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Editorial

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

by
Pradeep Wagle
1,* and
Prasanna H. Gowda
2
1
Grazinglands Research Laboratory, USDA Agricultural Research Service, El Reno, OK 73036, USA
2
Southeast Area, USDA Agricultural Research Service, Stoneville, MS 38776, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(18), 2146; https://doi.org/10.3390/rs11182146
Submission received: 11 September 2019 / Accepted: 15 September 2019 / Published: 15 September 2019
(This article belongs to the Special Issue Remote Sensing of Evapotranspiration (ET))
Versions Notes

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.

1. Introduction

Evapotranspiration (ET), a critical and major component of the water and energy balances, is a key variable for linking ecosystem functions and climate feedbacks [1], determination of crop water or irrigation requirements and crop coefficients [2], and estimation of productivity and water use efficiency of ecosystems [3,4]. Although the eddy covariance (EC) technique has been widely used for continuous measurements of ET in recent decades [5,6], it is not possible to measure ET by the EC technique at all places all the time and especially over heterogeneous landscapes. Thus, a wide range of remote sensing-based ET products at the global and regional scales has been developed in recent decades to complement the limited land surface coverage of the ground-based ET measurements [7,8,9]. These ET products include numerous remote sensing reanalysis-based [10,11,12], land surface model (LSM)-based [13,14], surface energy balance (SEB)-based [15,16,17], and empirical up-scaling of in situ ET observations [18,19]. The SEB-based models are gaining increased popularity because remote sensing in the thermal infrared provides information not only on the partitioning of the available energy to sensible and latent heat fluxes, but also on the predicting water stress levels [17,20]. However, a major shortcoming of SEB-based models is that they rely on available land surface temperature (LST) data from satellite observations. Consequently, SEB modeling estimates are not available for cloudy days. Thus, the process-based ET models are gaining more acceptance to generate continuous ET estimates by utilizing a variety of biophysical parameters derived from microwave and optical remote sensing observations [21,22]. It is also recognized that there are large differences among a wide range of ET products. Validations and inter-comparisons of various ET models or ET products under diverse ecosystems and agrometeorological conditions are needed due to different levels of uncertainties and accuracies that vary over space and time [23,24].
Although several remote sensing-based ET products are available, these datasets cannot generally provide ET data at both higher spatial and temporal resolutions to derive field-scale ET estimates over heterogeneous landscapes due to satellite orbital dynamics and physical limitations of the satellite sensors. Thus, downscaling and data fusion approaches have been employed to improve the higher spatial and temporal resolutions of remote sensing-based ET products [25,26,27,28].
Accurate ET estimates are crucial to manage water resources and to assess the impacts of climate on agriculture and food security [29]. High uncertainty in ET estimates is a major obstacle to examine spatial and temporal variability in regional hydrology [30]. Thus, understanding the uncertainty of ET estimates can help to better determine water availability for agriculture and livelihoods.
This special issue compiles contributions on research related to the above-mentioned various aspects of remote sensing of ET. The major topics covered by the 11 papers in this special issue include inter-comparison and performance evaluation of several ET models or products, data fusion approach to generate higher spatial and temporal resolution ET products, model development and/or improvement, and investigating uncertainties in ET estimates. A short summary of the varied contributions to this special issue is presented in the next section.

