Mapping Evapotranspiration At High Resolution With Internalized Calibration Research Articles

Comparative evaluation of four actual evapotranspiration models over different ecosystems and climate zones in China

ABSTRACT To better understand the discrepancies in evapotranspiration (ET) simulations between ET models, we intercompared four models in China: Priestley–Taylor Jet Propulsion Laboratory (PT-JPL), Penman–Montieth–Leuning Version 2 (PML-V2), Sigmoid Generalized Complementary Function (SGCF), Mapping Evapotranspiration at High Resolution with Internalized Calibration (METRIC). Data from 18 flux sites were used to evaluate the model performance at daytime (when incident shortwave radiation is greater than 20 W/m2) scales. To compare more fairly, we took the intersection of the outputs from four models for the analyses in the main text. All models yielded acceptable results, with PML-V2 or SGCF performing best at most sites. The average coefficient of determination and root mean square error among all sites of LE (latent heat of ET) were 0.72 and 51.71 W/m2 for PT-JPL, 0.80 and 46.65 W/m2 for PML-V2, 0.79 and 41.13 W/m2 for SGCF, 0.70 and 51.09 W/m2 for METRIC. PT-JPL and PML-V2 underestimated ET at most sites, whereas SGCF overestimated, potentially due to uncertainties in the vegetation indices and ET constraint parameters. Compared to measurements, PT-JPL underestimated the proportion of transpiration to evapotranspiration (0.81 versus 0.59), while PML-V2 overestimated (0.81 versus 0.90). Furthermore, all models performed best in the semi-arid zone dominated by grassland sites.

A review on models, products and techniques for evapotranspiration measurement, estimation, and validation

AbstractIn this review study, the major available methods for measurement and estimation of evapotranspiration (ET) are discussed briefly while explaining the latest developments. The best available validation methods are also reviewed and explained. It highlights the importance of accurate ET quantification in managing water resources, evaluating climate change impacts, and supporting crop water requirement management. Measurement methods such as scintillometry, lysimetry, and the eddy covariance (EC) flux method are presented. Additionally, hydrological models are discussed as estimation approaches for actual and potential ET. The paper explores various ET estimation products, particularly those based on remote sensing techniques. Specifically, methods like Mapping EvapoTranspiration at high Resolution with Internalized Calibration (METRIC), Simplified Surface Energy Balance Operational (SSEBop ET), Moderate Resolution Imaging Spectroradiometer (MOD16), Surface Energy Balance Algorithm for Land (SEBAL), Global Land Surface Evaporation: Amsterdam Methodology (GLEAM), Satellite Application Facility on Land Surface Analysis (LSA‐SAF), and Global Land Data Assimilation System (GLDAS) are described. The integration of machine learning (ML) with EC and remote sensing is investigated, with a comprehensive discussion of different ML approaches. Validation methods including the EC method, water balance method‐derived ET (WBET), and statistical techniques are explained. Overall, this review paper provides a comprehensive overview of ET quantification, covering measurement techniques, estimation approaches, remote sensing methods, and the integration of ML. The insights gained from this review contribute to a profound knowledge of ET dynamics and helps those sectors dealing with drought monitoring, water resource management and climate change assessments.

ESTIMATION OF WHEAT CROP PRODUCTIVITY OF DISTRICT NAWAB SHAH USING METRIC EEFLUX IMAGERY

Water scarcity and food security are global concerns, which require morefood production from less water. Therefore, for effective management of water resources and improved food production, identification of Crop water Productivity (CWP) is of prime importance. Current study was conducted at Center for Advanced Studies in Water, Mehran University of Engineering and Technology, Jamshoro, Pakistan during year 2020 estimates CWP of wheat during the Rabi season (winter season) in the Nawab Shah District of Sindh (located in Rorhi canal command area) using Mapping Evapotranspiration at High Resolution with internalized calibration (METRIC) - Earth Engine Flux (EEFLUX) application. Cloud corrections were applied where cloud cover was approximately 50% and missing data were interpolated. Actual evapotran spiration (ETa ) for each season (initial, development, mid and late, required for estimating CWP) were calculated from reference evapotranspiration (ETr ) by evapotranspiration factor (ETr F) which were available at the Food and Agriculture Organization (FAO) website, using geographical information system software package (ARC-GIS). Total ETa for the entire season came out to be 367.57 mm. Inshort, wheat crop productivity can be assessed by using matric EEFLUX imagery

