Using Sentinel-1A DInSAR interferometry and Landsat 8 data for monitoring water level changes in two lakes in Crete, Greece

References

• Agapiou A, Alexakis DD, Sarris A, Hadjimitsis DG. 2015. Linear 3-D transformations of Landsat 5 TM satellite images for the enhancement of archaeological signatures during the phenological cycle of crops. Int J Remote Sens. 36:20–35.10.1080/01431161.2014.990646
   Web of Science ®Google Scholar
• Alexakis DD, Sarris A. 2014. Integrated GIS and remote sensing analysis for landfill sitting in Western Crete. Environ Earth Sci. 72:467–482.10.1007/s12665-013-2966-y
   Web of Science ®Google Scholar
• Alexakis DD, Mexis F-DK, Vozinaki A-EK, Daliakopoulos IN, Tsanis IK. 2017. Soil moisture content estimation based on Sentinel-1 and auxiliary earth observation products. A hydrological approach. Sensors (Switzerland). 17: 1455.
   Google Scholar
• Alsdorf DE, Melack JM, Dunne T, Mertes LAK, Hess LL, Smith LC. 2000. Interferometric radar measurements of water level changes on the Amazon flood plain. Nature. 404:174–177.10.1038/35004560
   PubMed Web of Science ®Google Scholar
• Brisco B, Short N, van der Sanden J, Landry R, Raymond D. 2009. A semi-automated tool for surface water mapping with RADARSAT-1. Can J Remote Sens. 35:336–344.10.5589/m09-025
   Web of Science ®Google Scholar
• Chen CW, Zebker HA. 2002. Phase unwrapping for large SAR interferograms: statistical segmentation and generalized network models. IEEE Trans Geosci Remote Sens. 40: 1709–1719.
   Google Scholar
• Crétaux JF, Jelinski W, Calmant S, Kouraev A, Vuglinski V, Bergé-Nguyen M, Gennero MC, Nino F, Abarca Del Rio R, Cazenave A, et al. 2011. SOLS: a lake database to monitor in the near real time water level and storage variations from remote sensing data. Adv Space Res. 47:1497–1507.10.1016/j.asr.2011.01.004
   Web of Science ®Google Scholar
• Dai K, Li Z, Tomás R, Liu G, Yu B, Wang X, Cheng H, Chen J, Stockamp J. 2016. Monitoring activity at the Daguangbao mega-landslide (China) using Sentinel-1 TOPS time series interferometry. Remote Sens Environ. 186:501–513.10.1016/j.rse.2016.09.009
   Web of Science ®Google Scholar
• Ding XW, Li XF. 2011. Monitoring of the water-area variations of Lake Dongting in China with ENVISAT ASAR images. Int J Appl Earth Obs Geoinformation. 13:894–901.10.1016/j.jag.2011.06.009
   Web of Science ®Google Scholar
• Ferretti A, Prati C, Rocca F. 2001. Permanent scatterers in SAR interferometry. IEEE Trans Geosci Remote Sens. 39:8–20.10.1109/36.898661
   Web of Science ®Google Scholar
• Ferretti A, Monti-guarnieri A, Prati C, Rocca F, Massonnet D (CNES). 2007. InSAR processing: a mathematical approach (Part C). InSAR Principles: Guidelines for SAR Interferometry Processing and Interpretation. 120–234.
   Google Scholar
• Goldstein RM, Engelhardt H, Kamb B, Frolich RM. 1993. Satellite radar interferometry for monitoring ice sheet motion: application to an antarctic ice stream. Science. 262.
   PubMed Web of Science ®Google Scholar
• Gradstein SR, Smittenberg JH. 1977. Vegetatio. 34:65–86.10.1007/BF00054476
   Google Scholar
• Hellwich O, Ebner H. 2000. Geocoding SAR interferograms by least squares adjustment. ISPRS J Photogramm Remote Sens. 55:277–288.10.1016/S0924-2716(00)00025-3
   Web of Science ®Google Scholar
• Hong SH, Wdowinski S, Kim SW, Won JS. 2010. Multi-temporal monitoring of wetland water levels in the Florida Everglades using interferometric synthetic aperture radar (InSAR). Remote Sens Environ. 114:2436–2447.10.1016/j.rse.2010.05.019
   Web of Science ®Google Scholar
• Kasischke ES, Smith KB, Bourgeau-Chavez LL, Romanowicz EA, Brunzell S, Richardson CJ. 2003. Effects of seasonal hydrologic patterns in south Florida wetlands on radar backscatter measured from ERS-2 SAR imagery. Remote Sens Environ. 88:423–441.10.1016/j.rse.2003.08.016
   Web of Science ®Google Scholar
• Kim Sang-Wan, Hong Sang-Hoon, Won Joong-Sun. 2005. An application of L-band synthetic aperture radar to tide height measurement. IEEE Trans Geosci Remote Sens. 43:1472–1478.
   Web of Science ®Google Scholar
• Kim SW, Wdowinski S, Amelung F, Dixon TH, Won JS. 2013. Interferometric coherence analysis of the Everglades wetlands, South Florida. IEEE Trans Geosci Remote Sens. 51:5210–5224.10.1109/TGRS.2012.2231418
   Web of Science ®Google Scholar
• Kim J-W, Lu Z, Jones JW, Shum CK, Lee H, Jia Y. 2014. Monitoring Everglades freshwater marsh water level using L-band synthetic aperture radar backscatter. Remote Sens Environ. 150:66–81.10.1016/j.rse.2014.03.031
   Web of Science ®Google Scholar
• Martinis S, Twele A., Voigt S. 2009. Towards operational near real-time flood detection using a split-based automatic thresholding procedure on high resolution TerraSAR-X data. Nat Hazards Earth Syst Sci. 9:303–314.10.5194/nhess-9-303-2009
   Web of Science ®Google Scholar
• Moran M, McElroy S, Watts JM, Peters-Lidard CD. 2006. Radar remote sensing for estimation of surface soil moisture at the watershed scale. Model Remote Sens Appl Agric (US and Mexico). 91–106.