2. Overview of Contributions

2.1. Inter-Comparison and Performance Evaluation of Several ET Models or Products

Yang et al. [31] compared three Two-Source Energy Balance (TSEB) models for estimating ET and its components (evaporation, E and transpiration, T) in semiarid climates of China. Those three TSEB models were: TSEB model with the Priestley–Taylor equation (TSEB-PT), TSEB model with the Penman–Monteith equation (TSEB-PM), and TSEB model using component temperatures derived from vegetation fractional cover and land surface temperature (VFC/LST) space (TSEB-TC-TS). The study provided valuable insights into understanding the performances of TSEB models with different temperature decomposition methods since they were responsible for the observed discrepancies in the partitioned E and T fluxes. Based on the soil wetness isoline in the VFC/LST space, the VFC/LST-based temperature decomposition method can add a further constraint on vegetation T. This could also be used as a substitution for the interactive procedure adopted in the TSEB model.
Grosso et al. [32] employed the Surface Energy Balance Algorithm for Land (SEBAL) in a salt-affected and water-stressed maize field using Landsat images to map the spatial structure of water fluxes and crop yield. The SEBAL results were compared with ET estimates of the Food and Agriculture Organization (FAO) method and three-dimensional soil–plant simulations. The study highlighted that the integration of SEBAL with field observations and soil–plant simulations could be beneficial for precision agriculture practices (e.g., precision irrigation).
Li et al. [33] evaluated four popular global ET products: Global Land Evaporation Amsterdam Model version 3.0a (GLEAM3.0a), Modern Era Retrospective-Analysis for Research and Applications-Land (MERRA-Land), Global Land Data Assimilation System version 2.0 with the Noah model (GLDAS2.0-Noah), and EartH2Observe ensemble (EartH2Observe-En) over China using a stratification method, six validation criteria, and EC measurements at 12 sites. The model performances were evaluated by biome, elevation, and climate regime as well. The study recommended the use of multi-source ET datasets since no ET product consistently performed best for the selected validation criterion.
Delogu et al. [34] assessed the model predictions of water stress and ET components for the two proposed versions (the “patch” and “layer” resistances network) of the new dual-source Soil Plant Atmosphere and Remote Sensing Evapotranspiration (SPARSE) model over 20 in situ datasets encompassing diverse vegetation and climate conditions. The SPARSE model showed good estimates of latent and sensible heat fluxes and water stress over a large range of leaf area indexes and contrasting water stress levels.
Zheng et al. [22] used ETMonitor, a process-based model, with satellite earth observation datasets as main inputs to derive daily ET by utilizing surface soil moisture from microwave remote sensing and LST from thermal remote sensing. Estimated daily ET showed good agreement with EC-measured ET in Northeastern Thailand.
Khand et al. [35] developed an automated modeling framework to construct daily time series of ET maps, addressing the challenges related to processing and gap filling of non-continuous satellite data using the moderate resolution imaging spectroradiometer (MODIS) imagery and the Surface Energy Balance System (SEBS) model. The daily ET maps generated by this modeling framework captured the spatial and temporal variations (2001–2014) of ET across Oklahoma, USA. The proposed ET modeling framework provided a pathway to construct daily time series of ET maps at a regional scale and highlighted a range of potential applications for making informed decision and policies.
Lu et al. [36] evaluated the effects of differences among five representative ET products (Australian Water Availability Project (AWAP) as a reference, ET product developed by Commonwealth Scientific and Industrial Research Organization (CSIRO), LSM-based ET product from GLDAS, remote sensing-based ET product from MODIS, and water budget-based ET product from TerraClimate) on water resources and ecosystem management in the Murrumbidgee River catchment in Australia. Large differences in ET budgets among these five ET products propagated into the estimates of mean annual runoff, soil water storage, and irrigation demands.

2.2. Data Fusion Approach to Generate Higher Spatial and Temporal Resolution ET Products

Considering the lack of concurrent higher spatial and temporal resolution ET products, Yi et al. [37] employed a data fusion framework for predicting continuous daily ET at the field-scale over heterogeneous agricultural areas of Northwest China by fusing the advanced space-borne thermal emission reflectance radiometer (ASTER) and the MODIS data. Through a combination with the linear unmixing-based method, the spatial and temporal adaptive reflectance fusion model (STARFM) was modified to generate high-resolution ET estimates over heterogeneous areas. As compared with the original STARFM, the modified STARFM showed a significant improvement in daily ET estimation, preserved more spatial details for heterogeneous agricultural fields, and provided field-to-field variability in water use.
Wang et al. [38] proposed an improved ET fusion method— the Spatio-temporal Adaptive Data Fusion Algorithm for EvapoTranspiration mapping (SADFAET)—by introducing critical surface temperature (the corresponding temperature to determine soil moisture), importing the weights of surface ET-indicative similarity (the influencing factor of ET), 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). The study successfully fused daily MODIS and periodic Landsat 8 ET data in the SADFAET for producing ET at high spatial (30 m) and temporal (daily) resolutions.

