On the feasibility of temporally upscaling instantaneous evapotranspiration using weather forecast information

Quantification of land-surface evapotranspiration (ET) is highly significant in water resources management, climate change studies, and numerical weather prediction. The constant reference evaporative fraction method (EF_r, the ratio of the actual to reference ET), which assumes that the daily EF_r is equal to that at the satellite overpass time, is a scheme that has been widely applied to upscale remotely sensed instantaneous ET to daily ET. To overcome the difficulties encountered in the acquisition of tower-based meteorological variables, this study investigates the feasibility of using publicly available weather forecast information to estimate the daily reference ET using the constant EF_r method. A two-source energy balance model is adopted to compute the instantaneous ET using Moderate-Resolution Imaging Spectroradiometer (MODIS) remote-sensing data acquired between January 2011 and October 2012 at the Yucheng Comprehensive Experimental Station in the North China Plain. The results show that the daily maximum and minimum air temperatures from weather forecast information are consistent with the corresponding ground-based measurements, with a bias of 0.8 K and a root mean square error (RMSE) of <2.0 K. The daily global solar radiation and daily wind speed were poorly forecast when compared with the ground-based measurements. Using the meteorological variables from the daily weather forecast information produced a small bias of 0.1 mm day^–1 and an RMSE of 0.6 mm day^–1 when the estimated daily reference ET was compared with that derived using the ground-based meteorological measurements. When the remotely sensed instantaneous ET and half-hourly reference ET were as accurate as the ground-based measurements, the upscaling method produced the daily ET, using the meteorological variables from the weather forecast information, with a bias of 0.1 mm day^–1 and an RMSE of 0.7 mm day^–1.     

Estimating surface net solar radiation by use of Landsat-5 TM and digital elevation models

The ground surface net solar radiation is the energy that drives physical and chemical processes at the ground surface. In this paper, multi-spectral data from the Landsat-5 TM, topographic data from a gridded digital elevation model, field measurements, and the atmosphere model LOWTRAN 7 are used to estimate surface net solar radiation over the FIFE site. Firstly an improved method is presented and used for calculating total surface incoming radiation. Then, surface albedo is integrated from surface reflectance factors derived from remotely sensed data from Landsat-5 TM. Finally, surface net solar radiation is calculated by subtracting surface upwelling radiation from the total surface incoming radiation.     

Estimating surface solar radiation over complex terrain using moderate‐resolution satellite sensor data

It is well known that taking into account the influence of complex terrain is essential when using high‐resolution satellite remotely sensed data to estimate surface net solar radiation. This paper investigates whether this is also the case when using moderate‐resolution satellite remotely sensed data, such as Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR). Firstly, topographic data from a gridded digital elevation model, field measurements from the Tibetan Plateau, and results from the atmospheric 6S model are used to estimate surface incoming solar radiation over complex terrain. The associated error caused by not taking into account terrain complexity is then calculated, and the relative radiation error is estimated by standardizing the error. The results show that the standard deviation of the relative radiation error depends on the solar zenith angle, standard deviation of the height, and resolution of the digital elevation model (or resolution of the satellite sensor data). A single regression equation describes the change in the standard deviation of the relative radiation error with solar zenith angle, standard deviation of height, and resolution of the digital elevation model. This demonstrates that it is necessary to consider terrain complexity when using moderate‐resolution remotely sensed data.     

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.     

Integration of remote sensing and ecosystem modelling techniques to estimate forest net carbon uptake

Estimates of forest gross primary production (GPP) can be obtained using a parametric model (C‐Fix) that combines ground and remotely sensed data. A methodology is presented to convert these GPP estimates into values of net ecosystem exchange (NEE). The methodology is based on the use of a process model (BIOME‐BGC) that, after proper calibration, simulates all main functions of forest ecosystems at the climax condition. The estimated photosynthesis and respirations are transformed into net carbon fluxes of actual forests by using a simplified approach that relies on the difference between actual and potential stand biomass. The methodology was applied to eight forest sites in Italy where flux measurements were available and GPP estimates had been previously produced. The comparison of the obtained NEE estimates to the ground data indicates the potential of the approach and the prospects for future investigation.     

