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Remote Sensing of Evapotranspiration (ET)

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Biogeosciences Remote Sensing".

Deadline for manuscript submissions: closed (31 March 2019) | Viewed by 53695

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Special Issue Editors

Dr. Prasanna Gowda

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Guest Editor

USDA-ARS Grazinglands Research Laboratory, 7207 West Cheyenne Street, El Reno, OK 73036, USA
Interests: thermal remote sensing; irrigation scheduling; irrigation management; hydrologic and crop modeling; watershed modeling; ET measurement techniques; drought management; agricultural water management

Dr. Pradeep Wagle

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Guest Editor

USDA-ARS Grazinglands Research Laboratory, 7207 West Cheyenne Street, El Reno, OK 73036, USA
Interests: land surface-atmosphere interactions; eddy covariance measurements; agricultural water management; Flux measurements and modeling; impacts of land use change, extreme climatic events, and management practices on carbon and water dynamics of terrestrial ecosystems
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Special Issue Information

Dear Colleagues,

The main goal of this special issue is to report on advances in development and applications of ground-based evapotranspiration (ET) measuring instruments/sensors (Lysimeter, neutron probes, Eddy covariance, Bowen ratio, scintillometer, ET gauges, etc.) as well as remote sensing techniques for mapping ET/crop water use at plot, field, landscape and regional scales. Contributions on ET measurements, modeling and mapping may include (1) evaluation of existing/new instruments for their ability to measure ET/surface energy fluxes accurately in different agrometeorological conditions, limitation and challenges and footprint analysis; (2) recent advances in remote sensing based ET models; and (3) application of remote sensing based ET models in water rights, interstate compacts, invasive species, agricultural and urban allocations, endangered species protection, drought and food insecurity, large-scale land-surface and climate models, water conservation projects, irrigation performance, environmental impact assessment due to groundwater extractions, dryland water management, hydrological modeling, crop modeling, assessing crop water productivity, and irrigation scheduling, to name a few. Papers on coupling of CO2 fluxes and ET, and water use efficiency will also be considered.

Dr. Prasanna Gowda
Dr. Pradeep Wagle
Guest Editors

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Keywords

Evapotranspiration
Water use efficiency
Thermal remote sensing
Drought management
Groundwater management
Irrigation management
Watershed modeling
Surface energy balance models

Published Papers (12 papers)

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Editorial

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7 pages, 186 KiB

Open AccessEditorial

Editorial for the Special Issue “Remote Sensing of Evapotranspiration (ET)”

by Pradeep Wagle and Prasanna H. Gowda

Remote Sens. 2019, 11(18), 2146; https://doi.org/10.3390/rs11182146 - 15 Sep 2019

Cited by 3 | Viewed by 3086

Abstract

Evapotranspiration (ET) is a critical component of the water and energy balances, and the number of remote sensing-based ET products and estimation methods has increased in recent years. Various aspects of remote sensing of ET are reported in 11 papers published in this special issue. The major research topics covered by this special issue include inter-comparison and performance evaluation of widely used one- and two-source energy balance models, a new dual-source model (Soil Plant Atmosphere and Remote Sensing Evapotranspiration, SPARSE), and a process-based model (ETMonitor); assessment of multi-source (e.g., remote sensing, reanalysis, and land surface model) ET products; development or improvement of data fusion frameworks to provide continuous daily ET at a high spatial resolution (field-scale or 30 m) by fusing the advanced space-borne thermal emission reflectance radiometer (ASTER), the moderate resolution imaging spectroradiometer (MODIS), and Landsat data; and investigating uncertainties in ET estimates using an ET ensemble composed of 36 land surface models and four diagnostic datasets. The effects of the differences among ET products on water resources and ecosystem management were also investigated. More accurate ET estimates and improved understanding of remotely sensed ET products can help maximize crop productivity while minimizing water loses and management costs. Full article

(This article belongs to the Special Issue Remote Sensing of Evapotranspiration (ET))

