index, the Water Deficit Index (WDI, based on the trapezoid approach). The WDI was applied with multidirectional crop surface temperatures ( T s) and reflectance data acquired on a row-cotton crop with different water and cover conditions from 11 different view angles in the east/west plane. This data set allowed a biophysical analysis of this index both inside and outside its validity domain, initially limited in terms of T s measurements in a [−20°, +20°] view angles interval around nadir. Results showed that the WDI was a robust approach, since its calculation is based on the relationship between crop cover and T s. However, it yielded some directional errors in the case of sparse crops even in its validity domain where the relative variation of WDI between oblique angles and nadir could reach 14% (and more than 40% for larger view angles). The same degree of variability was observed between WDI values estimated on a same plot at two different times in a given day from a nadir observation. In a large range of crop heterogeneity, hourly sunlit soil fraction presented a stronger influence on T s than the total soil fraction. However, by adapting the view angle to daytime measurements and crop structure, it seemed possible to overcome sunlit soil effects. These experimental results were tested and extrapolated using a 3D crop energy balance model ( Thermo). It allowed simulations of directional T s measurements according to various sun/sensor angular configurations, crop structure, and water status characteristics. This confirmed the limitations of the trapezoid method both within and outside its validity domain. Moreover, Thermo allowed the computation of a “directional” WDI accounting for angular and hourly sunlit soil effects variability on T s. The interest of adapting the view angle to daytime measurements and crop structure was confirmed by comparing this “directional” WDI with the “theoretical” one (based on the original trapezoid approach). These results should encourage further development of water stress indices based on bidirectional thermal infrared and optical measurements to quantify and thus overcome sunlit soil fraction effects.

A simple model for minimum crop temperature forecasting during nocturnal cooling

A simple mechanistic model is developed to forecast the minimum temperature reached by the aerial elements of a crop during nocturnal cooling. The model, whose inputs are meteorological data registered at sunset, has two main characteristics: (1) it uses as a framework a two-layer scheme of the surface–atmosphere interaction which allows one to make a clear difference between the temperature of the crop and the temperature of the soil surface; (2) it does not deal with the rather complex resolution of the non-steady-state regime that most of the physical models intend to solve numerically or analytically; it is based upon a static representation of the soil–plant–atmosphere system assumed to be representative of the conditions reached at the end of the night, when minimum temperatures usually occur. The outputs of the model, i.e. minimum crop and soil surface temperatures, are compared to a set of experimental data collected on a pea crop grown near Paris in winter. It appears that the accuracy of the prediction depends mainly on the correct estimation of the nocturnal atmospheric radiation from the weather data observed at sunset. The model performs relatively well under clear sky conditions, but it is less accurate under cloudy conditions. Two simple procedures of estimating long-wave radiation are tested: their accuracy, however, turns out to be relatively poor. When used predictively, the model shows that all other conditions being kept equal, taller crops experience less severe frosts, while crops with greater leaf area index (LAI) experience more severe frosts. The role of soil characteristics (composition, moisture) is also assessed.

Relations between Soil Moisture and Satellite Vegetation Indices in the U.S. Corn Belt

Satellite-derived vegetation indices extracted over locations representative of midwestern U.S. cropland and forest for the period 1990–94 are analyzed to determine the sensitivity of the indices to neutron probe soil moisture measurements of the Illinois Climate Network (ICN). The deseasoned (i.e., departures from multiyear mean annual cycle) soil moisture measurements are shown to be weakly correlated with the deseasoned full resolution (1 km × 1 km) normalized difference vegetation index (NDVI) and fractional vegetation cover (FVC) data over both land cover types. The association, measured by the Pearson-moment-correlation coefficient, is stronger over forest than over cropland during the growing season (April–September). The correlations improve successively when the NDVI and FVC pixel data are aggregated to 3 km × 3 km, 5 km × 5 km, and 7 km × 7 km areas. The improved correlations are partly explained by the reduction in satellite navigation errors as spatial aggregation occurs, as well as the apparent scale dependence of the NDVI–soil moisture association. Similarly, stronger relations are obtained with soil moisture data that are lagged by up to 8 weeks with respect to the vegetation indices, implying that soil moisture may be a useful predictor of warm season satellite-derived vegetation conditions. This study suggests that a “long-term” memory of several weeks is present in the near-surface hydrological characteristics, especially soil water content, of the Midwest Corn Belt. The memory is integrated into the satellite vegetation indices and may be useful for predicting crop yield estimates and surface temperature anomalies.

