Estimation Of Net Radiation Research Articles

Estimation of instantaneous and daily net radiation from MODIS data under clear sky conditions: a case study in East Asia

The Moderate Resolution Imaging Spectroradiometer-based net radiation (R N) model was built and applied in East Asia in 2005. Because there have hardly been simple parameterization schemes developed over a large area using remote-sensing technology, the model was aimed to present physical simplicity in complex topography at multiple spatiotemporal scales. The model successfully reproduced the instantaneous R N values obtained at four flux tower sites having individually different ecohydrology. The diurnal cycle of R N was contextually simulated using a simple sine curve to determine the daily and monthly average net radiation. The diurnal R N estimation method was proven to be a reliable model as long as accurate boundary conditions, sunrise and sunset times, for example, were obtained. The monthly average net radiation (MANR) was estimated using the diurnal patterns of the instantaneous R N. Distribution of the monthly R N demonstrated that elevation and latitude were the primary factors affecting the MANR. The proposed R N algorithm turned out to be a promising method for valuable applications in various fields due to systematic simplicity and fewer input parameters.

Estimating Daily and 24-Hour Net Radiation for All Sky Conditions through Remote Sensing and Climatic Data

Net radiation is a key variable in hydrological studies. However, measured net radiation data are rarely available and are often subject to error because of equipment calibration or failure. Additionally, point measurements of net radiation do not represent the diversity of the regional net radiation values, which are needed for many physical and biological processes such as climate monitoring and evapotranspiration mapping. The authors present a methodology to estimate daytime net radiation using a combination of remote sensing and climatic data. This paper expands on previous original research by extending the estimation of net radiation to 24 h under all sky conditions. The procedure estimates daytime and nighttime net radiation and combines the results to calculate the 24-h net radiation values. The methodology combines information from satellite and local weather stations to estimate net radiation values. The procedure can estimate net radiation under all sky conditions using measured or estimated solar radiation. Two different methods are presented to estimate net radiation. Comparisons between measured and predicted daytime and 24-h net radiation using the two methods resulted in an average ratio ranging from 0.98–1.0 and a standard error of estimate ranging from 0.83–1.62 MJ m−2 day−1. Satellite data were used for the calculation of leaf area index, albedo, and ground temperature. Although satellite data are scarce, the methodology is not limited to satellite imagery. One can easily use ground level measurements of leaf area index, albedo, and temperature to estimate daytime and 24-h net radiation using the proposed methodology.

Estimation method of grass net radiation on the determination of potential evapotranspiration

Evapotranspiration depends primarily on the local energy balance, which is the most important variable of the physical environment when it comes to irrigation scheduling, especially throughout the production formation stages that are limited by water factors. The current manuscript will verify the applicability of estimates of grass net radiation to the performance of estimation methods of potential evapotranspiration under the climatic conditions in Piracicaba, Sao Paulo, Brazil. In order to proceed to simple linear regression studies, meteorological data were monitored by an automatic weather station throughout a period of 87 days for the calculation of potential evapotranspiration, taking into account both the estimated and calculated grass net radiation values. The use of grass net radiation estimated by a model based on the local energy balance showed an excellent performance for estimating potential evapotranspiration by means of the Penman–Monteith, adjusted Priestley–Taylor, and simplified Penman approaches, along with a good agreement (R2 > 0.91) related to the potential demand values obtained with grass net radiation measured by net radiometers. Copyright © 2012 Royal Meteorological Society

Estimating net radiation flux in the Tibetan Plateau by assimilating MODIS LST products with an ensemble Kalman filter and particle filter

