Estimating Evapotranspiration Estimates Research Articles

Novel algorithms for high-resolution prediction of canopy evapotranspiration in grapevine

Developing low-cost technology for custom water delivery to individual or small groups of plants is a critical next step to advance precision irrigation. Current systems for estimating evapotranspiration (ET), or plant water use, work on the scale of a full vineyard (e.g., 3–5 acres) or the scale of a single vine, but at a cost that prohibits monitoring past a small number of representative vines. To develop and evaluate low-cost ET sensors for individual grapevines, we used three head-pruned Zinfandel vines in pots and placed them on load cells to collect continuous weights indicative of actual ET. We mounted research-grade sensors for humidity, temperature, and wind speed on each vine and saved data at 2-minute intervals during three growing seasons. We developed three models based on first principles (Convective Mass Transfer or Mass Balance approaches) or simple correlations to predict actual single-plant ET from these data. We present here the results of a multi-year trial at the UC-Davis RMI vineyard to illustrate the performance of each of the models for ET estimation. Relative model performance was assessed by comparing model predictions to ground truth data provided by measurements from load cells–including assessments of estimated instantaneous ET rate, estimated cumulative water use over a one-hour window surrounding solar noon, and estimated cumulative water use over a full 24-hour period. The three algorithms developed consistently performed well, with single vine ET rate predictions showing a strong linear relationship with ground truth (range in r2 over three seasons CMT r2 = 0.61–0.86; MB r2 = 0.07–0.91; EM r2 = 0.57–0.92). The MB approach, which includes two measurements of relative humidity and temperature, was the most variable, likely due to the impact of sensor placement. In all seasons, we also examined the trend in the plant scaling factor found in each model, deemed As, which, based on model theory, is a function of vine size. Taken together, these results suggest that high-resolution irrigation (HRI) models are a promising new method for ET estimation at the single plant level.

ORBITAL REMOTE SENSING FOR THE MANAGEMENT OF AREAS IRRIGATED WITH A CENTRAL PIVOT SYSTEM

Irrigated agriculture is considered one of the most important techniques for agricultural production in the world, despite being the sector that uses water resources the most. Thus, technological advances have been constantly developed and applied in the sector as a way to improve water management and reduce its consumption, considering that some cultures are affected by economic costs and feasibilities of production, because the water cost has increased significantly in recent years. Remote sensing is a technology that has provided good results for estimating evapotranspiration (ETr) in producing areas, enabling more efficient irrigation management for low costs. This study aimed to answer the question about how the generation of irrigation management zones (IMZs) can be used to optimize water use and improve irrigation systems and which ETr models are better for it. Three ETr estimation models (Surface Energy Balance Algorithm for Land [SEBAL], Mapping Evapotranspiration at High Resolution and with Internalized Calibration [METRIC] and Simple Algorithm for Evapotranspiration Retrieving [SAFER]) were used to design IMZs in center pivot cultivated with cotton. The design of IMZs with ETr data proved to be a viable alternative, making it possible to improve water management in irrigated systems, reducing costs with viability irrigation for cotton and others cultures. The METRIC method displayed the greatest ease in obtaining the data used in the ETr estimation for generating the IMZs.

Evaluating spatial and temporal variations in sub-field level crop water demands

Variable rate irrigation (VRI) requires accurate knowledge of crop water demands at the sub-field level. Existing VRI practices commonly use one or more variables like soil electrical conductivity, historical yields, and topographic maps to delineate variable rate zones. However, these data sets do not quantify within season variability in crop water demands. Crop coefficients are widely used to help estimate evapotranspiration (ET) at different stages of a crop’s growth cycle, and past research has shown how remotely sensed data can identify differences in crop coefficients at regional and field levels. However, the amount of spatial and temporal variation in crop coefficients at the sub-field level (i.e. within a single center pivot system) has not been widely researched. This study aims to compare sub-field ET estimates from two remote sensing platforms and quantify spatial and temporal variations in aggregated sub-field level ET. Vegetation indices and reference ET data were collected at Kansas State University’s Southwest Research Extension Center (SWREC) and two Water Technology Farms during the 2020 corn growing season. Weekly maps of the Normalized Difference Vegetation Index (NDVI) and the Soil Adjusted Vegetation Index (SAVI) from aerial imagery are combined with empirical equations from existing literature to estimate both basal and combined crop coefficients at a 1-meter resolution. These ET estimates are aggregated to a 30 m resolution and compared to the Landsat Provisional Actual ET dataset. Finally, actual ET estimates from aerial images were aggregated using k-means clustering and stationary variable speed zones to determine if there is enough variation in actual ET at the sub-field level to build variable rate irrigation schedules. An equivalence test demonstrated that the aerial imagery and Landsat data sources produce significantly different crop coefficient estimates. However, the two datasets were moderately correlated with Pearson’s product-moment correlation coefficients ranging from -0.95 to 0.86. Both the aerial imaging and Landsat datasets showed high variability in crop coefficients during the first 5-6 weeks after emergence, with these coefficients becoming more spatially uniform later in the growing season. These crop coefficients may help irrigators make more informed irrigation management decisions during the growing season. However, more research is needed to validate these remotely sensed ET estimates and integrate them into an irrigation decision support system.

