Remote sensing techniques for water management and climate change monitoring in drought areas: case studies in Egypt and Tunisia

References

• Abdel-Kader, F. H. (2019). Assessment and monitoring of land degradation in the northwest coast region, Egypt using Earth observations data. Egyptian Journal of Remote Sensing and Space Science, 22(2), 2. https://doi.org/10.1016/j.ejrs.2018.02.001
   Web of Science ®Google Scholar
• Ahlström, A., Raupach, M. R., Schurgers, G., Smith, B., Arneth, A., Jung, M., Reichstein, M., Canadell, J. G., Friedlingstein, P., Jain, A. K., Kato, E., Poulter, B., Sitch, S., Stocker, B. D., Viovy, N., Wang, Y. P., Wiltshire, A., Zaehle, S., & Zeng, N. (2015). The dominant role of semi-arid ecosystems in the trend and variability of the land CO 2 sink. Science, 348(6237), 6237. https://doi.org/10.1126/science.aaa1668
   Web of Science ®Google Scholar
• Akinyemi, F. O. (2021). Vegetation trends, drought severity and land use‐land cover change during the growing season in semi‐arid contexts. Remote Sensing, 13(5), 836. https://doi.org/10.3390/rs13050836
   Web of Science ®Google Scholar
• Allam, M., Bakr, N., & Elbably, W. (2019). Multi-Temporal assessment of land use/land cover change in arid region based on landsat satellite imagery: Case Study in Fayoum Region, Egypt. Remote Sensing Applications: Society and Environment, 14. https://doi.org/10.1016/j.rsase.2019.02.002
   Google Scholar
• Anna, B., Mattia, F., Satalino, G., & Davidson, M. W. J. (2011). Dense temporal series of C- and L-band SAR data for soil moisture retrieval over agricultural crops. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 4(2). https://doi.org/10.1109/JSTARS.2010.2052916
   Web of Science ®Google Scholar
• Attema, E. P. W., & Fawwaz, T. U. (1978). Vegetation modeled as a water cloud. Radio Science, 13(2), 2. https://doi.org/10.1029/RS013i002p00357
   Web of Science ®Google Scholar
• Baghdadi, N., Gaultier, S., & King, C. (2002). Retrieving surface roughness and soil moisture from synthetic aperture radar (SAR) data using neural networks. Canadian Journal of Remote Sensing, 28(5), 701–23. https://doi.org/10.5589/m02-066
   Web of Science ®Google Scholar
• Bastiaanssen, W. G. M., Noordman, E. J. M., Pelgrum, H., Davids, G., Thoreson, B. P., & Allen, R. G. (2005). SEBAL Model with remotely sensed dta to improve water-resources management under actual field conditions. Journal of Irrigation and Drainage Engineering, 131(1), 1. https://doi.org/10.1061/(asce)0733-9437
   Web of Science ®Google Scholar
• Bhaskar, R. N. (2014). Comparative evaluation of different potential evapotranspiration estimation approaches. International Journal of Research in Engineering and Technology, 03(6), 544–552. https://doi.org/10.15623/ijret.2014.0306102
   Google Scholar
• Boulet, G., Delogu, E., Chebbi, W., Rafi, Z., Le Dantec, V., Mallick, K., Mougenot, B., Olioso, A., Zribi, M., Lili-Chabaane, Z., Er-Raki, S., Merlin O. (2019). Evapotranspiration and evaporation/transpiration retrieval using dual-source surface energy balance models integrating VIS/NIR/TIR data with satellite surface soil moisture information. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 42. https://doi.org/10.5194/isprs-archives-XLII-3-W6-9-2019.