Net radiation flux is a key physical variable of energy and water exchange processes in land–atmosphere interactions. Among the methods to estimate net radiation flux, measurements and estimates from modeling have their limitations. Data assimilation methods have potential to estimate net radiation flux more accurately by merging model dynamics and observations. In this paper, two data assimilation schemes are developed based on the Common Land Model (CoLM) using the ensemble Kalman filter (EnKF) and particle filter (PF) algorithms, respectively, to improve predictions of surface net radiation flux in the Tibetan Plateau. First, Moderate Resolution Imaging Spectroradiometer (MODIS) leaf area index (LAI) products were used to update the LAI in CoLM. It was found that sparse vegetation in the Tibetan Plateau has an obvious influence on the surface temperature. Then, the experiments of assimilating MODIS land surface temperature (LST) products were carried out with the EnKF and PF algorithms, respectively. The two schemes were tested and validated with observations from two automatic weather stations (AWSs) in Muztaga in the Tibetan Plateau during the period of October 1, 2009–September 30, 2010. The results show that the two assimilation schemes are both feasible and an improvement over the predictions of cases with no assimilation for the estimations of surface temperature and net radiation flux. Furthermore, the different surface types and the accuracy of MODIS LST products have an important influence on the assimilation results. In addition, the performances of EnKF and PF are almost same in the study.

Multimodel Analysis of Energy and Water Fluxes: Intercomparisons between Operational Analyses, a Land Surface Model, and Remote Sensing

Abstract Using data from seven global model operational analyses (OA), one land surface model, and various remote sensing retrievals, the energy and water fluxes over global land areas are intercompared for 2003/04. Remote sensing estimates of evapotranspiration (ET) are obtained from three process-based models that use input forcings from multisensor satellites. An ensemble mean (linear average) of the seven operational (mean-OA) models is used primarily to intercompare the fluxes with comparisons performed at both global and basin scales. At the global scale, it is found that all components of the energy budget represented by the ensemble mean of the OA models have a significant bias. Net radiation estimates had a positive bias (global mean) of 234 MJ m−2 yr−1 (7.4 W m−2) as compared to the remote sensing estimates, with the latent and sensible heat fluxes biased by 470 MJ m−2 yr−1 (13.3 W m−2) and −367 MJ m−2 yr−1 (11.7 W m−2), respectively. The bias in the latent heat flux is affected by the bias in the net radiation, which is primarily due to the biases in the incoming shortwave and outgoing longwave radiation and to the nudging process of the operational models. The OA models also suffer from improper partitioning of the surface heat fluxes. Comparison of precipitation (P) analyses from the various OA models, gauge analysis, and remote sensing retrievals showed better agreement than the energy fluxes. Basin-scale comparisons were consistent with the global-scale results, with the results for the Amazon in particular showing disparities between OA and remote sensing estimates of energy fluxes. The biases in the fluxes are attributable to a combination of errors in the forcing from the OA atmospheric models and the flux calculation methods in their land surface schemes. The atmospheric forcing errors are mainly attributable to high shortwave radiation likely due to the underestimation of clouds, but also precipitation errors, especially in water-limited regions.

Estimation of net surface shortwave radiation from MODIS data

Net surface shortwave radiation (NSSR) is a key quantity for the estimation of surface energy budget and is used in various land-surface models. In this article, two different methodologies, including three empirical algorithms and one advanced simplified theoretical algorithm for estimating instantaneous NSSR from Moderate Resolution Imaging Spectroradiometer (MODIS) data are explored and summarized. An advanced simplified theoretical algorithm is developed by combining two simplified radiative-transfer models with various MODIS atmosphere and land products. To comprehensively evaluate these algorithms, ground measurements from seven stations widely distributed in different climatic regions of China are used. The results indicate that under clear-sky conditions, the three empirical algorithms present appreciable difference in accuracy, while under cloudy skies, they produce similar, but not very good, predictions. Compared with these empirical methods, the simplified theoretical algorithm we adopt can significantly improve accuracy. The root mean square difference (RMSD) yielded by this algorithm is approximately 54 W m−2 under clear skies and 83 W m−2 under cloudy skies, respectively. Since the utility of instantaneous NSSR estimates is limited compared to that of the daily average value, a simple scheme to acquire the daily average NSSR is established, which is based on instantaneous estimations from two satellite MODIS sensors (Terra: AM and Aqua: PM), and the daily average NSSR over the Beijing area is also mapped.

