Reviewing the Potential of Sentinel-2 in Assessing the Drought
Abstract
:1. Introduction
2. Properties of Sentinel-2
3. Progress in Remote Sensing of Drought from a Climatological and Ecological Perspective
3.1. Remote Sensing of Vegetation in Drought
3.1.1. Leaf Area Index
3.1.2. Canopy Water Content
3.1.3. Leaf Chlorophyll Content
3.2. Remote Sensing of Land Use and Land Cover Change in Drought
3.3. Remote Sensing of Evapotranspiration in Drought
3.4. Remote Sensing of Soil Moisture in Drought
3.5. Remote Sensing of Surface Water and Wetland Analysis in Drought
4. Summary and Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
- Wilhite, D.A.; Glantz, M.H. Understanding: The drought phenomenon: The role of definitions. Water Int. 1985, 10, 111–120. [Google Scholar] [CrossRef] [Green Version]
- Passioura, J.B. Drought and drought tolerance. Plant Growth Regul. 1996, 20, 79–83. [Google Scholar] [CrossRef]
- Tsakiris, G.; Vangelis, H. Establishing a drought index incorporating evapotranspiration. Eur. Water 2005, 9, 3–11. [Google Scholar]
- Palmer, W.C. Meteorological Drought; U.S. Weather Bureau: Washington, DC, USA, 1965.
- Wilhelmi, O.V.; Wilhite, D.A. Assessing vulnerability to agricultural drought: A Nebraska case study. Nat. Hazards 2002, 25, 37–58. [Google Scholar] [CrossRef]
- Nalbantis, I.; Tsakiris, G. Assessment of hydrological drought revisited. Water Resour. Manag. 2009, 23, 881–897. [Google Scholar] [CrossRef]
- Zhao, M.; Huang, S.; Huang, Q.; Wang, H.; Leng, G.; Xie, Y. Assessing socio-economic drought evolution characteristics and their possible meteorological driving force. Geomat. Nat. Hazards Risk 2019, 10, 1084–1101. [Google Scholar] [CrossRef] [Green Version]
- Guerrero-Salazar, P.; Yevjevich, V. Analysis of Drought Characteristics by the Theory of Runs; Hydrology Paper 80; Colorado State University: Fort Collins, CO, USA, 1975. [Google Scholar]
- World Meteorological Organization. Drought Monitoring and Early Warning: Concepts, Progress and Future Challenges; World Meteorological Organization: Geneva, Switzerland, 2006; ISBN 978-92-63-11006-0. [Google Scholar]
- Wang, W.; Ertsen, M.W.; Svoboda, M.D.; Hafeez, M. Propagation of drought: From meteorological drought to agricultural and hydrological drought. Adv. Meteorol. 2016, 2016, 1–5. [Google Scholar] [CrossRef]
- Pulwarty, R.S.; Sivakumar, M.V.K. Information systems in a changing climate: Early warnings and drought risk management. Weather Clim. Extrem. 2014, 3, 14–21. [Google Scholar] [CrossRef] [Green Version]
- Zhang, L.; Jiao, W.; Zhang, H.; Huang, C.; Tong, Q. Studying drought phenomena in the Continental United States in 2011 and 2012 using various drought indices. Remote Sens. Environ. 2017, 190, 96–106. [Google Scholar] [CrossRef]
- Hayes, M.; Svoboda, M.; Wall, N.; Widhalm, M. The lincoln declaration on drought indices: Universal meteorological drought index recommended. Bull. Am. Meteorol. Soc. 2011, 92, 485–488. [Google Scholar] [CrossRef] [Green Version]
- Anderson, L.O.; Malhi, Y.; Aragão, L.E.O.C.; Ladle, R.; Arai, E.; Barbier, N.; Phillips, O. Remote sensing detection of droughts in Amazonian forest canopies. New Phytol. 2010, 187, 733–750. [Google Scholar] [CrossRef]
- Zhang, A.; Jia, G. Monitoring meteorological drought in semiarid regions using multi-sensor microwave remote sensing data. Remote Sens. Environ. 2013, 134, 12–23. [Google Scholar] [CrossRef]
- Maybank, J.; Bonsai, B.; Jones, K.; Lawford, R.; O’Brien, E.G.; Ripley, E.A.; Wheaton, E. Drought as a natural disaster. Atmosphere-Ocean 1995, 33, 195–222. [Google Scholar] [CrossRef]
- Dilley, M.; Chen, R.S.; Deichmann, U.; Lerner-Lam, A.L.; Arnold, M. Natural Disaster Hotspots; World Bank: Washington, DC, USA, 2005. [Google Scholar]
- Below, R.; Grover-Kopec, E.; Dilley, M. Documenting drought-related Disasters: A global reassessment. J. Environ. Dev. 2007, 16, 328–344. [Google Scholar] [CrossRef]
- Wilhite, D.A.; Svoboda, M.D.; Hayes, M.J. Understanding the complex impacts of drought: A key to enhancing drought mitigation and preparedness. Water Resour. Manag. 2007, 21, 763–774. [Google Scholar] [CrossRef] [Green Version]
- Gupta, A.K.; Tyagi, P.; Sehgal, V.K. Drought disaster challenges and mitigation in India: Strategic appraisal. Curr. Sci. 2011, 100, 1795–1860. [Google Scholar]
- White, D.H.; Walcott, J.J. The role of seasonal indices in monitoring and assessing agricultural and other droughts: A review. Crop Pasture Sci. 2009, 60, 599–616. [Google Scholar] [CrossRef]
- Brito, S.S.B.; Cunha, A.P.M.A.; Cunningham, C.C.; Alvalá, R.C.; Marengo, J.A.; Carvalho, M.A. Frequency, duration and severity of drought in the Semiarid Northeast Brazil region. Int. J. Climatol. 2018, 38, 517–529. [Google Scholar] [CrossRef]
- McKee, T.B.; Nolan, J.; Kleist, J. The relationship of drought frequency and duration to time scales. In Proceedings of the 8th Conference on Applied Climatology, Boston, MA, USA, 17–22 January 1993. [Google Scholar]
- Svoboda, M.; LeComte, D.; Hayes, M.; Heim, R.; Gleason, K.; Angel, J.; Rippey, B.; Tinker, R.; Palecki, M.; Stooksbury, D.; et al. The drought monitor. Bull. Am. Meteorol. Soc. 2002, 83, 1181–1190. [Google Scholar] [CrossRef] [Green Version]
- Keyantash, J.; Dracup, J.A. The quantification of drought: An evaluation of drought indices. Bull. Am. Meteorol. Soc. 2002, 83, 1167–1180. [Google Scholar] [CrossRef]
- Vicente-Serrano, S.M. Evaluating the impact of drought using remote sensing in a Mediterranean, Semi-arid Region. Nat. Hazards 2007, 40, 173–208. [Google Scholar] [CrossRef]
- Wilhite, D.A. Drought as a natural hazard: Concepts and definitions. In Drought: A Global Assessment; Routledge: London, UK, 2000. [Google Scholar]
- Mishra, A.K.; Singh, V.P. A review of drought concepts. J. Hydrol. 2010, 391, 202–216. [Google Scholar] [CrossRef]
- McVicar, T.R.; Jupp, D.L.B. The current and potential operational uses of remote sensing to aid decisions on drought exceptional circumstances in Australia: A review. Agric. Syst. 1998, 57, 399–468. [Google Scholar] [CrossRef]
- Dai, A. Drought under global warming: A review. Wiley Interdiscip. Rev. Clim. Chang. 2011, 2, 45–65. [Google Scholar] [CrossRef] [Green Version]
- Chen, Y.; Sun, L.; Wang, W.; Pei, Z. Application of sentinel 2 data for drought monitoring in Texas, America. In Proceedings of the 2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics), Istanbul, Turkey, 16–19 July 2019. [Google Scholar]
- Šebenik, U.; Brilly, M.; Šraj, M. Drought analysis using the standardized precipitation index (SPI). Acta Geogr. Slov. 2017, 57, 31–49. [Google Scholar] [CrossRef] [Green Version]
- Knutson, C. Methods and Tools for Drought Analysis and Management. Eos 2008, 89, 206. [Google Scholar] [CrossRef]
