A review of surface energy balance models for estimating actual evapotranspiration with remote sensing at high spatiotemporal resolution over large extents

McShane, Ryan R.; Driscoll, Katelyn P.; Sando, Roy

doi:10.3133/sir20175087

Cited by 51 publications

(35 citation statements)

References 15 publications

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“…More specifically, the validation test against individual AmeriFlux towers shows about 10–30% uncertainty associated with the MOD16 ET products (Mu et al, 2011; Velpuri et al, 2013). Furthermore, all remote sensing‐based ET products will have certain assumptions leading to biases (McShane et al, 2017). Our model also has the advantage of learning how to use each input data set given its relationship to the training data, and thus can implicitly account for biases in each product.…”

Section: Discussionmentioning

confidence: 99%

Groundwater Withdrawal Prediction Using Integrated Multitemporal Remote Sensing Data Sets and Machine Learning

Majumdar

Smith

Butler

et al. 2020

Water Resources Research

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Effective monitoring of groundwater withdrawals is necessary to help mitigate the negative impacts of aquifer depletion. In this study, we develop a holistic approach that combines water balance components with a machine learning model to estimate groundwater withdrawals. We use both multitemporal satellite and modeled data from sensors that measure different components of the water balance and land use at varying spatial and temporal resolutions. These remote sensing products include evapotranspiration, precipitation, and land cover. Due to the inherent complexity of integrating these data sets and subsequently relating them to groundwater withdrawals using physical models, we apply random forests-a state of the art machine learning algorithm-to overcome such limitations. Here, we predict groundwater withdrawals per unit area over a highly monitored portion of the High Plains aquifer in the central United States at 5 km resolution for the Years 2002-2019. Our modeled withdrawals had high accuracy on both training and testing data sets (R 2 ≈ 0.99 and R 2 ≈ 0.93, respectively) during leave-one-out (year) cross validation with low mean absolute error (MAE) ≈ 4.31 mm and root-mean-square error (RMSE) ≈ 13.50 mm for the year 2014. Moreover, we found that even for the extreme drought year of 2012, we have a satisfactory test score (R 2 ≈ 0.84) with MAE ≈ 9.72 mm and RMSE ≈ 24.17 mm. Therefore, the proposed machine learning approach should be applicable to similar regions for proactive water management practices. Plain Language Summary Groundwater is an essential component of global water resources and is the largest source of Earth's liquid freshwater. It is extensively used for drinking water and to support global food production. Consequently, groundwater consumption has significantly increased owing to the pressing demands for water, food, and energy primarily driven by the increasing global population. Despite its critical role in the water-food-energy nexus, very few regions in the United States or elsewhere actively monitor their groundwater withdrawals (extraction or pumping) for implementing sustainable water management solutions. We develop a novel approach combining water balance components and land use measured using openly available remote sensing products with a machine learning model to estimate groundwater withdrawals. This framework automatically learns the interrelationships among these variables and groundwater withdrawals. Our study area is a portion of the High Plains Aquifer in Kansas (central United States) where overpumping has caused substantial groundwater storage loss. Also, a large amount of groundwater pumping data are available for validation. The results indicate good accuracy even for extreme drought years. Thus, this approach should be applicable to similar regions having sparsely or moderately available groundwater pumping data, enabling water managers to proactively implement sustainable solutions addressing water security issues.

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Section: Discussionmentioning

confidence: 99%

Groundwater Withdrawal Prediction Using Integrated Multitemporal Remote Sensing Data Sets and Machine Learning

Majumdar

Smith

Butler

et al. 2020

Water Resources Research

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“…According to [ 107 ]; for the cold pixel, the ratio

and

is assumed to be 1.05. However, this assumption is not always true at the beginning or outside of the growing season when the vegetation is much less than the alfalfa [ 18 ]. Therefore, the ratios of the

and

for the cold and hot pixels are calculated by NDVI [ 19 ].…”