A market-based mechanism for long-term groundwater management using remotely sensed data

Groundwater markets improve the agricultural economy by transferring water entitlements from low-efficient users to high-efficient ones to maximize productivity. Aiming at developing an efficient groundwater market, the environmental effects of the market mechanism should be assessed, and a reliable method for monitoring water consumption needs to be employed. Toward this end, this paper proposes three annual smart groundwater market mechanisms to maximize water net benefits, minimize groundwater withdrawal, and precisely measure water consumption in agricultural fields. To guarantee the aquifer's safe yield in each mechanism, a groundwater simulation model (i.e., Groundwater Modeling System (GMS)) is used to control groundwater table drawdown at the end of the planning horizon. In addition, the fields' evapotranspiration (ET) is estimated using Surface Energy Balance Algorithm for Land (SEBAL) and Mapping Evapo Transpiration at high Resolution with Internalized Calibration (METRIC) algorithm to measure the net groundwater consumption during the market. In this regard, we evaluated the algorithms' performances using observed data from a local lysimeter. They are applied to the Nough plain in Iran to assess the effectiveness of the proposed market framework. The findings illustrate their efficiency in recovering approximately 80% (23.33 million cubic meters (MCM)) of groundwater loss due to overexploitation in the study area and increasing the users' annual benefits by 10.6% compared to the non-market condition. In addition, results imply that the METRIC model approximates daily crop ET with a higher accuracy level than the SEBAL model with RMSE, MAE, and Percentage Error of 0.37 mm/day, 0.32 mm/day, and 14.92%, respectively. This research revealed that the proposed market framework is a powerful tool for reallocating water entitlements and increasing water productivity in arid and semi-arid regions.

Faster evapotranspiration recovery compared to canopy development post clearcutting in a floodplain forest

Actual evapotranspiration (ETa) is a key component of the water balance, and its accurate quantification of spatiotemporal variation is essential to improve the understanding of soil–plant-atmosphere-hydrosphere interactions. While in situ point measurements allow for the determination of the ETa of a particular ecosystem or land cover type, remote sensing methods make it possible to divide and evaluate the rate of ETa of different ecosystems in the landscape and would allow better management of the water resource system, especially if those are partly or permanently limited. The goal of this paper was to analyze the impact of clearcutting during standard forest management practices in floodplain forests and its effect on the water regime of the area. We selected the Lanžhot floodplain forest area (48.681°N 16.950°E, 155 m a.s.l.) that represents a biologically and hydrologically unique area of remnant floodplain forest within the upper Danube River basin. Since clearcutting represents a drastic intervention into the ecosystem including changes in canopy structure and local microclimate, we hypothesized that the impact of the clearcut on the overall water balance is long-term in the order of decades as a result consequence of logging of the mature forest and changes in local microclimatic conditions in disturbed areas. To analyze the impacts of clearcutting on ETa, we applied the METRIC (Mapping EvapoTranspiration at high Resolution with Internalized Calibration) remote sensing model. METRIC was validated in the Central European climatic conditions using two ground eddy covariance measurements systems over grassland and floodplain forest. The evaluated model METRIC was then applied to quantify ETa in three areas of interest in the floodplain forest, where in 2015–2021, mature vegetation was targeted for removal, modification of the soil surface, and subsequent planting of a new generation of English oak trees (Quercus robur L.). Immediately after the removal of vegetation cover, METRIC detected an annual decrease in ETa by 35–40%, while leaf area index (LAI) decreased by 70–80%. This was followed by a quick recovery of the ETa/ETo (where ETo represents FAO-56 reference evapotranspiration), reaching similar values as uncut surrounding vegetation during the third year, while LAI recovery was detected in the fifth year after the removal of vegetation cover. The rapid recovery of the ETa/ETo rejected the initial hypothesis, and studies showed that the newly formed stand replaced the ETa/ETo rate of the mature stand in the floodplain forest conditions of Central Europe in the fourth year, suggesting that the impacts of clearcutting on the hydrology may be temporally relatively limited.