Estimation of net surface shortwave radiation over the tropical Indian Ocean using geostationary satellite observations: Algorithm and validation

[1] This paper presents the development of a methodology to estimate the net surface shortwave radiation (SWR) over tropical oceans using half-hourly geostationary satellite estimates of outgoing longwave radiation (OLR). The collocated data set of SWR measured at 13 buoy locations over the Indian Ocean and a Meteosat-derived OLR for the period of 2002–2009 have been used to derive an empirical relationship. The information from the solar zenith angle that determines the amount of solar radiation received at a particular location is used to normalize the SWR to nadir observation in order to make the empirical relationship location independent. As the relationship between SWR and OLR is valid mostly over the warm-pool regions, the present study restricts SWR estimation in the tropical Indian Ocean domain (30°E–110°E, 30°S–30°N). The SWR estimates are validated with an independent collocated data set and subsequently compared with the SWR estimates from the Global Energy and Water Cycle Experiment-Surface Radiation Budget V3.0 (GEWEX-SRB), International Satellite Cloud Climatology Project-Flux Data (ISCCP-FD), and National Centers for Environmental Prediction (NCEP) reanalysis for the year 2007. The present algorithm provides significantly better accuracy of SWR estimates, with a root-mean-square error of 27.3 W m−2 as compared with the values of 32.7, 37.5, and 59.6 W m−2 obtained from GEWEX-SRB, ISCCP-FD, and NCEP, respectively. The present algorithm also provides consistently better SWR compared with other available products under different sky conditions and seasons over Indian Ocean warm-pool regions.

Estimation of net surface shortwave radiation from land surface temperature in regional scale

The method to estimate NSSR (net surface shortwave radiation) from LST (land surface temperature) in regional scale is discussed. First, an elliptical model between the time series of normalized LST and NSSR was developed using the daily evolution of LST and NSSR. Second, time series of LST and NSSR were simulated by common land model (CoLM) and were proved to be of high accuracy. On the basis of these, a non-linear least square ellipse fitting using the genetic algorithm method was used to fit the normalized LST and NSSR. Finally, LST was inverted using MODIS (moderate resolution imaging spectroradiometer) data with the split-window algorithm, and the regional NSSR was then estimated with LST and an elliptical model. The validation result shows that the derived average NSSR of 50×50 pixels of MODIS data was quite close to the observed data, and the distribution was reasonable, which indicates that the proposed method was capable of estimating NSSR on a regional scale.

Temporal patterns of thermal emission directionality of crop canopies

[1] Many field experiments have observed significant temporal variations of thermal infrared (TIR) emission directionality, making it necessary to explain this phenomenon quantitatively to exploit potential applications of the directional remotely sensed TIR observation. The main objective of this paper is to determine when and how the significant directional effect appears. Two models, TRGM and Cupid, are linked to simulate the temporal variations of directional brightness temperature TB(θ) of crop canopies, including winter wheat and summer corn. Two indicators are defined: (1) ΔTB,AVG representing the mean difference between nadir TB(0) and off-nadir TB(55) and (2) ΔTB,STD representing the standard deviation of TB(55) for different view azimuth angles. Simulation results show that the highest ΔTB,AVG of up to 4°C appears mostly at midday (1200–1300 LT), while the lowest ΔTB,AVG appears mostly in the early morning (0700–0800 LT) or late afternoon (1700–1800 LT). The ΔTB,STD is about one third of ΔTB,AVG and should not be neglected given its considerable value at around 1400 LT. This trend has been proven through field measurements at both wheat and corn sites. The major factors affecting the trend are also identified using sensitivity analysis. Among the major factors, soil water content, LAI, and solar radiation are the three most significant factors, whereas the wind speed and air temperature have a larger effect on ΔTB,AVG than air humidity. It is interesting that ΔTB,AVG reaches a maximum value when the LAI is around 0.8. Further analysis shows that ΔTB,AVG is related to soil surface net radiation, which will be useful in net radiation estimation.

Improved Estimation of Shortwave Radiation Over Equatorial Indian Ocean Using Geostationary Satellite Data

An algorithm for improved estimation of daily net shortwave radiation (SWR) over the equatorial Indian Ocean is proposed in this study. This makes use of high temporal sampling by geostationary satellites (in our study, Meteosat-7) of outgoing longwave radiation and in situ observations in the equatorial Indian Ocean warm pool region. Results indicate significant improvement over the algorithm developed earlier with polar satellites (referred to as SH98 hereafter). The SWR accuracy with the present algorithm is better than 40 W·m-2, while that by SH98 is greater than 50 W·m-2 at this location. The study also makes a comparative assessment of various available SWR products in simulating sea surface temperature (SST) with an Indian Ocean model at the buoy location. The study shows that SST was simulated significantly better with SWR estimated by the present algorithm in comparison to SH98.