Effect of the Shadow Pixels on Evapotranspiration Inversion of Vineyard: A High-Resolution UAV-Based and Ground-Based Remote Sensing Measurements

Due to the proliferation of precision agriculture, the obstacle of estimating evapotranspiration (ET) and its components from shadow pixels acquired from remote sensing technology should not be neglected. To accurately detect shaded soil and leaf pixels and quantify the implications of shadow pixels on ET inversion, a two-year field-scale observation was carried out in the growing season for a pinot noir vineyard. Based on high-resolution remote sensing sensors covering visible light, thermal infrared, and multispectral light, the supervised classification was applied to detect shadow pixels. Then, we innovatively combined the normalized difference vegetation index with the three-temperature model to quantify the proportion of plant transpiration (T) and soil evaporation (E) in the vineyard ecosystem. Finally, evaluated with the eddy covariance system, we clarified the implications of the shadow pixels on the ET estimation and the spatiotemporal patterns of ET in a vineyard system by considering where shadow pixels were presented. Results indicated that the shadow detection process significantly improved reliable assessment of ET and its components. (1) The shaded soil pixels misled the land cover classification, with the mean canopy cover ignoring shadows 1.68–1.70 times more often than that of shaded area removal; the estimation accuracy of ET can be improved by 4.59–6.82% after considering the effect of shaded soil pixels; and the accuracy can be improved by 0.28–0.89% after multispectral correction. (2) There was a 2 °C canopy temperature discrepancy between sunlit leaves and shaded leaves, meaning that the estimation accuracy of T can be improved by 1.38–7.16% after considering the effect of shaded canopy pixels. (3) Simultaneously, the characteristics showed that there was heterogeneity of ET in the vineyard spatially and that E and T fluxes accounted for 238.05 and 208.79 W·m−2, respectively; the diurnal variation represented a single-peak curve, with a mean of 0.26 mm/h. Our findings provide a better understanding of the influences of shadow pixels on ET estimation using remote sensing techniques.

Spatio-Temporal Assessment of Global Gridded Evapotranspiration Datasets across Iran

Estimating evapotranspiration (ET), the main water output flux within basins, is an important step in assessing hydrological changes and water availability. However, direct measurements of ET are challenging, especially for large regions. Global products now provide gridded estimates of ET at different temporal resolution, each with its own method of estimating ET based on various data sources. This study investigates the differences between ERA5, GLEAM, and GLDAS datasets of estimated ET at gridded points across Iran, and their accuracy in comparison with reference ET. The spatial and temporal discrepancies between datasets are identified, as well as their co-variation with forcing variables. The ET reference values used to check the accuracy of the datasets were based on the water balance (ETwb) from Iran’s main basins, and co-variation of estimated errors for each product with forcing drivers of ET. The results indicate that ETERA5 provides higher base average values and lower maximum annual average values than ETGLEAM. Temporal changes at the annual scale are similar for GLEAM, ERA5, and GLDAS datasets, but differences at seasonal and monthly time scales are identified. Some discrepancies are also recorded in ET spatial distribution, but generally, all datasets provide similarities, e.g., for humid regions basins. ETERA5 has a higher correlation with available energy than available water, while ETGLEAM has higher correlation with available water, and ETGLDAS does not correlate with none of these drivers. Based on the comparison of ETERA5 and ETGLEAM with ETwb, both have similar errors in spatial distribution, while ETGLDAS provided over and under estimations in northern and southern basins, respectively, compared to them (ETERA5 and ETGLEAM). All three datasets provide better ET estimates (values closer to ETWB) in hyper-arid and arid regions from central to eastern Iran than in the humid areas. Thus, the GLEAM, ERA5, and GLDAS datasets are more suitable for estimating ET for arid rather than humid basins in Iran.

