Abstract
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 (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> 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 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> 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 (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ≥ 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 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> , 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.
Highlights
A RID landscapes are seriously facing challenge of spatial and temporal distribution of soil salinity, during drought periods [1], due to water quality and scarcity, the high temperature and the increased evapotranspiration rate [2]
The Root Mean Square Difference (RMSD) values are null between the NIR and shortwave infrared (SWIR) homologous bands, and are insignificant for the other bands (i.e., 0.003 for coastal and 0.001 for the blue, green, and red bands)
The calculated RMSD for the Semi-Empirical Predictive Model (SEPM) vary between 0.003 and 0.5 dS.m−1 reflecting a relative error that varies between 0.001 and 0.05 for salinity classes varying between 2.5 and 600 dS.m−1. This difference is identical to the electrical conductivity accuracy measurement in the filed using electronical instruments [83]. These results pointed out that multispectral instruments (MSI) and Operational Land Imager (OLI) sensors can be combined for high temporal frequency to monitor soil salinity dynamic in time and space in an arid landscape
Summary
A RID landscapes are seriously facing challenge of spatial and temporal distribution of soil salinity, during drought periods [1], due to water quality and scarcity, the high temperature and the increased evapotranspiration rate [2]. In addition to water stress, these landscapes are vulnerable to salinization, marginality, and desertification as a consequence of human activities [3] and global climate change impact [4]. Soil salinity affect approximately 40% to 45% of the Earth land, especially in semi-arid and arid landscapes [6], and the global cost of irrigation-induced salinity is estimated around 11 billion US$ a year [7]. To remedy this situation in vulnerable landscape to salinization, there are methods available to slow down the processes and, sometimes, even reverse them. Farmers, soil managers, scientists, and agricultural engineers need accurate and reliable information on the nature, extent, magnitude, severity, and spatial distribution of the salinity against which they could take appropriate measures [8]
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