   Google Scholar
• Chen, Y., Dengsheng, L., Moran, E., Batistella, M., Vieira Dutra, L., Del Arco Sanches, I., Bicudo da Silva, R. F., Huang, J., José Barreto Luiz, A., & Falcão de Oliveira, M. A. (2018). Mapping croplands, cropping patterns, and crop types using MODIS time-series data. International Journal of Applied Earth Observation and Geoinformation, 69. https://doi.org/10.1016/j.jag.2018.03.005
   PubMed Web of Science ®Google Scholar
• de Jong, R., de Bruin, S., de Wit, A., Schaepman, M. E., & Dent, D. L. (2011). Analysis of monotonic greening and browning trends from global NDVI time-series. Remote Sensing of Environment, 115(2),692–702. Elsevier. https://doi.org/10.1016/J.RSE.2010.10.011
   Web of Science ®Google Scholar
• Del Frate, F., Ferrazzoli, P., Schiavon, G., Ahlström, A., Raupach, M. R., Schurgers, G., Smith, B., Arneth, A., Jung, M., Reichstein, M., Canadell, J. G., Friedlingstein, P., Jain, A. K., Kato, E., Poulter, B., Sitch, S., Stocker, B. D., Viovy, N., Wang, Y. P., … Zeng, N. (2003). Retrieving Soil moisture and agricultural variables by microwave radiometry using neural networks. Remote Sensing of Environment, 84(2), 174–183. https://doi.org/10.1016/S0034-4257(02)00105-0
   Web of Science ®Google Scholar
• Dobson, M. C., Ulaby, F. T., Hallikainen, M. T., & El-Rayes, M. A. (1985). Microwave dielectric behavior of wet soil-Part II: Dielectric mixing models. IEEE Transactions on Geoscience and Remote SensingGE-, 23, 1. https://doi.org/10.1109/TGRS.1985.289498
   Web of Science ®Google Scholar
• Ekundayo, O. Y., Okogbue, E. C., Akinluyi, F. O., Kalumba, A. M., & Orimoloye, I. R. (2021). Spatiotemporal drought assessment using vegetation health index and standardized precipitation index over Sudano-Sahelian region of Nigeria. African Geographical Review, 40(4), 4. https://doi.org/10.1080/19376812.2020.1841658
   Web of Science ®Google Scholar
• El-Shirbeny, Alsersy, A., Saleh, N. H., Abu-Taleb, K. A., & El-Shirbeny, M. A. (2015). Changes in irrigation water consumption in the Nile Delta of Egypt assessed by remote sensing. Arabian Journal of Geosciences, 8(12), 12. https://doi.org/10.1007/s12517-015-2005-2
   Web of Science ®Google Scholar
• Fayech, D., & Tarhouni, J. (2021). Climate variability and its effect on normalized difference vegetation index (NDVI) using remote sensing in semi-arid Area. Modeling Earth Systems and Environment, 7(3), 3. https://doi.org/10.1007/s40808-020-00896-6
   Web of Science ®Google Scholar
• Fishar, M. R. (2018). Nile Delta (Egypt). In The Wetland Book II: Distribution, Description, and Conservation The Wetland Book. Springer (Vol. 2, pp. 1251–1260). Dordrecht: Springer Nature. https://doi.org/10.1007/978-94-007-4001-3_216
   Google Scholar
• Fung, A. K. (1994. . Microwave Scattering and Emission Models and Their Applications (Artech House Publishers) 0890065233 .
   Google Scholar
• Gao, Q., Zribi, M., Jose Escorihuela, M., & Baghdadi, N. (2017). Synergetic use of Sentinel-1 and Sentinel-2 data for soil moisture mapping at 100 m resolution. Sensors (Switzerland), 17(9), 1966. https://doi.org/10.3390/s17091966
   PubMed Web of Science ®Google Scholar
• Gidey, E., Dikinya, O., Sebego, R., Segosebe, E., & Zenebe, A. (2018). Modeling the spatio-temporal meteorological drought characteristics using the standardized precipitation index (SPI) in Raya and its environs, Northern Ethiopia. Earth Systems and Environment, 2(2). https://doi.org/10.1007/s41748-018-0057-7
   Google Scholar
• Giorgi, F., & Lionello, P. (2008). Climate change projections for the Mediterranean region. Global and Planetary Change, 63(2–3), 2–3. https://doi.org/10.1016/j.gloplacha.2007.09.005
   Web of Science ®Google Scholar
• Hachani, A., Ouessar, M., Paloscia, S., Santi, E., & Pettinato, S. (2019). Soil moisture retrieval from Sentinel-1 acquisitions in an arid environment in Tunisia: Application of artificial neural networks techniques. International Journal of Remote Sensing, 40(24), 24. https://doi.org/10.1080/01431161.2019.1629503
   Web of Science ®Google Scholar
• Hamza, W. 2006. The Nile Estuary. Handbook of Environmental Chemistry, Volume 5, Water Pollution 5 (PART H). https://doi.org/10.1007/698-5-025
   Google Scholar
• Hesham, Y.H., Khalil, A.A., M. El-Sayed, M. (2015). Atlas for Drought Monitoring over Egypt 978-977-302-445-1 . https://www.academia.edu/17344925/ATLAS_FOR_DROUGHT_MONITORING_OVER_EGYPT
   Google Scholar
• Hornáček, M., Wagner, W., Sabel, D., Linh Truong, H., Snoeij, P., Hahmann, T., Diedrich, E., & Doubková, M. (2012). Potential for high resolution systematic global surface soil moisture retrieval via change detection using Sentinel-1. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5(4), 4. https://doi.org/10.1109/JSTARS.2012.2190136
   Web of Science ®Google Scholar
• Huete, A., Didan, K., Miura, T., Rodriguez, E. P., Gao, X., & Ferreira, L. G. (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83(1–2), 1–2. https://doi.org/10.1016/S0034-4257(02)00096-2
   Web of Science ®Google Scholar
• IPCC. (2019). Climate Change, Desertification, Land Degradation, Sustainable Land Management, Food Security, and Greenhouse gas fluxes in Terrestrial Ecosystems. IPCC. https://www.ipcc.ch/site/assets/uploads/2019/11/SRCCL-Full-Report-Compiled-191128.pdf.