Estimating stomatal conductance and evapotranspiration of winter wheat using a soil-plant water relations-based stress index

Stomatal conductance, closely related to water flow in the soil-plant-atmosphere continuum, is an important parameter in the Penman-Monteith (P-M) model for estimating evapotranspiration (ET). In this study, a novel soil water stress index ω, considering intrinsic soil-plant water relations, was introduced into the Jarvis empirical estimation model of stomatal conductance to improve the representation of the effect of soil water stress on stomatal conductance. The index ω accounted not only for current water availability by combing the effects of relative distribution of soil water to roots and nonlinear stomatal response, but also for the hysteresis effect of water stress by means of the inclusion of a recovery coefficient. Combined plant and soil-based measurements from a greenhouse experiment provided the basis for investigating the relationship between leaf stomatal conductance gs and root zone soil water stress represented by ω. The response of gs to root-weighted soil matric potential was found to be nonlinear. The relationship between gs and the extent of previous water stress (i.e. the water stress recovery coefficient curve) was generalized by a power function and was verified and confirmed using results obtained from the literature. The reliability of ω was tested by coupling it into the Jarvis model to estimate leaf (gs) and canopy (gc) stomatal conductance, and thereupon into the P-M model to estimate cumulative ET (CET) in the greenhouse experiment and two field experiments. The estimated gs, gc and CET agreed well with the measurements, with root mean squared error not more than 0.0006 m s−1, 0.0020 m s−1 and 8.2 mm, respectively, and determination coefficient (Nash-Sutcliffe efficiency coefficient) consistently greater than 65% (0.14). Therefore, ω should be feasible and reliable to delineate the response of stomatal physiological reaction to water stress, and hence helpful for accurate estimation of ET using Jarvis-based P-M models.

Evapotranspiration Estimation with Small UAVs in Precision Agriculture.

Estimating evapotranspiration (ET) has been one of the most critical research areas in agriculture because of water scarcity, the growing population, and climate change. The accurate estimation and mapping of ET are necessary for crop water management. Traditionally, researchers use water balance, soil moisture, weighing lysimeters, or an energy balance approach, such as Bowen ratio or eddy covariance towers to estimate ET. However, these ET methods are point-specific or area-weighted measurements and cannot be extended to a large scale. With the advent of satellite technology, remote sensing images became able to provide spatially distributed measurements. However, the spatial resolution of multispectral satellite images is in the range of meters, tens of meters, or hundreds of meters, which is often not enough for crops with clumped canopy structures, such as trees and vines. Unmanned aerial vehicles (UAVs) can mitigate these spatial and temporal limitations. Lightweight cameras and sensors can be mounted on the UAVs and take high-resolution images. Unlike satellite imagery, the spatial resolution of the UAV images can be at the centimeter-level. UAVs can also fly on-demand, which provides high temporal imagery. In this study, the authors examined different UAV-based approaches of ET estimation at first. Models and algorithms, such as mapping evapotranspiration at high resolution with internalized calibration (METRIC), the two-source energy balance (TSEB) model, and machine learning (ML) are analyzed and discussed herein. Second, challenges and opportunities for UAVs in ET estimation are also discussed, such as uncooled thermal camera calibration, UAV image collection, and image processing. Then, the authors share views on ET estimation with UAVs for future research and draw conclusive remarks.

Estimation of evapotranspiration in lesser Himalayas using remote sensing based surface energy balance algorithm

In this study, remote sensing-based algorithm, METRIC is used to estimate evapotranspiration (ET) over a mountainous watershed in the lesser Himalayas in Uttarakhand, India. ET estimates of METRIC are compared with the ET values determined by using FAO Penman-Monteith equation and crop coefficients. In its original form, METRIC underestimated the ET values considerably with mean absolute error (MAE) of 0.95 mm/day. A modification is incorporated in METRIC for better representation of the effect of rainfall on the ET process. The proposed modification estimates ET at the hot pixel using daily rainfall and meteorological data. Using the modification, the MAE is reduced to 0.44 mm/day and underestimation of ET is reduced from 20.9% to 8.4%. This study contributes to the better understanding of ET in the lesser Himalayas of Uttarakhand. It also underlines the importance of accurate assignment of ET to the hot pixel for accurate estimation of ET using METRIC.