- Hu, X.; Ren, H.; Tansey, K.; Zheng, Y.; Ghent, D.; Liu, X.; Yan, L. Agricultural drought monitoring using European Space Agency Sentinel 3A land surface temperature and normalized difference vegetation index imageries. Agric. For. Meteorol. 2019, 279, 107707. [Google Scholar] [CrossRef]
- Asner, G.P.; Alencar, A. Drought impacts on the Amazon forest: The remote sensing perspective. New Phytol. 2010, 187, 569–578. [Google Scholar] [CrossRef]
- Kogan, F.N. Global Drought Watch from Space. Bull. Am. Meteorol. Soc. 1997, 78, 621–636. [Google Scholar] [CrossRef]
- Rojas, O.; Vrieling, A.; Rembold, F. Assessing drought probability for agricultural areas in Africa with coarse resolution remote sensing imagery. Remote Sens. Environ. 2011, 115, 343–352. [Google Scholar] [CrossRef]
- Volden, E. New Capabilities in Earth Observation for Agriculture; European Space Agency: Budapest, Hungary, 2017. [Google Scholar]
- Shahabfar, A.; Ghulam, A.; Eitzinger, J. Drought monitoring in Iran using the perpendicular drought indices. Int. J. Appl. Earth Obs. Geoinf. 2012, 18, 119–127. [Google Scholar] [CrossRef]
- Ghulam, A.; Qin, Q.; Kusky, T.; Li, Z.L. A re-examination of perpendicular drought indices. Int. J. Remote Sens. 2008, 29, 6037–6044. [Google Scholar] [CrossRef]
- Wang, Q.; Blackburn, G.A.; Onojeghuo, A.O.; Dash, J.; Zhou, L.; Zhang, Y.; Atkinson, P.M. Fusion of Landsat 8 OLI and Sentinel-2 MSI Data. IEEE Trans. Geosci. Remote Sens. 2017, 55, 3885–3899. [Google Scholar] [CrossRef] [Green Version]
- Hao, P.-Y.; Tang, H.-J.; Chen, Z.-X.; Yu, L.; Wu, M.-Q. High resolution crop intensity mapping using harmonized Landsat-8 and Sentinel-2 data. J. Integr. Agric. 2019, 18, 2883–2897. [Google Scholar] [CrossRef]
- Liu, Y.; Gong, W.; Hu, X.; Gong, J. Forest type identification with random forest using Sentinel-1A, Sentinel-2A, multi-temporal Landsat-8 and DEM data. Remote Sens. 2018, 10, 946. [Google Scholar] [CrossRef] [Green Version]
- Roy, D.P.; Huang, H.; Boschetti, L.; Giglio, L.; Yan, L.; Zhang, H.H.; Li, Z. Landsat-8 and Sentinel-2 burned area mapping-A combined sensor multi-temporal change detection approach. Remote Sens. Environ. 2019, 231, 111254. [Google Scholar] [CrossRef]
- Wang, Q.; Atkinson, P.M. Spatio-temporal fusion for daily Sentinel-2 images. Remote Sens. Environ. 2018, 204, 31–42. [Google Scholar] [CrossRef] [Green Version]
- Wang, Q.; Shi, W.; Li, Z.; Atkinson, P.M. Fusion of Sentinel-2 images. Remote Sens. Environ. 2016, 187, 241–252. [Google Scholar] [CrossRef] [Green Version]
- Kaplan, G.; Avdan, U. Sentinel-1 and Sentinel-2 data fusion for wetlands mapping: Balikdami, Turkey. Int Arch. Photogram. Remote Sens. Spat. Inf. Sci. 2018, 42. [Google Scholar] [CrossRef] [Green Version]
- Useya, J.; Chen, S. Comparative Performance Evaluation of Pixel-Level and Decision-Level Data Fusion of Landsat 8 OLI, Landsat 7 ETM+ and Sentinel-2 MSI for Crop Ensemble Classification. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2018, 11, 4441–4451. [Google Scholar] [CrossRef]
- West, H.; Quinn, N.; Horswell, M.; White, P. Assessing vegetation response to soil moisture fluctuation under extreme drought using sentinel-2. Water 2018, 10, 838. [Google Scholar] [CrossRef] [Green Version]
- Rossi, S.; Weissteiner, C.; Laguardia, G.; Kurnik, B.; Robustelli, M.; Niemeyer, S.; Gobron, N. Potential of MERIS fAPAR for Drought Detection; European Space Agency Communication Production Office: Frascati, Italy, 2008. [Google Scholar]
- Putzenlechner, B.; Castro, S.; Kiese, R.; Ludwig, R.; Marzahn, P.; Sharp, I.; Sanchez-Azofeifa, A. Validation of Sentinel-2 fAPAR products using ground observations across three forest ecosystems. Remote Sens. Environ. 2019, 232, 111310. [Google Scholar] [CrossRef]
- Karnieli, A.; Agam, N.; Pinker, R.T.; Anderson, M.; Imhoff, M.L.; Gutman, G.G.; Panov, N.; Goldberg, A. Use of NDVI and land surface temperature for drought assessment: Merits and limitations. J. Clim. 2010, 23, 618–633. [Google Scholar] [CrossRef]
- Zhang, H.; Chen, H.-L.; Shen, S. The application of normalized multi-band drought index (NMDI) method in cropland drought monitoring. Proc. SPIE 2009, 7472. [Google Scholar] [CrossRef]
- Park, S.; Im, J.; Jang, E.; Rhee, J. Drought assessment and monitoring through blending of multi-sensor indices using machine learning approaches for different climate regions. Agric. For. Meteorol. 2016, 216, 157–169. [Google Scholar] [CrossRef]
- Wieland, M.; Martinis, S. Large-scale surface water change observed by Sentinel-2 during the 2018 drought in Germany. Int. J. Remote Sens. 2020, 41, 4742–4756. [Google Scholar] [CrossRef]
- Vaudour, E.; Gomez, C.; Fouad, Y.; Lagacherie, P. Sentinel-2 image capacities to predict common topsoil properties of temperate and Mediterranean agroecosystems. Remote Sens. Environ. 2019, 223, 21–33. [Google Scholar] [CrossRef]
- Chen, F.; Ming, C.; Li, J.; Wang, C.; Claverie, M. A Comparison of Sentinel-2a and Sentinel-2B with Preliminary Results; IEEE: Manhattan, NY, USA, 2018. [Google Scholar]
- Breshears, D.D.; Cobb, N.S.; Rich, P.M.; Price, K.P.; Allen, C.D.; Balice, R.G.; Romme, W.H.; Kastens, J.H.; Floyd, M.L.; Belnap, J.; et al. Regional vegetation die-off in response to global-change-type drought. Proc. Natl. Acad. Sci. USA 2005, 102, 15144–15148. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Ji, L.; Peters, A.J. Assessing vegetation response to drought in the northern Great Plains using vegetation and drought indices. Remote Sens. Environ. 2003, 87, 85–98. [Google Scholar] [CrossRef]
- Adams, H.D.; Guardiola-Claramonte, M.; Barron-Gafford, G.A.; Villegas, J.C.; Breshears, D.D.; Zou, C.B.; Troch, P.A.; Huxman, T.E. Temperature sensitivity of drought-induced tree mortality portends increased regional die-off under global-change-type drought. Proc. Natl. Acad. Sci. USA 2009, 106, 7063–7066. [Google Scholar] [CrossRef] [Green Version]
- Camarero, J.J.; Gazol, A.; Sangüesa-Barreda, G.; Oliva, J.; Vicente-Serrano, S.M. To die or not to die: Early warnings of tree dieback in response to a severe drought. J. Ecol. 2015, 103, 44–57. [Google Scholar] [CrossRef] [Green Version]
- Brown, J.F.; Wardlow, B.D.; Tadesse, T.; Hayes, M.J.; Reed, B.C. The Vegetation Drought Response Index (VegDRI): A new integrated approach for monitoring drought stress in vegetation. GIScience Remote Sens. 2008, 45, 16–46. [Google Scholar] [CrossRef]
- Masek, J.; Ju, J.; Roger, J.C.; Skakun, S.; Claverie, M.; Dungan, J. Harmonized Landsat/sentinel-2 products for land monitoring. In Proceedings of the IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain, 22–27 July 2018. [Google Scholar]
- Claverie, M.; Ju, J.; Masek, J.G.; Dungan, J.L.; Vermote, E.F.; Roger, J.C.; Skakun, S.V.; Justice, C. The Harmonized Landsat and Sentinel-2 surface reflectance data set. Remote Sens. Environ. 2018, 219, 145–161. [Google Scholar] [CrossRef]
