Mapping Vegetation Index-Derived Actual Evapotranspiration across Croplands Using the Google Earth Engine Platform
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Site
2.2. Satellite Data and Preprocessing
2.3. Calculation of NDVIs and ET-NDVIs
2.4. Evaluation of ET-VIs and VIs
3. Results
3.1. ETa Products Comparison
3.2. ET-VIs versus Ground-Based Data
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
Parameter | ET-NDVI* | ET-NDVI*scaled | ET-NDVIKc | ET-EVI2 |
---|---|---|---|---|
Z-Value | −3.54 | −3.26 | −0.42 | −0.32 |
p-value | 0.0004 | 0.001 | 0.67 | 0.75 |
Sen’s slope | −4 | −4.91 | −0.58 | −0.79 |
Average | 635 | 760 | 971 | 740 |
References
- Misra, A.K. Climate change and challenges of water and food security. Int. J. Sustain. Built Environ. 2014, 3, 153–165. [Google Scholar] [CrossRef]
- Łabędzki, L.Ł.; Bąk, B. Impact of meteorological drought on crop water deficit and crop yield reduction in Polish agriculture. J. Water Land Dev. 2017, 34, 181–190. [Google Scholar] [CrossRef]
- Biggs, T.W.; Marshall, M.; Messina, A. Mapping daily and seasonal evapotranspiration from irrigated crops using global climate grids and satellite imagery: Automation and methods comparison. Water Resour. Res. 2016, 52, 7311–7326. [Google Scholar] [CrossRef]
- Tadesse, T.; Senay, G.B.; Berhan, G.; Regassa, T.; Beyene, S. Evaluating a satellite-based seasonal evapotranspiration product and identifying its relationship with other satellite-derived products and crop yield: A case study for Ethiopia. Int. J. Appl. Earth Obs. Geoinf. 2015, 40, 39–54. [Google Scholar] [CrossRef]
- Meza, I.; Siebert, S.; Döll, P.; Kusche, J.; Herbert, C.; Eyshi Rezaei, E.; Nouri, H.; Gerdener, H.; Popat, E.; Frischen, J.; et al. Global-scale drought risk assessment for agricultural systems. Nat. Hazards Earth Syst. Sci. 2020, 20, 695–712. [Google Scholar] [CrossRef]
- Blatchford, M.; Mannaerts, C.M.; Zeng, Y.; Nouri, H.; Karimi, P. Influence of Spatial Resolution on Remote Sensing-Based Irrigation Performance Assessment Using WaPOR Data. Remote Sens. 2020, 12, 2949. [Google Scholar] [CrossRef]
- Amani, M.; Ghorbanian, A.; Ahmadi, S.A.; Kakooei, M.; Moghimi, A.; Mirmazloumi, S.M.; Moghaddam, S.H.A.; Mahdavi, S.; Ghahremanloo, M.; Parsian, S.; et al. Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2020, 13, 5326–5350. [Google Scholar] [CrossRef]
- Zhang, K.; Kimball, J.S.; Running, S.W. A review of remote sensing based actual evapotranspiration estimation. Wiley Interdiscip. Rev. Water 2016, 3, 834–853. [Google Scholar] [CrossRef]
- Senay, G.B.; Bohms, S.; Singh, R.K.; Gowda, P.H.; Velpuri, N.M.; Alemu, H.; Verdin, J.P. Operational Evapotranspiration Mapping Using Remote Sensing and Weather Datasets: A New Parameterization for the SSEB Approach. JAWRA J. Am. Water Resour. Assoc. 2013, 49, 577–591. [Google Scholar] [CrossRef]
- Bastiaanssen, W.G.M.; Cheema, M.J.M.; Immerzeel, W.W.; Miltenburg, I.J.; Pelgrum, H. Surface energy balance and actual evapotranspiration of the transboundary Indus Basin estimated from satellite measurements and the ETLook model. Water Resour. Res. 2012, 48, 1–16. [Google Scholar] [CrossRef] [Green Version]
- Glenn, E.P.; Nagler, P.L.; Huete, A.R. Vegetation Index Methods for Estimating Evapotranspiration by Remote Sensing. Surv. Geophys. 2010, 31, 531–555. [Google Scholar] [CrossRef]