Estimation of Actual Evapotranspiration Using Satellite-Based Surface Energy Balance Derived from Landsat Imagery in Northern Thailand

In this study, satellite-based measures of surface energy balance and the mapping evapotranspiration at high resolution with internalized calibration (METRIC) from Landsat imagery were used to estimate the spatiotemporal distribution of actual evapotranspiration (ETa) in northern Thailand, constituting a procedure that has rarely been performed in southeast Asia. Subsequently, we compared the ETa obtained from METRIC with that calculated using the FAO-56 dual-crop coefficient method via the SIMDualKc software and found a strong correlation. An assessment of the accuracy of all the sample plots revealed the R2, Root-Mean-Square Error (RMSE), and mean absolute error (MAE) values to be 0.830, 0.730, and 0.575 mm d−1, respectively. Differences in the cumulative ETa values derived from SIMDualKc and METRIC ranged in magnitude from 0.93–3.57% for rice and 3.08–7.99% for longan. The ETa values for forestland and waterbodies were higher than those for agricultural areas and areas with other forms of land use. The spatiotemporal distribution of the seasonal ETa during the dry season was consistent with the climate, vegetation, and anthropogenic activity. Thus, our results indicate that METRIC is a reliable tool for estimating ETa for water resource management under different environmental conditions and improving water use efficiency over large areas.

The sensitivity of maize evapotranspiration to vapor pressure deficit and soil moisture with lagged effects under extreme drought in Southwest China

Evapotranspiration (ET) is regulated by vapor pressure deficit (VPD) and soil moisture (SM) which are expected to change in global climate change. Understanding the impacts of VPD and SM on maize ET is essential for global food security and water cycle. Here, we estimate the actual maize ET in Southwest China during 2001–2011 based on Mapping Evapotranspiration at High Resolution with Internalized Calibration (METRIC) model using remote-sensing data and reanalysis data. Then, we define the extreme drought events in 2009–2011 using the smoothed standardized anomaly of the three-month Standardized Precipitation Evapotranspiration Index (SPEI) to investigate the responses of maize ET to VPD and SM during extreme drought events in Southwest China. We detected the lag times of maize ET responses to VPD and SM, and then analyzed the sensitivity of maize ET to VPD and SM with lagged effects. We found pervasive lagged responses of maize ET with a shorter lag time of VPD (1.12 ±0.006 months, P < 0.05) than SM (1.28±0.006 months, P < 0.05). We analyzed the lag times of maize ET in relation to elevation, temperature, and precipitation to investigate the abiotic impacts on the lagged responses and found the lagged responses were more prevalent in areas at higher altitudes with lower temperatures but there was less variation across precipitation gradients. Maize ET was more sensitive to VPD than SM with lagged effects. High VPD enhanced maize ET under sufficient SM but during an extreme drought event, maize ET might be dramatically reduced due to SM depletion in spite of high VPD. The study highlights the significance of atmospheric water demand for crop water consumption under extreme climate events.

Modeling Actual Evapotranspiration with MSI-Sentinel Images and Machine Learning Algorithms

The modernization of computational resources and application of artificial intelligence algorithms have led to advancements in studies regarding the evapotranspiration of crops by remote sensing. Therefore, this research proposed the application of machine learning algorithms to estimate the ETrF (Evapotranspiration Fraction) of sugar can crop using the METRIC (Mapping Evapotranspiration at High Resolution with Internalized Calibration) model with data from the Sentinel-2 satellites constellation. In order to achieve this goal, images from the MSI sensor (MultiSpectral Instrument) from the Sentinel-2 and the OLI (Operational Land Imager) and TIRS (Thermal Infrared Sensor) sensors from the Landsat-8 were acquired nearly at the same time between the years 2018 and 2020 for sugar cane crops. Images from OLI and TIR sensors were intended to calculate ETrF through METRIC (target variable), while for the MSI sensor images, the explanatory variables were extracted in two approaches, using 10 m (approach 1) and 20 m (approach 2) spatial resolution. The results showed that the algorithms were able to identify patterns in the MSI sensor data to predict the ETrF of the METRIC model. For approach 1, the best predictions were XgbLinear (R2 = 0.80; RMSE = 0.15) and XgbTree (R2 = 0.80; RMSE = 0.15). For approach 2, the algorithm that demonstrated superiority was the XgbLinear (R2 = 0.91; RMSE = 0.10), respectively. Thus, it became evident that machine learning algorithms, when applied to the MSI sensor, were able to estimate the ETrF in a simpler way than the one that involves energy balance with the thermal band used in the METRIC model.