   Google Scholar
• O’Grady D, Leblanc M, Bass A. 2014. The use of radar satellite data from multiple incidence angles improves surface water mapping. Remote Sens Environ. 140:652–664.10.1016/j.rse.2013.10.006
   Web of Science ®Google Scholar
• Pan F, Liao J, Li X, Guo H. 2013. Application of the inundation area-lake level rating curves constructed from the SRTM DEM to retrieving lake levels from satellite measured inundation areas. Comput Geosci. 52:168–176.10.1016/j.cageo.2012.09.024
   Web of Science ®Google Scholar
• Peng D, Xiong L, Guo S, Shu N. 2005. Study of Dongting Lake area variation and its influence on water level using MODIS data / Etude de la variation de la surface du Lac Dongting et de son influence sur le niveau d’eau, grâce à des données MODIS. Hydrol Sci J. 50:31–44.
   Web of Science ®Google Scholar
• Qiu Z, Yue J, Wang X, Yue S, Wong FH. 2016. Spaceborne SAR imaging algorithm for coherence optimized. Deng Y, editor. PloS one. 11:e0148823.
   Google Scholar
• Ramillien G, Frappart F, Seoane L. 2014. Application of the regional water mass variations from GRACE satellite gravimetry to large-scale water management in Africa. Remote Sens. 6:7379–7405.10.3390/rs6087379
   Google Scholar
• Raucoules D, Le Cozannet G, de Michele M, Capo S. 2017. Observing water-level variations from space-borne high-resolution Synthetic Aperture Radar (SAR) image correlation. Geocarto International. 6049:1–11.10.1080/10106049.2017.1323962
   Google Scholar
• Reschke J, Bartsch A, Schlaffer S, Schepaschenko D. 2012. Capability of C-Band SAR for operational wetland monitoring at high latitudes. Remote Sens. 4:2923–2943.10.3390/rs4102923
   Google Scholar
• Schmitt A. 2016. Multiscale and multidirectional multilooking for SAR image enhancement. IEEE Trans Geosci Remote Sens. 54:5117–5134.10.1109/TGRS.2016.2555624
   Web of Science ®Google Scholar
• Schwatke C, Dettmering D, Bosch W, Seitz F. 2015. DAHITI – an innovative approach for estimating water level time series over inland waters using multi-mission satellite altimetry. Hydrol Earth Syst Sci. 19:4345–4364.10.5194/hess-19-4345-2015
   Web of Science ®Google Scholar
• Simon RN, Tormos T, Danis P-A. 2015. Very high spatial resolution optical and radar imagery in tracking water level fluctuations of a small inland reservoir. Int J Appl Earth Obs Geoinformation. 38:36–39.10.1016/j.jag.2014.12.007
   Web of Science ®Google Scholar
• Tong X, Sandwell DT, Fialko Y. 2010. Coseismic slip model of the 2008 Wenchuan earthquake derived from joint inversion of interferometric synthetic aperture radar, GPS, and field data. J Geophys Res. 115:B04314.
   Web of Science ®Google Scholar
• Wdowinski S, Kim S-W, Amelung F, Dixon TH, Miralles-Wilhelm F, Sonenshein R. 2008. Space-based detection of wetlands’ surface water level changes from L-band SAR interferometry. Remote Sens Environ. 112:681–696.10.1016/j.rse.2007.06.008
   Web of Science ®Google Scholar
• White L, Brisco B, Dabboor M, Schmitt A, Pratt A. 2015. A collection of SAR methodologies for monitoring wetlands. [place unknown].
   Google Scholar
• Yoon G-W, Kim S-W, Lee Y-W, Won J-S, Yoon G, Kim S, Lee Y, Won J. 2013. Remote sensing letters measurement of the water level in reservoirs from TerraSAR-X SAR interferometry and amplitude images measurement of the water level in reservoirs from TerraSAR-X SAR interferometry and amplitude images. Remote Sens Lett. 4:446–454.10.1080/2150704X.2012.747708
   Web of Science ®Google Scholar
• Zhang G, Xie H, Kang S, Yi D, Ackley SF. 2011. Monitoring lake level changes on the Tibetan Plateau using ICESat altimetry data (2003–2009). Remote Sens Environ. 115:1733–1742.10.1016/j.rse.2011.03.005
   Web of Science ®Google Scholar
• Zhang M, Li Z, Tian B, Zhou J, Tang P. 2016. The backscattering characteristics of wetland vegetation and water-level changes detection using multi-mode SAR: a case study. Int J Appl Earth Obs Geoinformation. 45:1–13.
   Web of Science ®Google Scholar
• Zhou X, Chang N-B, Li S. 2009. Applications of SAR interferometry in earth and environmental science research (Basel, Switzerland). Sensors. 9:1876–1912.
   PubMed Web of Science ®Google Scholar
• Zhu W, Jia S, Lv A. 2014. Monitoring the fluctuation of lake Qinghai using multi-source remote sensing data. Remote Sens. 6:10457–10482.10.3390/rs61110457
   Google Scholar