Evaporative fraction and its application in estimating daily evapotranspiration of water-saving irrigated rice field

Evaporative fraction (EF), which is known to exhibit variation in response to changes in crop species, soil and meteorologic conditions, plays an important role in interpreting the components of energy budget and estimating evapotranspiration (ET), while such information is scarce for humid rice fields. The present study discussed the variation of energy balance components and then examined the pattern of hourly, daytime and daily EF after monitoring energy components by eddy covariance for water-saving irrigated (WSI) rice paddies of 2015 and 2016. Then the daily ET was estimated by an improved EF up-scaling method in the subtropical monsoon climate region of East China. Diurnally, hourly EF is deemed as an approximately concave-up shape in different growth stages of rice season. The seasonal average EF varies gently, with a minimum around 10:00–11:00 AM. Seasonally, the mean daytime EF for the whole growth stage is 0.86, 7% lower than the daily value. Daily EF exhibits mostly higher than 0.8 except later yellow ripening period, approaching 1.0 in the milk stage. EF over WSI rice exhibits obviously greater than the reports that from upland crops. In addition, differences are noted in the results with respect to the daily ET estimation by EF up-scaling method. The estimated daily ET (ETEF,d) from hourly EF during 10:00–11:00 h is highly correlated to the measured ET (ETtrue) by the weighed micro-lysimeters though the ETEF,d value is underestimated. Such a considerable gap serves in forming a relationship between ETEF,d and ETtrue,d, that is, by simply multiplying the representativeness ET value based on the EF up-scaling method by a correction procedure calibrated for this region. In conclusion, an improved EF up-scaling method is proposed for extrapolating remote sensing based ET estimates to daily values.

Evapotranspiration Estimation for Tibetan Plateau Headwaters Using Conjoint Terrestrial and Atmospheric Water Balances and Multisource Remote Sensing

AbstractSatellite remote sensing combined with water balance calculations provides a promising approach to estimating evapotranspiration (ET), a critical variable in water‐energy exchange. Here we compare ET estimates from terrestrial and atmospheric water balances, multisource remote sensing (AVHRR, GLEAM, and MOD16), and a land surface model (GLDAS NOAH) for headwaters on the Tibetan Plateau (TP), that is, headwaters of the Brahmaputra (HBR), Salween (HSR), Mekong (HMR), Yangtze (HYR), and Huang (Yellow; HHR) Rivers, for the 2003–2012 period. Results show that (1) ET estimated from terrestrial and atmospheric water balances agrees closely in three basins (HMR, HYR, and HHR) but has large discrepancies in the other two basins (HBR and HSR), mainly caused by uncertainties in the terrestrial water balance; (2) agreement between various ET products and water balance‐derived ET baselines is highest for GLEAM in two basins (HMR and HYR) and GLDAS NOAH in another two basins (HSR and HHR); and (3) large discrepancies between water balance‐derived ET and all ET products are found in the most glacierized HBR, which may reflect the importance of sublimation in the ET process. The decadal mean ET based on water balance‐derived ET baselines is highest in the HHR (447 mm/year) and HSR (430 mm/year) and lowest in the HBR (238 mm/year), ranging from 51% to 78% of mean precipitation in the five TP headwaters. These findings have important implications for ET estimation on the TP headwaters, which greatly influences downstream water availability.

COMPARISON OF SAFER AND METRIC-BASED ACTUAL EVAPOTRANSPIRATION MODELS IN A SUBTROPICAL AREA OF BRAZIL

COMPARISON OF SAFER AND METRIC-BASED ACTUAL EVAPOTRANSPIRATION MODELS IN A SUBTROPICAL AREA OF BRAZIL
 
 
 CÉSAR DE OLIVEIRA FERREIRA SILVA1; RODRIGO LILLA MANZIONE2 E JOSÉ LUIZ ALBUQUERQUE FILHO3
 
 1Agronomical Sciences Faculty, Universidade Estadual Paulista, Av. Universitária, 3780, Altos do Paraíso, 18610-034, São Paulo/SP, Brazil, cesaroliveira.f.silva@gmail.com. 
 2School of Sciences and Engineering, Universidade Estadual Paulista, Rua Domingos da Costa Lopes,780, Jd. Itaipu, 17602-496, Tupã/SP, Brazil, rlmanzione@gmail.com. 
 3Department of Hydrogeology, Institute of Technological Research (IPT), Av. Prof. Almeida Prado, 532, Cid. Universitária – Butantã, 05508-901, São Paulo/SP, Brazil, albuze@ipt.br. 
 