Hourly evapotranspiration derived from NOAA-AVHRR visible and GOES-IMAGER thermal infrared data

In previous studies, remotely sensed values of evapotranspiration are generally computed using a simplified surface energy budget model that employs a semi-empirical coefficient with combinations of Sun-synchronous satellite data and ground-based data. This approach has two main limitations, however: Sun-synchronous satellites have low temporal resolution and the estimation is limited only to a local point around the meteorological station because the models require the aid of ground-base measurements, especially air temperature. This study reduced both limitations through the supplemental use of geostationary satellite (GOES-8) data and remotely sensed estimates of all necessary parameters, including net radiation, air temperature and surface temperature. In particular, air temperature, which is an important meteorological parameter in evapotranspiration estimation, was reproduced through third-order polynomial multiple regressions (R² = 0.88; root mean square (rms) error = 2.21 °C). The coefficient needed for the hourly estimate of evapotranspiration was represented through both a Gaussian model and a plane model. The models were constructed using surface roughness length and Sun hour angle, which replaced wind speed – a parameter that is difficult to estimate remotely over land. Assessments show that the models can depict the temporal distributions of empirical coefficients over various land-cover types. The standard error of this coefficient estimate was 0.002 mm h^?1 K^?1 for both time periods. A strong correlation (R² > 0.87; rms. error <0.17 mm h^?1) was found in comparisons between the selected potential and remotely sensed actual evapotranspiration for four land-cover classes.     

Using ASTER satellite data to calculate riparian evapotranspiration in the Middle Rio Grande,New Mexico

Riparian evapotranspiration (ET) in the Rio Grande Basin in New Mexico, USA is a major component of the hydrological system. Over a period of several years, ET has been measured in selected locations of dense saltcedar and cottonwood vegetation. Riparian vegetation varies in density, species and soil moisture availability, and to obtain accurate measurements, multiple sampling points are needed, making the process costly and impractical. An alternative solution involves using remotely sensed data to estimate ET over large areas. In this study, daily ET values were measured using eddy covariance flux towers installed in areas of saltcedar and cottonwood vegetation. At these sites, remotely sensed satellite data from the National Aeronautics and Space Administration (NASA) Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) were used to calculate the albedo, normalized difference vegetation index (NDVI) and surface temperature. A surface energy balance model was used to calculate ET values from the ASTER data, which were available for 7 days in the year. Comparison between the daily ET values of saltcedar and cottonwood measured from the flux towers and calculated from remote sensing resulted in a mean square error (MSE) of 0.16 and 0.37 mm day^?1, respectively. The regional map of ET generated from the remote sensing data demonstrated considerable variation in ET, ranging from 0 to 9.8 mm day^?1, with a mean of 5.5 mm day^?1 and standard deviation of 1.85 mm day^?1 (n = 427481 pixels) excluding open water. This was due to variations in plant variety and density, soil type and moisture availability, and the depth to water table.     

Application of remote sensing for estimating crop water requirements,yield and water productivity of wheat in the Gezira Scheme

An accurate estimate of the evapotranspiration (ET) and crop water productivity (WP) at the regional scale plays a significant role in managing water-saving irrigation in the dry region. Three sites in the Gezira scheme representing research field (Gezira Research Station, GRS), farmers' fields with participatory water management approach (Abdelhakam block) and ordinary farmers' fields (Madina block) were selected to estimate spatial ET, crop yield and WP of wheat using remote sensing data coupled with ground observations. The methodology is based on surface energy balance to estimate sensible and latent heat fluxes by combining remotely sensed data from Landsat 7 ETM+ and Moderate Resolution Imaging Spectroradiometer (MODIS) with common meteorological data. A comparison between Surface Energy Balance Algorithm for Land (SEBAL) estimated ET and ET calculated based on reference evapotranspiration (ET_o)-crop coefficient (k _c) approach over the growing season exhibited a good agreement. The average SEBAL-based ET of wheat for the entire season at GRS was 396 mm. SEBAL estimated k _c were 0.46, 1.07 and 0.3 at the initial, mid season and late season stages, respectively, whereas the k _c values determined from water depletion measurements were 0.5, 1.15 and 0.5, respectively. The average crop yield at GRS, Abdelhakam block and Madina block were 2.4, 1.9 and 1.4 t ha^?1, respectively. The mean values of WP for the three locations were 0.52 (coefficient of variation, CV?=?0.31), 0.50 (CV?=?0.33) and 0.43 (CV?=?0.45) kg m^?3, respectively. Achieving a coefficient of variation (CV?=?0.15), water savings at the three locations could reach 21%, 42% and 53%, respectively.     