METRIC Modeli ve Google Earth Engine Flux ile Hesaplanan Günlük Bitki Su Tüketimi ve Referans Bitki Su Tüketimi Fraksiyonu Değerlerinin Karşılaştırılması

Evapotranspirasyonun (ET) hassas bir biçimde belirlenmesi su kaynakları yönetiminde oldukça önemlidir. Son yıllarda ET’nin uzaktan algılama teknikleri yardımıyla belirlenmesinde çeşitli modeller geliştirilmiştir. Bu modeller arasında Evapotranspirasyon Haritalamasında Yüksek Çözünürlük ve İçsel Kalibrasyon Modeli (METRIC) en yaygın kullanılanlardan biridir. METRIC modelinde içsel kalibrasyon amacıyla ET’nin potansiyel seviyede ve ET’nin sıfır olduğu iki ekstrem koşulu temsilen seçimler yapılması gerekmektedir. Bu seçimleri hem uzaktan algılama hem de ET üzerine deneyimli kişilerin yapması gerekmektedir. Ancak bu durum METRIC modelinin kullanımını kısıtlamaktadır. Oluşan bu boşluğun doldurulması için Google Earth Engine platformunda Earth Engine Evapotranspiration Flux (EEFlux) uygulaması geliştirilmiştir. Bu uygulamayla METRIC modeli otomatik olarak kalibre edilmektedir. Bu çalışmanın amacı elle METRIC modeli ile EEFlux modelinden elde edilen günlük ET (ETd) ve Referans ET fraksiyonu (ETrF) değerlerinin karşılaştırılmasıdır. Çalışma Amasya ili Merzifon ilçesinde yer alan Uzunyazı, Çayırözü ve Yeşilören köylerindeki arpa, ayçiçeği, buğday, mısır, şeker pancarı, patates ve soğan yetiştiriciliği yapılan tarım alanlarında gerçekleştirilmiştir. Araştırmada üç farklı tarihe ait Landsat 8 uydu görüntüleri kullanılmıştır. Çalışmadan elde edilen sonuçlara göre METRIC ve EEFLUX ile hesaplanan ETd değerleri uyumlu olmasına rağmen (R2=0,87), genel olarak EEFlux-ETd değerleri METRIC-ETd değerlerinden daha düşük gerçekleşmiştir (RMSE=2,5 mm gün-1 ve MAE=2,38 mm gün-1). ETrF değerleri ise ETd değerlerine benzer uyumla hesaplanmıştır (R2=0,88, RMSE=0,11 ve MAE=0,09). Buna göre EEFLUX ve METRIC ile hesaplanan ETd ve ETrF değerleri arasında belirli farklılıklar olmasına rağmen, EEFLUX ile oldukça hızlı, yerel iklim verileri ve deneyimli bir kullanıcı ihtiyacı olmadan ETd değerleri belirlenebilmektedir.

Application of the METRIC model for mapping evapotranspiration over the Sundarban Biosphere Reserve, India

This study is an attempt to estimate the spatial distribution of daily evapotranspiration (ET) in the Indian part of the Sundarban Biosphere Reserve by utilizing the (Mapping Evapo Transpiration at high resolution with internalized Calibration (METRIC) model. Landsat 8 OLI satellite image was used to run the METRIC model and calculate ET to explain the overall surface energy balance in the area under the present climatic conditions. The result obtained shows that, the ET minimum value ranges from 6.874 mm/day in cropland to a maximum of 12.321 mm/day in the forest and water bodies. We used a (ETrF) value of 1 for forests since the forest surface temperature was lower than the air temperature, resulting in a negative sensible heat flow. The use of METRIC energy balancing procedures for validation purposes using Landsat imagery shows that the per hourly, monthly, and yearly ET estimations acquired using MODIS (Moderate Resolution Imaging Spectro radiometer) data may be effectively reproduced. The research includes a novel component that verifies the efficacy of this technique for water conservation, as well as satellite-based technology, which offers effective and efficient tools for integrated evapotranspiration estimation.