 
 1 ABSTRACT
 
 Remote sensing algorithms are well known to estimate surface energy fluxes in regional to global scales with low cost. The remote sensing approach has an advantage of estimating evapotranspiration (ET) on larger spatiotemporal scales when compared with traditional methods. This study compared the result of ET estimates from the “Simple Algorithm for Evapotranspiration Retrieving” (SAFER) and “Mapping Evapotranspiration at high Resolution with Internal Calibration” (METRIC) models on varied land uses of a subtropical area located in Southeast Brazil by using a image from the sensor OLI of LANDSAT-8. The results showed similarity of ET estimate from both models, although slight deviation especially at high ET values. It happened due differences as the need of anchor pixel in METRIC, which requires two points with extrem thermohydrological conditions in the same area. Minimum ground data requirement is the major advantage of the METRIC over the SAFER model. The maximum value, the sum and ET range by METRIC was higher than SAFER. This study has considered both models feasible for estimation of ET from satellite data in the study area.
 
 Keywords: remote sensing, modelling, superficial temperature, Landsat-8, agriwater.
 
 
 SILVA, C. O. F.; MANZIONE, R. L.; ALBUQUERQUE FILHO, J. L. 
 COMPARAÇÃO DE MODELOS DE EVAPOTRANSPIRAÇÃO REAL SAFER E METRIC EM UMA ÁREA SUBTROPICAL DO BRASIL
 
 
 2 RESUMO
 
 Algoritmos de sensoriamento remoto são conhecidos por estimar fluxos de energia de superfície em escalas regionais a globais com baixo custo. A abordagem de sensoriamento remoto tem a vantagem de estimar a evapotranspiração (ET) em escalas espaço-temporais maiores que os métodos tradicionais. Este estudo compara o resultado da estimativa de ET do “Simple Algorithm for Evapotranspiration Retrieving” (SAFER) e “Mapping Evapotranspiration at high Resolution with Internal Calibration” (METRIC) em variados usos da terra de uma área subtropical localizada no Sudeste do Brasil usando uma imagem do sensor OLI do satélite LANDSAT-8. Os resultados mostraram similaridade da estimativa da ET em ambos os modelos, embora houvesse desvio, especialmente em altos valores de ET. Isto é devido a diferenças como a necessidade de âncora de pixel no modelo METRIC, que necessita de dois pontos com condições termohidrológicas extremas em uma mesma área. A exigência mínima de dados terrestres é a principal vantagem do METRIC sobre o modelo SAFER. Esse estudo considerou ambos os modelos viáveis ​​para a estimativa de ET a partir de dados de satélite na área de estudo. Neste estudo, o valor máximo, a soma e a variação do ET pelo METRIC foram maiores que o do SAFER.
 
 Palavras-chave: sensoriamento remoto, modelagem, temperatura de superfície, Landsat-8, agriwater.

Impact of the Revisit of Thermal Infrared Remote Sensing Observations on Evapotranspiration Uncertainty—A Sensitivity Study Using AmeriFlux Data

Thermal infrared remote sensing observations have been widely used to provide useful information on surface energy and water stress for estimating evapotranspiration (ET). However, the revisit time of current high spatial resolution (<100 m) thermal infrared remote sensing systems, sixteen days for Landsat for example, can be insufficient to reliably derive ET information for water resources management. We used in situ ET measurements from multiple Ameriflux sites to (1) evaluate different scaling methods that are commonly used to derive daytime ET estimates from time-of-day observations; and (2) quantify the impact of different revisit times on ET estimates at monthly and seasonal time scales. The scaling method based on a constant evaporative ratio between ET and the top-of-atmosphere solar radiation provided slightly better results than methods using the available energy, the surface solar radiation or the potential ET as scaling reference fluxes. On average, revisit time periods of 2, 4, 8 and 16 days resulted in ET uncertainties of 0.37, 0.55, 0.73 and 0.90 mm per day in summer, which represented 13%, 19%, 23% and 31% of the monthly average ET calculated using the one-day revisit dataset. The capability of a system to capture rapid changes in ET was significantly reduced for return periods higher than eight days. The impact of the revisit on ET depended mainly on the land cover type and seasonal climate, and was higher over areas with high ET. We did not observe significant and systematic differences between the impacts of the revisit on monthly ET estimates that are based on morning or afternoon observations. We found that four-day revisit scenarios provided a significant improvement in temporal sampling to monitor surface ET reducing by around 40% the uncertainty of ET products derived from a 16-day revisit system, such as Landsat for instance.