Estimating evapotranspiration of European forests from NOAA-imagery at satellite overpass time: Towards an operational processing chain for integrated optical and thermal sensor data products

Evapotranspiration (ET) using the Integral NOAA-imagery processing Chain (iNOAA-Chain) is quantified by implementing visible and thermal satellite information on a regional scale. ET is calculated based on the energy balance closure principle. The combination of evaporative fraction (EF), soil heat flux and instantaneous net radiation, results in an instantaneous spatial distribution of ET values. Surface broadband albedo and land surface temperature (LST) serve to determine EF. EF is derived using four methods based on NOAA/AVHRR satellite imagery. Instantaneous evapotranspiration, i.e. at time of satellite overpass, on European continental scale with emphasis on forest stands is estimated using the iNOAA-Chain. Finally, the estimated net radiation (R_n), soil heat fluxes (G₀) and evaporative fraction and evapotranspiration at time of satellite overpass are validated against EUROFLUX site data for the growing season of 1997 (March-October). The regression line for the pooled R_n (iNOAA-Chain versus EUROFLUX) has a slope, intercept, Pearson product moment correlation coefficient (R²) and relative root mean square error (RRMSE) of respectively 0.943, 17.120, 0.926 and 5.5%. The soil heat fluxes, calculated with two approaches are not-well modelled with slopes smaller than − 3.000 and a R² in the order of zero. We observe a slight underestimation of the iNOAA Chain estimated EF. The regression line for pooled EF data for the best performing method (SPLIT-method) has a slope of 0.935, an intercept of 0.041 and the R² is 0.847. A pooled RRMSE EF value of 12.3% is found. The pooled slope, intercept, R² and RRMSE for EF derived with SORT-method 1 are respectively 0.449, 0.251, 0.043 and 65.1%, with SORT-method 2, 0.567, 0.203, 0.174 and 39.1%, and with SORT-method 3, 0.568, 0.254, 0.288, and 32.8%. Also instantaneous evapotranspiration is underestimated with a pooled RRMSE on ET of 23.4%. The regression curve of pooled ET data for the best performing method has a slope of 0.889 an intercept of 15.880 and a Pearson product moment correlation coefficient of 0.771. The other method gives a slope of 0.781, an intercept of 17.541 and a R² of 0.776. Error propagation analysis reveals that the relative error on evapotranspiration at satellite overpass time is at least 27%.     

Evaluating evapotranspiration and water-use efficiency of terrestrial ecosystems in the conterminous United States using MODIS and AmeriFlux data

In this study, we used the remotely-sensed data from the Moderate Resolution Imaging Spectrometer (MODIS), meteorological and eddy flux data and an artificial neural networks (ANNs) technique to develop a daily evapotranspiration (ET) product for the period of 2004-2005 for the conterminous U.S. We then estimated and analyzed the regional water-use efficiency (WUE) based on the developed ET and MODIS gross primary production (GPP) for the region. We first trained the ANNs to predict evapotranspiration fraction (EF) based on the data at 28 AmeriFlux sites between 2003 and 2005. Five remotely-sensed variables including land surface temperature (LST), normalized difference vegetation index (NDVI), normalized difference water index (NDWI), leaf area index (LAI) and photosynthetically active radiation (PAR) and ground-measured air temperature and wind velocity were used. The daily ET was calculated by multiplying net radiation flux derived from remote sensing products with EF. We then evaluated the model performance by comparing modeled ET with the data at 24 AmeriFlux sites in 2006. We found that the ANNs predicted daily ET well (R² = 0.52-0.86). The ANNs were applied to predict the spatial and temporal distributions of daily ET for the conterminous U.S. in 2004 and 2005. The ecosystem WUE for the conterminous U.S. from 2004 to 2005 was calculated using MODIS GPP products (MOD17) and the estimated ET. We found that all ecosystems' WUE-drought relationships showed a two-stage pattern. Specifically, WUE increased when the intensity of drought was moderate; WUE tended to decrease under severe drought. These findings are consistent with the observations that WUE does not monotonously increase in response to water stress. Our study suggests a new water-use efficiency mechanism should be considered in ecosystem modeling. In addition, this study provides a high spatial and temporal resolution ET dataset, an important product for climate change and hydrological cycling studies for the MODIS era.     