Evapotranspiration estimation of Urmia Lake Basin using GCOM-C thermal imagery

Basin-scale monthly and annual evapotranspiration (ET) is estimated for Urmia Lake Basin by applying the Global Change Observation Mission for Climate (GCOM-C) global ETindex estimation algorithm to thermal imagery observed by the GCOM-C satellite. In total, 297 satellite images acquired during 2018-2019 were used in this study. ET estimation accuracy was examined for an area dominated by apple fields using traditional surface irrigation. The estimated ET was 15% lower than the standard crop ET, which was computed using a procedure suggested by the Food and Agriculture Organization of the United Nations on a monthly timescale, and was 8% lower on an annual timescale. Comparison of estimated ET with a satellite-based ET map derived by using the Mapping EvapoTranspiration at high Resolution with Internalized Calibration (METRIC) model showed a similar difference. The 8%–15% differences among the different sources of ET were small, given that a similar or wider range of uncertainty is frequently available even in ground-based ET measurements. Comparison between the estimated ET and the MODIS ET Product (MOD16) revealed a greater difference in the evaluated area of the apple fields. Given the climatic ET demands and the irrigation practices of the area, ET estimation accuracy is more likely to be higher using the dataset derived from this study than using MOD16. The GCOM-C satellite started routine surface observations in January 2018. Its contribution to agricultural water management, such as by estimating ET as presented in this study, will increase as the amount of historical data stored continues to accumulate.

Satellite-Based Water and Energy Balance Model for the Arid Region to Determine Evapotranspiration: Development and Application

Actual evapotranspiration (ETa) plays an important role in irrigation planning and supervision. Traditionally, the estimation of ETa was approximated using different in situ techniques, having high initial and maintenance costs with low spatial resolution. In this context, satellite imagery models play an effective role in water management practices by estimating ETa in small and large-scale areas. All existing models have been widely used for the estimation of ETa around the globe, but there is no definite conclusion on which approach is best for the hot and hyper-arid region of Oman. Our study introduces an innovative approach that uses in situ, meteorological, and satellite imagery (Landsat-OLI/TIRS) datasets to estimate ETa. The satellite-based water and energy balance model for the arid region to determine evapotranspiration (SMARET) was developed under the hot and hyper-arid region conditions of Oman by incorporating soil temperature in the sensible heat flux. The performance of SMARET ran through accuracy assessment against in situ measurements via sap flow sensors and lysimeters. The SMARET was also evaluated against three existing models, including the surface energy balance algorithm for land (SEBAL), mapping evapotranspiration at high-resolution with internalized calibration (METRIC), and the Penman–Monteith (PM) model. The study resulted in a significant correlation between SMARET (R2 = 0.73), as well as the PM model (R2 = 0.72), and the ETa values calculated from Lysimeter. The SMARET model also showed a significant correlation (R2 = 0.66) with the ETa values recorded using the sap flow meter. The strong relationship between SMARET, sap flow measurement, and lysimeter observation suggests that SMARET has application capability in hot and hyper-arid regions.

Above ground carbon stock mapping over Coimbatore and Nilgiris Biosphere: a key source to the C sink

Mapping and quantifying above ground carbon (AGC) Stocks reflect significant dynamics in the terrestrial carbon cycle and cascade climate change. Estimation of such key driver was performed for the dominant species (Bamboo, Eucalyptus and Teak) over Coimbatore and Nilgiris Biosphere (2006 − 2018 quadruple interval) of Tamilnadu, India with the developed global stepwise multiple linear regression (SMLR) and local geographically weighted regression (GWR) models using multi-dynamic variables. Evapotranspiration (ET) developed using Mapping Evapotranspiration at High Resolution with Internalized Calibration (METRIC) model for the region was analysed with the best-fitted AGC estimation model to understand AGC—ET synergistic pertinence dynamics. The study compared and validated the estimation by the models and indicated that AGC estimation using SMLR exhibiting a high degree of accuracy ( ) with nominal negative bias in the estimation ranging from with amplification of GWR prediction indicted positive bias with comparatively least mean accuracy ( ). The ET—AGC reciprocity for the dominant species resulted that, bamboo with lower AGC correlated with higher ET tailed by teak with higher AGC and ET and eucalyptus with relatively higher AGC and lower ET and respectively. The analysis resulted in minimal biasness in AGC mapping using SMLR, and both the model signifies that the region can potentially be considered a long-term carbon sink.