- Nagler, P.; Glenn, E.; Nguyen, U.; Scott, R.; Doody, T. Estimating Riparian and Agricultural Actual Evapotranspiration by Reference Evapotranspiration and MODIS Enhanced Vegetation Index. Remote Sens. 2013, 5, 3849–3871. [Google Scholar] [CrossRef]
- Nouri, H.; Glenn, E.; Beecham, S.; Chavoshi Boroujeni, S.; Sutton, P.; Alaghmand, S.; Noori, B.; Nagler, P. Comparing Three Approaches of Evapotranspiration Estimation in Mixed Urban Vegetation: Field-Based, Remote Sensing-Based and Observational-Based Methods. Remote Sens. 2016, 8, 492. [Google Scholar] [CrossRef]
- Allen, R.G.; Pereira, L.S.; Raes, D.; Smith, M. Crop Evapotranspiration: Guidelines for Computing Crop Water Requirements; Food and Agriculture Organization of the United States: Rome, Italy, 1988; ISBN 9251042195. [Google Scholar]
- Sishodia, R.P.; Ray, R.L.; Singh, S.K. Applications of Remote Sensing in Precision Agriculture: A Review. Remote Sens. 2020, 12, 3136. [Google Scholar] [CrossRef]
- Allen, R. Using the FAO-56 dual crop coefficient method over an irrigated region as part of an evapotranspiration intercomparison study. J. Hydrol. 2000, 229, 27–41. [Google Scholar] [CrossRef]
- Nouri, H.; Beecham, S.; Kazemi, F.; Hassanli, A.M.; Anderson, S. Remote sensing techniques for predicting evapotranspiration from mixed vegetated surfaces. Hydrol. Earth Syst. Sci. Discuss. 2013, 10, 3897–3925. [Google Scholar] [CrossRef]
- Akdim, N.; Alfieri, S.; Habib, A.; Choukri, A.; Cheruiyot, E.; Labbassi, K.; Menenti, M. Monitoring of Irrigation Schemes by Remote Sensing: Phenology versus Retrieval of Biophysical Variables. Remote Sens. 2014, 6, 5815–5851. [Google Scholar] [CrossRef]
- Hunsaker, D.J.; Pinter, P.J.; Barnes, E.M.; Kimball, B.A. Estimating cotton evapotranspiration crop coefficients with a multispectral vegetation index. Irrig Sci 2003, 22, 95–104. [Google Scholar] [CrossRef]
- Nagler, P.; Morino, K.; Murray, R.S.; Osterberg, J.; Glenn, E. An Empirical Algorithm for Estimating Agricultural and Riparian Evapotranspiration Using MODIS Enhanced Vegetation Index and Ground Measurements of ET. I. Description of Method. Remote Sens. 2009, 1, 1273–1297. [Google Scholar] [CrossRef]
- Murray, R.S.; Nagler, P.; Morino, K.; Glenn, E. An Empirical Algorithm for Estimating Agricultural and Riparian Evapotranspiration Using MODIS Enhanced Vegetation Index and Ground Measurements of ET. II. Application to the Lower Colorado River, U.S. Remote Sens. 2009, 1, 1125–1138. [Google Scholar] [CrossRef] [Green Version]
- Glenn, E.P.; Neale, C.M.U.; Hunsaker, D.J.; Nagler, P.L. Vegetation index-based crop coefficients to estimate evapotranspiration by remote sensing in agricultural and natural ecosystems. Hydrol. Process. 2011, 25, 4050–4062. [Google Scholar] [CrossRef]
- French, A.N.; Hunsaker, D.J.; Sanchez, C.A.; Saber, M.; Gonzalez, J.R.; Anderson, R. Satellite-based NDVI crop coefficients and evapotranspiration with eddy covariance validation for multiple durum wheat fields in the US Southwest. Agric. Water Manag. 2020, 239, 106266. [Google Scholar] [CrossRef]
- Nagler, P.; Scott, R.; Westenburg, C.; Cleverly, J.; Glenn, E.; Huete, A. Evapotranspiration on western U.S. rivers estimated using the Enhanced Vegetation Index from MODIS and data from eddy covariance and Bowen ratio flux towers. Remote Sens. Environ. 2005, 97, 337–351. [Google Scholar] [CrossRef]
- Khand, K.; Taghvaeian, S.; Hassan-Esfahani, L. Mapping Annual Riparian Water Use Based on the Single-Satellite-Scene Approach. Remote Sens. 2017, 9, 832. [Google Scholar] [CrossRef]