Research

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18 pages, 2615 KiB

Open AccessArticle

Differences among Evapotranspiration Products Affect Water Resources and Ecosystem Management in an Australian Catchment

by Zhixiang Lu, Yan Zhao, Yongping Wei, Qi Feng and Jiali Xie

Remote Sens. 2019, 11(8), 958; https://doi.org/10.3390/rs11080958 - 22 Apr 2019

Cited by 17 | Viewed by 3184

Abstract

Evapotranspiration (ET) is a critical component of the water and energy balance of climate–soil–vegetation interactions and can account for a water loss of about 90% in arid regions. It is recognized that there are differences among different ET products, but it is not known what the range of this difference is and to what extent it impacts on water resources and ecosystem management. In this study, we assess the effects of value differences of five representative ET products on water resources and ecosystem management in the Murrumbidgee River catchment in Australia. The results show there are obvious differences in the annual and monthly ET values among these five ET products, which lead to huge differences on the estimations of mean annual runoff, soil water storage changes, and yearly irrigation water per area. Meanwhile, they result in different relationships between the annual gross primary productivity and ET and different water-use efficiency values for both forest and grassland, but the influence of ET variations on forest is less obvious than on grassland. The effects of the variations among the ET products on water resources and ecosystem management are remarkable and need to be the subject of more attention. Full article

(This article belongs to the Special Issue Remote Sensing of Evapotranspiration (ET))

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16 pages, 4757 KiB

Open AccessArticle

Uncertainties in Evapotranspiration Estimates over West Africa

by Hahn Chul Jung, Augusto Getirana, Kristi R. Arsenault, Thomas R.H. Holmes and Amy McNally

Remote Sens. 2019, 11(8), 892; https://doi.org/10.3390/rs11080892 - 12 Apr 2019

Cited by 27 | Viewed by 4455

Abstract

An evapotranspiration (ET) ensemble composed of 36 land surface model (LSM) experiments and four diagnostic datasets (GLEAM, ALEXI, MOD16, and FLUXNET) is used to investigate uncertainties in ET estimate over five climate regions in West Africa. Diagnostic ET datasets show lower uncertainty estimates and smaller seasonal variations than the LSM-based ET values, particularly in the humid climate regions. Overall, the impact of the choice of LSMs and meteorological forcing datasets on the modeled ET rates increases from north to south. The LSM formulations and parameters have the largest impact on ET in humid regions, contributing to 90% of the ET uncertainty estimates. Precipitation contributes to the ET uncertainty primarily in arid regions. The LSM-based ET estimates are sensitive to the uncertainty of net radiation in arid region and precipitation in humid region. This study serves as support for better determining water availability for agriculture and livelihoods in Africa with earth observations and land surface models. Full article

(This article belongs to the Special Issue Remote Sensing of Evapotranspiration (ET))

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21 pages, 3856 KiB

Open AccessArticle

An Improved Spatio-Temporal Adaptive Data Fusion Algorithm for Evapotranspiration Mapping

by Tong Wang, Ronglin Tang, Zhao-Liang Li, Yazhen Jiang, Meng Liu and Lu Niu

Remote Sens. 2019, 11(7), 761; https://doi.org/10.3390/rs11070761 - 29 Mar 2019

Cited by 14 | Viewed by 3442

Abstract

Continuous high spatio-temporal resolution monitoring of evapotranspiration (ET) is critical for water resource management and the quantification of irrigation water efficiency at both global and local scales. However, available remote sensing satellites cannot generally provide ET data at both high spatial and temporal resolutions. Data fusion methods have been widely applied to estimate ET at a high spatio-temporal resolution. Nevertheless, most fusion methods applied to ET are initially used to integrate land surface reflectance, the spectral index and land surface temperature, and few studies completely consider the influencing factor of ET. To overcome this limitation, this paper presents an improved ET fusion method, namely, the spatio-temporal adaptive data fusion algorithm for evapotranspiration mapping (SADFAET), by introducing critical surface temperature (the corresponding temperature to decide soil moisture), importing the weights of surface ET-indicative similarity (the influencing factor of ET, which is estimated from remote sensing data) and modifying the spectral similarity (the differences in spectral characteristics of different spatial resolution images) for the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM). We fused daily Moderate Resolution Imaging Spectroradiometer (MODIS) and periodic Landsat 8 ET data in the SADFAET for the experimental area downstream of the Heihe River basin from April to October 2015. The validation results, based on ground-based ET measurements, indicated that the SADFAET could successfully fuse MODIS and Landsat 8 ET data (mean percent error: −5%), with a root mean square error of 45.7 W/m², whereas the ESTARFM performed slightly worse, with a root mean square error of 50.6 W/m². The more physically explainable SADFAET could be a better alternative to the ESTARFM for producing ET at a high spatio-temporal resolution. Full article