Maximum and Actual Evapotranspiration for Barley (Hordeum vulgare L.) through NOAA Satellite Images in Castilla-La Mancha, Spain

An easy-to-follow methodology is developed for the assessment of regional evapotranspiration in Castilla-La Mancha, a semi-arid region of Spain. The methodology is applied to barley crops to monitor the irrigation scheduling over the region, by using remote sensing techniques supplemented by ground measurements. The methodology can be based on either of two models. In the first one, established by Caselles and Delegido, 1the reference evapotranspiration, ET o, derives from the expression ET o= AR g (T a) m a x+ BR g+ Cwhere A, Band Care empirical coefficients, depending on climatic parameters and determined for each region; R gis the daily global radiation; and ( T a) m a xis the maximum air temperature. The second model, proposed by Jackson et al., 2considers the actual evapotranspiration ER= R n+ D( T a− T s) where R n, is the net radiation, T aand T sare the air and crop surface temperatures, respectively, and Dis a semi-empirical coefficient. Both methods were compared with the method of Penman (considered standard), and resulted in differences of ±1 mm d −1. The developed methodology has been applied to map the reference and the actual evapotranspiration over a 10×10 km area, using the thermal-infrared information provided by the AVHRR (advanced very high resolution radiometer) sensor on board the NOAA (national oceanic atmosphere administration) satellite on a selected date.

A simple algorithm to retrieve soil moisture and vegetation biomass using passive microwave measurements over crop fields

A simple algorithm to retrieve sail moisture and vegetation water content from passive microwave measurements is analyzed in this study. The approach is based on a zeroth-order solution of the radiative transfer equations in a vegetation layer. In this study, the single scattering albedo accounts for scattering effects and two parameters account for the dependence of the optical thickness on polarization, incidence angle, and frequency. The algorithm requires only ancillary information about crop type and surface temperature. Retrievals of the surface parameters from two radiometric data sets acquired over a soybean and a wheat crop have been attempted. The model parameters have been fitted in order to achieve best match between measured and retrieved surface data. The results of the inversion are analyzed for different configurations of the radiometric observations: one or several look angles, L-band, C-band or (L-band and C-band). Sensitivity of the retrievals to the best fit values of the model parameters has also been investigated. The best configurations, requiring simultaneous measurements at L- and C-band, produce retrievals of soil moisture and biomass with a 15% estimated precision (about 0.06 m 3/m 3 for soil moisture and 0.3 kg/m 2 for biomass) and exhibit a limited sensitivity to the best fit parameters.

Calibration and use of a pyroelectric thermal camera and imaging system for greenhouse infrared heating evaluation

Infrared sensing instruments were calibrated and used to measure crop surface temperature distributions in a gas-fired, infrared heated greenhouse. These instruments included thermistors, narrow field of view infrared thermometers for small spot areas, and a pyroelectric thermal vision camera, equipped with a variable automatic gain to optimize the grey scale circuit. The thermal vision camera was used for both qualitative and quantitative temperature measurement by evaluating temperatures from crop images, using machine vision. Calibration was conducted using hot and cold reference surfaces of known temperature. Images were digitized with a computer and frame grabber, and analyzed with a commercial software package. Grey-scale values of the calibration surfaces correlated well ( r 2 = 0.98) with other corresponding temperature measurements. Crop surface temperature under radiant heating in a greenhouse was observed to be quite uniform ±2.5°C. Sources of radiant energy loss were identified in the greenhouse, operated at a suboptimal air temperature, for improved heating efficiency.

On the use of satellite thermal data for determining evapotranspiration in partially vegetated areas

Abstract The use of satellite data for determining daily evapotranspiration over completely vegetated areas has been analysed by different authors. However, if the soil surface is partially covered by vegetation the problem becomes more complicated, because in this case the value of the temperature measured by the satellite varies with the viewing geometry. In this work we have studied the influence of the satellite observation angle for crops with different cover degree, field geometry and ground emissivity. For this purpose we have used the simplified relation proposed by Jackson et al.LET d=Rnd−B(T3−T a)iwhere ET 4 and R nd are the daily values of evapotranspiration and net radiation respectively, (T 3−T a)i is the difference between crop surface and air temperatures measured at midday, B is a semi-empirical parameter, and L is the latent heat of vaporization of water. And we have concluded that the imposition of a single value of B is unacceptable and specific values must be chosen according to certai...