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Remote Sensing-Based Evapotranspiration Models

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: closed (15 March 2023) | Viewed by 34890

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Dr. Vivek Sharma

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Agricultural and Biological Engineering Department, Institute of Food and Agricultural Sciences, University of Florida, Gainesville, FL 32611, USA
Interests: precision water management; soil and water conservation; irrigation scheduling; evapotranspiration and surface energy balance fluxes; soil water and crop dynamics; crop water productivity; remote sensing
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Dr. Aditya Singh

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Department of Agricultural and Biological Engineering, University of Florida, Gainesville, FL 32611, USA
Interests: remote sensing; forestry; spectroscopy; water quality; wildlife habitat use
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Special Issue Information

Dear Colleagues,

Evapotranspiration (ET) plays a significant role in local, regional, and global climate by impacting relationships between land-use/land cover change and microclimate/climate energy balance in the hydrological cycle and has important applications in agriculture and natural system. Over the years, various remote sensing-based techniques have been developed to understand and estimate ET and its interactions over local to regional spatial scales. This special issue aims to provide a forum of discussion for recent developments and advances in Remote Sensing-based ET models and their applications in diverse ecosystems and agrometeorological conditions. The special issue aims at targeting studies related to the advances of large-scale remote sensing-based ET modeling, model and algorithm validation, uncertainty analysis, and calibration aiming at improvements of surface energy and water vapor fluxes computations under different climate and land-use scenarios. Specific topics include but are not limited to:

Development, validation, and inter-comparison of new and improved remote sensing-based ET models in diverse ecosystems and agrometeorological conditions.
Integration remote sensing model ET with hydrological and crop models, and machine learning.
Downscaling and data fusion techniques to improve spatio-temporal resolution of remote sensing-based ET products.
Application of remote sensing-based ET models in agriculture, natural, and urban environment, food security, water resources management under irrigated and rainfed settings.

Dr. Vivek Sharma
Dr. Aditya Singh
Guest Editors

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Keywords

Evapotranspiration
Surface energy balance models
land-use/land cover change
Water resources management
Water use efficiency
Data fusion
Machine learning
Crop and watershed modelling

Published Papers (9 papers)

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17 pages, 9518 KiB

Open AccessArticle

Accounting for Turbulence-Induced Canopy Heat Transfer in the Simulation of Sensible Heat Flux in SEBS Model

by Sammy M. Njuki, Chris M. Mannaerts and Zhongbo Su

Remote Sens. 2023, 15(6), 1578; https://doi.org/10.3390/rs15061578 - 14 Mar 2023

Cited by 1 | Viewed by 1138

Abstract

Surface turbulent heat fluxes are crucial for monitoring drought, heat waves, urban heat islands, agricultural water management, and other hydrological applications. Energy Balance Models (EBMs) are widely used to simulate surface heat fluxes from a combination of remote sensing-derived variables and meteorological data. Single-source EBMs, in particular, are preferred in mapping surface turbulent heat fluxes due to their relative simplicity. However, most single-source EBMs suffer from uncertainties inherent to the parameter

k B^{- 1}

, which is used to account for differences in the source of heat and the sink of momentum when representing aerodynamic resistance in single-source EBMs. For instance, the parameterization of

k B^{- 1}

in the commonly used single-source Surface Energy Balance System (SEBS) model uses a constant value of the foliage heat transfer coefficient (

C_{t}

), in the parameterization of the vegetation component of

k B^{- 1}

(

k B_{v}^{- 1}

). Thus, SEBS ignores the effect of turbulence on canopy heat transfer. As a result, SEBS has been found to greatly underestimate sensible heat flux in tall forest canopies, where turbulence is a key contributor to canopy heat transfer. This study presents a revised parameterization of

k B_{v}^{- 1}

for the SEBS model. A physically based formulation of

C_{t}

, which considers the effect of turbulence on

C_{t}

, is used in deriving the revised parameterization. Simulation results across 15 eddy covariance (EC) flux tower sites show that the revised parameterization significantly reduces the underestimation of sensible heat flux compared to the original parameterization under tall forest canopies. The revised parameterization is relatively simple and does not require additional information on canopy structure compared to some more complex parameterizations proposed in the literature. As such, the revised parameterization is suitable for mapping surface turbulent heat fluxes, especially under tall forest canopies. Full article

(This article belongs to the Special Issue Remote Sensing-Based Evapotranspiration Models)