(This article belongs to the Special Issue Remote Sensing of Evapotranspiration (ET))

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20 pages, 6818 KiB

Open AccessArticle

A Modeling Framework for Deriving Daily Time Series of Evapotranspiration Maps Using a Surface Energy Balance Model

by Kul Khand, Saleh Taghvaeian, Prasanna Gowda and George Paul

Remote Sens. 2019, 11(5), 508; https://doi.org/10.3390/rs11050508 - 02 Mar 2019

Cited by 7 | Viewed by 4405

Abstract

Surface energy balance models have been one of the most widely used approaches to estimate spatially distributed evapotranspiration (ET) at varying landscape scales. However, more research is required to develop and test an operational framework that can address all challenges related to processing and gap filling of non-continuous satellite data to generate time series of ET at regional scale. In this study, an automated modeling framework was developed to construct daily time series of ET maps using MODIS imagery and the Surface Energy Balance System model. The ET estimates generated from this modeling framework were validated against observations of three eddy-covariance towers in Oklahoma, United States during a two-year period at each site. The modeling framework overestimated ET but captured its spatial and temporal variability. The overall performance was good with mean bias errors less than 30 W m⁻² and root mean square errors less than 50 W m⁻². The model was then applied for a 14-year period (2001–2014) to study ET variations across Oklahoma. The statewide annual ET varied from 841 to 1100 mm yr⁻¹, with an average of 994 mm yr⁻¹. The results were also analyzed to estimate the ratio of estimated ET to reference ET, which is an indicator of water scarcity. The potential applications and challenges of the ET modeling framework are discussed and the future direction for the improvement and development of similar automated approaches are highlighted. Full article

(This article belongs to the Special Issue Remote Sensing of Evapotranspiration (ET))

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17 pages, 7232 KiB

Open AccessArticle

Earth Observations-Based Evapotranspiration in Northeastern Thailand

by Chaolei Zheng, Li Jia, Guangcheng Hu and Jing Lu

Remote Sens. 2019, 11(2), 138; https://doi.org/10.3390/rs11020138 - 12 Jan 2019

Cited by 18 | Viewed by 3961

Abstract

Thailand is characterized by typical tropical monsoon climate, and is suffering serious water related problems, including seasonal drought and flooding. These issues are highly related to the hydrological processes, e.g., precipitation and evapotranspiration (ET), which are helpful to understand and cope with these problems. It is critical to study the spatiotemporal pattern of ET in Thailand to support the local water resource management. In the current study, daily ET was estimated over Thailand by ETMonitor, a process-based model, with mainly satellite earth observation datasets as input. One major advantage of the ETMonitor algorithm is that it introduces the impact of soil moisture on ET by assimilating the surface soil moisture from microwave remote sensing, and it reduces the dependence on land surface temperature, as the thermal remote sensing is highly sensitive to cloud, which limits the ability to achieve spatial and temporal continuity of daily ET. The ETMonitor algorithm was further improved in current study to take advantage of thermal remote sensing. In the improved scheme, the evaporation fraction was first obtained by land surface temperature—vegetation index triangle method, which was used to estimate ET in the clear days. The soil moisture stress index (SMSI) was defined to express the constrain of soil moisture on ET, and clear sky SMSI was retrieved according to the estimated clear sky ET. Clear sky SMSI was then interpolated to cloudy days to obtain the SMSI for all sky conditions. Finally, time-series ET at daily resolution was achieved using the interpolated spatio-temporal continuous SMSI. Good agreements were found between the estimated daily ET and flux tower observations with root mean square error ranging between 1.08 and 1.58 mm d⁻¹, which showed better accuracy than the ET product from MODerate resolution Imaging Spectroradiometer (MODIS), especially for the forest sites. Chi and Mun river basins, located in Northeast Thailand, were selected to analyze the spatial pattern of ET. The results indicate that the ET had large fluctuation in seasonal variation, which is predominantly impacted by the monsoon climate. Full article

(This article belongs to the Special Issue Remote Sensing of Evapotranspiration (ET))