Sensitivity of Evapotranspiration Components in Remote Sensing-Based Models

Accurately estimating evapotranspiration (ET) at large spatial scales is essential to our understanding of land-atmosphere coupling and the surface balance of water and energy. Comparisons between remote sensing-based ET models are difficult due to diversity in model formulation, parametrization and data requirements. The constituent components of ET have been shown to deviate substantially among models as well as between models and field estimates. This study analyses the sensitivity of three global ET remote sensing models in an attempt to isolate the error associated with forcing uncertainty and reveal the underlying variables driving the model components. We examine the transpiration, soil evaporation, interception and total ET estimates of the Penman-Monteith model from the Moderate Resolution Imaging Spectroradiometer (PM-MOD), the Priestley-Taylor Jet Propulsion Laboratory model (PT-JPL) and the Global Land Evaporation Amsterdam Model (GLEAM) at 42 sites where ET components have been measured using field techniques. We analyse the sensitivity of the models based on the uncertainty of the input variables and as a function of the raw value of the variables themselves. We find that, at 10% added uncertainty levels, the total ET estimates from PT-JPL, PM-MOD and GLEAM are most sensitive to Normalized Difference Vegetation Index (NDVI) (%RMSD = 100.0), relative humidity (%RMSD = 122.3) and net radiation (%RMSD = 7.49), respectively. Consistently, systemic bias introduced by forcing uncertainty in the component estimates is mitigated when components are aggregated to a total ET estimate. These results suggest that slight changes to forcing may result in outsized variation in ET partitioning and relatively smaller changes to the total ET estimates. Our results help to explain why model estimates of total ET perform relatively well despite large inter-model divergence in the individual ET component estimates.

Microwave implementation of two-source energy balance approach for estimating evapotranspiration

A newly developed microwave (MW) land surface temperature (LST) product is used to substitute thermal infrared (TIR) based LST in the Atmosphere Land Exchange Inverse (ALEXI) modelling framework for estimating ET from space. ALEXI implements a two-source energy balance (TSEB) land surface scheme in a time-differential approach, designed to minimize sensitivity to absolute biases in input records of LST through the analysis of the rate of temperature change in the morning. Thermal infrared (TIR) retrievals of the diurnal LST curve, traditionally from geostationary platforms, are hindered by cloud cover, reducing model coverage on any given day. This study tests the utility of diurnal temperature information retrieved from a constellation of satellites with microwave radiometers that together provide 6-8 observations of Ka-band brightness temperature per location per day. This represents the first ever attempt at a global implementation of ALEXI with MW-based LST and is intended as the first step towards providing all-weather capability to the ALEXI framework. The analysis is based on 9-year long, global records of ALEXI ET generated using both MW and TIR based diurnal LST information as input. In this study, the MW-LST sampling is restricted to the same clear sky days as in the IR-based implementation to be able to analyse the impact of changing the LST dataset separately from the impact of sampling all-sky conditions. The results show that long-term bulk ET estimates from both LST sources agree well, with a spatial correlation of 92% for total ET in the Europe/Africa domain and agreement in seasonal (3-month) totals of 83-97 % depending on the time of year. Most importantly, the ALEXI-MW also matches ALEXI-IR very closely in terms of 3-month inter-annual anomalies, demonstrating its ability to capture the development and extent of drought conditions. Weekly ET output from the two parallel ALEXI implementations is further compared to a common ground measured reference provided by the FLUXNET consortium. Overall, the two model implementations generate similar performance metrics (correlation and RMSE) for all but the most challenging sites in terms of spatial heterogeneity and level of aridity. It is concluded that a constellation of MW satellites can effectively be used to provide LST for estimating ET through ALEXI, which is an important step towards all-sky satellite-based retrieval of ET using an energy balance framework.