Development of a hybrid method for estimating land surface shortwave net radiation from MODIS data

Accurate estimation of shortwave net radiation ( S n ) at a high spatial resolution is critical for regional and global land surface models. Current surface radiation budget products have fine temporal resolutions, but only coarse spatial resolutions that are not suitable for land applications. A hybrid algorithm was developed in this study to estimate S n from MODIS data under both clear and cloudy sky conditions without requiring coarser resolution ancillary data. This algorithm was validated using ground measurements at seven sites of the SURFace RADiation budget observing network (SURFRAD) in the United States. Instantaneous S n estimated by this method was also compared with GEWEX/SRB and ISCCP data, and other methods. The results indicate that our algorithm can produce S n at 1-km resolution with improved accuracy and is easily implemented to generate operational global products. Daily integrated S n is estimated at 1-km resolution using instantaneous S n . These finer spatial resolution datasets capture the specific sequence of the redistribution of the available energy at the Earth's surface. Therefore, they support recent high resolution land surface models.

Estimation of net radiation from the MODIS data under all sky conditions: Southern Great Plains case study

Net radiation is a key component in the surface radiation budget. Numerous studies have developed frameworks to estimate net radiation or its components (upwelling or downwelling longwave and/or shortwave radiation) from remote sensing data for clear sky conditions. Application of existing methodologies to estimate net radiation for cloudy sky conditions from remote sensing sensors remains a significant challenge. In this paper, we present a framework to estimate instantaneous and daily average net radiation under all sky conditions from using the data from the MODerate Resolution Imaging Spectroradiometer (MODIS), onboard from the Terra satellites. Bisht et al. (2005) methodology is used for the clear sky portion of the MODIS overpass; while for cloudy portion of the MODIS overpass an extension of Bisht et al. (2005) methodology is applied. The extension of Bisht et al. (2005) methodology utilizes the MODIS cloud data product (MOD06_L2) for cloud top temperature, cloud fraction, cloud emissivity, cloud optical thickness and land surface temperature for cloudy days. The methodology is applied over the Southern Great Plains (SGP) for a time period covering all seasons of 2006. During the MODIS-Terra overpasses in 2006 over the SGP, only 24% of day-overpasses and 9% of night-overpasses had 75% or more of the study region as cloud free. Thus, this proposed study is applicable to a large portion of the MODIS-Terra overpasses. The root mean square errors (RMSE) of instantaneous and daily average net radiation estimated under cloudy conditions using the MOD06_L2 product, comparing to ground-based measurements are 37 W m − 2 and 38 W m − 2 , respectively. The strength of the proposed methodology is that it can rely exclusively on remote sensing data in the absence of ancillary ground observations, thus it has a potential to estimate surface energy budget globally.

Net Radiation Dynamics: Performance of 20 Daily Net Radiation Models as Related to Model Structure and Intricacy in Two Climates