Forest evapotranspiration dynamics over a fragmented forest landscape under drought in southwestern Amazonia

Ongoing climate change and human conversion of forests to other land uses alter regional evapotranspiration dynamics and, consequently, impact associated hydrological systems in Amazonia. We studied the effects of drought and fragmentation on forest evapotranspiration using the surface energy balance-based model METRIC (Mapping Evapotranspiration at high Resolution with Internalized Calibration) for a fragmented forest landscape in Brazil's Amazonian state of Rondônia.Dry season (June-August) forest evapotranspiration estimates were produced for the 2009-2011 period that encompassed the 2010 drought event, one of the extreme droughts in the Amazon. METRIC evapotranspiration data were analyzed in relation to climate (monthly precipitation and cumulative water deficit) and forest fragmentation (edge distance from 100m to 1000m from forest edge and edge density). During the dry season of 2009, pre-drought, forest evapotranspiration did not fall below 110mm/month. However, the 2010 drought year showed a drastic decline in evapotranspiration by 32%, to 75mm/month, from July to August. In 2011, evapotranspiration rates were still depressed with August rates dropping as low as 85mm/month. Forest evapotranspiration dynamics were driven mainly by precipitation and corresponding water deficits in the drier years (2010 and 2011), although evapotranspiration deficits along the edges of forest fragments were locally significant, at the landscape scale. The forests near edges (to 100m) had progressively lower evapotranspiration levels than interior forests as dry seasons progressed and these differences were greatest in the 2010 drought year, reaching almost 5%.Our results suggest that during the driest months, fragmentation exacerbated both the rate and extent of evapotranspiration reductions over forest areas up to 100m from edges, equivalent to ~20% of the forested landscape in our study area.

Comparison of Satellite Driven Surface Energy Balance Models in Estimating Crop Evapotranspiration in Semi-Arid to Arid Inter-Mountain Region

The regional-scale estimation of crop evapotranspiration (ETc) over a heterogeneous surface is an important tool for the decision-makers in managing and allocating water resources. This is especially critical in the arid to semi-arid regions that require supplemental water due to insufficient precipitation, soil moisture, or groundwater. Over the years, various remote sensing-based surface energy balance (SEB) models have been developed to accurately estimate ETc over a regional scale. However, it is important to carry out the SEB model assessment for a particular geographical setting to ensure the suitability of a model. Thus, in this study, four commonly used and contrasting remote sensing models viz. METRIC (mapping evapotranspiration at high resolution with internalized calibration), SEBAL (surface energy balance algorithm for land), S-SEBI (simplified surface energy balance index), and SEBS (surface energy balance system) were compared and used to quantify and map the spatio-temporal variation of ETc in the semi-arid to arid inter-mountain region of Big Horn Basin, Wyoming (Landsat Path/Row: 37/29). Model estimates from 19 cloud-free Landsat 7 and 8 images were compared with the Bowen ratio energy balance system (BREBS) flux stationed in a center pivot irrigated field during 2017 (sugar beet), 2018 (dry bean), and 2019 (barley) growing seasons. The results indicated that all SEB models are effective in capturing the variation of ETc with R2 ranging in between 0.06 to 0.95 and RMSD between 0.07 to 0.15 mm h−1. Pooled data over three vegetative surfaces for three years under irrigated conditions revealed that METRIC (NSE = 0.9) performed better across all land cover types, followed by SEBS (NSE = 0.76), S-SEBI (NSE = 0.73), and SEBAL (NSE = 0.65). In general, all SEB models substantially overestimated ETc and underestimated sensible heat (H) fluxes under dry conditions when only crop residue was available at the surface. A mid-season density plot and absolute difference maps at image scale between the models showed that models involving METRIC, SEBAL, and S-SEBI are close in their estimates of daily crop evapotranspiration (ET24) with pixel-wise RMSD ranged from 0.54 to 0.76 mm d−1 and an average absolute difference across the study area ranged from 0.47 to 0.56 mm d−1. Likewise, all the SEB models underestimated the seasonal ETc, except SEBS.