2.3. Model Development and/or Improvement

Considering the knowledge gaps in differences among final ET estimates resulting from subjectivity in selecting “hot” and “cold” pixel pair, Dhungel and Barber [39] tested the assumption of low variability of surface properties by first applying an automated calibration pixel selection process for a SEB model—Mapping EvapoTranspiration at high Resolution with Internalized Calibration (METRIC). Consequently, they computed vertical near-surface temperature differences (dT) vs. surface temperature (Ts) relationships at all pixels, which could potentially be used for model calibration to explore ET variance among the outcomes from multiple calibration schemes where normalized difference vegetation index (NDVI) and Ts variability are intrinsically negligible. Significant variability in ET (ranging from 5% to 20%) and a high and statistically consistent variability in dT suggested that additional surface properties, which were not captured when using only NDVI and Ts, affected the calibration process. This approach of quantifying ET variability based on candidate pixel selection helps to quantify the biases inadvertently introduced by user subjectivity as well as to improve the model’s usability and performance.
Zheng et al. [22] developed and applied a new scheme in ETMonitor, a process-based model, to take advantage of thermal remote sensing. In the improved scheme, the evaporation fraction was obtained by LST-vegetation index triangle method to estimate ET in clear days. The soil moisture stress index (SMSI) was defined to express the impact of soil moisture on ET. Clear sky SMSI, retrieved according to the estimated clear sky ET, was interpolated to cloudy days to obtain the SMSI for all sky conditions. Finally, interpolated spatio-temporal continuous SMSI was used to derive daily time-series ET.
Wang et al. [38] developed an improved ET fusion method (SADFAET) based on ESTARFM. The improvements in SADFAET were as follows: consideration of soil moisture by introducing the critical surface temperature while selecting similar pixels, use of multiple spectral bands, and introduction of the surface ET-indicative similarity to calculate the weights of similar pixels. This new method can effectively fuse ET at high and low spatial resolutions.

2.4. Investigating Uncertainties in ET Estimates

Jung et al. [40] investigated uncertainties in ET estimates over five different climatic regions in West Africa using an ET ensemble composed of 36 LSM experiments and four diagnostic datasets (GLEAM, ALEXI, MOD16, and FLUXNET). The LSM-based ET values had greater uncertainty estimates and larger seasonal variations than the diagnostic ET datasets. The LSM formulations and parameters had the largest impact on ET in humid regions (contributing to 90% of the ET uncertainty estimates), while precipitation contributed to the ET uncertainty primarily in arid regions. The results indicated that assimilating diagnostic ET datasets into LSMs or hydrological models could improve the accuracy of ET estimates.

3. Conclusions

The 11 papers published in this special issue highlight a variety of topics related to remote sensing of ET. This special issue provides valuable insights into understanding the performances of different ET models and products under diverse ecosystems and agrometeorological conditions. In addition, improvements on the ET models have also been proposed. Proposed ET data fusion approaches provide unique means of monitoring continuous daily ET at higher spatial resolutions (e.g., field-scale or less) over heterogeneous landscapes. More accurate ET estimates and improved understanding of remotely sensed ET products are crucial to maximize crop productivity while minimizing water losses and management costs.

Author Contributions

P.W. wrote the editorial and P.G. revised and contributed for intellectual contents.

