TY - JOUR
T1 - Sentinel-MSI and Landsat-OLI Data Quality Characterization for High Temporal Frequency Monitoring of Soil Salinity Dynamic in an Arid Landscape
AU - Bannari, Abdou
AU - Hameid Mohamed Musa, Nadir
AU - Abuelgasim, Abdelgadir
AU - El-Battay, Ali
N1 - Funding Information:
Manuscript received September 29, 2019; revised April 30, 2020 and May 13, 2020; accepted May 15, 2020. Date of publication May 21, 2020; date of current version June 4, 2020. This work was supported in part by the Arabian Gulf University (Kingdom of Bahrain) and in part by the United Arab Emirates University (United Arab Emirates). (Corresponding author: Abdou Bannari.) Abdou Bannari is with the Space Pix-Map International Inc., Gatineau J8R 3R7, QC, Canada (e-mail: abannari@agu.edu.bh).
Funding Information:
The authors would like to thank the Arabian Gulf University (Kingdom of Bahrain) and United Arab Emirates University (United Arab Emirates) for their financial support. They acknowledge the NASA-USGS and ESA for the Landsat-OLI and Sentinel-MSI free data. They also express their gratitude to the anonymous reviewers for their constructive comments.
Publisher Copyright:
© 2008-2012 IEEE.
PY - 2020
Y1 - 2020
N2 - Although the Sentinel-MSI and Landsat-OLI are designed to be similar, they have different spectral, spatial and radiometric resolutions. In addition, relative spectral response profiles characterizing the filters responsivities of the both instruments are not identical between the homologous bands. This paper analyse the difference between the reflectance in the homologous spectral bands of MSI and OLI sensors, VNIR and SWIR, for high temporal frequency monitoring of soil salinity dynamic in an arid landscapes. In addition, their conversion in term of Soil Salinity and Sodicity Index (SSSI) and in term of Semi-Empirical Predictive Model (SEPM) for soil salinity mapping were compared. To achieve these, analyses were performed on simulated data and on two pairs of images (MSI and OLI) acquired over the same area in July 2015 and August 2017 with one day difference between each pair. The results obtained demonstrate that the statistical fits between SMI and OLI simulated reflectance over a wide range of soil samples with different salinity degrees reveals an excellent linear relationship (R2 of 0.99) for all bands, as well as for SSSI and SEPM. The Root Mean Square Difference (RMSD) values are null between the NIR and SWIR homologous bands, and are insignificant for the other bands. Moreover, the SSSI show an RMSD of 0.0007 and the SEPM express an excellent RMSD around 0.5 dS.m-1 reflecting a relative error between 0.001 and 0.05 for non-saline and extreme salinity classes, respectively. Likewise, the two used pairs of images exhibited very significant fits (R2 ≥ 0.93) for spectral band reflectance's, as well for SSSI and SEPM, yielding a RMSD values less than 0.029 for bands and less than 0.004 for SSSI. While, for SEPM, the RMSD fluctuate between 0.12 and 2.65 dS.m-1, respectively, of non-saline and extreme salinity classes. Accordingly, we can conclude that the MSI and OLI sensors can be used jointly to monitor accurately the soil salinity and it's dynamic in time and space in arid landscape, provided that rigorous preprocessing issues must be addressed before.
AB - Although the Sentinel-MSI and Landsat-OLI are designed to be similar, they have different spectral, spatial and radiometric resolutions. In addition, relative spectral response profiles characterizing the filters responsivities of the both instruments are not identical between the homologous bands. This paper analyse the difference between the reflectance in the homologous spectral bands of MSI and OLI sensors, VNIR and SWIR, for high temporal frequency monitoring of soil salinity dynamic in an arid landscapes. In addition, their conversion in term of Soil Salinity and Sodicity Index (SSSI) and in term of Semi-Empirical Predictive Model (SEPM) for soil salinity mapping were compared. To achieve these, analyses were performed on simulated data and on two pairs of images (MSI and OLI) acquired over the same area in July 2015 and August 2017 with one day difference between each pair. The results obtained demonstrate that the statistical fits between SMI and OLI simulated reflectance over a wide range of soil samples with different salinity degrees reveals an excellent linear relationship (R2 of 0.99) for all bands, as well as for SSSI and SEPM. The Root Mean Square Difference (RMSD) values are null between the NIR and SWIR homologous bands, and are insignificant for the other bands. Moreover, the SSSI show an RMSD of 0.0007 and the SEPM express an excellent RMSD around 0.5 dS.m-1 reflecting a relative error between 0.001 and 0.05 for non-saline and extreme salinity classes, respectively. Likewise, the two used pairs of images exhibited very significant fits (R2 ≥ 0.93) for spectral band reflectance's, as well for SSSI and SEPM, yielding a RMSD values less than 0.029 for bands and less than 0.004 for SSSI. While, for SEPM, the RMSD fluctuate between 0.12 and 2.65 dS.m-1, respectively, of non-saline and extreme salinity classes. Accordingly, we can conclude that the MSI and OLI sensors can be used jointly to monitor accurately the soil salinity and it's dynamic in time and space in arid landscape, provided that rigorous preprocessing issues must be addressed before.
KW - Arid landscape
KW - images data
KW - landsat-OLI
KW - semi-empirical model
KW - sentinel-MSI
KW - simulated data
KW - soil salinity
KW - soil salinity and sodicity index (SSSI)
KW - spectroradiometric measurements
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U2 - 10.1109/JSTARS.2020.2995543
DO - 10.1109/JSTARS.2020.2995543
M3 - Article
AN - SCOPUS:85086465515
SN - 1939-1404
VL - 13
SP - 2434
EP - 2450
JO - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
JF - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
M1 - 9097955
ER -