Estimation of evapotranspiration fluxes from Sal (Shorea robusta Gaertn.f) forest using METRIC model: case study of Doon Valley, India

The present study focusses on the estimation of ET using Mapping Evapotranspiration at high Resolution with Internalized Calibration (METRIC) model over the Sal (Shorea robusta Gaertn.f) forest in the subtropical Doon Valley, India for the dry periods of 2016–18. Parameterization of METRIC model was done for surface temperature (Ts), momentum roughness length (zom), and surface albedo (α) estimations to make it suitable for Forest ET assessment. Results showed good agreement between the daily ET derived from the METRIC model and observed ET by eddy covariance method at Barkot Flux Tower (BFT) site. Results of parameterized METRIC model showed improvement over non-parameterized model with higher coefficient of determination (0.81), Nash Sutcliffe Efficiency (0.71) and lower Root Mean Square Error (0.48 mm). Average ET for the Sal forest ranged from 1.18-4.78 mm/day. Monthly and seasonal ET was derived using cubic spline interpolation-based time integration of reference ET fraction (ETrF).

Automatically selecting hot and cold pixels for satellite actual evapotranspiration estimation under different topographic and climatic conditions

The persistent monitoring of evapotranspiration (ET) over the regions suffering from water scarcity is critical for sustainable agricultural water management. Remote sensing provides time- and cost-effective capability to investigate daily ET rates at large scales. Satellite-based actual evapotranspiration (ETa) algorithms typically rely on specifying the upper and lower boundaries of ETa rate over agricultural and pasture fields, commonly known as hot (dry) and cold (wet) pixels selection. These boundaries are to be recognized by an expert through a subjective and labor-intensive task. In this study, a method has been introduced to automatically select appropriate anchor pixels (i.e., hot and cold pixels) independent from land use/cover maps with the simplest possible way, quickly applied even by an inexperienced operator. Subsequently, ETa was calculated using Mapping EvapoTranspiration at high Resolution with Internalized Calibration (METRIC), Surface Energy Balance Algorithm for Land (SEBAL), and Surface Energy Balance System (SEBS) algorithms and evaluated against measured data. In this method, the mountains and foothills were removed using the Shuttle Radar Topography Mission Digital Elevation Model (SRTM DEM) and the subsequent product was a slope mask. Then, filters were applied based on the Normalized Difference Vegetation Index (NDVI), Albedo, and Land Surface Temperature (LST) images to identify potential candidate pixels for hot and cold pixels. In the end, the best-conditioned pixel being closest to the meteorological station was selected. The method was assessed in five different regions with different topographic and climatic conditions. The selected pixels were first visually validated in Landsat images, and then the fluctuations and values were discussed in time series of anchor pixels and LST histograms. The visual interpretation was indicative of selecting the anchor pixels in fallow (hot pixel) and densely vegetated (cold pixel) surfaces. Also, the hot and cold pixels were suitably situated in the upper and lower quartiles of the LST histogram, respectively. The range of cold pixels variations throughout the study periods was lower compared with the hot pixels (44.2, 55.3, 35.5, 66.5, and 25.2 K for hot pixels against 34.2, 45.7, 25.6, 52.2, and 17 K for cold pixels) as expected, which emanated from the lower fluctuations of temperature over vegetation against the soil. The results were indicative of the better performance of METRIC compared with SEBAL and SEBS with greater values of R2 in all the regions. Therefore, using the introduced method, the expert subjective interference was eliminated and processing time reduced significantly from about 1 h per image to a few minutes.

Modeling Evapotranspiration of Winter Wheat Using Contextual and Pixel-Based Surface Energy Balance Models

HighlightsThree contextual-based (CB) and two pixel-based (PB) models were evaluated to estimate ET of rainfed winter wheat.Instantaneous available energy estimation and ET upscaling impacted model performance.The CB models performed better at instantaneous and daily scales compared to the PB models.ET estimation biases increased during low vegetation and drier conditions, especially for the PB models.Abstract. Surface energy balance (SEB) models based on thermal remote sensing data are widely used in research applications to map evapotranspiration (ET) across various landscapes. However, their ability to capture ET from winter wheat remains underexplored, especially in practical applications such as integrated resource management and drought preparedness. Investigating winter wheat ET dynamics is important in agricultural regions such as the Southern Great Plains of the U.S., where winter wheat is extensively cultivated. The goal of this study was to evaluate the performance of five fully automated SEB models, three contextual-based (CB) and two pixel-based (PB), in estimating instantaneous and daily ET of winter wheat by comparing the model results with flux tower observations. The CB models included Surface Energy Balance Algorithm for Land (SEBAL), Mapping Evapotranspiration at high Resolution with Internalized Calibration (METRIC), and Triangular Vegetation Temperature (TVT). The PB models included Surface Energy Balance System (SEBS) and Two-Source Energy Balance (TSEB). Model evaluation during two winter wheat growing seasons (2016-2018) using 28 Landsat images showed that the instantaneous ET estimates from METRIC and TSEB had the smallest (RMSE = 0.14 mm h-1) and largest (RMSE = 0.27 mm h-1) errors, respectively. At the daily scale, SEBAL was the best performing model (RMSE = 1.0 mm d-1), followed by TVT (RMSE = 1.1 mm d-1), METRIC (RMSE = 1.2 mm d-1), SEBS (RMSE = 1.3 mm d-1), and TSEB (RMSE = 1.5 mm d-1). Overall, the CB models provided smaller errors than the PB models. Larger errors in daily ET estimation were observed during low vegetation and drier conditions, especially for the PB models. Keywords: Flux tower, Landsat, Southern Great Plains, Water use.