Global estimates of evapotranspiration and gross primary production based on MODIS and global meteorology data

The simulation of gross primary production (GPP) at various spatial and temporal scales remains a major challenge for quantifying the global carbon cycle. We developed a light use efficiency model, called EC-LUE, driven by only four variables: normalized difference vegetation index (NDVI), photosynthetically active radiation (PAR), air temperature, and the Bowen ratio of sensible to latent heat flux. The EC-LUE model may have the most potential to adequately address the spatial and temporal dynamics of GPP because its parameters (i.e., the potential light use efficiency and optimal plant growth temperature) are invariant across the various land cover types. However, the application of the previous EC-LUE model was hampered by poor prediction of Bowen ratio at the large spatial scale. In this study, we substituted the Bowen ratio with the ratio of evapotranspiration (ET) to net radiation, and revised the RS-PM (Remote Sensing-Penman Monteith) model for quantifying ET. Fifty-four eddy covariance towers, including various ecosystem types, were selected to calibrate and validate the revised RS-PM and EC-LUE models. The revised RS-PM model explained 82% and 68% of the observed variations of ET for all the calibration and validation sites, respectively. Using estimated ET as input, the EC-LUE model performed well in calibration and validation sites, explaining 75% and 61% of the observed GPP variation for calibration and validation sites respectively.Global patterns of ET and GPP at a spatial resolution of 0.5° latitude by 0.6° longitude during the years 2000-2003 were determined using the global MERRA dataset (Modern Era Retrospective-Analysis for Research and Applications) and MODIS (Moderate Resolution Imaging Spectroradiometer). The global estimates of ET and GPP agreed well with the other global models from the literature, with the highest ET and GPP over tropical forests and the lowest values in dry and high latitude areas. However, comparisons with observed GPP at eddy flux towers showed significant underestimation of ET and GPP due to lower net radiation of MERRA dataset. Applying a procedure to correct the systematic errors of global meteorological data would improve global estimates of GPP and ET. The revised RS-PM and EC-LUE models will provide the alternative approaches making it possible to map ET and GPP over large areas because (1) the model parameters are invariant across various land cover types and (2) all driving forces of the models may be derived from remote sensing data or existing climate observation networks.     

Estimating crop-specific evapotranspiration using remote-sensing imagery at various spatial resolutions for improving crop growth modelling

By governing water transfer between vegetation and atmosphere, evapotranspiration (ET) can have a strong influence on crop yields. An estimation of ET from remote sensing is proposed by the EUMETSAT ‘Satellite Application Facility’ (SAF) on Land Surface Analysis (LSA). This ET product is obtained operationally every 30 min using a simplified SVAT scheme that uses, as input, a combination of remotely sensed data and atmospheric model outputs. The standard operational mode uses other LSA-SAF products coming from SEVIRI imagery (the albedo, the downwelling surface shortwave flux, and the downwelling surface longwave flux), meteorological data, and the ECOCLIMAP database to identify and characterize the land cover.

With the overall objective of adapting this ET product to crop growth monitoring necessities, this study focused first on improving the ET product by integrating crop-specific information from high and medium spatial resolution remote-sensing data. A Landsat (30 m)-based crop type classification is used to identify areas where the target crop, winter wheat, is located and where crop-specific Moderate Resolution Imaging Spectroradiometer (MODIS) (250 m) time series of green area index (GAI) can be extracted. The SVAT model was run for 1 year (2007) over a study area covering Belgium and part of France using this supplementary information. Results were compared to those obtained using the standard operational mode.

ET results were also compared with ground truth data measured in an eddy covariance station. Furthermore, transpiration and potential transpiration maps were retrieved and compared with those produced using the Crop Growth Monitoring System (CGMS), which is run operationally by the European Commission's Joint Research Centre to produce in-season forecast of major European crops. The potential of using ET obtained from remote sensing to improve crop growth modelling in such a framework is studied and discussed.

Finally, the use of the ET product is also explored by integrating it in a simpler modelling approach based on light-use efficiency. The Carnegie–Ames–Stanford Approach (CASA) agroecosystem model was therefore applied to obtain net primary production, dry matter productivity, and crop yield using only LSA-SAF products. The values of yield were compared with those obtained using CGMS, and the dry matter productivity values with those produced at the Flemish Institute for Technological Research (VITO). Results showed the potential of using this simplified remote-sensing method for crop monitoring.     