- Urban, M.; Berger, C.; Mudau, T.E.; Heckel, K.; Truckenbrodt, J.; Odipo, V.O.; Smit, I.P.J.; Schmullius, C. Surface moisture and vegetation cover analysis for drought monitoring in the southern Kruger National Park using Sentinel-1, Sentinel-2, and Landsat-8. Remote Sens. 2018, 10, 1482. [Google Scholar] [CrossRef] [Green Version]
- Bolton, D.K.; Gray, J.M.; Melaas, E.K.; Moon, M.; Eklundh, L.; Friedl, M.A. Continental-scale land surface phenology from harmonized Landsat 8 and Sentinel-2 imagery. Remote Sens. Environ. 2020, 240, 111685. [Google Scholar] [CrossRef]
- Shao, Z.; Cai, J.; Fu, P.; Hu, L.; Liu, T. Deep learning-based fusion of Landsat-8 and Sentinel-2 images for a harmonized surface reflectance product. Remote Sens. Environ. 2019, 235, 111425. [Google Scholar] [CrossRef]
- Nguyen, M.D.; Baez-Villanueva, O.M.; Bui, D.D.; Nguyen, P.T.; Ribbe, L. Harmonization of landsat and sentinel 2 for crop monitoring in drought prone areas: Case studies of Ninh Thuan (Vietnam) and Bekaa (Lebanon). Remote Sens. 2020, 12, 281. [Google Scholar] [CrossRef] [Green Version]
- Puletti, N.; Chianucci, F.; Castaldi, C. Use of Sentinel-2 for forest classification in Mediterranean environments. Ann. Silvic. Res. 2018, 42, 32–38. [Google Scholar] [CrossRef]
- Hill, M.J. Vegetation index suites as indicators of vegetation state in grassland and savanna: An analysis with simulated SENTINEL 2 data for a North American transect. Remote Sens. Environ. 2013, 137, 94–111. [Google Scholar] [CrossRef]
- Pastick, N.J.; Wylie, B.K.; Wu, Z. Spatiotemporal analysis of Landsat-8 and Sentinel-2 data to support monitoring of dryland ecosystems. Remote Sens. 2018, 10, 791. [Google Scholar] [CrossRef] [Green Version]
- Misra, G.; Cawkwell, F.; Wingler, A. Status of phenological research using sentinel-2 data: A review. Remote Sens. 2020, 12, 2760. [Google Scholar] [CrossRef]
- Singh, R.P.; Roy, S.; Kogan, F. Vegetation and temperature condition indices from NOAA AVHRR data for drought monitoring over India. Int. J. Remote Sens. 2003, 24, 4393–4402. [Google Scholar] [CrossRef]
- Zhou, X.; Wang, P.; Tansey, K.; Zhang, S.; Li, H.; Wang, L. Developing a fused vegetation temperature condition index for drought monitoring at fi eld scales using Sentinel-2 and MODIS imagery. Comput. Electron. Agric. 2020, 168, 105144. [Google Scholar] [CrossRef]
- Croft, H.; Chen, J.M.; Zhang, Y.; Simic, A.; Noland, T.L.; Nesbitt, N.; Arabian, J. Evaluating leaf chlorophyll content prediction from multispectral remote sensing data within a physically-based modelling framework. ISPRS J. Photogramm. Remote Sens. 2015, 102, 85–95. [Google Scholar] [CrossRef]
- Kim, K.; Wang, M.-C.; Ranjitkar, S.; Liu, S.-H.; Xu, J.-C.; Zomer, R.J. Using leaf area index (LAI) to assess vegetation response to drought in Yunnan province of China. J. Mt. Sci. 2017, 14, 1863–1872. [Google Scholar] [CrossRef]
- Korhonen, L.; Hadi; Packalen, P.; Rautiainen, M. Comparison of Sentinel-2 and Landsat 8 in the estimation of boreal forest canopy cover and leaf area index. Remote Sens. Environ. 2017, 195, 259–274. [Google Scholar] [CrossRef]
- Meyer, L.H.; Heurich, M.; Beudert, B.; Premier, J.; Pflugmacher, D. Comparison of Landsat-8 and Sentinel-2 data for estimation of leaf area index in temperate forests. Remote Sens. 2019, 11, 1160. [Google Scholar] [CrossRef] [Green Version]
- Majasalmi, T.; Rautiainen, M. The potential of Sentinel-2 data for estimating biophysical variables in a boreal forest: A simulation study. Remote Sens. Lett. 2016, 7, 427–436. [Google Scholar] [CrossRef]
- Dong, T.; Liu, J.; Qian, B.; He, L.; Liu, J.; Wang, R.; Jing, Q.; Champagne, C.; McNairn, H.; Powers, J.; et al. Estimating crop biomass using leaf area index derived from Landsat 8 and Sentinel-2 data. ISPRS J. Photogramm. Remote Sens. 2020, 168, 236–250. [Google Scholar] [CrossRef]
- Cogato, A.; Pagay, V.; Marinello, F.; Meggio, F.; Grace, P.; Migliorati, M.D.A. Assessing the feasibility of using sentinel-2 imagery to quantify the impact of heatwaves on irrigated vineyards. Remote Sens. 2019, 11, 2869. [Google Scholar] [CrossRef] [Green Version]
- Cernicharo, J.; Verger, A.; Camacho, F. Empirical and physical estimation of Canopy Water Content: From CHRIS/PROBA Data. Remote Sens. 2013, 5, 5265–5284. [Google Scholar] [CrossRef] [Green Version]
- Zhang, F.; Zhou, G. Estimation of canopy water content by means of hyperspectral indices based on drought stress gradient experiments of maize in the north plain China. Remote Sens. 2015, 7, 15203–15223. [Google Scholar] [CrossRef] [Green Version]
- Paz-Kagan, T.; Vaughn, N.R.; Martin, R.E.; Brodrick, P.G.; Stephenson, N.L.; Das, A.J.; Nydick, K.R.; Asner, G.P. Landscape-scale variation in canopy water content of giant sequoias during drought. For. Ecol. Manag. 2018, 419–420, 291–304. [Google Scholar] [CrossRef] [Green Version]
- Asner, G.P.; Brodrick, P.G.; Anderson, C.B.; Vaughn, N.; Knapp, D.E.; Martin, R.E. Progressive forest canopy water loss during the 2012-2015 California drought. Proc. Natl. Acad. Sci. USA 2016, 113, E249–E255. [Google Scholar] [CrossRef] [Green Version]
- Boren, E.J.; Boschetti, L. Landsat-8 and Sentinel-2 Canopy Water Content Estimation in Croplands through Radiative Transfer Model Inversion. Remote Sens. 2020, 12, 2803. [Google Scholar] [CrossRef]
- Marusig, D.; Petruzzellis, F.; Tomasella, M.; Napolitano, R.; Altobelli, A.; Nardini, A. Correlation of field-measured and remotely sensed plant water status as a tool to monitor the risk of drought-induced forest decline. Forests 2020, 11, 77. [Google Scholar] [CrossRef] [Green Version]
- Dotzler, S.; Hill, J.; Buddenbaum, H.; Stoffels, J. The potential of EnMAP and sentinel-2 data for detecting drought stress phenomena in deciduous forest communities. Remote Sens. 2015, 7, 14227–14258. [Google Scholar] [CrossRef] [Green Version]
- Coluzzi, R.; Fascetti, S.; Imbrenda, V.; Italiano, S.S.P.; Ripullone, F.; Lanfredi, M. Exploring the Use of Sentinel-2 Data to Monitor Heterogeneous Effects of Contextual Drought and Heatwaves on Mediterranean Forests. Land 2020, 9, 325. [Google Scholar] [CrossRef]
- Lin, S.; Li, J.; Liu, Q.; Li, L.; Zhao, J.; Yu, W. Evaluating the effectiveness of using vegetation indices based on red-edge reflectance from Sentinel-2 to estimate gross primary productivity. Remote Sens. 2019, 11, 1303. [Google Scholar] [CrossRef] [Green Version]
- Puletti, N.; Mattioli, W.; Bussotti, F.; Pollastrini, M. Monitoring the effects of extreme drought events on forest health by Sentinel-2 imagery. J. Appl. Remote Sens. 2019, 13, 020501. [Google Scholar] [CrossRef]
- Kościelniak, J.; Filek, W.; Biesaga-Kościelniak, J. The effect of drought stress on chlorophyll fluorescence in Lolium-Festuca hybrids. Acta Physiol. Plant. 2006, 28, 149–158. [Google Scholar] [CrossRef]