   Google Scholar
• Jaafar, H. H., & Ahmad, F. A. (2015). Crop yield prediction from remotely sensed vegetation indices and primary productivity in arid and semi-arid lands. International Journal of Remote Sensing, 36(18), 18. https://doi.org/10.1080/01431161.2015.1084434
   Web of Science ®Google Scholar
• Javzandulam, T., Tateishi, R., & Sanjaa, T. (2005). Analysis of vegetation indices for monitoring vegetation degradation in semi-arid and arid areas of Mongolia. International Journal of Environmental Studies, 62(2), 2. https://doi.org/10.1080/00207230500034123
   Google Scholar
• JICA, Yachiyo Engineering Co., Kaihatsu Management Consulting, and Corporation Ingérosec. 2015. “Project on Regional Development Planning of the Southern Region in the Republic of Tunisia Final Report Project on Regional Development Planning of the Southern Region in the Republic of Tunisia Final Report”, no. Mdici. https://openjicareport.jica.go.jp/pdf/12246732.pdf.
   Google Scholar
• John, R., Chen, J., Nan, L., Guo, K., Liang, C., Wei, Y., Noormets, A., Keping, M., & Han, X. (2008). Predicting plant diversity based on remote sensing products in the semi-arid region of inner Mongolia. Remote Sensing of Environment, 112(5), 2018–2032. https://doi.org/10.1016/j.rse.2007.09.013
   Web of Science ®Google Scholar
• Justice, C. O., Vermote, E., Townshend, J. R. G., Defries, R., Roy, D. P., Hall, D. K., Salomonson, V. V., Privette, J. L., Riggs, G., Strahler, A., Lucht, W., Myneni, R. B., Knyazikhin, Y., Running, S. W., Nemani, R. R., Wan, Z., Huete, A. R., van Leeuwen, W., Wolfe, R. E., … Barnsley, M. J. (1998). The moderate resolution imaging spectroradiometer (MODIS): Land remote sensing for global change research. IEEE Transactions on Geoscience and Remote Sensing, 36(4), 4. https://doi.org/10.1109/36.701075
   Web of Science ®Google Scholar
• Khan, M. S., Baik, J., & Choi, M. (2020). Inter-Comparison of evapotranspiration datasets over heterogeneous landscapes across Australia. Advances in Space Research, 66(3), 3. https://doi.org/10.1016/j.asr.2020.04.037
   Web of Science ®Google Scholar
• Kogan, F. N. (1995). Application of vegetation index and brightness temperature for drought detection. Advances in Space Research, 15(11), 91–100. https://doi.org/10.1016/0273-1177(95)00079-T
   PubMed Web of Science ®Google Scholar
• Kogan, F. N. (2001). Operational space technology for global vegetation assessment. Bulletin of the American Meteorological Society, 82(9), 1949–1964. https://doi.org/10.1175/1520-0477(2001)082<1949:OSTFGV>2.3.CO;2
   Web of Science ®Google Scholar
• Kottek, M., Grieser, J., Beck, C., Rudolf, B., & Rubel, F. (2006). World Map of the Köppen-Geiger Climate Classification Updated. Meteorologische Zeitschrift, 15(3), 3. https://doi.org/10.1127/0941–2948/2006/0130
   Web of Science ®Google Scholar
• Lal, R. (2004 Carbon Sequestration in Dryland Ecosystems Environmental Management). . In (Vol. 33 4 , pp. 528–544). . http://link.springer.com/10.1007/s00267-003-9110-9 doi:10.1007/s00267-003-9110-9
   PubMed Web of Science ®Google Scholar
• Lionello, P., & Scarascia, L. (2018). The relation between climate change in the Mediterranean region and global warming. Regional Environmental Change, 18(5), 5. https://doi.org/10.1007/s10113-018-1290-1
   Web of Science ®Google Scholar
• Mckee, T. B. T. B., Doesken, N. J. N. J., Kleist, J., Doesken, N. J. N. J., Kleist, J., Mckee, T. B. T. B., Nolan J. N.J, D., & Kleist, J. 1993. “The relationship of drought frequency and duration to time scales. Eighth Conference on Applied Climatology. American Meteorological Society, Boston”. Eighth Conference on Applied Climatology, no. 1.