We compared daily net radiation (Rn) estimates from 19 methods with the ASCE-EWRI Rn estimates in two climates: Clay Center, Nebraska (sub-humid) and Davis, California (semi-arid) for the calendar year. The performances of all 20 methods, including the ASCE-EWRI Rn method, were then evaluated against Rn data measured over a non-stressed maize canopy during two growing seasons in 2005 and 2006 at Clay Center. Methods differ in terms of inputs, structure, and equation intricacy. Most methods differ in estimating the cloudiness factor, emissivity (e), and calculating net longwave radiation (Rnl). All methods use albedo (a) of 0.23 for a reference grass/alfalfa surface. When comparing the performance of all 20 Rn methods with measured Rn, we hypothesized that the a values for grass/alfalfa and non-stressed maize canopy were similar enough to only cause minor differences in Rn and grass- and alfalfa-reference evapotranspiration (ETo and ETr) estimates. The measured seasonal average a for the maize canopy was 0.19 in both years. Using a = 0.19 instead of a = 0.23 resulted in 6% overestimation of Rn. Using a = 0.19 instead of a = 0.23 for ETo and ETr estimations, the 6% difference in Rn translated to only 4% and 3% differences in ETo and ETr, respectively, supporting the validity of our hypothesis. Most methods had good correlations with the ASCE-EWRI Rn (r2 > 0.95). The root mean square difference (RMSD) was less than 2 MJ m-2 d-1 between 12 methods and the ASCE-EWRI Rn at Clay Center and between 14 methods and the ASCE-EWRI Rn at Davis. The performance of some methods showed variations between the two climates. In general, r2 values were higher for the semi-arid climate than for the sub-humid climate. Methods that use dynamic e as a function of mean air temperature performed better in both climates than those that calculate e using actual vapor pressure. The ASCE-EWRI-estimated Rn values had one of the best agreements with the measured Rn (r2 = 0.93, RMSD = 1.44 MJ m-2 d-1), and estimates were within 7% of the measured Rn. The Rn estimates from six methods, including the ASCE-EWRI, were not significantly different from measured Rn. Most methods underestimated measured Rn by 6% to 23%. Some of the differences between measured and estimated Rn were attributed to the poor estimation of Rnl. We conducted sensitivity analyses to evaluate the effect of Rnl on Rn, ETo, and ETr. The Rnl effect on Rn was linear and strong, but its effect on ETo and ETr was subsidiary. Results suggest that the Rn data measured over green vegetation (e.g., irrigated maize canopy) can be an alternative Rn data source for ET estimations when measured Rn data over the reference surface are not available. In the absence of measured Rn, another alternative would be using one of the Rn models that we analyzed when all the input variables are not available to solve the ASCE-EWRI Rn equation. Our results can be used to provide practical information on which method to select based on data availability for reliable estimates of daily Rn in climates similar to Clay Center and Davis.

Surface Aerodynamic Temperature Modeling over Rainfed Cotton

Evapotranspiration (ET) or latent heat flux (LE) can be spatially estimated as an energy balance (EB) residual for land surfaces using remote sensing inputs. The EB equation requires the estimation of net radiation (Rn), soil heat flux (G), and sensible heat flux (H). Rn and G can be estimated with an acceptable accuracy. In computing H, radiometric surface temperature (Ts) is often used instead of surface aerodynamic temperature (To), as To is neither measured nor easily estimated. This may cause an underestimation of ET because H will be overestimated as Ts is typically larger than To for unstable atmospheric conditions. The objectives of this study were to (1) model To to improve the estimation of H and consequently ET for advective environments in the semi-arid Texas High Plains, and (2) assess the accuracy of the To model using three different methods (aerodynamic profile, lysimeter, and eddy covariance). A 6.5 m tower platform was used to measure profiles of wind speed, air temperature, and relative humidity in and above cotton canopy near a large weighing lysimeter managed under rainfed conditions at the USDA-ARS Conservation and Production Research Laboratory, Bushland, Texas. The To was modeled using H as a residual from the EB at the lysimeter location. Results indicated that To was better modeled as a linear function of Ts, air temperature, and surface aerodynamic resistance. Modeled To showed a very small estimation error (0.1% mean bias error and 3.8% root mean square error) when compared to To values measured using the aerodynamic profile data. Even though excellent results were found in this study, the model is only valid for dryland cotton with a leaf area index ranging from 0.2 to 1.3 m2 m-2. Furthermore, more research is needed to expand the To model to cover cotton grown under irrigated conditions and showing larger crop percent cover and leaf area index values, and under different environmental and atmospheric conditions.