- Zhang, Y.; Chen, J.M.; Thomas, S.C. Retrieving seasonal variation in chlorophyll content of overstory and understory sugar maple leaves from leaf-level hyperspectral data. Can. J. Remote Sens. 2007, 33, 406–415. [Google Scholar] [CrossRef]
- Clevers, J.G.P.W.; Kooistra, L.; van den Brande, M.M.M. Using Sentinel-2 data for retrieving LAI and leaf and canopy chlorophyll content of a potato crop. Remote Sens. 2017, 9, 405. [Google Scholar] [CrossRef] [Green Version]
- Clevers, J.G.P.W.; Gitelson, A.A. Remote estimation of crop and grass chlorophyll and nitrogen content using red-edge bands on sentinel-2 and-3. Int. J. Appl. Earth Obs. Geoinf. 2013, 23, 344–351. [Google Scholar] [CrossRef]
- Darvishzadeh, R.; Skidmore, A.; Abdullah, H.; Cherenet, E.; Ali, A.; Wang, T.; Nieuwenhuis, W.; Heurich, M.; Vrieling, A.; O’Connor, B.; et al. Mapping leaf chlorophyll content from Sentinel-2 and RapidEye data in spruce stands using the invertible forest reflectance model. Int. J. Appl. Earth Obs. Geoinf. 2019, 79, 58–70. [Google Scholar] [CrossRef] [Green Version]
- Zarco-Tejada, P.J.; Hornero, A.; Beck, P.S.A.; Kattenborn, T.; Kempeneers, P.; Hernández-Clemente, R. Chlorophyll content estimation in an open-canopy conifer forest with Sentinel-2A and hyperspectral imagery in the context of forest decline. Remote Sens. Environ. 2019, 223, 320–335. [Google Scholar] [CrossRef] [PubMed]
- Delegido, J.; Verrelst, J.; Alonso, L.; Moreno, J. Evaluation of sentinel-2 red-edge bands for empirical estimation of green LAI and chlorophyll content. Sensors 2011, 11, 7063–7081. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Gholizadeh, A.; Mišurec, J.; Kopačková, V.; Mielke, C.; Rogass, C. Assessment of red-edge position extraction techniques: A case study for norway spruce forests using hymap and simulated sentinel-2 data. Forests 2016, 7, 226. [Google Scholar] [CrossRef] [Green Version]
- Steinhausen, M.J.; Wagner, P.D.; Narasimhan, B.; Waske, B. Combining Sentinel-1 and Sentinel-2 data for improved land use and land cover mapping of monsoon regions. Int. J. Appl. Earth Obs. Geoinf. 2018, 73, 595–604. [Google Scholar] [CrossRef]
- Thanh Noi, P.; Kappas, M. Comparison of Random Forest, k-Nearest Neighbor, and Support Vector Machine Classifiers for Land Cover Classification Using Sentinel-2 Imagery. Sensors 2017, 18, 18. [Google Scholar] [CrossRef] [Green Version]
- Scheffler, D.; Frantz, D.; Segl, K. Spectral harmonization and red edge prediction of Landsat-8 to Sentinel-2 using land cover optimized multivariate regressors. Remote Sens. Environ. 2020, 241, 111723. [Google Scholar] [CrossRef]
- Forkuor, G.; Dimobe, K.; Serme, I.; Tondoh, J.E. Landsat-8 vs. Sentinel-2; examining the added value of sentinel-2′s red-edge bands to land-use and land-cover mapping in Burkina Faso. GIScience Remote Sens. 2018, 55, 331–354. [Google Scholar] [CrossRef]
- Eskandari, S.; Jaafari, M.R.; Oliva, P.; Ghorbanzadeh, O.; Blaschke, T. Mapping land cover and tree canopy cover in Zagros forests of Iran: Application of Sentinel-2, google earth, and field data. Remote Sens. 2020, 12, 1912. [Google Scholar] [CrossRef]
- Tong, X.; Brandt, M.; Hiernaux, P.; Herrmann, S.; Rasmussen, L.V.; Rasmussen, K.; Tian, F.; Tagesson, T.; Zhang, W.; Fensholt, R. The forgotten land use class: Mapping of fallow fields across the Sahel using Sentinel-2. Remote Sens. Environ. 2020, 239, 111598. [Google Scholar] [CrossRef]
- Panigada, C.; Tagliabue, G.; Zaady, E.; Rozenstein, O.; Garzonio, R.; Di Mauro, B.; De Amicis, M.; Colombo, R.; Cogliati, S.; Miglietta, F.; et al. A new approach for biocrust and vegetation monitoring in drylands using multi-temporal Sentinel-2 images. Prog. Phys. Geogr. 2019, 43, 496–520. [Google Scholar] [CrossRef]
- Mazzia, V.; Khaliq, A.; Chiaberge, M. Improvement in land cover and crop classification based on temporal features learning from Sentinel-2 data using recurrent-Convolutional Neural Network (R-CNN). Appl. Sci. 2020, 10, 238. [Google Scholar] [CrossRef] [Green Version]
- Rind, D.; Goldberg, R.; Hansen, J.; Rosenzweig, C.; Ruedy, R. Potential evapotranspiration and the likelihood of future drought. J. Geophys. Res. 1990, 95, 9983–10004. [Google Scholar] [CrossRef]
- Teuling, A.J.; Van Loon, A.F.; Seneviratne, S.I.; Lehner, I.; Aubinet, M.; Heinesch, B.; Bernhofer, C.; Grünwald, T.; Prasse, H.; Spank, U. Evapotranspiration amplifies European summer drought. Geophys. Res. Lett. 2013, 40, 2071–2075. [Google Scholar] [CrossRef]
- Um, M.J.; Kim, Y.; Park, D.; Jung, K.; Wang, Z.; Kim, M.M.; Shin, H. Impacts of potential evapotranspiration on drought phenomena in different regions and climate zones. Sci. Total Environ. 2020, 703, 135590. [Google Scholar] [CrossRef]
- Zhao, J.; Chen, X.; Zhang, J.; Zhao, H.; Song, Y. Higher temporal evapotranspiration estimation with improved SEBS model from geostationary meteorological satellite data. Sci. Rep. 2019, 9, 14981. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Kustas, W.P.; Norman, J.M. Use of remote sensing for evapotranspiration monitoring over land surfaces. Hydrol. Sci. J. 1996, 41, 495–516. [Google Scholar] [CrossRef]
- Liou, Y.A.; Kar, S.K. Evapotranspiration estimation with remote sensing and various surface energy balance algorithms-a review. Energies 2014, 7, 2821–2849. [Google Scholar] [CrossRef] [Green Version]
- Kalma, J.D.; McVicar, T.R.; McCabe, M.F. Estimating land surface evaporation: A review of methods using remotely sensed surface temperature data. Surv. Geophys. 2008, 29, 421–469. [Google Scholar] [CrossRef]
- Bisquert, M.; Sánchez, J.M.; López-Urrea, R.; Caselles, V. Estimating high resolution evapotranspiration from disaggregated thermal images. Remote Sens. Environ. 2016, 187, 423–433. [Google Scholar] [CrossRef]
- Cai, J.; Liu, Y.; Lei, T.; Pereira, L.S. Estimating reference evapotranspiration with the FAO Penman-Monteith equation using daily weather forecast messages. Agric. For. Meteorol. 2007, 145, 22–35. [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]
- Bogawski, P.; Bednorz, E. Comparison and Validation of Selected Evapotranspiration Models for Conditions in Poland (Central Europe). Water Resour. Manag. 2014, 28, 5021–5038. [Google Scholar] [CrossRef] [Green Version]
- Farg, E.; Arafat, S.M.; Abd El-Wahed, M.S.; El-Gindy, A.M. Estimation of Evapotranspiration ETc and Crop Coefficient K c of Wheat, in south Nile Delta of Egypt Using integrated FAO-56 approach and remote sensing data. Egypt. J. Remote Sens. Space Sci. 2012, 15, 83–89. [Google Scholar] [CrossRef] [Green Version]
- Murray, R.S.; Nagler, P.L.; Morino, K.; Glenn, E.P. 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]