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21 pages, 12602 KiB

Open AccessArticle

Mapping Vegetation Index-Derived Actual Evapotranspiration across Croplands Using the Google Earth Engine Platform

by Neda Abbasi, Hamideh Nouri, Kamel Didan, Armando Barreto-Muñoz, Sattar Chavoshi Borujeni, Christian Opp, Pamela Nagler, Prasad S. Thenkabail and Stefan Siebert

Remote Sens. 2023, 15(4), 1017; https://doi.org/10.3390/rs15041017 - 12 Feb 2023

Cited by 3 | Viewed by 11676

Abstract

Precise knowledge of crop water consumption is essential to better manage agricultural water use, particularly in regions where most countries struggle with increasing water and food insecurity. Approaches such as cloud computing and remote sensing (RS) have facilitated access, process, and visualization of big geospatial data to map and monitor crop water requirements. To find the most reliable Vegetation Index (VI)-based evapotranspiration (ETa) for croplands in drylands, we modeled and mapped ETa using empirical RS methods across the Zayandehrud river basin in Iran for two decades (2000–2019) on the Google Earth Engine platform using the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index 2 (EVI2). Developed ET-VI products in this study comprise three NDVI-based ETa (ET-NDVI*, ET-NDVI*_scaled, and ET-NDVI_Kc) and an EVI2-based ETa (ET-EVI2). We (a) applied, for the first time, the ET-NDVI* method to croplands as a crop-independent index and then compared its performance with the ET-EVI2 and crop ET, and (b) assessed the ease and feasibility of the transferability of these methods to other regions. Comparing four ET-VI products showed that annual ET-EVI2 and ET-NDVI*_scaled estimations were close. ET-NDVI_Kc consistently overestimated ETa. Our findings indicate that ET-EVI2 and ET-NDVI_Kc were easy to parametrize and adopt to other regions, while ET-NDVI* and ET-NDVI*_scaled are site-dependent and sensitive to image acquisition time. ET-EVI2 performed robustly in arid and semi-arid regions making it a better tool. Future research should further develop and confirm these findings by characterizing the accuracy of VI-based ETa over croplands in drylands by comparing them with available ETa products and examining their performance using crop-specific comparisons. Full article

(This article belongs to the Special Issue Remote Sensing-Based Evapotranspiration Models)

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25 pages, 6324 KiB

Open AccessArticle

Improving the Operational Simplified Surface Energy Balance Evapotranspiration Model Using the Forcing and Normalizing Operation

by Gabriel B. Senay, Gabriel E. L. Parrish, Matthew Schauer, MacKenzie Friedrichs, Kul Khand, Olena Boiko, Stefanie Kagone, Ray Dittmeier, Saeed Arab and Lei Ji

Remote Sens. 2023, 15(1), 260; https://doi.org/10.3390/rs15010260 - 01 Jan 2023

Cited by 9 | Viewed by 4955

Abstract

Actual evapotranspiration modeling is providing useful information for researchers and resource managers in agriculture and water resources around the world. The performance of models depends on the accuracy of forcing inputs and model parameters. We developed an improved approach to the parameterization of the Operational Simplified Surface Energy Balance (SSEBop) model using the Forcing and Normalizing Operation (FANO). SSEBop has two key model parameters that define the model boundary conditions. The FANO algorithm computes the wet-bulb boundary condition using a linear FANO Equation relating surface temperature, surface psychrometric constant, and the Normalized Difference Vegetation Index (NDVI). The FANO parameterization was implemented on two computing platforms using Landsat and gridded meteorological datasets: (1) Google Earth Engine (GEE) and (2) Earth Resources Observation and Science (EROS) Center Science Processing Architecture (ESPA). Evaluation was conducted by comparing modeled actual evapotranspiration (ETa) estimates with AmeriFlux eddy covariance (EC) and water balance ETa from level-8 Hydrologic Unit Code sub-basins in the conterminous United States. FANO brought substantial improvements in model accuracy and operational implementation. Compared to the earlier version (v0.1.7), SSEBop FANO (v0.2.6) reduced grassland bias from 47% to −2% while maintaining comparable bias for croplands (11% versus −7%) against EC data. A water balance-based ETa bias evaluation showed an overall improvement from 7% to −1%. Climatology versus annual gridded reference evapotranspiration (ETr) produced comparable ETa results, justifying the use of climatology ETr for the global SSEBop Landsat ETa that is accessible through the ESPA website. Besides improvements in model accuracy, SSEBop FANO increases the spatiotemporal coverage of ET modeling due to the elimination of high NDVI requirements for model parameterization. Because of the existence of potential biases from forcing inputs and model parameters, continued evaluation and bias corrections are necessary to improve the absolute magnitude of ETa for localized water budget applications. Full article