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20 pages, 2434 KiB

Open AccessArticle

Evaluation of the SPARSE Dual-Source Model for Predicting Water Stress and Evapotranspiration from Thermal Infrared Data over Multiple Crops and Climates

by Emilie Delogu, Gilles Boulet, Albert Olioso, Sébastien Garrigues, Aurore Brut, Tiphaine Tallec, Jérôme Demarty, Kamel Soudani and Jean-Pierre Lagouarde

Remote Sens. 2018, 10(11), 1806; https://doi.org/10.3390/rs10111806 - 15 Nov 2018

Cited by 19 | Viewed by 3144

Abstract

Using surface temperature as a signature of the surface energy balance is a way to quantify the spatial distribution of evapotranspiration and water stress. In this work, we used the new dual-source model named Soil Plant Atmosphere and Remote Sensing Evapotranspiration (SPARSE) based on the Two Sources Energy Balance (TSEB) model rationale which solves the surface energy balance equations for the soil and the canopy. SPARSE can be used (i) to retrieve soil and vegetation stress levels from known surface temperature and (ii) to predict transpiration, soil evaporation, and surface temperature for given stress levels. The main innovative feature of SPARSE is that it allows to bound each retrieved individual flux component (evaporation and transpiration) by its corresponding potential level deduced from running the model in prescribed potential conditions, i.e., a maximum limit if the surface water availability is not limiting. The main objective of the paper is to assess the SPARSE model predictions of water stress and evapotranspiration components for its two proposed versions (the “patch” and “layer” resistances network) over 20 in situ data sets encompassing distinct vegetation and climate. Over a large range of leaf area index values and for contrasting vegetation stress levels, SPARSE showed good retrieval performances of evapotranspiration and sensible heat fluxes. For cereals, the layer version provided better latent heat flux estimates than the patch version while both models showed similar performances for sparse crops and forest ecosystems. The bounded layer version of SPARSE provided the best estimates of latent heat flux over different sites and climates. Broad tendencies of observed and retrieved stress intensities were well reproduced with a reasonable difference obtained for most of the points located within a confidence interval of 0.2. The synchronous dynamics of observed and retrieved estimates underlined that the SPARSE retrieved water stress estimates from Thermal Infra-Red data were relevant tools for stress detection. Full article

(This article belongs to the Special Issue Remote Sensing of Evapotranspiration (ET))

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25 pages, 6218 KiB

Open AccessArticle

Estimating Calibration Variability in Evapotranspiration Derived from a Satellite-Based Energy Balance Model

by Sulochan Dhungel and Michael E. Barber

Remote Sens. 2018, 10(11), 1695; https://doi.org/10.3390/rs10111695 - 26 Oct 2018

Cited by 15 | Viewed by 5601

Abstract

Computing evapotranspiration (ET) with satellite-based energy balance models such as METRIC (Mapping EvapoTranspiration at high Resolution with Internalized Calibration) requires internal calibration of sensible heat flux using anchor pixels (“hot” and “cold” pixels). Despite the development of automated anchor pixel selection methods that classify a pool of candidate pixels using the amount of vegetation (normalized difference vegetation index, NDVI) and surface temperature (T_s), final pixel selection still relies heavily on operator experience. Yet, differences in final ET estimates resulting from subjectivity in selecting the final “hot” and “cold” pixel pair (from within the candidate pixel pool) have not yet been investigated. This is likely because surface properties of these candidate pixels, as quantified by NDVI and surface temperature, are generally assumed to have low variability that can be attributed to random noise. In this study, we test the assumption of low variability by first applying an automated calibration pixel selection process to 42 nearly cloud-free Landsat images of the San Joaquin area in California taken between 2013 and 2015. We then compute T_s (vertical near-surface temperature differences) vs. T_s relationships at all pixels that could potentially be used for model calibration in order to explore ET variance between the results from multiple calibration schemes where NDVI and T_s variability is intrinsically negligible. Our results show significant variability in ET (ranging from 5% to 20%) and a high—and statistically consistent—variability in dT values, indicating that there are additional surface properties affecting the calibration process not captured when using only NDVI and T_s. Our findings further highlight the potential for calibration improvements by showing that the dT vs. T_s calibration relationship between the cold anchor pixel (with lowest dT) and the hot anchor pixel (with highest dT) consistently provides the best daily ET estimates. This approach of quantifying ET variability based on candidate pixel selection and the accompanying results illustrate an approach to quantify the biases inadvertently introduced by user subjectivity and can be used to inform improvements on model usability and performance. Full article