- Marta, A.D.; Chirico, G.B.; Bolognesi, S.F.; Mancini, M.; D’Urso, G.; Orlandini, S.; De Michele, C.; Altobelli, F. Integrating sentinel-2 imagery with Aquacrop for dynamic assessment of tomato water requirements in southern Italy. Agronomy 2019, 9, 404. [Google Scholar] [CrossRef] [Green Version]
- Vanino, S.; Nino, P.; De Michele, C.; Falanga Bolognesi, S.; D’Urso, G.; Di Bene, C.; Pennelli, B.; Vuolo, F.; Farina, R.; Pulighe, G.; et al. Capability of Sentinel-2 data for estimating maximum evapotranspiration and irrigation requirements for tomato crop in Central Italy. Remote Sens. Environ. 2018, 215, 452–470. [Google Scholar] [CrossRef]
- Pasqualotto, N.; D’Urso, G.; Bolognesi, S.F.; Belfiore, O.R.; Van Wittenberghe, S.; Delegido, J.; Pezzola, A.; Winschel, C.; Moreno, J. Retrieval of evapotranspiration from sentinel-2: Comparison of vegetation indices, semi-empirical models and SNAP biophysical processor approach. Agronomy 2019, 9, 663. [Google Scholar] [CrossRef] [Green Version]
- Vanella, D.; Ramírez-Cuesta, J.M.; Intrigliolo, D.S.; Consoli, S. Combining electrical resistivity tomography and satellite images for improving evapotranspiration estimates of citrus orchards. Remote Sens. 2019, 11, 373. [Google Scholar] [CrossRef] [Green Version]
- Choudhury, B.J.; Ahmed, N.U.; Idso, S.B.; Reginato, R.J.; Daughtry, C.S.T. Relations between evaporation coefficients and vegetation indices studied by model simulations. Remote Sens. Environ. 1994, 50, 1–17. [Google Scholar] [CrossRef]
- Pôças, I.; Paço, T.A.; Paredes, P.; Cunha, M.; Pereira, L.S. Estimation of actual crop coefficients using remotely sensed vegetation indices and soil water balance modelled data. Remote Sens. 2015, 7, 2373–2400. [Google Scholar] [CrossRef] [Green Version]
- Rozenstein, O.; Haymann, N.; Kaplan, G.; Tanny, J. Estimating cotton water consumption using a time series of Sentinel-2 imagery. Agric. Water Manag. 2018, 207, 44–52. [Google Scholar] [CrossRef]
- Ramírez-Cuesta, J.M.; Mirás-Avalos, J.M.; Rubio-Asensio, J.S.; Intrigliolo, D.S. A novel ArcGIS toolbox for estimating crop water demands by integrating the dual crop coefficient approach with multi-satellite imagery. Water 2018, 11, 38. [Google Scholar] [CrossRef] [Green Version]
- Mirás-Avalos, J.M.; Rubio-Asensio, J.S.; Ramírez-Cuesta, J.M.; Maestre-Valero, J.F.; Intrigliolo, D.S. Irrigation-advisor-a decision support system for irrigation of vegetable crops. Water 2019, 11, 2245. [Google Scholar] [CrossRef] [Green Version]
- Longo-Minnolo, G.; Vanella, D.; Consoli, S.; Intrigliolo, D.S.; Ramírez-Cuesta, J.M. Integrating forecast meteorological data into the ArcDualKc model for estimating spatially distributed evapotranspiration rates of a citrus orchard. Agric. Water Manag. 2020, 231, 105967. [Google Scholar] [CrossRef]
- Huryna, H.; Cohen, Y.; Karnieli, A.; Panov, N.; Kustas, W.P.; Agam, N. Evaluation of TsHARP utility for thermal sharpening of Sentinel-3 satellite images using Sentinel-2 visual imagery. Remote Sens. 2019, 11, 2304. [Google Scholar] [CrossRef] [Green Version]
- Guo, L.J.; Moore, J.M.M. Pixel block intensity modulation: Adding spatial detail to tm band 6 thermal imagery. Int. J. Remote Sens. 1998, 19, 2477–2491. [Google Scholar] [CrossRef]
- Weng, Q.; Fu, P.; Gao, F. Generating daily land surface temperature at Landsat resolution by fusing Landsat and MODIS data. Remote Sens. Environ. 2014, 145, 55–67. [Google Scholar] [CrossRef]
- Guzinski, R.; Nieto, H. Evaluating the feasibility of using Sentinel-2 and Sentinel-3 satellites for high-resolution evapotranspiration estimations. Remote Sens. Environ. 2019, 221, 157–172. [Google Scholar] [CrossRef]
- Gao, F.; Kustas, W.P.; Anderson, M.C. A data mining approach for sharpening thermal satellite imagery over land. Remote Sens. 2012, 4, 3287–3319. [Google Scholar] [CrossRef] [Green Version]
- Bellvert, J.; Jofre-Ĉekalović, C.; Pelechá, A.; Mata, M.; Nieto, H. Feasibility of using the two-source energy balance model (TSEB) with Sentinel-2 and Sentinel-3 images to analyze the spatio-temporal variability of vine water status in a vineyard. Remote Sens. 2020, 12, 2299. [Google Scholar] [CrossRef]
- Guzinski, R.; Nieto, H.; Sandholt, I.; Karamitilios, G. Modelling high-resolution actual evapotranspiration through Sentinel-2 and Sentinel-3 data fusion. Remote Sens. 2020, 12, 1433. [Google Scholar] [CrossRef]
- Flint, A.L.; Childs, S.W. Use of the Priestley-Taylor evaporation equation for soil water limited conditions in a small forest clearcut. Agric. For. Meteorol. 1991, 56, 247–260. [Google Scholar] [CrossRef]
- Mokhtari, A.; Noory, H.; Pourshakouri, F.; Haghighatmehr, P.; Afrasiabian, Y.; Razavi, M.; Fereydooni, F.; Sadeghi Naeni, A. Calculating potential evapotranspiration and single crop coefficient based on energy balance equation using Landsat 8 and Sentinel-2. ISPRS J. Photogramm. Remote Sens. 2019, 154, 231–245. [Google Scholar] [CrossRef]
- Antônio, A.H. Determining regional actual evapotranspiration of irrigated crops and natural vegetation in the São Francisco river basin (Brazil) using remote sensing and Penman-Monteith equation. Remote Sens. 2010, 2, 1287–1319. [Google Scholar] [CrossRef] [Green Version]
- Silva, C.D.O.F.; Teixeira, A.H.D.C.; Manzione, R.L. Agriwater: An R package for spatial modelling of energy balance and actual evapotranspiration using satellite images and agrometeorological data. Environ. Model. Softw. 2019, 120, 104497. [Google Scholar] [CrossRef]
- Hunsaker, D.J.; Fitzgerald, G.J.; French, A.N.; Clarke, T.R.; Ottman, M.J.; Pinter, P.J. Wheat irrigation management using multispectral crop coefficients: I. Crop evapotranspiration prediction. Trans. ASABE 2007, 50, 2017–2033. [Google Scholar] [CrossRef]
- Singh, R.K.; Khand, K.; Kagone, S.; Schauer, M.; Senay, G.B.; Wu, Z. A novel approach for next generation water-use mapping using Landsat and Sentinel-2 satellite data. Hydrol. Sci. J. 2020, 65, 2508–2519. [Google Scholar] [CrossRef]
- 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]
- Chen, Y.; Xia, J.; Liang, S.; Feng, J.; Fisher, J.B.; Li, X.; Li, X.; Liu, S.; Ma, Z.; Miyata, A.; et al. Comparison of satellite-based evapotranspiration models over terrestrial ecosystems in China. Remote Sens. Environ. 2014, 140, 279–293. [Google Scholar] [CrossRef]
- Ambrosone, M.; Matese, A.; Di Gennaro, S.F.; Gioli, B.; Tudoroiu, M.; Genesio, L.; Miglietta, F.; Baronti, S.; Maienza, A.; Ungaro, F.; et al. Retrieving soil moisture in rainfed and irrigated fields using Sentinel-2 observations and a modified OPTRAM approach. Int. J. Appl. Earth Obs. Geoinf. 2020, 89, 102113. [Google Scholar] [CrossRef]
- Liang, W.L.; Hung, F.X.; Chan, M.C.; Lu, T.H. Spatial structure of surface soil water content in a natural forested headwater catchment with a subtropical monsoon climate. J. Hydrol. 2014, 516, 210–221. [Google Scholar] [CrossRef]