   Google Scholar
• MedECC. (2020). Climate and environmental change in the Mediterranean basin – current situation and risks for the future. First Mediterranean assessment report. Climate and Environmental Change in the Mediterranean Basin – Current Situation and Risks for the Future (pp.632). ISBN 978-2-9577416-0-1; doi: 10.5281/zenodo.4768833
   Google Scholar
• Mingzhu, H., Kimball, J. S., Yonghong, Y., Running, S. W., Guan, K., Moreno, A., Xiaocui, W., & Maneta, M. (2019). Satellite data-driven modeling of field scale evapotranspiration in croplands using the MOD16 algorithm framework. Remote Sensing of Environment, 230. https://doi.org/10.1016/j.rse.2019.05.020
   Web of Science ®Google Scholar
• Ning, Z., Hong, Y., Qin, Q., & Liu, L. (2013). VSDI: A visible and shortwave infrared drought index for monitoring soil and vegetation moisture based on optical remote sensing. International Journal of Remote Sensing, 34, 13. https://doi.org/10.1080/01431161.2013.779046
   Web of Science ®Google Scholar
• Olmos-Trujillo, E., González-Trinidad, J., Júnez-Ferreira, H., Pacheco-Guerrero, A., Bautista-Capetillo, C., Avila-Sandoval, C., & Galván-Tejada, E. (2020). Spatio-temporal response of vegetation indices to rainfall and temperature in a semiarid region. Sustainability (Switzerland), 12(5). https://doi.org/10.3390/su12051939
   Google Scholar
• Paloscia, S., Pettinato, S., Santi, E., Notarnicola, C., Pasolli, L., & Reppucci, A. (2013). Soil moisture mapping using Sentinel-1 images: algorithm and preliminary validation. Remote Sensing of Environment, 134. https://doi.org/10.1016/j.rse.2013.02.027
   Web of Science ®Google Scholar
• Qiaozhen, M., Zhao, M., & Running, S. W. (2011). Improvements to a MODIS global terrestrial evapotranspiration algorithm. Remote Sensing of Environment, 115, 8. https://doi.org/10.1016/j.rse.2011.02.019
   Web of Science ®Google Scholar
• Rodríguez-Fernández, N. J., Aires, F., Richaume, P., Kerr, Y. H., Prigent, C., Kolassa, J., Cabot, F., Jiménez, C., Mahmoodi, A., & Drusch, M. (2015). Soil moisture retrieval using neural networks: Application to SMOS. IEEE Transactions on Geoscience and Remote Sensing, 53(11), 5991–6007. https://doi.org/10.1109/TGRS.2015.2430845
   Web of Science ®Google Scholar
• Roy, D. P., Wulder, M. A., Loveland, T. R., Woodcock, C. E., Allen, R. G., Anderson, M. C., Helder, D., Irons, J. R., Johnson, D. M., Kennedy, R., Scambos, T. A., Schaaf, C. B., Schott, J. R., Sheng, Y., Vermote, E. F., Belward, A. S., Bindschadler, R., Cohen, W. B., Gao, F., … Zhu, Z. (2014). Landsat-8: Science and product vision for terrestrial global change research. Remote Sensing of Environment, 145, 154–172. https://doi.org/10.1016/j.rse.2014.02.001
   Web of Science ®Google Scholar
• Ryan, J. G., & Spencer, D. C. (2001). Future challenges and opportunities for agricultural development in the semi-arid tropics. Patanchura, 502, 324 http://oar.icrisat.org/id/eprint/1107.