(This article belongs to the Special Issue Remote Sensing of Evapotranspiration (ET))

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21 pages, 7548 KiB

Open AccessArticle

Continuous Daily Evapotranspiration Estimation at the Field-Scale over Heterogeneous Agricultural Areas by Fusing ASTER and MODIS Data

by Zhenyan Yi, Hongli Zhao and Yunzhong Jiang

Remote Sens. 2018, 10(11), 1694; https://doi.org/10.3390/rs10111694 - 26 Oct 2018

Cited by 13 | Viewed by 3169

Abstract

Continuous daily evapotranspiration (ET) monitoring at the field-scale is crucial for water resource management in irrigated agricultural areas in arid regions. Here, an integrated framework for daily ET, with the required spatiotemporal resolution, is described. Multi-scale surface energy balance algorithm evaluations and a data fusion algorithm are combined to optimally exploit the spatial and temporal characteristics of image datasets, collected by the advanced space-borne thermal emission reflectance radiometer (ASTER) and the moderate resolution imaging spectroradiometer (MODIS). Through combination with a linear unmixing-based method, the spatial and temporal adaptive reflectance fusion model (STARFM) is modified to generate high-resolution ET estimates for heterogeneous areas. The performance of this methodology was evaluated for irrigated agricultural fields in arid and semiarid areas of Northwest China. Compared with the original STARFM, a significant improvement in daily ET estimation accuracy was obtained by the modified STARFM (overall mean absolute percentage error (MAP): 12.9% vs. 17.2%; root mean square error (RMSE): 0.7 mm d⁻¹ vs. 1.2 mm d⁻¹). The modified STARFM additionally preserved more spatial details than the original STARFM for heterogeneous agricultural fields, and provided field-to-field variability in water use. Improvements were further evident in the continuous daily ET, where the day-to-day dynamics of ET estimates were captured. ET data fusion provides a unique means of monitoring continuous daily crop ET values at the field-scale in agricultural areas, and may have value in supporting operational water management decisions. Full article

(This article belongs to the Special Issue Remote Sensing of Evapotranspiration (ET))

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28 pages, 8203 KiB

Open AccessArticle

Assessment of Multi-Source Evapotranspiration Products over China Using Eddy Covariance Observations

by Shijie Li, Guojie Wang, Shanlei Sun, Haishan Chen, Peng Bai, Shujia Zhou, Yong Huang, Jie Wang and Peng Deng

Remote Sens. 2018, 10(11), 1692; https://doi.org/10.3390/rs10111692 - 26 Oct 2018

Cited by 30 | Viewed by 4638

Abstract

As an essential variable in linking water, carbon, and energy cycles, evapotranspiration (ET) is difficult to measure. Remote sensing, reanalysis, and land surface model-based ET products offer comprehensive alternatives at different spatio-temporal intervals, but their performance varies. In this study, we selected four popular ET global products: The Global Land Evaporation Amsterdam Model version 3.0a (GLEAM3.0a), the Modern Era Retrospective-Analysis for Research and Applications-Land (MERRA-Land) project, the Global Land Data Assimilation System version 2.0 with the Noah model (GLDAS2.0-Noah) and the EartH2Observe ensemble (EartH2Observe-En). Then, we comprehensively evaluated the performance of these products over China using a stratification method, six validation criteria, and high-quality eddy covariance (EC) measurements at 12 sites. The aim of this research was to provide important quantitative information to improve and apply the ET models and to inform choices about the appropriate ET product for specific applications. Results showed that, within one stratification, the performance of each ET product based on a certain criterion differed among classifications of this stratification. Furthermore, the optimal ET (OET) among these products was identified by comparing the magnitudes of each criterion. Results suggested that, given a criterion (a stratification classification), the OETs varied among stratification classifications (the selected six criteria). In short, no product consistently performed best, according to the selected validation criterion. Thus, multi-source ET datasets should be employed in future studies to enhance confidence in ET-related conclusions. Full article

(This article belongs to the Special Issue Remote Sensing of Evapotranspiration (ET))