- Liang, M.; Pause, M.; Prechtel, N.; Schramm, M. Regionalization of coarse scale soil moisture products using fine-scale vegetation indices-prospects and case study. Remote Sens. 2020, 12, 551. [Google Scholar] [CrossRef] [Green Version]
- El Hajj, M.; Baghdadi, N.; Zribi, M.; Bazzi, H. Synergic use of Sentinel-1 and Sentinel-2 images for operational soil moisture mapping at high spatial resolution over agricultural areas. Remote Sens. 2017, 9, 1292. [Google Scholar] [CrossRef] [Green Version]
- Peters-Lidard, C.D.; Mocko, D.M.; Garcia, M.; Santanello, J.A.; Tischler, M.A.; Moran, M.S.; Wu, Y. Role of precipitation uncertainty in the estimation of hydrologic soil properties using remotely sensed soil moisture in a semiarid environment. Water Resour. Res. 2008, 44, 5. [Google Scholar] [CrossRef] [Green Version]
- Zhang, D.; Zhou, G. Estimation of soil moisture from optical and thermal remote sensing: A review. Sensors 2016, 16, 1308. [Google Scholar] [CrossRef] [Green Version]
- Lin, H. Earth’s Critical Zone and hydropedology: Concepts, characteristics, and advances. Hydrol. Earth Syst. Sci. 2010, 14, 25–45. [Google Scholar] [CrossRef] [Green Version]
- Curcio, J.A.; Petty, C.C. The Near Infrared Absorption Spectrum of Liquid Water. J. Opt. Soc. Am. 1951, 41, 302. [Google Scholar] [CrossRef]
- Weare, B. Monitoring and Predicting Agricultural Drought: A Global Study. Vadose Zone J. 2006, 5, 1293. [Google Scholar] [CrossRef]
- Ghazaryan, G.; Dubovyk, O.; Graw, V.; Kussul, N.; Schellberg, J. Local-scale agricultural drought monitoring with satellite-based multi-sensor time-series. GIScience Remote Sens. 2020, 57, 704–718. [Google Scholar] [CrossRef]
- Han, H.; Bai, J.; Yan, J.; Yang, H.; Ma, G. A combined drought monitoring index based on multi-sensor remote sensing data and machine learning. Geocarto Int. 2019, 36, 1161–1177. [Google Scholar] [CrossRef]
- Engman, E.T. Applications of microwave remote sensing of soil moisture for water resources and agriculture. Remote Sens. Environ. 1991, 35, 213–226. [Google Scholar] [CrossRef]
- Topp, G.C. State of the art of measuring soil water content. Hydrol. Process. 2003, 17, 2993–2996. [Google Scholar] [CrossRef]
- Thavorntam, W.; Tantemsapya, N.; Armstrong, L. A combination of meteorological and satellite-based drought indices in a better drought assessment and forecasting in Northeast Thailand. Nat. Hazards 2015, 77, 1453–1474. [Google Scholar] [CrossRef]
- Wu, B.; Ma, Z.; Yan, N. Agricultural drought mitigating indices derived from the changes in drought characteristics. Remote Sens. Environ. 2020, 244, 111813. [Google Scholar] [CrossRef]
- Sadeghi, M.; Babaeian, E.; Tuller, M.; Jones, S.B. The optical trapezoid model: A novel approach to remote sensing of soil moisture applied to Sentinel-2 and Landsat-8 observations. Remote Sens. Environ. 2017, 198, 52–68. [Google Scholar] [CrossRef] [Green Version]
- Mananze, S.; Pôças, I. Agricultural drought monitoring based on soil moisture derived from the optical trapezoid model in Mozambique. J. Appl. Remote Sens. 2019, 13, 024519. [Google Scholar] [CrossRef]
- Hassanpour, R.; Zarehaghi, D.; Neyshabouri, M.R.; Feizizadeh, B.; Rahmati, M. Modification on optical trapezoid model for accurate estimation of soil moisture content in a maize growing field. J. Appl. Remote Sens. 2020, 14, 034519. [Google Scholar] [CrossRef]
- Ceccato, P.; Flasse, S.; Tarantola, S.; Jacquemoud, S.; Grégoire, J.M. Detecting vegetation leaf water content using reflectance in the optical domain. Remote Sens. Environ. 2001, 77, 22–33. [Google Scholar] [CrossRef]
- Mattia, F.; Balenzano, A.; Satalino, G.; Lovergine, F.; Peng, J.; Wegmuller, U.; Cartus, O.; Davidson, M.W.J.; Kim, S.; Johnson, J.; et al. Sentinel-1 & Sentinel-2 for soil moisture retrieval at field scale. In Proceedings of the IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain, 22–27 July 2018. [Google Scholar]
- Gangat, R.; van Deventer, H.; Naidoo, L.; Adam, E. Estimating soil moisture using Sentinel-1 and Sentinel-2 sensors for dryland and palustrine wetland areas. S. Afr. J. Sci. 2020, 116, 1–9. [Google Scholar] [CrossRef]
- Attarzadeh, R.; Amini, J. Towards an object-based multi-scale soil moisture product using coupled Sentinel-1 and Sentinel-2 data. Remote Sens. Lett. 2019, 10, 619–628. [Google Scholar] [CrossRef]
- Zhuo, W.; Huang, J.; Li, L.; Zhang, X.; Ma, H.; Gao, X.; Huang, H.; Xu, B.; Xiao, X. Assimilating Soil Moisture Retrieved from Sentinel-1 and Sentinel-2 Data into WOFOST Model to Improve Winter Wheat Yield Estimation. Remote Sens. 2019, 11, 1618. [Google Scholar] [CrossRef] [Green Version]
- Pan, H.; Chen, Z.; de Allard, W.; Ren, J. Joint assimilation of leaf area index and soil moisture from sentinel-1 and sentinel-2 data into the WOFOST model for winter wheat yield estimation. Sensors 2019, 19, 3161. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Wang, S.G.; Li, X.; Han, X.J.; Jin, R. Estimation of surface soil moisture and roughness from multi-angular ASAR imagery in theWatershed Allied Telemetry Experimental Research (WATER). Hydrol. Earth Syst. Sci. 2011, 15, 1415–1426. [Google Scholar] [CrossRef] [Green Version]
- Bousbih, S.; Zribi, M.; El Hajj, M.; Baghdadi, N.; Lili-Chabaane, Z.; Gao, Q.; Fanise, P. Soil moisture and irrigation mapping in a semi-arid region, based on the synergetic use of Sentinel-1 and Sentinel-2 data. Remote Sens. 2018, 10, 1953. [Google Scholar] [CrossRef] [Green Version]
- Gao, Q.; Zribi, M.; Escorihuela, M.J.; Baghdadi, N. Synergetic use of sentinel-1 and sentinel-2 data for soil moisture mapping at 100 m resolution. Sensors 2017, 17, 1966. [Google Scholar] [CrossRef] [Green Version]
- Ma, C.; Li, X.; McCabe, M.F. Retrieval of high-resolution soil moisture through combination of Sentinel-1 and Sentinel-2 data. Remote Sens. 2020, 12, 2303. [Google Scholar] [CrossRef]
- Attarzadeh, R.; Amini, J.; Notarnicola, C.; Greifeneder, F. Synergetic use of Sentinel-1 and Sentinel-2 data for soil moisture mapping at plot scale. Remote Sens. 2018, 10, 1285. [Google Scholar] [CrossRef] [Green Version]
- He, B.; Xing, M.; Bai, X. A synergistic methodology for soil moisture estimation in an alpine prairie using radar and optical satellite data. Remote Sens. 2014, 6, 10966–10985. [Google Scholar] [CrossRef] [Green Version]
- Alexakis, D.D.; Mexis, F.D.K.; Vozinaki, A.E.K.; Daliakopoulos, I.N.; Tsanis, I.K. Soil moisture content estimation based on Sentinel-1 and auxiliary earth observation products. A hydrological approach. Sensors 2017, 17, 1455. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Thoma, D.P.; Moran, M.S.; Bryant, R.; Rahman, M.M.; Collins, C.D.H.; Keefer, T.O.; Noriega, R.; Osman, I.; Skrivin, S.M.; Tischler, M.A.; et al. Appropriate scale of soil moisture retrieval from high resolution radar imagery for bare and minimally vegetated soils. Remote Sens. Environ. 2008, 112, 403–414. [Google Scholar] [CrossRef]