Evaluation of a moderate resolution imaging spectroradiometer triangle-based algorithm for evapotranspiration estimates in subalpine regions

The current study evaluates the application of a moderate resolution imaging spectroradiometer (MODIS) triangle-based method to estimate evapotranspiration (ET) in subalpine environments. Topographic corrections and improved soil moisture representation are applied to a previously developed net radiation (Rn) model and triangle algorithm to develop an 8-day average ET product based solely on satellite products. We evaluate modeled Rn and MODIS ET (MOD-ET) against ground-based values at four sites in the Sierra Nevada of northern California and also present a comparison between two monthly distributed ET datasets [operational simplified surface energy balance (SSEBop) and MODIS MOD16]. Modeled daily Rn results indicate a systematic underestimation (between −83 and −110 W/m2 bias). Consequently, Rn is bias-corrected before calculating MOD-ET. MOD-ET validation shows correlations between 0.15 and 0.45 with errors between 73 and 126 W/m2. MOD-ET and SSEBop ET report correlations of 0.36 and 0.20, respectively, on average, compared to ground-based monthly ET. MOD16 underestimates monthly totals, with bias values on the range of −14 to −144 W/m2. Semiarid conditions and scale differences between the MODIS pixel and station contribute to errors with respect to observation. Overall, MOD-ET provides reasonable ET estimates and may better capture temporal dynamics in environments undergoing chronic disturbance.

Assessment of a Remote Sensing Energy Balance Methodology (SEBAL) Using Different Interpolation Methods to Determine Evapotranspiration in a Citrus Orchard

A surface energy balance algorithm for land (SEBAL) for estimating evapotranspiration (ET) has been parameterized and tested in a 400-ha drip irrigated citrus orchard. Simultaneously, during three growing seasons, energy fluxes were measured using Eddy Covariance. Instantaneous fluxes obtained with SEBAL using 10 images from Landsat-5 were compared with the measured fluxes. The Perrier function was the best method for properly estimating the roughness momentum length for discontinuous canopies, as in citrus orchards. Crop height was estimated using LIDAR data. In general, SEBAL performed well for net radiation estimation but failed in soil heat flux estimation. Latent heat estimations from the SEBAL model had a relative root mean square error (rRMSE) of 0.06 when compared with measurements obtained by Eddy Covariance. Three procedures were tested for up-scaling the instantaneous ET estimates from SEBAL to daily ET values: 1) assuming the fraction between the actual ET and the reference ET is constant throughout the day; 2) using actual local crop coefficient curves; and 3) using an up-scaling factor where the fraction of hourly ET to daily ET equals the ratio of hourly to daily global solar radiation. This last method gave acceptable results for daily ET estimations $({\bf rRMSE} = {\bf 0.09})$ and for 15-day ET $({\bf rRMSE} = {\bf 0.19})$ , and its main advantage is that no local data are required. It is concluded that the SEBAL methodology can be successfully applied for determining actual ET, even in discontinuous citrus canopies. However, additional parameterizations of momentum roughness length were needed in order to obtain reliable ET determinations.

Evapotranspiration based on equilibrated relative humidity (ETRHEQ): Evaluation over the continental U.S.

AbstractA novel method of estimating evapotranspiration (ET), referred to as the ETRHEQ method, is further developed, validated, and applied across the U.S. from 1961 to 2010. The ETRHEQ method estimates the surface conductance to water vapor transport, which is the key rate‐limiting parameter of typical ET models, by choosing the surface conductance that minimizes the vertical variance of the calculated relative humidity profile averaged over the day. The ETRHEQ method, which was previously tested at five AmeriFlux sites, is modified for use at common weather stations and further validated at 20 AmeriFlux sites that span a wide range of climates and limiting factors. Averaged across all sites, the daily latent heat flux RMSE is ∼26 W·m−2 (or 15%). The method is applied across the U.S. at 305 weather stations and spatially interpolated using ANUSPLIN software. Gridded annual mean ETRHEQ ET estimates are compared with four data sets, including water balance‐derived ET, machine‐learning ET estimates based on FLUXNET data, North American Land Data Assimilation System project phase 2 ET, and a benchmark product that integrates 14 global ET data sets, with RMSEs ranging from 8.7 to 12.5 cm·yr−1. The ETRHEQ method relies only on data measured at weather stations, an estimate of vegetation height derived from land cover maps, and an estimate of soil thermal inertia. These data requirements allow it to have greater spatial coverage than direct measurements, greater historical coverage than satellite methods, significantly less parameter specification than most land surface models, and no requirement for calibration.