Acknowledgments

We would like to thank all the authors who contributed to the special issue and the staff in the editorial office.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Fisher, J.B.; Melton, F.; Middleton, E.; Hain, C.; Anderson, M.; Allen, R.; McCabe, M.F.; Hook, S.; Baldocchi, D.; Townsend, P.A.; et al. The future of evapotranspiration: Global requirements for ecosystem functioning, carbon and climate feedbacks, agricultural management, and water resources. Water Resour. Res. 2017, 53, 2618–2626. [Google Scholar] [CrossRef]
  2. Marek, T.; Piccinni, G.; Schneider, A.; Howell, T.; Jett, M.; Dusek, D. Weighing lysimeters for the determination of crop water requirements and crop coefficients. Appl. Eng. Agric. 2006, 22, 851–856. [Google Scholar] [CrossRef]
  3. Law, B.E.; Falge, E.; Gu, L.V.; Baldocchi, D.D.; Bakwin, P.; Berbigier, P.; Davis, K.; Dolman, A.J.; Falk, M.; Fuentes, J.D.; et al. Environmental controls over carbon dioxide and water vapor exchange of terrestrial vegetation. Agric. For. Meteorol. 2002, 113, 97–120. [Google Scholar] [CrossRef] [Green Version]
  4. Wagle, P.; Gowda, P.H.; Xiao, X.; Anup, K.C. Parameterizing ecosystem light use efficiency and water use efficiency to estimate maize gross primary production and evapotranspiration using MODIS EVI. Agric. For. Meteorol. 2016, 222, 87–97. [Google Scholar] [CrossRef] [Green Version]
  5. Baldocchi, D.D.; Hincks, B.B.; Meyers, T.P. Measuring Biosphere-Atmosphere Exchanges of Biologically Related Gases with Micrometeorological Methods. Ecology 1988, 69, 1331–1340. [Google Scholar] [CrossRef]
  6. Rana, G.; Katerji, N. Measurement and estimation of actual evapotranspiration in the field under Mediterranean climate: a review. Eur. J. Agron. 2000, 13, 125–153. [Google Scholar] [CrossRef]
  7. Mu, Q.; Heinsch, F.A.; Zhao, M.; Running, S.W. Development of a global evapotranspiration algorithm based on MODIS and global meteorology data. Remote Sens. Environ. 2007, 111, 519–536. [Google Scholar] [CrossRef]
  8. Mueller, B.; Seneviratne, S.I.; Jimenez, C.; Corti, T.; Hirschi, M.; Balsamo, G.; Ciais, P.; Dirmeyer, P.; Fisher, J.B.; Guo, Z.; et al. Evaluation of global observations-based evapotranspiration datasets and IPCC AR4 simulations. Geophys. Res. Lett. 2011, 38. [Google Scholar] [CrossRef] [Green Version]
  9. Yuan, W.; Liu, S.; Yu, G.; Bonnefond, J.M.; Chen, J.; Davis, K.; Desai, A.R.; Goldstein, A.H.; Gianelle, D.; Rossi, F.; et al. Global estimates of evapotranspiration and gross primary production based on MODIS and global meteorology data. Remote Sens. Environ. 2010, 114, 1416–1431. [Google Scholar] [CrossRef] [Green Version]
  10. Onogi, K.; Tsutsui, J.; Koide, H.; Sakamoto, M.; Kobayashi, S.; Hatsushika, H.; Matsumoto, T.; Yamazaki, N.; Kamahori, H.; Takahashi, K.; et al. The JRA-25 reanalysis. J. Meteorol. Soc. Jpn. 2007, 85, 369–432. [Google Scholar] [CrossRef]
  11. Dee, D.P.; Uppala, S.M.; Simmons, A.J.; Berrisford, P.; Poli, P.; Kobayashi, S.; Andrae, U.; Balmaseda, M.A.; Balsamo, G.; Bauer, D.P.; et al. The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Q. J. R. Meteorol. Soc. 2011, 137, 553–597. [Google Scholar] [CrossRef]
  12. Bosilovich, M.G.; Robertson, F.R.; Takacs, L.; Molod, A.; Mocko, D. Atmospheric water balance and variability in the MERRA-2 reanalysis. J. Clim. 2017, 30, 1177–1196. [Google Scholar] [CrossRef]
  13. Rodell, M.; Houser, P.R.; Jambor, U.E.A.; Gottschalck, J.; Mitchell, K.; Meng, C.J.; Arsenault, K.; Cosgrove, B.; Radakovich, J.; Bosilovich, M.; et al. The global land data assimilation system. Bull. Am. Meteorol. Soc. 2004, 85, 381–394. [Google Scholar] [CrossRef]