Section: Methodsmentioning

confidence: 99%

“…Remote sensing models have been useful in accounting for the spatial variability of ET at regional scales when using satellite platforms such as Landsat and ASTER [ 14 , 15 , 16 , 17 ]. Since the satellite started being applied [ 18 ], several remote sensing models have been developed to estimate ET, such as surface energy balance algorithm for land (SEBAL) [ 8 , 15 ], mapping evapotranspiration with internalized calibration (METRIC) [ 19 ], the dual temperature difference (DTD) [ 20 ], and the Priestley–Taylor TSEB (TSEB-PT) [ 21 ]. Remote sensing techniques can provide information such as normalized difference vegetation index (NDVI), leaf area index (LAI), surface temperature, and surface albedo.…”

Section: Introductionmentioning

confidence: 99%

Evapotranspiration Estimation with Small UAVs in Precision Agriculture

Niu

Hollenbeck

Zhao

et al. 2020

Sensors

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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.

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“…This study aimed at evaluating spatial ET maps from UAS based remote sensing (RS) data relative to satellite-based RS data for crops grown in commercial farming operations. As the conventional METRIC model is well-established for spatial ET mapping for its corroboration in over 25 countries and in almost all types of agroclimatic zones [22], the additional field validation was deemed not necessary, i.e., outside the scope of the study. Thus, we did not conduct ground-reference ET measurements.…”

Section: Output Comparisonsmentioning

confidence: 99%

High Resolution Geospatial Evapotranspiration Mapping of Irrigated Field Crops Using Multispectral and Thermal Infrared Imagery with METRIC Energy Balance Model

Chandel

Molaei

Khot

et al. 2020

Drones

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Geospatial crop water use mapping is critical for field-scale site-specific irrigation management. Landsat 7/8 satellite imagery with a widely adopted METRIC (Mapping Evapotranspiration at high Resolution with Internalized Calibration) energy balance model (LM approach) estimates accurate evapotranspiration (ET) but limits field-scale spatiotemporal (30 m pixel−1, ~16 days) mapping. A study was therefore conducted to map actual ET of commercially grown irrigated-field crops (spearmint, potato, and alfalfa) at very high-resolution (7 cm pixel−1). Six small unmanned aerial system (UAS)-based multispectral and thermal infrared imagery campaigns were conducted (two for each crop) at the same time as the Landsat 7/8 overpass. Three variants of METRIC model were used to process the UAS imagery; UAS-METRIC-1, -2, and -3 (UASM-1, -2, and -3) and outputs were compared with the standard LM approach. ET root mean square differences (RMSD) between LM-UASM-1, LM-UASM-2, and LM-UASM-3 were in the ranges of 0.2–2.9, 0.5–0.9, and 0.5–2.7 mm day−1, respectively. Internal calibrations and sensible heat fluxes majorly resulted in such differences. UASM-2 had the highest similarity with the LM approach (RMSD: 0.5–0.9, ETdep,abs (daily ET departures): 2–14%, r (Pearson correlation coefficient) = 0.91). Strong ET correlations between UASM and LM approaches (0.7–0.8, 0.7–0.8, and 0.8–0.9 for spearmint, potato, and alfalfa crops) suggest equal suitability of UASM approaches as LM to map ET for a range of similar crops. UASM approaches (Coefficient of variation, CV: 6.7–24.3%) however outperformed the LM approach (CV: 2.1–11.2%) in mapping spatial ET variations due to large number of pixels. On-demand UAS imagery may thus help in deriving high resolution site-specific ET maps, for growers to aid in timely crop water management.

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A review of surface energy balance models for estimating actual evapotranspiration with remote sensing at high spatiotemporal resolution over large extents

Cited by 51 publications

References 15 publications

Groundwater Withdrawal Prediction Using Integrated Multitemporal Remote Sensing Data Sets and Machine Learning

Groundwater Withdrawal Prediction Using Integrated Multitemporal Remote Sensing Data Sets and Machine Learning

Evapotranspiration Estimation with Small UAVs in Precision Agriculture

High Resolution Geospatial Evapotranspiration Mapping of Irrigated Field Crops Using Multispectral and Thermal Infrared Imagery with METRIC Energy Balance Model

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