Estimation of Net Radiation in Iraq

Estimation of Net Radiation in Iraq

Modeling the land surface heat exchange process with the aid of moderate resolution imaging spectroradiomer images

Most ecosystems and crops experience water stress in arid and semiarid areas of the Inner Mongolia grassland, Northern China. Yet the lack of long-term in situ monitoring data hinders the managerial capacity of changing water vapor environment, which is tied with sustaining the grassland in the Inner Mongolia. Environmental remote sensing monitoring and modeling may provide synergistic means of observing changes in thermodynamic balance during drought onset at the grassland surface, providing reliable projections accounting for variations and correlations of water vapor and heat fluxes. It is the aim of this paper to present a series of estimates of latent heat, sensible heat, and net radiation using an innovative first-principle, physics-based model (GEOMOD: GEO-model estimated the land surface heat with MODis data) with the aid of integrated satellite remote sensing and in situ eddy covariance data. Based on the energy balance principle and aerodynamics diffusion theory, the GEOMOD model is featured with MODIS (Moderate Resolution Imaging Spectroradiometer) data with 250 m spatial resolution to collectively reflect the spatial heterogeneity of surface properties, supplement missing data with the neighborhood values across both spatial and temporal domains, estimate the surface roughness height and zero-plane displacement with dynamic look-up table, and implement a fast iterative algorithm to calculate sensible heat. Its analytical framework is designed against overreliance on local micro-meteorological parameters. Practical implementation was assessed in the study area, the Xilin Gol River Basin, a typical grassland environment, Northern China. With 179 days of MODIS data in support of modeling, coincident ground-based observations between 2000 and 2006 were selected for model calibration. The findings indicate that GEOMOD performs reasonably well in modeling the land surface heat exchange process, as demonstrated by a case study of Inner Mongolia.

Estimation of Daytime Net Radiation from Shortwave Radiation Measurements and Meteorological Observations

Abstract Changes in surface net radiation Rn control the earth’s climate, the hydrological cycle, and plant photosynthesis. However, Rn is not readily available. This study develops a method to estimate surface daytime Rn from solar shortwave radiation measurements as well as conventional meteorological observations (or satellite retrievals) including daily minimum temperature, daily temperature range, and relative humidity, and vegetation indices from satellite data. Measurements collected at 22 U.S. and 2 Tibetan Plateau, China, sites from 2000 to 2006 are used to develop and validate the method. Land cover types include desert, semidesert, croplands, grasslands, and forest. Site elevations range from 98 to 4700 m. The results show that the method estimates Rn under clear and cloudy conditions accurately over a range of land cover types, elevations, and climates without requiring local calibration. The results show that the method estimates Rn accurately. The bias varies from −7.8 to 9.7 W m−2 (±3% in relative value) for different sites, and the root-mean-square error ranges from 12.8 to 21 W m−2 (from +5% to +9% in relative value) for different sites, with an average of 16.9 W m−2 (+6% relative) for all sites. The correlation coefficient for all sites is about 0.99. The correlation coefficient between the measured and predicted annual anomaly (year average subtracted from multiyear average) in daytime Rn is as high as 0.91, demonstrating that the method accurately estimates long-term variation in Rn.

Estimation of instantaneous net surface longwave radiation from MODIS cloud-free data

This paper develops a statistical regression method to estimate the instantaneous Downwelling Surface Longwave Radiation (DSLR) for cloud-free skies using only the satellite-based radiances measured at the Top Of the Atmosphere (TOA), and subsequently combines the DSLR with the MODIS land surface temperature/emissivity products (MOD11_L2) to estimate the instantaneous Net Surface Longwave Radiation (NSLR). The proposed method relates the DSLR directly to the TOA radiances in the MODIS Thermal InfraRed (TIR) channels provided that the terrain altitude and the satellite Viewing Zenith Angle (VZA) are known. The simulation analysis shows that the instantaneous DSLR could be estimated by the proposed method with the Root Mean Square Error (RMSE) of 12.4 W/m 2 for VZA = 0 and terrain altitude z = 0 km. Similar results are obtained for the other VZAs and altitudes. Considering the MODIS instrumental errors of 0.25 K for the TOA brightness temperatures in channels 28, 33 and 34, and of 0.05 K for channels 29 and 31, and of 0.35 K for channel 36, the overall retrieval accuracy in terms of the RMSE is decreased to 13.1 W/m 2 for the instantaneous DSLR. Moreover, a comparison of MODIS derived DSLR and NSLR are done with the field measurements made at six sites of the Surface Radiation Budget Network (SURFRAD) in the United States for days with cloud-free conditions at the moment of MODIS overpass in 2006. The results show that the bias, RMSE and the square of the correlation coefficient ( R 2) between the MODIS derived DSLR with the proposed method and the field measured DSLR are 20.3 W/m 2, 30.1 W/m 2 and 0.91 respectively, and bias = 11.7 W/m 2, RMSE = 26.1 W/m 2 and R 2 = 0.94 for NSLR. In addition, the scheme proposed by Bisht et al. [Bisht, G., Venturini, V., Islam, S., & Jiang, L. (2005). Estimation of the net radiation using MODIS (Moderate Resolution Imaging Spectroradiometer) data for clear-sky days. Remote Sensing of Environment, 97, 52–67], which requires the MODIS atmospheric profile product (MOD07) and also the MODIS land surface temperature/emissivity products (MOD11_L2) as inputs, is used to estimate the instantaneous DSLR and NSLR for comparison with the field measurements as well as the MODIS derived DSLR and NSLR using our proposed method. The results of the comparisons show that, at least for our cases, our proposed method for estimating DSLR from the MODIS radiances at the TOA and the resultant NSLR gives results comparable to those estimated with Bisht et al.'s scheme [Bisht, G., Venturini, V., Islam, S., & Jiang, L. (2005). Estimation of the net radiation using MODIS (Moderate Resolution Imaging Spectroradiometer) data for clear-sky days. Remote Sensing of Environment, 97, 52–67].