- Nagler, P.L.; Barreto-Muñoz, A.; Chavoshi Borujeni, S.; Nouri, H.; Jarchow, C.J.; Didan, K. Riparian Area Changes in Greenness and Water Use on the Lower Colorado River in the USA from 2000 to 2020. Remote Sens. 2021, 13, 1332. [Google Scholar] [CrossRef]
- Nagler, P.; Sall, I.; Barreto-Muñoz, A.; Gómez-Sapiens, M.; Nouri, H.; Chavoshi Borujeni, S.; Didan, K. Effect of restoration on plant greenness and water use in relation to drought in the riparian corridor of the Colorado River delta. J. Am. Water Resour. Assoc. 2022, 58, 746–784. [Google Scholar] [CrossRef]
- Jarchow, C.J.; Waugh, W.J.; Nagler, P.L. Calibration of an evapotranspiration algorithm in a semiarid sagebrush steppe using a 3-ha lysimeter and Landsat normalized difference vegetation index data. Ecohydrology 2022, 15. [Google Scholar] [CrossRef]
- Nouri, H.; Beecham, S.; Anderson, S.; Nagler, P. High Spatial Resolution WorldView-2 Imagery for Mapping NDVI and Its Relationship to Temporal Urban Landscape Evapotranspiration Factors. Remote Sens. 2014, 6, 580–602. [Google Scholar] [CrossRef]
- Vulova, S.; Meier, F.; Rocha, A.D.; Quanz, J.; Nouri, H.; Kleinschmit, B. Modeling urban evapotranspiration using remote sensing, flux footprints, and artificial intelligence. Sci. Total Environ. 2021, 786, 147293. [Google Scholar] [CrossRef] [PubMed]
- Bausch, W.C.; Neale, C.M.U. Crop Coefficients Derived from Reflected Canopy Radiation: A Concept. Trans. ASAE 1987, 30, 703–709. [Google Scholar] [CrossRef]
- Kamble, B.; Kilic, A.; Hubbard, K. Estimating Crop Coefficients Using Remote Sensing-Based Vegetation Index. Remote Sens. 2013, 5, 1588–1602. [Google Scholar] [CrossRef]
- Duchemin, B.; Hadria, R.; Erraki, S.; Boulet, G.; Maisongrande, P.; Chehbouni, A.; Escadafal, R.; Ezzahar, J.; Hoedjes, J.; Kharrou, M.H.; et al. Monitoring wheat phenology and irrigation in Central Morocco: On the use of relationships between evapotranspiration, crops coefficients, leaf area index and remotely-sensed vegetation indices. Agric. Water Manag. 2006, 79, 1–27. [Google Scholar] [CrossRef]
- Groeneveld, D.P.; Baugh, W.M. Correcting satellite data to detect vegetation signal for eco-hydrologic analyses. J. Hydrol. 2007, 344, 135–145. [Google Scholar] [CrossRef]
- Senay, G.B.; Leake, S.; Nagler, P.L.; Artan, G.; Dickinson, J.; Cordova, J.T.; Glenn, E.P. Estimating basin scale evapotranspiration (ET) by water balance and remote sensing methods. Hydrol. Process. 2011, 25, 4037–4049. [Google Scholar] [CrossRef]
- Jarchow, C.J.; Nagler, P.L.; Glenn, E.P. Greenup and evapotranspiration following the Minute 319 pulse flow to Mexico: An analysis using Landsat 8 Normalized Difference Vegetation Index (NDVI) data. Ecol. Eng. 2017, 106, 776–783. [Google Scholar] [CrossRef]
- Bresloff, C.J.; Nguyen, U.; Glenn, E.P.; Waugh, J.; Nagler, P.L. Effects of grazing on leaf area index, fractional cover and evapotranspiration by a desert phreatophyte community at a former uranium mill site on the Colorado Plateau. J. Environ. Manag. 2013, 114, 92–104. [Google Scholar] [CrossRef]
- Jarchow, C.J.; Waugh, W.J.; Didan, K.; Barreto-Muñoz, A.; Herrmann, S.; Nagler, P.L. Vegetation-groundwater dynamics at a former uranium mill site following invasion of a biocontrol agent: A time series analysis of Landsat normalized difference vegetation index data. Hydrol. Process. 2020, 34, 2739–2749. [Google Scholar] [CrossRef]