Quantification and Mapping of Satellite Driven Surface Energy Balance Fluxes in Semi-Arid to Arid Inter-Mountain Region

Crop evapotranspiration (ETc) estimates, on a regional scale, hold enormous potential in managing surface and groundwater resources. This is particularly important for the headwater state of Wyoming, which provides water to found major river basins of the US. In this study, METRIC (Mapping evapotranspiration at high resolution with internalized calibration), a satellite-based image processing model, was used to map and quantify daily, monthly, and seasonal ETc and other energy balance fluxes, i.e., net radiation (Rn), sensible heat (H), and soil heat flux (G) dynamics for different land-use classes. Monthly and seasonal ETc estimated were further used to approximate regional water consumption patterns for different land-use types for nine irrigation districts in semi-arid to arid intermountain region of Big Horn Basin (BHB), Wyoming. The validation of METRIC retrievals against Bowen ratio energy balance system (BREBS) fluxes measured over three vegetative surfaces, viz. sugar beet in 2017, dry bean in 2018, and barley in 2019, indicated high accuracy. The pooled correlation observed between estimated (pooled) and measured instantaneous fluxes had R2 values of 0.91 (RMSE = 0.08 mm h−1, NSE = 0.91), 0.81 (RMSE = 49.6 Wm−2, NSE = 0.67), 0.53 (RMSE = 27.1 Wm−2, NSE = 0.53), and 0.86 (RMSE = 59.2 Wm−2, NSE = 0.84) for ETc, Rn, G, and H, respectively. The biggest discrepancy between measured and estimated monthly ETc values was observed during times when BREBS flux tower footprint was devoid of any crops or the crops at footprint were not actively transpiring. Validation results improved when comparisons were made on monthly scales with METRIC underestimating growing season ETc in the range between 3.2% to 6.0%. Seasonal ETc by land-use type showed significant variation over the study area where crop ETc was 52% higher than natural vegetation ETc. Furthermore, it was found that, in the arid to semi-arid intermountain region of Wyoming, the contribution of irrigation to total seasonal ETc varied in the range of 73–81% in nine irrigation districts that fall within the study area. The high relative contribution of irrigation highlights the importance of identifying and quantifying ETc for improved management in irrigation system design and water allocation.

Satellite‐based regional‐scale evapotranspiration estimation mapping of the rice bowl of Tamil Nadu: A little water to spare*

AbstractImproved regular and rapid monitoring techniques are needed to quantify evapotranspiration (ET) precisely on agricultural fields to enhance the efficient usage of water resources and protect water quality and the environment. This study proposes estimating and mapping ET using a vegetation index coupled with a crop coefficient estimation method over the famous rice bowl region of Tamil Nadu, India. The study utilized a very high‐resolution satellite—Sentinel—of resolution 10 m. The study is based on a crop coefficient—Normalized Difference Vegetation Index (NDVI)—based ET estimation, which is one of the most adaptable practical approaches in quantifying crop water usage in multiple phases of crop growth on a regional scale. The results have been further calibrated using the surface energy‐based Mapping Evapotranspiration at High Resolution with Internalized Calibration (METRIC) model to assess estimation correctness. The estimations analysed for the 2016–2019 kharif cropping season show good overall correlation with the coefficient of determination (R2) of ≥0.72, with mean bias error (MBE) of ≤0.0009 mm day−1 and root mean square error (RMSE) of ≤0.67 mm day−1, indicating a positive assessment of ET for the delta region of Tamil Nadu for reaching vindicable intendance of crop water in this region.