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17 pages, 5204 KiB

Open AccessArticle

Mapping Maize Evapotranspiration at Field Scale Using SEBAL: A Comparison with the FAO Method and Soil-Plant Model Simulations

by Carla Grosso, Gabriele Manoli, Marco Martello, Yann H. Chemin, Diego H. Pons, Pietro Teatini, Ilaria Piccoli and Francesco Morari

Remote Sens. 2018, 10(9), 1452; https://doi.org/10.3390/rs10091452 - 11 Sep 2018

Cited by 33 | Viewed by 6318

Abstract

The surface energy balance algorithm for land (SEBAL) has been successfully applied to estimate evapotranspiration (ET) and yield at different spatial scales. However, ET and yield patterns have never been investigated under highly heterogeneous conditions. We applied SEBAL in a salt-affected and water-stressed maize field located at the margin of the Venice Lagoon, Italy, using Landsat images. SEBAL results were compared with estimates of evapotranspiration by the Food and Agriculture Organization (FAO) method (ET_c) and three-dimensional soil-plant simulations. The biomass production routine in SEBAL was then tested using spatially distributed crop yield measurements and the outcomes of a soil-plant numerical model. The results show good agreement between SEBAL evapotranspiration and ET_c. Instantaneous ET simulated by SEBAL is also consistent with the soil-plant model results (R² = 0.7047 for 2011 and R² = 0.6689 for 2012). Conversely, yield predictions (6.4 t/ha in 2011 and 3.47 t/ha in 2012) are in good agreement with observations (8.64 t/ha and 3.86 t/ha, respectively) only in 2012 and the comparison with soil-plant simulations (8.69 t/ha and 5.49 t/ha) is poor. In general, SEBAL underestimates land productivity in contrast to the soil-plant model that overestimates yield in dry years. SEBAL provides accurate predictions under stress conditions due to the fact that it does not require knowledge of the soil/root characteristics. Full article

(This article belongs to the Special Issue Remote Sensing of Evapotranspiration (ET))

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20 pages, 7564 KiB

Open AccessArticle

Intercomparison of Three Two-Source Energy Balance Models for Partitioning Evaporation and Transpiration in Semiarid Climates

by Yongmin Yang, Jianxiu Qiu, Renhua Zhang, Shifeng Huang, Sheng Chen, Hui Wang, Jiashun Luo and Yue Fan

Remote Sens. 2018, 10(7), 1149; https://doi.org/10.3390/rs10071149 - 20 Jul 2018

Cited by 24 | Viewed by 5034

Abstract

Evaporation (E) and transpiration (T) information is crucial for precise water resources planning and management in arid and semiarid areas. Two-source energy balance (TSEB) methods based on remotely-sensed land surface temperature provide an important modeling approach for estimating evapotranspiration (ET) and its components of E and T. Approaches for accurate decomposition of the component temperature and E/T partitioning from ET based on TSEB requires careful investigation. In this study, three TSEB models are used: (i) the TSEB model with the Priestley-Taylor equation, i.e., TSEB-PT; (ii) the TSEB model using the Penman-Monteith equation, i.e., TSEB-PM, and (iii) the TSEB using component temperatures derived from vegetation fractional cover and land surface temperature (VFC/LST) space, i.e., TSEB-T_C-T_S. These models are employed to investigate the impact of component temperature decomposition on E/T partitioning accuracy. Validation was conducted in the large-scale campaign of Heihe Watershed Allied Telemetry Experimental Research-Multi-Scale Observation Experiment on Evapotranspiration (HiWATER-MUSOEXE) in the northwest of China, and results showed that root mean square errors (RMSEs) of latent and sensible heat fluxes were respectively lower than 76 W/m² and 50 W/m² for all three approaches. Based on the measurements from the stable oxygen and hydrogen isotopes system at the Daman superstation, it was found that all three models slightly overestimated the ratio of E/ET. In addition, discrepancies in E/T partitioning among the three models were observed in the kernel experimental area of MUSOEXE. Further intercomparison indicated that different temperature decomposition methods were responsible for the observed discrepancies in E/T partitioning. The iterative procedure adopted by TSEB-PT and TSEB-PM produced higher LE_C and lower T_C when compared to TSEB-T_C-T_S. Overall, this work provides valuable insights into understanding the performances of TSEB models with different temperature decomposition mechanisms over semiarid regions. Full article

(This article belongs to the Special Issue Remote Sensing of Evapotranspiration (ET))

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