- Nouri, H.; Nagler, P.; Chavoshi Borujeni, S.; Barreto Munez, A.; Alaghmand, S.; Noori, B.; Galindo, A.; Didan, K. Effect of spatial resolution of satellite images on estimating the greenness and evapotranspiration of urban green spaces. Hydrol. Process. 2020, 34, 3183–3199. [Google Scholar] [CrossRef]
- Abbasi, N.; Nouri, H.; Didan, K.; Barreto-Muñoz, A.; Chavoshi Borujeni, S.; Salemi, H.; Opp, C.; Siebert, S.; Nagler, P. Estimating Actual Evapotranspiration over Croplands Using Vegetation Index Methods and Dynamic Harvested Area. Remote Sens. 2021, 13, 5167. [Google Scholar] [CrossRef]
- D’Urso, G. Current Status and Perspectives for the Estimation of Crop Water Requirements from Earth Observation. Ital. J. Agron. 2010, 5, 107. [Google Scholar] [CrossRef] [Green Version]
- Allen, R.G.; Pereira, L.S.; Howell, T.A.; Jensen, M.E. Evapotranspiration information reporting: I. Factors governing measurement accuracy. Agric. Water Manag. 2011, 98, 899–920. [Google Scholar] [CrossRef]
- Belmonte, A.C.; Jochum, A.M.; GarcÍa, A.C.; Rodríguez, A.M.; Fuster, P.L. Irrigation management from space: Towards user-friendly products. Irrig Drain. Syst 2005, 19, 337–353. [Google Scholar] [CrossRef]
- Mohammadian, M.; Arfania, R.; Sahour, H. Evaluation of SEBS Algorithm for Estimation of Daily Evapotranspiration Using Landsat-8 Dataset in a Semi-Arid Region of Central Iran. Open, J. Geol. 2017, 07, 335–347. [Google Scholar] [CrossRef]
- Zamani, O.; Grundmann, P.; Libra, J.A.; Nikouei, A. Limiting and timing water supply for agricultural production—The case of the Zayandeh-Rud River Basin, Iran. Agric. Water Manag. 2019, 222, 322–335. [Google Scholar] [CrossRef]
- Sarvari, H.; Rakhshanifar, M.; Tamošaitienė, J.; Chan, D.W.; Beer, M. A Risk Based Approach to Evaluating the Impacts of Zayanderood Drought on Sustainable Development Indicators of Riverside Urban in Isfahan-Iran. Sustainability 2019, 11, 6797. [Google Scholar] [CrossRef]
- Gohari, A.; Eslamian, S.; Abedi-Koupaei, J.; Massah Bavani, A.; Wang, D.; Madani, K. Climate change impacts on crop production in Iran’s Zayandeh-Rud River Basin. Sci. Total Environ. 2013, 442, 405–419. [Google Scholar] [CrossRef] [PubMed]
- Gorelick, N.; Hancher, M.; Dixon, M.; Ilyushchenko, S.; Thau, D.; Moore, R. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 2017, 202, 18–27. [Google Scholar] [CrossRef]
- Roy, D.P.; Kovalskyy, V.; Zhang, H.K.; Vermote, E.F.; Yan, L.; Kumar, S.S.; Egorov, A. Characterization of Landsat-7 to Landsat-8 reflective wavelength and normalized difference vegetation index continuity. Remote Sens. Environ. 2016, 185, 57–70. [Google Scholar] [CrossRef] [PubMed]
- Nagler, P.L.; Barreto-Muñoz, A.; Chavoshi Borujeni, S.; Jarchow, C.J.; Gómez-Sapiens, M.M.; Nouri, H.; Herrmann, S.M.; Didan, K. Ecohydrological responses to surface flow across borders: Two decades of changes in vegetation greenness and water use in the riparian corridor of the Colorado River delta. Hydrol. Process. 2020, 34, 4851–4883. [Google Scholar] [CrossRef]
- Hamed, K.H.; Ramachandra Rao, A. A modified Mann-Kendall trend test for autocorrelated data. J. Hydrol. 1998, 204, 182–196. [Google Scholar] [CrossRef]
- Sen, P.K. Estimates of the regression coefficient based on Kendall’s tau. J. Am. Stat. 1968, 63, 1379–1389. [Google Scholar] [CrossRef]
- R Core Team. A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2021. [Google Scholar]
- QGIS.org. QGIS Geographic Information System; QGIS Association, 2022. Available online: http://www.qgis.org (accessed on 2 February 2022).