卫星遥感数据的地表直射光辐射计算与改正

定量讨论了像元地表的起伏和地理位置的不同对地表直射光辐射的影响以及这种影响造成的像元遥感数值的变化。并在此基础上建立了像元地表直射光辐射的计算公式和像元遥感数据直射光分量的改正公式。     

卫星遥感数字图像的地面辐射改正研究

讨论了地形、大气以及地理位置对卫片像元土尤辐射三分量(直射、散射、邻坡反射)的影响和这种影响所引起的卫星遥感数据变化。研究了地面光辐射、地形、大气和遥感数据之间的定量关来，并以此为基础，研究了卫星遥感数字图像地面辐射改正的原理和方法。经地面辐射改正后的卫星遥感数据能满足遥感应用基础研究和地面辐射状况研完的需要。     

Daily net radiation estimated from air temperature and NOAA-AVHRR data: A case study for the Iberian Peninsula

In this work we present a methodology to obtain the daily net radiation flux from NOAA-AVHRR data. To achieve this we need first to obtain shortwave net radiation flux from the solar global radiation flux and the albedo map. Secondly, we need to obtain the upward longwave radiation flux from surface temperature and emissivity and the downward longwave radiation flux from air temperature and vapour pressure. A daily net radiation flux image corresponding to flat areas of the Iberian Peninsula is presented as an example in which we show that daily net radiation flux can be obtained with an uncertainty of approximately 1.5 mm day^-1.     

Global estimates of evapotranspiration for climate studies using multi-sensor remote sensing data: Evaluation of three process-based approaches

Three process based models are used to estimate terrestrial heat fluxes and evapotranspiration (ET) at the global scale: a single source energy budget model, a Penman-Monteith based approach, and a Priestley-Taylor based approach. All models adjust the surface resistances or provide ecophysiological constraints to account for changing environmental factors. Evaporation (or sublimation) over snow-covered regions is calculated consistently for all models using a modified Penman equation. Instantaneous fluxes of latent heat computed at the time of satellite overpass are linearly scaled to the equivalent daily evapotranspiration using the computed evaporative fraction and the day-time net radiation. A constant fraction (10% of daytime evaporation) is used to account for the night time evaporation. Interception losses are computed using a simple water budget model. We produce daily evapotranspiration and sensible heat flux for the global land surface at 5 km spatial resolution for the period 2003-2006. With the exception of wind and surface pressure, all model inputs and forcings are obtained from satellite remote sensing.Satellite-based inputs and model outputs were first carefully evaluated at the site scale on a monthly-mean basis, then as a four-year mean against a climatological estimate of ET over 26 major basins, and finally in terms of a latitudinal profile on an annual basis. Intercomparison of the monthly model estimates of latent and sensible heat fluxes with 12 eddy-covariance towers across the U.S. yielded mean correlation of 0.57 and 0.54, respectively. Satellite-based meteorological datasets of 2 m temperature (0.83), humidity (0.70), incident shortwave radiation (0.64), incident longwave radiation (0.67) were found to agree well at the tower scale, while estimates of wind speed correlated poorly (0.17). Comparisons of the four year mean annual ET for 26 global river basins and global latitudinal profiles with a climatologically estimated ET resulted in a Kendall's τ > 0.70. The seasonal cycle over the continents is well represented in the Hovmöeller plots and the suppression of ET during major droughts in Europe, Australia and the Amazon are well picked up. This study provides the first ever moderate resolution estimates of ET on a global scale using only remote sensing based inputs and forcings, and furthermore the first ever multi-model comparison of process-based remote sensing estimates using the same inputs.     

A simple single layer model to estimate transpiration from vegetation using multi-spectral and meteorological data

A methodology is developed here to model evapotranspiration (λE_c ) from the canopy layer over large areas by combining satellite and ground measurements of biophysical and meteorological variables. The model developed here follows the energy balance approach, where λE_c is estimated as a residual when the net radiation (Rn), sensible heat flux (H) and ground flux (G) are known. Multi-spectral measurements from the NOAA Advanced Very High Resolution Radiometer (AVHRR) were used along with routine meteorological measurements made on the ground to estimate components of the energy balance. The upwelling long wave radiation, and H from the canopy layer were modelled using the canopy temperature, obtained from a linear relation between the Normalized Difference Vegetation Index (NDVI) and surface temperature. This method separates flux measurements from the canopy and bare soil without the need for a complex two layer model. From theoretical analysis of canopy reflectance, leaf area, and canopy resistance, a model is developed to scale the transpiration estimates from the full canopy to give an area averaged estimate from the mean NDVI of the study area. The model was tested using data collected from the First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE), and the results show that the modelled values of total surface evapotranspiration from the soil and canopy layers vary from the ground measurements by less than 9 per cent.     