  14. Schellekens, J.; Dutra, E.; la Torre, A.M.D.; Balsamo, G.; van Dijk, A.; Weiland, F.S.; Minvielle, M.; Calvet, J.C.; Decharme, B.; Eisner, S.; et al. A global water resources ensemble of hydrological models: the eartH2Observe Tier-1 dataset. Earth Syst. Sci. Data 2017, 9, 389–413. [Google Scholar] [CrossRef] [Green Version]
  15. Bastiaanssen, W.G.; Menenti, M.; Feddes, R.A.; Holtslag, A.A.M. A remote sensing surface energy balance algorithm for land (SEBAL). 1. Formulation. J. Hydrol. 1998, 212, 198–212. [Google Scholar] [CrossRef]
  16. Roerink, G.J.; Su, Z.; Menenti, M. S-SEBI: A simple remote sensing algorithm to estimate the surface energy balance. Phys. Chem. Earth 2000, 25, 147–157. [Google Scholar] [CrossRef]
  17. Allen, R.G.; Tasumi, M.; Trezza, R. Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC)—Model. J. Irrig. Drain. Eng. 2007, 133, 380–394. [Google Scholar] [CrossRef]
  18. Jung, M.; Reichstein, M.; Bondeau, A. Towards global empirical upscaling of FLUXNET eddy covariance observations: validation of a model tree ensemble approach using a biosphere model. Biogeosciences 2009, 6, 2001–2013. [Google Scholar] [Green Version]
  19. Wagle, P.; Xiao, X.; Gowda, P.; Basara, J.; Brunsell, N.; Steiner, J.; Anup, K.C. Analysis and estimation of tallgrass prairie evapotranspiration in the central United States. Agric. For. Meteorol. 2017, 232, 35–47. [Google Scholar] [CrossRef]
  20. Bhattarai, N.; Wagle, P.; Gowda, P.H.; Kakani, V.G. Utility of remote sensing-based surface energy balance models to track water stress in rain-fed switchgrass under dry and wet conditions. ISPRS J. Photogramm. Remote Sens. 2017, 133, 128–141. [Google Scholar] [CrossRef]
  21. Hu, G.; Jia, L. Monitoring of evapotranspiration in a semi-arid inland river basin by combining microwave and optical remote sensing observations. Remote Sens. 2015, 7, 3056–3087. [Google Scholar] [CrossRef]
  22. Zheng, C.; Jia, L.; Hu, G.; Lu, J. Earth Observations-Based Evapotranspiration in Northeastern Thailand. Remote Sens. 2019, 11, 138. [Google Scholar] [CrossRef]
  23. Ershadi, A.; McCabe, M.F.; Evans, J.P.; Chaney, N.W.; Wood, E.F. Multi-site evaluation of terrestrial evaporation models using FLUXNET data. Agric. For. Meteorol. 2014, 187, 46–61. [Google Scholar] [CrossRef]
  24. Wagle, P.; Bhattarai, N.; Gowda, P.H.; Kakani, V.G. Performance of five surface energy balance models for estimating daily evapotranspiration in high biomass sorghum. ISPRS J. Photogramm. Remote Sens. 2017, 128, 192–203. [Google Scholar] [CrossRef] [Green Version]
  25. Singh, R.; Senay, G.; Velpuri, N.; Bohms, S.; Verdin, J. On the downscaling of actual evapotranspiration maps based on combination of MODIS and Landsat-based actual evapotranspiration estimates. Remote Sens. 2014, 6, 10483–10509. [Google Scholar] [CrossRef]
  26. Ke, Y.; Im, J.; Park, S.; Gong, H. Downscaling of MODIS One kilometer evapotranspiration using Landsat-8 data and machine learning approaches. Remote Sens. 2016, 8, 215. [Google Scholar] [CrossRef]
  27. Cammalleri, C.; Anderson, M.C.; Gao, F.; Hain, C.R.; Kustas, W.P. A data fusion approach for mapping daily evapotranspiration at field scale. Water Resour. Res. 2013, 49, 4672–4686. [Google Scholar] [CrossRef]
  28. Cammalleri, C.; Anderson, M.C.; Gao, F.; Hain, C.R.; Kustas, W.P. Mapping daily evapotranspiration at field scales over rainfed and irrigated agricultural areas using remote sensing data fusion. Agric. For. Meteorol. 2014, 186, 1–11. [Google Scholar] [CrossRef]