- Salemi, H.; Toomanian, N.; Jalali, A.; Nikouei, A.; Khodagholi, M.; Rezaei, M. Determination of Net Water Requirement of Crops and Gardens in Order to Optimize the Management of Water Demand in Agricultural Sector; Springer: Cham, Switzerand, 2020; pp. 331–360. [Google Scholar] [CrossRef]
- Karamouz, M.; Rasouli, K.; Nazif, S. Development of a Hybrid Index for Drought Prediction: Case Study. J. Hydrol. Eng. 2009, 14, 617–627. [Google Scholar] [CrossRef]
- Marston, L.; Konar, M. Drought impacts to water footprints and virtual water transfers of the C entral V alley of C alifornia. Water Resour. Res. 2017, 53, 5756–5773. [Google Scholar] [CrossRef]
- Rezaei, E.E.; Ghazaryan, G.; Moradi, R.; Dubovyk, O.; Siebert, S. Crop harvested area, not yield, drives variability in crop production in Iran. Environ. Res. Lett. 2021, 16, 64058. [Google Scholar] [CrossRef]
- Lesk, C.; Rowhani, P.; Ramankutty, N. Influence of extreme weather disasters on global crop production. Nature 2016, 529, 84–87. [Google Scholar] [CrossRef] [PubMed]
- Jiang, Z.; Huete, A.R.; Kim, Y.; Didan, K. 2-band enhanced vegetation index without a blue band and its application to AVHRR data. In Remote Sensing and Modeling of Ecosystems for Sustainability IV; Optical Engineering + Applications; Gao, W., Ustin, S.L., Eds.; SPIE: San Diego, CA, USA, 2007; p. 667905. [Google Scholar]
- Moran, M.S.; Clarke, T.R.; Inoue, Y.; Vidal, A. Estimating crop water deficit using the relation between surface-air temperature and spectral vegetation index. Remote Sens. Environ. 1994, 49, 246–263. [Google Scholar] [CrossRef]
- Huete, A.; DIDAN, K.; MIURA, T.; Rodriguez, E.; Gao, X.; Ferreira, L. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens. Environ. 2002, 83, 195–213. [Google Scholar] [CrossRef]
- Jiang, Z.; Huete, A.; DIDAN, K.; MIURA, T. Development of a two-band enhanced vegetation index without a blue band. Remote Sens. Environ. 2008, 112, 3833–3845. [Google Scholar] [CrossRef]
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Abbasi, N.; Nouri, H.; Didan, K.; Barreto-Muñoz, A.; Chavoshi Borujeni, S.; Opp, C.; Nagler, P.; Thenkabail, P.S.; Siebert, S. Mapping Vegetation Index-Derived Actual Evapotranspiration across Croplands Using the Google Earth Engine Platform. Remote Sens. 2023, 15, 1017. https://doi.org/10.3390/rs15041017
Abbasi N, Nouri H, Didan K, Barreto-Muñoz A, Chavoshi Borujeni S, Opp C, Nagler P, Thenkabail PS, Siebert S. Mapping Vegetation Index-Derived Actual Evapotranspiration across Croplands Using the Google Earth Engine Platform. Remote Sensing. 2023; 15(4):1017. https://doi.org/10.3390/rs15041017
Chicago/Turabian StyleAbbasi, Neda, Hamideh Nouri, Kamel Didan, Armando Barreto-Muñoz, Sattar Chavoshi Borujeni, Christian Opp, Pamela Nagler, Prasad S. Thenkabail, and Stefan Siebert. 2023. "Mapping Vegetation Index-Derived Actual Evapotranspiration across Croplands Using the Google Earth Engine Platform" Remote Sensing 15, no. 4: 1017. https://doi.org/10.3390/rs15041017