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Detecting and Mapping Salt-Affected Soil with Arid Integrated Indices in Feature Space Using Multi-Temporal Landsat Imagery.

Authors :
AbdelRahman, Mohamed A. E.
Afifi, Ahmed A.
D'Antonio, Paola
Gabr, Safwat S.
Scopa, Antonio
Source :
Remote Sensing. Jun2022, Vol. 14 Issue 11, p2599-2599. 22p.
Publication Year :
2022

Abstract

Salinity systems are well known as extreme environmental systems that occur either naturally or by certain human activities, in arid and semiarid regions, which may harm crop production. Soil salinity identification is essential for soil management and reclamation projects. Information derived from space data acquisition systems (e.g., Landsat, ASTER) is considered as one of the most rapid techniques in mapping Salt-Affected Soil (SAfSoil). The current study tested the previously proposed salinity indices on the northern Nile Delta region, Egypt. The results indicated that most of the indices were not suitable to detect the SAfSoil in the area, due to the interaction between the bare soils, salts, and urbanization. To resolve this issue, the current work suggested a new index for detecting and monitoring the SAfSoil in the Nile Delta region. The newly proposed index takes into consideration plant health, the salt crust at the surface of the soils, as well as urbanization. It facilitates the mapping processes of SAfSoil in the area compared to any other previously proposed index. In this respect, multi-temporal Landsat-7 and 8 satellite data, acquired in 2002, 2016, and 2021, were used. The new index was prepared using the 2002 data and verified using the 2016 and 2021 data. Field measurements and data collected during 2002, 2016, and 2021 were utilized as ground truth data to assess the accuracy of the results obtained from the proposed index. The evaluation of the results indicated that the accuracy assessment for 2002, 2016, and 2021 images was 94.58, 96.08, and 95.68%, respectively. Finally, the effectiveness of using remote sensing in detecting and mapping SAfSoil is outlined. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20724292
Volume :
14
Issue :
11
Database :
Academic Search Index
Journal :
Remote Sensing
Publication Type :
Academic Journal
Accession number :
157368983
Full Text :
https://doi.org/10.3390/rs14112599