- Kim, H.; Park, J.; Yoo, J.; Kim, T.W. Assessment of drought hazard, vulnerability, and risk: A case study foradministrative districts in South Korea. J. Hydro-Environ. Res. 2015, 9, 28–35. [Google Scholar] [CrossRef]
- Dumitraşcu, M.; Mocanu, I.; Mitrică, B.; Dragotă, C.; Grigorescu, I.; Dumitrică, C. The assessment of socio-economic vulnerability to drought in Southern Romania (Oltenia Plain). Int. J. Disaster Risk Reduct. 2018, 27, 142–154. [Google Scholar] [CrossRef]
- Pei, W.; Fu, Q.; Liu, D.; Li, T.; Cheng, K.; Cui, S. A Novel Method for Agricultural Drought Risk Assessment. Water Resour. Manag. 2019, 33, 2033–2047. [Google Scholar] [CrossRef]
- Thomas, T.; Jaiswal, R.K.; Galkate, R.; Nayak, P.C.; Ghosh, N.C. Drought indicators-based integrated assessment of drought vulnerability: A case study of Bundelkhand droughts in central India. Nat. Hazards 2016, 81, 1627–1652. [Google Scholar] [CrossRef]
- Pandey, R.P.; Pandey, A.; Galkate, R.V.; Byun, H.R.; Mal, B.C. Integrating Hydro-Meteorological and Physiographic Factors for Assessment of Vulnerability to Drought. Water Resour. Manag. 2010, 24, 4199–4217. [Google Scholar] [CrossRef]
- Zhang, Q.; Sun, P.; Li, J.; Xiao, M.; Singh, V.P. Assessment of drought vulnerability of the Tarim River basin, Xinjiang, China. Theor. Appl. Climatol. 2015, 121, 337–347. [Google Scholar] [CrossRef]
- Rahman, M.R.; Lateh, H. Meteorological drought in Bangladesh: Assessing, analysing and hazard mapping using SPI, GIS and monthly rainfall data. Environ. Earth Sci. 2016, 75, 1026. [Google Scholar] [CrossRef]
- Pei, W.; Fu, Q.; Liu, D.; Li, T.-X.; Cheng, K.; Cui, S. Spatiotemporal analysis of the agricultural drought risk in Heilongjiang Province, China. Theor. Appl. Climatol. 2018, 133, 151–164. [Google Scholar] [CrossRef]
- Darvishzadeh, R.; Wang, T.; Skidmore, A.; Vrieling, A.; O’Connor, B.; Gara, T.W.; Ens, B.J.; Paganini, M. Analysis of Sentinel-2 and rapidEye for retrieval of leaf area index in a saltmarsh using a radiative transfer model. Remote Sens. 2019, 11, 671. [Google Scholar] [CrossRef] [Green Version]
- Cao, C.X.; Zhao, J.; Gong, P.; Ma, G.R.; Bao, D.M.; Tian, K.; Tian, R.; Niu, Z.G.; Zhang, H.; Xu, M.; et al. Wetland changes and droughts in southwestern China. Geomat. Nat. Hazards Risk 2012, 3, 79–95. [Google Scholar] [CrossRef]
- Murthy, C.S.; Laxman, B.; Sesha Sai, M.V.R. Geospatial analysis of agricultural drought vulnerability using a composite index based on exposure, sensitivity and adaptive capacity. Int. J. Disaster Risk Reduct. 2015, 12, 163–171. [Google Scholar] [CrossRef]
- Mohsenipour, M.; Shahid, S.; Chung, E.-S.; Wang, X.-J. Changing Pattern of Droughts during Cropping Seasons of Bangladesh. Water Resour. Manag. 2018, 32, 1555–1568. [Google Scholar] [CrossRef]
- Jiao, W.; Tian, C.; Chang, Q.; Novick, K.A.; Wang, L. A new multi-sensor integrated index for drought monitoring. Agric. For. Meteorol. 2019, 268, 74–85. [Google Scholar] [CrossRef] [Green Version]
- Mitsch, W.; Gosselink, J. Wetlands: Human Use and Science. Wetlands 2015, 5, 3–24. [Google Scholar]
- Tiner, R.W.; Lang, M.W.; Klemas, V.V. Remote Sensing of Wetlands: Applications and Advances; CRC Press: Boca Raton, FL, USA, 2015; ISBN 9781482237382. [Google Scholar]
- Ozesmi, S.L.; Bauer, M.E. Satellite remote sensing of wetlands. Wetl. Ecol. Manag. 2002, 10, 381–402. [Google Scholar] [CrossRef]
- Kulawardhana, R.W.; Thenkabail, P.S.; Vithanage, J.; Biradar, C.; Islam, M.A.; Gunasinghe, S.; Alankara, R. Evaluation of the wetland mapping methods using Landsat ETM+ and SRTM data. J. Spat. Hydrol. 2007, 7, 2. [Google Scholar]
- Islam, M.A.; Thenkabail, P.S.; Kulawardhana, R.W.; Alankara, R.; Gunasinghe, S.; Edussriya, C.; Gunawardana, A. Semi-automated methods for mapping wetlands using Landsat ETM+ and SRTM data. Int. J. Remote Sens. 2008, 29, 7077–7106. [Google Scholar] [CrossRef]
- Bwangoy, J.R.B.; Hansen, M.C.; Roy, D.P.; De Grandi, G.; Justice, C.O. Wetland mapping in the Congo Basin using optical and radar remotely sensed data and derived topographical indices. Remote Sens. Environ. 2010, 114, 73–86. [Google Scholar] [CrossRef]
- Davranche, A.; Poulin, B.; Lefebvre, G. Mapping flooding regimes in Camargue wetlands using seasonal multispectral data. Remote Sens. Environ. 2013, 138, 165–171. [Google Scholar] [CrossRef] [Green Version]
- Ronchetti, G.; Sona, G. Pan-sharpening methods applied on Sentinel-2 imagery for mapping inland water bodies. Geophys. Res. Abstr. 2018, 20, 7760. [Google Scholar]
- Tuan, V.A.; Hang, L.T.T.; Quang, N.H. Monitoring urban surface water bodies change using mndwi estimated from pan-sharpened optical satellite images. In Proceedings of the FIG Working Week, Hanoi, Vietnam, 22–26 April 2019. [Google Scholar]
- Du, Y.; Zhang, Y.; Ling, F.; Wang, Q.; Li, W.; Li, X. Water bodies’ mapping from Sentinel-2 imagery with Modified Normalized Difference Water Index at 10-m spatial resolution produced by sharpening the swir band. Remote Sens. 2016, 8, 354. [Google Scholar] [CrossRef] [Green Version]
- Gong, P.; Liu, H.; Zhang, M.; Li, C.; Wang, J.; Huang, H.; Clinton, N.; Ji, L.; Li, W.; Bai, Y.; et al. Stable classification with limited sample: Transferring a 30-m resolution sample set collected in 2015 to mapping 10-m resolution global land cover in 2017. Sci. Bull. 2019, 64, 370–373. [Google Scholar] [CrossRef] [Green Version]
- Wang, Z.; Liu, J.; Li, J.; Zhang, D.D. Multi-SpectralWater Index (MuWI): A Native 10-m Multi-SpectralWater Index for accuratewater mapping on sentinel-2. Remote Sens. 2018, 10, 1643. [Google Scholar] [CrossRef] [Green Version]
- Yang, X.; Chen, L. Evaluation of automated urban surface water extraction from Sentinel-2A imagery using different water indices. J. Appl. Remote Sens. 2017, 11, 026016. [Google Scholar] [CrossRef]
- Yesou, H.; Pottier, E.; Mercier, G.; Grizonnet, M.; Haouet, S.; Giros, A.; Faivre, R.; Huber, C.; Michel, J. Synergy of Sentinel-1 and Sentinel-2 imagery for wetland monitoring information extraction from continuous flow of sentinel images applied to water bodies and vegetation mapping and monitoring. In Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China, 10–15 July 2016. [Google Scholar]
- Carlson, B.Z.; Hébert, M.; Van Reeth, C.; Bison, M.; Laigle, I.; Delestrade, A. Monitoring the seasonal hydrology of alpine wetlands in response to snow cover dynamics and summer climate: A novel approach with Sentinel-2. Remote Sens. 2020, 12, 1959. [Google Scholar] [CrossRef]
- Yang, X.; Zhao, S.; Qin, X.; Zhao, N.; Liang, L. Mapping of urban surface water bodies from sentinel-2 MSI imagery at 10 m resolution via NDWI-based image sharpening. Remote Sens. 2017, 9, 596. [Google Scholar] [CrossRef] [Green Version]