(This article belongs to the Special Issue Remote Sensing-Based Evapotranspiration Models)

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14 pages, 2781 KiB

Open AccessArticle

Assessing Suitability of Auto-Selection of Hot and Cold Anchor Pixels of the UAS-METRIC Model for Developing Crop Water Use Maps

by Behnaz Molaei, R. Troy Peters, Lav R. Khot and Claudio O. Stöckle

Remote Sens. 2022, 14(18), 4454; https://doi.org/10.3390/rs14184454 - 07 Sep 2022

Cited by 4 | Viewed by 1587

Abstract

The METRIC energy balance model uses an auto-selection approach for identifying hot (dry, bare soil) and cold (fully transpiring crop) anchor pixels for the internal calibration of the model. When an unmanned aerial system (UAS) is used for imagery, the small image size and the varying crop and soil water status of agricultural fields make the identification of reliable hot and cold pixels challenging. In this study, we used an experimental spearmint field under three irrigation levels (75%, 100%, and 125% of crop evapotranspiration, ET_c). As a way of providing diverse field conditions, six different extents (Extent 1 to Extent 6) were selected from each day of the seven days of UAS imagery campaigns of the same field for generating UAS-based ET_c maps using auto-selection of hot and cold anchor pixels for the internal calibration of the model. Extent 1 had the smallest coverage area of the field, including only plants that were irrigated at 75% of ET_c, while the fields of view of the other extents increased to where the Extent 6 covered the spearmint field and all the surroundings including trees, a nearby water canal, irrigated grass, and irrigated and non-irrigated soil. The results showed that different sizes of extent resulted in the selection of variable hot (bare, but moist soil in small extents, and dry bare soil at the larger extents) and cold anchor pixels (crop under water stress at the small extents, and tree canopy or grass alongside the water canal at the larger extents). This variation resulted in significantly different ET_c estimation for the same spearmint crop field, indicative of a potential limitation for the use auto-selection of hot and cold pixels when using the UAS-METRIC model. Full article

(This article belongs to the Special Issue Remote Sensing-Based Evapotranspiration Models)

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22 pages, 9392 KiB

Open AccessArticle

Remote Sensing Data Fusion to Evaluate Patterns of Regional Evapotranspiration: A Case Study for Dynamics of Film-Mulched Drip-Irrigated Cotton in China’s Manas River Basin over 20 Years

by Xuejin Qiao, Guang Yang, Jianchu Shi, Qiang Zuo, Lining Liu, Mu Niu, Xun Wu and Alon Ben-Gal

Remote Sens. 2022, 14(14), 3438; https://doi.org/10.3390/rs14143438 - 17 Jul 2022

Cited by 5 | Viewed by 1889

Abstract

The accurate quantification of evapotranspiration (ET) is critical to the sustainable management of irrigated agriculture. In this study, we proposed a remote sensing data fusion method for predicting ET, coupling a surface energy balance system model with an enhanced spatial and temporal adaptive reflectance fusion model utilizing remote sensing inversion with satellite data from Landsat and MODIS. The method was tested for a case study with cotton fields under film-mulched drip irrigation (FMDI) in the Manas River Basin. Areas under FMDI were identified, and ET patterns were evaluated for a 21-year period spanning from 2000 to 2020. A field experiment, a regional survey, and data retrieved from the literature provided results demonstrating that the method allowed reliable estimation of ET distribution with simultaneously, relatively high spatial and temporal resolutions at both field and regional scales. ET was found to decline from upstream to downstream in the basin, with the difference gradually diminishing over time. Supported by the promotion of FMDI technology, the area under cotton production in the basin increased by an average of 4.9% annually. Limited irrigation quotas to farmers and, therefore, water application per area led to a decreasing ratio of relative water supply for potential ET and, thus, to a reduction in average actual ET of 7.5 mm year⁻¹. The average ET in the basin declined to about 415.7 mm in 2020, 287.2 mm lower than its water demand. The dynamics of fused ET provide a reliable scientific basis for agricultural water resources planning and management and for the sustainable utilization of water and soil resources in the basin. The method, with simultaneously high temporal and spatial resolutions, should have both local and global practical potential. Full article

(This article belongs to the Special Issue Remote Sensing-Based Evapotranspiration Models)

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21 pages, 8985 KiB

Open AccessArticle

How High to Fly? Mapping Evapotranspiration from Remotely Piloted Aircrafts at Different Elevations

by Logan A. Ebert, Ammara Talib, Samuel C. Zipper, Ankur R. Desai, Kyaw Tha Paw U, Alex J. Chisholm, Jacob Prater and Mallika A. Nocco

