Статья

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

Giuliano RamatCNR-IFAC, Institute of Applied Physics–National Research Council of Italy, Florence, ItalyE. SantiCNR-IFAC, Institute of Applied Physics–National Research Council of Italy, Florence, ItalyS. PalosciaCNR-IFAC, Institute of Applied Physics–National Research Council of Italy, Florence, ItalyGiacomo FontanelliCNR-IFAC, Institute of Applied Physics–National Research Council of Italy, Florence, ItalyS. PettinatoCNR-IFAC, Institute of Applied Physics–National Research Council of Italy, Florence, ItalyLeonardo SanturriCNR-IFAC, Institute of Applied Physics–National Research Council of Italy, Florence, ItalyN. SouissiCNR-IFAC, Institute of Applied Physics–National Research Council of Italy, Florence, ItalyEmmanuel Da PonteGerman Aerospace Center - German Remote Sensing Data Center DLR-DFD,Weßling, GermanyMagdy Abdel WahabFaculty of Science, Cairo University, Giza, EgyptAlaa A. KhalilCentral Lab. for Agricultural Climate- CLAC-ARC,Giza, EgyptYassmin Hesham EssaCentral Lab. for Agricultural Climate- CLAC-ARC,Giza, EgyptMohamed OuessarArid Regions Institute, IRA, Medenine, TunisiaHanen DhaouArid Regions Institute, IRA, Medenine, TunisiaAbderrahman SghaierArid Regions Institute, IRA, Medenine, TunisiaA. HachaniArid Regions Institute, IRA, Medenine, TunisiaZeineb KassoukGREEN TEAM, Institut National Agronomique de Tunisie, UCAR/INAT/LR17AGR01 (GREEN-TEAM), TunisiaZohra Lili‐ChabaaneGREEN TEAM, Institut National Agronomique de Tunisie, UCAR/INAT/LR17AGR01 (GREEN-TEAM), Tunisia

2023en

ABI

Аннотация

This study focused on monitoring the water status of vegetation and soil by exploiting the synergy of optical and microwave satellite data with the aim of improving the knowledge of water cycle in cultivated lands in Egyptian Delta and Tunisian areas. Environmental analysis approaches based on optical and synthetic aperture radar data were carried out to set up the basis for future implementation of practical and cost-effective methods for sustainable water use in agriculture. Long-term behaviors of vegetation indices were thus analyzed between 2000 and 2018. By using SAR data from Sentinel-1, an Artificial Neural Network-based algorithm was implemented for estimating soil moisture and monthly maps for 2018 have been generated to be compared with information derived from optical indices. Moreover, a novel drought severity index was developed and applied to available data. The index was obtained by combining vegetation soil difference index, derived from optical data, and soil moisture content derived from SAR data. The proposed index was found capable of complementing optical and microwave sensitivity to drought-related parameters, although ground data are missing for correctly validating the results, by capturing drought patterns and their temporal evolution better than indices based only on microwave or optical data..

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Идентификаторы

DOI: 10.1080/22797254.2022.2157335

Цитирования и источники

Цитирований: 2Использованных источников: 0

Показатели — AkademScholar