   Google Scholar
• Santi, E. (2016). Neural networks applications for the remote sensing of hydrological parameters. Artificial Neural Networks - Models and Applications. https://doi.org/10.5772/63165
   Google Scholar
• Santi, E., Paloscia, S., Pettinato, S., Brocca, L., & Ciabatta, L. (2016). Robust assessment of an operational algorithm for the retrieval of soil moisture from AMSR-E data in central Italy. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(6), 6. https://doi.org/10.1109/JSTARS.2016.2575361
   Web of Science ®Google Scholar
• Santi, E., Paloscia, S., Pettinato, S., & Fontanelli, G. (2016). Application of artificial neural networks for the soil moisture retrieval from active and passive microwave spaceborne sensors. International Journal of Applied Earth Observation and Geoinformation, 48. https://doi.org/10.1016/j.jag.2015.08.002
   Web of Science ®Google Scholar
• Santi, E., Pettinato, S., Paloscia, S., Pampaloni, P., MacElloni, G., & Brogioni, M. (2012). An algorithm for generating soil moisture and snow depth maps from microwave spaceborne radiometers: HydroAlgo. Hydrology and Earth System Sciences, 16(10), 10. https://doi.org/10.5194/hess-16-3659-2012
   Web of Science ®Google Scholar
• Senay, G.B., Velpuri, N.M., Bohms, S., Budde, M., Young, C., Rowland, J., Verdin, J.P. (2015). Drought Monitoring and Assessment: Remote Sensing and Modeling Approaches for the Famine Early Warning Systems Network. In Shroder, John F., Di Baldassarre, Giuliano, Paron, Paolo (Eds.), Hydro-Meteorological Hazards, Risks and Disasters (Elsevier) 233–262. ISBN: 9780123948465. https://doi.org/10.1016/B978-0-12-394846-5.00009-6
   Google Scholar
• Sommer, R., Ryan, J., Masri, S., Singh, M., & Diekmann, J. (2011). Effect of shallow tillage, moldboard plowing, straw management and compost addition on soil organic matter and nitrogen in a dryland barley/wheat-vetch rotation. Soil and Tillage Research, 115–116. https://doi.org/10.1016/j.still.2011.06.003
   Web of Science ®Google Scholar
• Sun, D., & Kafatos, M. (2007). Note on the NDVI-LST relationship and the use of temperature-related drought Indices over North America. Geophysical Research Letters, 34(24), 24. https://doi.org/10.1029/2007GL031485
   Web of Science ®Google Scholar
• Suzuki. 2020. “Contract signed for new copernicus ROSE-L Mission.” https://www.esa.int/Applications/Observing_the_Earth/Copernicus/Contract_signed_for_new_Copernicus_ROSE-L_mission
   Google Scholar
• Tarpanelli, A., Santi, E., Tourian, M. J., Filippucci, P., Amarnath, G., & Brocca, L. (2019). Daily river discharge estimates by merging satellite optical sensors and radar altimetry through artificial neural network. IEEE Transactions on Geoscience and Remote Sensing, 57(1), 1. https://doi.org/10.1109/TGRS.2018.2854625
   Web of Science ®Google Scholar
• Tramblay, Y., Koutroulis, A., Samaniego, L., Vicente-Serrano, S. M., Volaire, F., Boone, A., Le Page, M., Llasat, M.C., Albergel, C., Burak, S., Cailleret, M., Cindrić Kalin, K., Davi, H., Dupuy, J., Greve, P., Grillakis, M. et al. (2020). Challenges for drought assessment in the mediterranean region under future climate scenarios. Earth-Science Reviews, https://doi.org/10.1016/j.earscirev.2020.103348
   Web of Science ®Google Scholar
• Wardlow, B. D., & Egbert, S. L. (2010). A Comparison of MODIS 250-m EVI and NDVI data for crop mapping: A case study for Southwest Kansas. International Journal of Remote Sensing, 31(3), 3. https://doi.org/10.1080/01431160902897858
   Web of Science ®Google Scholar
• Xie, C., Zhang, X., Zhuang, L., Zhu, R., & Guo, J. (2022). . In Scientific Reports, Vol.). Nature Publishing Group UK. . https://doi.org/10.1038/s41598-022-06363-9.
   Google Scholar
• Yuan, L., Yaoming, M., Chen, X., Wang, Y., & Zhaoguo, L. (2021). An enhanced MOD16 evapotranspiration model for the Tibetan Plateau during the unfrozen season. Journal of Geophysical Research: Atmospheres, 126(7). https://doi.org/10.1029/2020JD032787
   PubMed Web of Science ®Google Scholar
• Zhu, X., Liu, Y., Kun, X., & Pan, Y. (2021). Effects of drought on vegetation productivity of farmland ecosystems in the drylands of northern China. Remote Sensing, 13(6). https://doi.org/10.3390/rs13061179
   PubMed Web of Science ®Google Scholar
• Zribi, M., Baghdadi, N., Bousbih, S., El-Hajj, M., & Gao, Q. (2019). Surface moisture and irrigation mapping at agricultural field scale using the synergy Sentinel-1/Sentinel-2 Data. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 42. https://doi.org/10.5194/isprs-archives-XLII-3-W6-357-2019
   Google Scholar