Remote Sens. 2022, 14(7), 1660; https://doi.org/10.3390/rs14071660 - 30 Mar 2022

Cited by 5 | Viewed by 2475

Abstract

Recent advancements in remotely piloted aircrafts (RPAs) have made frequent, low-flying imagery collection more economical and feasible than ever before. The goal of this work was to create, compare, and quantify uncertainty associated with evapotranspiration (ET) maps generated from different conditions and image capture elevations. We collected optical and thermal data from a commercially irrigated potato (Solanum tuberosum) field in the Wisconsin Central Sands using a quadcopter RPA system and combined multispectral/thermal camera. We conducted eight mission sets (24 total missions) during the 2019 growing season. Each mission set included flights at 90, 60, and 30 m above ground level. Ground reference measurements of surface temperature and soil moisture were collected throughout the domain within 15 min of each RPA mission set. Evapotranspiration values were modeled from the flight data using the High-Resolution Mapping of Evapotranspiration (HRMET) model. We compared HRMET-derived ET estimates to an Eddy Covariance system within the flight domain. Additionally, we assessed uncertainty for each flight using a Monte Carlo approach. Results indicate that the primary source of uncertainty in ET estimates was the optical and thermal data. Despite some additional detectable features at low elevation, we conclude that the tradeoff in resources and computation does not currently justify low elevation flights for annual vegetable crop management in the Midwest USA. Full article

(This article belongs to the Special Issue Remote Sensing-Based Evapotranspiration Models)

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26 pages, 17206 KiB

Open AccessEditor’s ChoiceArticle

Using Remote Sensing to Estimate Scales of Spatial Heterogeneity to Analyze Evapotranspiration Modeling in a Natural Ecosystem

by Ayman Nassar, Alfonso Torres-Rua, Lawrence Hipps, William Kustas, Mac McKee, David Stevens, Héctor Nieto, Daniel Keller, Ian Gowing and Calvin Coopmans

Remote Sens. 2022, 14(2), 372; https://doi.org/10.3390/rs14020372 - 13 Jan 2022

Cited by 12 | Viewed by 3704

Abstract

Understanding the spatial variability in highly heterogeneous natural environments such as savannas and river corridors is an important issue in characterizing and modeling energy fluxes, particularly for evapotranspiration (ET) estimates. Currently, remote-sensing-based surface energy balance (SEB) models are applied widely and routinely in agricultural settings to obtain ET information on an operational basis for use in water resources management. However, the application of these models in natural environments is challenging due to spatial heterogeneity in vegetation cover and complexity in the number of vegetation species existing within a biome. In this research effort, small unmanned aerial systems (sUAS) data were used to study the influence of land surface spatial heterogeneity on the modeling of ET using the Two-Source Energy Balance (TSEB) model. The study area is the San Rafael River corridor in Utah, which is a part of the Upper Colorado River Basin that is characterized by arid conditions and variations in soil moisture status and the type and height of vegetation. First, a spatial variability analysis was performed using a discrete wavelet transform (DWT) to identify a representative spatial resolution/model grid size for adequately solving energy balance components to derive ET. The results indicated a maximum wavelet energy between 6.4 m and 12.8 m for the river corridor area, while the non-river corridor area, which is characterized by different surface types and random vegetation, does not show a peak value. Next, to evaluate the effect of spatial resolution on latent heat flux (LE) estimation using the TSEB model, spatial scales of 6 m and 15 m instead of 6.4 m and 12.8 m, respectively, were used to simplify the derivation of model inputs. The results indicated small differences in the LE values between 6 m and 15 m resolutions, with a slight decrease in detail at 15 m due to losses in spatial variability. Lastly, the instantaneous (hourly) LE was extrapolated/upscaled to daily ET values using the incoming solar radiation (R_s) method. The results indicated that willow and cottonwood have the highest ET rates, followed by grass/shrubs and treated tamarisk. Although most of the treated tamarisk vegetation is in dead/dry condition, the green vegetation growing underneath resulted in a magnitude value of ET. Full article

(This article belongs to the Special Issue Remote Sensing-Based Evapotranspiration Models)

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31 pages, 11753 KiB

Open AccessArticle

Mapping Daily Evapotranspiration at Field Scale Using the Harmonized Landsat and Sentinel-2 Dataset, with Sharpened VIIRS as a Sentinel-2 Thermal Proxy

by Jie Xue, Martha C. Anderson, Feng Gao, Christopher Hain, Yun Yang, Kyle R. Knipper, William P. Kustas and Yang Yang