  29. Lei, F.; Crow, W.T.; Holmes, T.R.; Hain, C.; Anderson, M.C. Global investigation of soil moisture and latent heat flux coupling strength. Water Resour. Res. 2018, 54, 8196–8215. [Google Scholar] [CrossRef]
  30. Li, K.Y.; Coe, M.T.; Ramankutty, N. Investigation of hydrological variability in West Africa using land surface models. J. Clim. 2005, 18, 3173–3188. [Google Scholar] [CrossRef]
  31. Yang, Y.; Qiu, J.; Zhang, R.; Huang, S.; Chen, S.; Wang, H.; Luo, J.; Fan, Y. Intercomparison of three two-source energy balance models for partitioning evaporation and transpiration in semiarid climates. Remote Sens. 2018, 10, 1149. [Google Scholar] [CrossRef]
  32. Grosso, C.; Manoli, G.; Martello, M.; Chemin, Y.; Pons, D.; Teatini, P.; Piccoli, I.; Morari, F. Mapping maize evapotranspiration at field scale using SEBAL: A comparison with the FAO method and soil-plant model simulations. Remote Sens. 2018, 10, 1452. [Google Scholar] [CrossRef]
  33. Li, S.; Wang, G.; Sun, S.; Chen, H.; Bai, P.; Zhou, S.; Huang, Y.; Wang, J.; Deng, P. Assessment of Multi-Source Evapotranspiration Products over China Using Eddy Covariance Observations. Remote Sens. 2018, 10, 1692. [Google Scholar] [CrossRef]
  34. Delogu, E.; Boulet, G.; Olioso, A.; Garrigues, S.; Brut, A.; Tallec, T.; Demarty, J.; Soudani, K.; Lagouarde, J.P. Evaluation of the SPARSE Dual-Source Model for Predicting Water Stress and Evapotranspiration from Thermal Infrared Data over Multiple Crops and Climates. Remote Sens. 2018, 10, 1806. [Google Scholar] [CrossRef]
  35. Khand, K.; Taghvaeian, S.; Gowda, P.; Paul, G. A modeling framework for deriving daily time series of evapotranspiration maps using a surface energy balance model. Remote Sens. 2019, 11, 508. [Google Scholar] [CrossRef]
  36. Lu, Z.; Zhao, Y.; Wei, Y.; Feng, Q.; Xie, J. Differences among Evapotranspiration Products Affect Water Resources and Ecosystem Management in an Australian Catchment. Remote Sens. 2019, 11, 958. [Google Scholar] [CrossRef]
  37. Yi, Z.; Zhao, H.; Jiang, Y. Continuous Daily Evapotranspiration Estimation at the Field-Scale over Heterogeneous Agricultural Areas by Fusing ASTER and MODIS Data. Remote Sens. 2018, 11, 1694. [Google Scholar] [CrossRef]
  38. Wang, T.; Tang, R.; Li, Z.L.; Jiang, Y.; Liu, M.; Niu, L. An Improved Spatio-Temporal Adaptive Data Fusion Algorithm for Evapotranspiration Mapping. Remote Sens. 2019, 11, 761. [Google Scholar] [CrossRef]
  39. Dhungel, S.; Barber, M. Estimating Calibration Variability in Evapotranspiration Derived from a Satellite-Based Energy Balance Model. Remote Sens. 2018, 10, 1695. [Google Scholar] [CrossRef]
  40. Jung, H.C.; Getirana, A.; Arsenault, K.R.; Holmes, T.R.; McNally, A. Uncertainties in Evapotranspiration Estimates over West Africa. Remote Sens. 2019, 11, 892. [Google Scholar] [CrossRef]

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Wagle, P.; Gowda, P.H. Editorial for the Special Issue “Remote Sensing of Evapotranspiration (ET)”. Remote Sens. 2019, 11, 2146. https://doi.org/10.3390/rs11182146

AMA Style

Wagle P, Gowda PH. Editorial for the Special Issue “Remote Sensing of Evapotranspiration (ET)”. Remote Sensing. 2019; 11(18):2146. https://doi.org/10.3390/rs11182146

Chicago/Turabian Style

Wagle, Pradeep, and Prasanna H. Gowda. 2019. "Editorial for the Special Issue “Remote Sensing of Evapotranspiration (ET)”" Remote Sensing 11, no. 18: 2146. https://doi.org/10.3390/rs11182146

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