- Yang, X.; Qin, Q.; Grussenmeyer, P.; Koehl, M. Urban surface water body detection with suppressed built-up noise based on water indices from Sentinel-2 MSI imagery. Remote Sens. Environ. 2018, 219, 259–270. [Google Scholar] [CrossRef]
- Araya-López, R.A.; Lopatin, J.; Fassnacht, F.E.; Hernández, H.J. Monitoring Andean high altitude wetlands in central Chile with seasonal optical data: A comparison between Worldview-2 and Sentinel-2 imagery. ISPRS J. Photogramm. Remote Sens. 2018, 145, 213–224. [Google Scholar] [CrossRef]
- Kaplan, G.; Avdan, U. Mapping and Monitoring Wetlands Using Sentinel-2 Satellite Imagery. ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci. 2017, IV-4/W4, 271–277. [Google Scholar] [CrossRef] [Green Version]
- Morales, S.; Ruiz, M.; Soria, J.M. Water level fluctuations in Gallocanta Lake (Spain) using Sentinel-2 remote. Preprints 2020, 1–12. [Google Scholar] [CrossRef]
- Bhatnagar, S.; Ghosh, B.; Regan, S.; Naughton, O.; Johnston, P.; Gill, L. Monitoring environmental supporting conditions of a raised bog using remote sensing techniques. Proc. Int. Assoc. Hydrol. Sci. 2018, 380, 9–15. [Google Scholar] [CrossRef]
- Bhaga, T.D.; Dube, T.; Shoko, C. Satellite monitoring of surface water variability in the drought prone Western Cape, South Africa. Phys. Chem. Earth 2020, in press. [Google Scholar] [CrossRef]
- Pena-Regueiro, J.; Sebastiá-Frasquet, M.T.; Estornell, J.; Aguilar-Maldonado, J.A. Sentinel-2 application to the surface characterization of small water bodies in Wetlands. Water 2020, 12, 1487. [Google Scholar] [CrossRef]
- Kaplan, G.; Avdan, U. Sentinel-1 and Sentinel-2 Data Fusion for Mapping and Monitoring Wetlands. Preprints 2018, 2018070244. [Google Scholar] [CrossRef]
- Ludwig, C.; Walli, A.; Schleicher, C.; Weichselbaum, J.; Riffler, M. A highly automated algorithm for wetland detection using multi-temporal optical satellite data. Remote Sens. Environ. 2019, 224, 333–351. [Google Scholar] [CrossRef]
- Sekertekin, A.; Cicekli, S.Y.; Arslan, N. Index-Based Identification of Surface Water Resources Using Sentinel-2 Satellite Imagery. In Proceedings of the 2018 2nd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), Ankara, Turkey, 19–21 October 2018. [Google Scholar]
- Yang, X.; Qin, Q.; Yésou, H.; Ledauphin, T.; Koehl, M.; Grussenmeyer, P.; Zhu, Z. Monthly estimation of the surface water extent in France at a 10-m resolution using Sentinel-2 data. Remote Sens. Environ. 2020, 244, 111803. [Google Scholar] [CrossRef]
- Slagter, B.; Tsendbazar, N.-E.; Vollrath, A.; Reiche, J. Mapping wetland characteristics using temporally dense Sentinel-1 and Sentinel-2 data: A case study in the St. Lucia wetlands, South Africa. Int. J. Appl. Earth Obs. Geoinf. 2020, 86, 102009. [Google Scholar] [CrossRef]
- Mahdianpari, M.; Salehi, B.; Mohammadimanesh, F.; Homayouni, S.; Gill, E. The first wetland inventory map of newfoundland at a spatial resolution of 10 m using sentinel-1 and sentinel-2 data on the Google Earth Engine cloud computing platform. Remote Sens. 2019, 11, 43. [Google Scholar] [CrossRef] [Green Version]
- Whyte, A.; Ferentinos, K.P.; Petropoulos, G.P. A new synergistic approach for monitoring wetlands using Sentinels -1 and 2 data with object-based machine learning algorithms. Environ. Model. Softw. 2018, 104, 40–54. [Google Scholar] [CrossRef] [Green Version]
- Chatziantoniou, A.; Petropoulos, G.P.; Psomiadis, E. Co-Orbital Sentinel 1 and 2 for LULC mapping with emphasis on wetlands in a mediterranean setting based on machine learning. Remote Sens. 2017, 9, 1259. [Google Scholar] [CrossRef] [Green Version]
Band No. | Spatial Resolution (m) | Sentinel-2 A | Sentinel-2 B | SNR @ Lref | ||
---|---|---|---|---|---|---|
Central Wavelength (nm) | Band WIDTH (nm) | Central Wavelength (nm | Band Width (nm) | |||
B1 | 60 | 442.7 | 21 | 442.2 | 21 | 129 |
B2 | 10 | 492.4 | 66 | 492.1 | 66 | 158 |
B3 | 10 | 559.8 | 36 | 559.0 | 36 | 168 |
B4 | 10 | 664.6 | 31 | 664.9 | 31 | 148 |
B5 | 20 | 704.1 | 15 | 103.8 | 16 | 117 |
B6 | 20 | 740.5 | 15 | 739.1 | 15 | 89 |
B7 | 20 | 782.8 | 20 | 779.7 | 20 | 105 |
B8 | 10 | 832.8 | 106 | 832.9 | 106 | 174 |
B8a | 20 | 864.7 | 21 | 864.0 | 22 | 72 |
B9 | 60 | 945.1 | 20 | 442.2 | 21 | 114 |
B10 | 60 | 1373.5 | 31 | 943.2 | 21 | 50 |
B11 | 20 | 1613.7 | 91 | 1610.4 | 94 | 100 |
B12 | 20 | 2202.4 | 175 | 2185.7 | 185 | 100 |
Aspect | Method | References |
---|---|---|
Remote Sensing of Vegetation in Drought | Extended super-resolution convolutional neural network (ESRCNN) | [67] |
Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) | [74] | |
SAFYE | [80] | |
PROSPECT model | [93] | |
Red-Edge Position (REP) | [99] | |
Remote Sensing of Land Use and Land Cover Change in Drought | The random forest wrapper approach | [101] |
Support vector machine | [102] | |
Random forest | [102] | |
k-Nearest Neighbor | [102] | |
Pixel R-CNN | [107] | |
Remote Sensing of Evapotranspiration in Drought | Surface energy balance models | [113,114] |
FAO-56 Penman-Montecito (FAO-56 PM) | [116,117,121] | |
Soil water balance model | [122] | |
PBIM | [132] | |
SADFAT | [133] | |
Data Mining Sharpening | [134,135,136] | |
Multi-Sensor Data Fusion approach (MSDF-ET) | [139] | |
Simple Algorithm For ET Retrieving (SAFER) | [140,141] | |
Remote Sensing of Soil Moisture in Drought | Change Detection Method | [65,169,172,177] |
OPtical TRApezoid Model (OPTRAM) | [146,161,162,163,164] | |
ANN | [149] | |
Water Cloud Model (WCM) | [149,168,170,171,175,176] | |
Integral Equation Model (IEM) | [170,175] | |
Cost Function | [173] | |
Support Vector Regression | [174] | |
Remote Sensing of Surface Water and Wetland Analysis in Drought | ML algorithm-Random Forest da | [28,219,220] |
Unsupervised Classification | [47,208,209,210] | |
Supervised Classification | [47,209,211] | |
Object-based Classification | [47,209,214] | |
Gram-Schmidt pan sharpening methods | [198] | |
OTSU algorithm | [200,203,216] | |
Index-based Classification | [209] | |
Change Detection using Vegetation Indices | [212,213] | |
Tile-based Image Thresholding | [215] | |
Rule-based Super Pixel (RBSP) approach | [217] | |
ML algorithm-Support Vector Machine (SVM) | [220,221] |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Varghese, D.; Radulović, M.; Stojković, S.; Crnojević, V. Reviewing the Potential of Sentinel-2 in Assessing the Drought. Remote Sens. 2021, 13, 3355. https://doi.org/10.3390/rs13173355
Varghese D, Radulović M, Stojković S, Crnojević V. Reviewing the Potential of Sentinel-2 in Assessing the Drought. Remote Sensing. 2021; 13(17):3355. https://doi.org/10.3390/rs13173355
Chicago/Turabian StyleVarghese, Dani, Mirjana Radulović, Stefanija Stojković, and Vladimir Crnojević. 2021. "Reviewing the Potential of Sentinel-2 in Assessing the Drought" Remote Sensing 13, no. 17: 3355. https://doi.org/10.3390/rs13173355