Development of novel embedded system for evapotranspiration estimation in Central India

Automation in the agriculture domain has gained importance due to the need of effective water utilisation. Developed countries have centralised irrigation management information systems, which uses a network of automated weather stations across the country to record real-time data of solar radiation, soil temperature, air temperature, vapour pressure, wind speed, relative humidity, and precipitation. This data helps in estimating evapotranspiration (ET) which is further used for irrigation scheduling. Developing and under developed countries do not have networked weather stations, hence the estimation of ET becomes difficult, time consuming and cost prohibitive. Such countries have an indirect method using meteorological data as the only alternative. This problem leads to a high importance of finding cost effective ways to estimate ET using real time data. A major challenge is to collect weather data. This paper describes a proposed embedded system for the estimation of reference ET for irrigation control. The Penman-Monteith equation is used to compute the reference ET. The real time weather parameter has been recorded for the computation and the result has been compared with the Hargreaves ET equation for missing climatic data. The designed system is cost prohibitive and comparatively simple.

Assessment of the MODIS global evapotranspiration algorithm using eddy covariance measurements and hydrological modelling in the Rio Grande basin

Remote sensing is considered the most effective tool for estimating evapotranspiration (ET) over large spatial scales. Global terrestrial ET estimates over vegetated land surfaces are now operationally produced at 1-km spatial resolution using data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the MOD16 algorithm. To evaluate the accuracy of this product, ground-based measurements of energy fluxes obtained from eddy covariance sites installed in tropical biomes and from a hydrological model (MGB-IPH) were used to validate MOD16 products at local and regional scales. We examined the accuracy of the MOD16 algorithm at two sites in the Rio Grande basin, Brazil, one characterized by a sugar-cane plantation (USE), the other covered by natural savannah vegetation (PDG) for the year 2001. Inter-comparison between 8-day average MOD16 ET estimates and flux tower measurements yielded correlations of 0.78 to 0.81, with root mean square errors (RMSE) of 0.78 and 0.46 mm d-1, at PDG and USE, respectively. At the PDG site, the annual ET estimate derived by the MOD16 algorithm was 19% higher than the measured amount. For the average annual ET at the basin-wide scale (over an area of 145 000 km2), MOD16 estimates were 21% lower than those from the hydrological model MGB-IPH. Misclassification of land use and land cover was identified as the largest contributor to the error from the MOD16 algorithm. These estimates improve significantly when results are integrated into monthly or annual time intervals, suggesting that the algorithm has a potential for spatial and temporal monitoring of the ET process, continuously and systematically, through the use of remote sensing data.Editor D. Koutsoyiannis; Associate editor T. WagenerCitation Ruhoff, A.L., Paz, A.R., Aragao, L.E.O.C., Mu, Q., Malhi, Y., Collischonn, W., Rocha, H.R., and Running, S.W., 2013. Assessment of the MODIS global evapotranspiration algorithm using eddy covariance measurements and hydrological modelling in the Rio Grande basin. Hydrological Sciences Journal, 58 (8), 1658–1676.

Estimating evapotranspiration with land data assimilation systems

ABSTRACTAdvancements in both land surface models (LSMs) and land surface data assimilation, especially over the last decade, have substantially advanced the ability of land data assimilation systems (LDASs) to estimate evapotranspiration (ET). This article provides a historical perspective on international LSM intercomparison efforts and the development of LDASs, both of which have improved LSM ET skill. In addition, an assessment of ET estimates for current LDASs is provided along with current research that demonstrates improvement in LSM ET estimates due to assimilating satellite‐based soil moisture products. Using the Ensemble Kalman Filter in the Land Information System, we assimilate both NASA and Land Parameter Retrieval Model soil moisture products into the Noah LSM Version 3.2 with the North American LDAS Phase 2 (NLDAS‐2), forcing to mimic the NLDAS‐2 configuration. Through comparisons with two global reference ET products, one based on interpolated flux tower data and one from a new satellite ET algorithm, over the NLDAS‐2 domain, we demonstrate improvement in ET estimates only when assimilating the LPRM soil moisture product. Copyright © 2011 John Wiley & Sons, Ltd.