Estimation of surface longwave radiation components from ground‐based historical net radiation and weather data

A methodology for estimating ground upwelling, clear‐sky and cloud downwelling longwave radiations (L↑, Lsky↓, and Lcld↓) and net shortwave radiation (Sn) at 30‐min temporal scales based on long‐term ground‐based net radiations and meteorological observations is described. Components of surface radiation can be estimated from empirical models, cloud radiation models, and remote sensing observations. The proposed method combines the local calibration of empirical models and the radiative energy balance method to obtain the dual‐directional, dual‐spectral components of the surface radiation for the offline land surface process modeling and ecosystem study. By extracting information of radiation components from long‐term net radiation and concurrent weather data, the utility of tower net radiation observations is maximized. Four test sites with multiyears' radiation records were used to evaluate the method. The results show that when compared with the results of empirical models using default parameters the proposed method is able to produce more accurate estimates of longwave surface components (Lg↑, Lsky↓, Lcld↓) and net shortwave radiation (Sn). Overall, the estimated and observed surface radiation components show high correlations (>0.90), high index of agreement (>0.89), and low errors (root mean square error <30 W m−2 and bias <11 W m−2) at all of the test sites. The advantage of this scheme is that the refinement is achieved using the information from the historical net radiation and weather data at each observation site without additional measurements. This method is applicable for many existing observation sites worldwide which have long‐term (at least 1 year) continuous net radiation records.

Estimating stomatal conductance with thermal imagery

Most thermal methods for the study of drought responses in plant leaves are based on the calculation of 'stress indices'. This paper proposes and compares three main extensions of these for the direct estimation of absolute values of stomatal conductance to water vapour (gs) using infrared thermography (IRT). All methods use the measured leaf temperature and two environmental variables (air temperature and boundary layer resistance) as input. Additional variables required, depending on the method, are the temperatures of wet and dry reference surfaces, net radiation and relative humidity. The methods were compared using measured gs data from a vineyard in Southern Portugal. The errors in thermal estimates of conductance were of the same order as the measurement errors using a porometer. Observed variability was also compared with theoretical estimates of errors in estimated gs determined on the basis of the errors in the input variables (leaf temperature, boundary layer resistance, net radiation) and the partial derivatives of the energy balance equations used for the gs calculations. The full energy balance approach requires accurate estimates of net radiation absorbed, which may not be readily available in field conditions, so alternatives using reference surfaces are shown to have advantages. A new approach using a dry reference leaf is particularly robust and recommended for those studies where the specific advantages of thermal imagery, including its non-contact nature and its ability to sample large numbers of leaves, are most apparent. Although the results suggest that estimates of the absolute magnitude of gs are somewhat subjective, depending on the skill of the experimenter at selecting evenly exposed leaves, relative treatment differences in conductance are sensitively detected by different experimenters.