利用正演计算方法分析地形和噪音对多波段遥感数据的影响

在没有干扰和噪音影响的理想条件下,多波段遥感数据中不同地物在散点图上的位置是一
个点。但在实际情况中,地形和噪音大大地改变了目标分布形状。利用DEM数字高程数据以及太
阳高度角和方位角等参数计算出地表接收到的辐射能量,然后设置已知目标,利用散点图来分析不
同反射率下目标所占的空间位置。再通过增加不同强度的噪音,分析目标在散点图上的变化规律,
以便寻找目标分离的有效特征。     

Development and comparison of Landsat radiometric and snowpack model inversion techniques for estimating geothermal heat flux

We present the first quantitative representation of the intensity of Yellowstone National Park's surficial geothermal activity mapped continuously in space. A radiative thermal anomaly was remotely sensed throughout a 19,682-km² landscape covering Yellowstone National Park in the northern Rocky Mountains, USA. The anomaly is the residual terrestrial emittance measured using the Landsat Enhanced Thematic Mapper after accounting for elevation and solar effects, and was hypothesized to be an estimator of a lower bound for geothermal heat flux (GHF). Continuous variations in the anomaly were measured ranging from 0 W m^− 2 up to a maximum heat flux of at least 94 W m^− 2 (at the 28.5 m pixel scale). An independent method was developed for measuring GHF at smaller scales, based on inversion of a snowpack simulation model, combined with field mapping of snow-free perimeters around selected geothermal features. These perimeters were assumed to be approximately isothermal, with a mean GHF estimated as the minimum heat flux required to ablate the simulated snowpack at that location on the day of field survey. The remotely sensed thermal anomaly correlated well (r = 0.82) with the snowpack-inversion measurements, and supported the hypothesis that the anomaly estimates a lower bound for GHF. These methods enable natural resource managers to identify, quantify and predict changes in heat flux over time in geothermally active areas. They also provide a quantitative basis for understanding the degree to which Yellowstone's famous wildlife herds are actually dependent on geothermal activity.     

Development of a global evapotranspiration algorithm based on MODIS and global meteorology data

The objective of this research is to develop a global remote sensing evapotranspiration (ET) algorithm based on Cleugh et al.'s [Cleugh, H.A., R. Leuning, Q. Mu, S.W. Running (2007) Regional evaporation estimates from flux tower and MODIS satellite data. Remote Sensing of Environment 106, page 285-304- 2007 (doi: 10.1016/j.rse.2006.07.007).] Penman-Monteith based ET (RS-PM). Our algorithm considers both the surface energy partitioning process and environmental constraints on ET. We use ground-based meteorological observations and remote sensing data from the MODerate Resolution Imaging Spectroradiometer (MODIS) to estimate global ET by (1) adding vapor pressure deficit and minimum air temperature constraints on stomatal conductance; (2) using leaf area index as a scalar for estimating canopy conductance; (3) replacing the Normalized Difference Vegetation Index with the Enhanced Vegetation Index thereby also changing the equation for calculation of the vegetation cover fraction (F_C); and (4) adding a calculation of soil evaporation to the previously proposed RS-PM method.We evaluate our algorithm using ET observations at 19 AmeriFlux eddy covariance flux towers. We calculated ET with both our Revised RS-PM algorithm and the RS-PM algorithm using Global Modeling and Assimilation Office (GMAO v. 4.0.0) meteorological data and compared the resulting ET estimates with observations. Results indicate that our Revised RS-PM algorithm substantially reduces the root mean square error (RMSE) of the 8-day latent heat flux (LE) averaged over the 19 towers from 64.6 W/m² (RS-PM algorithm) to 27.3 W/m² (Revised RS-PM) with tower meteorological data, and from 71.9 W/m² to 29.5 W/m² with GMAO meteorological data. The average LE bias of the tower-driven LE estimates to the LE observations changed from 39.9 W/m² to − 5.8 W/m² and from 48.2 W/m² to − 1.3 W/m² driven by GMAO data. The correlation coefficients increased slightly from 0.70 to 0.76 with the use of tower meteorological data. We then apply our Revised RS-PM algorithm to the globe using 0.05° MODIS remote sensing data and reanalysis meteorological data to obtain the annual global ET (MODIS ET) for 2001. As expected, the spatial pattern of the MODIS ET agrees well with that of the MODIS global terrestrial gross and net primary production (MOD17 GPP/NPP), with the highest ET over tropical forests and the lowest ET values in dry areas with short growing seasons. This MODIS ET product provides critical information on the regional and global water cycle and resulting environment changes.