Remote Sens. 2021, 13(17), 3420; https://doi.org/10.3390/rs13173420 - 28 Aug 2021

Cited by 24 | Viewed by 4324

Abstract

Accurate and frequent monitoring of evapotranspiration (ET) at sub-field scales can provide valuable information for agricultural water management, quantifying crop water use and stress toward the goal of increasing crop water use efficiency and production. Using land-surface temperature (LST) data retrieved from Landsat thermal infrared (TIR) imagery, along with surface reflectance data describing albedo and vegetation cover fraction, surface energy balance models can generate ET maps down to a 30 m spatial resolution. However, the temporal sampling by such maps can be limited by the relatively infrequent revisit period of Landsat data (8 days for combined Landsats 7 and 8), especially in cloudy areas experiencing rapid changes in moisture status. The Sentinel-2 (S2) satellites, as a good complement to the Landsat system, provide surface reflectance data at 10–20 m spatial resolution and 5 day revisit period but do not have a thermal sensor. On the other hand, the Visible Infrared Imaging Radiometer Suite (VIIRS) provides TIR data on a near-daily basis with 375 m resolution, which can be refined through thermal sharpening using S2 reflectances. This study assesses the utility of augmenting the Harmonized Landsat and Sentinel-2 (HLS) dataset with S2-sharpened VIIRS as a thermal proxy source on S2 overpass days, enabling 30 m ET mapping at a potential combined frequency of 2–3 days (including Landsat). The value added by including VIIRS-S2 is assessed both retrospectively and operationally in comparison with flux tower observations collected from several U.S. agricultural sites covering a range of crop types. In particular, we evaluate the performance of VIIRS-S2 ET estimates as a function of VIIRS view angle and cloud masking approach. VIIRS-S2 ET retrievals (MAE of 0.49 mm d⁻¹ against observations) generally show comparable accuracy to Landsat ET (0.45 mm d⁻¹) on days of commensurate overpass, but with decreasing performance at large VIIRS view angles. Low-quality VIIRS-S2 ET retrievals linked to imperfect VIIRS/S2 cloud masking are also discussed, and caution is required when applying such data for generating ET timeseries. Fused daily ET time series benefited during the peak growing season from the improved multi-source temporal sampling afforded by VIIRS-S2, particularly in cloudy regions and over surfaces with rapidly changing vegetation conditions, and value added for real-time monitoring applications is discussed. This work demonstrates the utility and feasibility of augmenting the HLS dataset with sharpened VIIRS TIR imagery on S2 overpass dates for generating high spatiotemporal resolution ET products. Full article

(This article belongs to the Special Issue Remote Sensing-Based Evapotranspiration Models)

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14 pages, 1749 KiB

Open AccessTechnical Note

Modeling Evapotranspiration at Larger Temporal Scales: Effects of Temporal Aggregation and Data Gaps

by K. V. Athira, R. Eswar, Gilles Boulet, Rahul Nigam and Bimal K. Bhattacharya

Remote Sens. 2022, 14(17), 4142; https://doi.org/10.3390/rs14174142 - 23 Aug 2022

Viewed by 1307

Abstract

Evapotranspiration (ET) at weekly and monthly time scales is often needed for various applications. When using remote sensing (RS)-based models, this can be achieved either by averaging all the required input variables to the intended time scale and simulating ET using models (input aggregation), or by estimating daily ET from the models and averaging to weekly or monthly ET (output aggregation). It is not clear if both these aggregation approaches yield the same outcome when using RS-based models for the estimation of ET. Another issue in obtaining ET at longer time scales is the lack of enough satellite observations to estimate ET with reasonable accuracy. This study aimed to compare the input and output aggregation approaches to obtain ET at weekly and monthly time scales using three RS ET models, namely, Priestley–Taylor Jet Propulsion Lab (PT-JPL), Soil Plant Atmosphere and Remote Sensing Evapotranspiration (SPARSE), and Surface Temperature Initiated Closure (STIC) models. The study was conducted using in situ data over six sites of different agro-climatic conditions in India, Tunisia, and France. The results indicate that the input aggregation provided relatively better results for monthly and weekly ET values than the output aggregation, having a lower RMSE (1–40%). Further, it was found that at least seven to eight satellite observations per month are required to obtain reliable ET estimate when using RS-based models. Full article

(This article belongs to the Special Issue Remote Sensing-Based Evapotranspiration Models)

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