Characterization of Slightly and Moderately Saline and Sodic Soils in Irrigated Agricultural Land using Simulated Data of Advanced Land Imaging (EO‐1) Sensor

Abstract Around the world, especially in semi‐arid regions, millions of hectares of irrigated agricultural land are abandoned each year because of the adverse effects of irrigation, mainly secondary salinity and sodicity. Accurate information about the extent, magnitude, and spatial distribution of salinity and sodicity will help create sustainable development of agricultural resources. In Morocco, south of the Mediterranean region, the growth of the vegetation and potential yield are limited by the joint influence of high temperatures and water deficit. Consequently, the overuse of surface and groundwater, coupled with agricultural intensification, generates secondary soils salinity and sodicity. This research focuses on the potential and limits of the advance land imaging (EO‐1 ALI) sensor spectral bands for the discrimination of slight and moderate soil salinity and sodicity in the Tadla's irrigated agricultural perimeter, Morocco. To detect affected soils, empirical relationships (second‐order regression analysis) were calculated between the electrical conductivity (EC) and different spectral salinity indices. To achieve our goal, spectroradiometric measurements (350 to 2500 nm), field observation, and laboratory analysis (EC of a solution extracted from a water‐saturated soil), and soil reaction (pH) were used. The spectroradiometric data were acquired using the ASD (analytical spectral device) above 28 bare soil samples with various degrees of soil salinity and sodicity, as well as unaffected soils. All of the spectroradiometric data were resampled and convolved in the solar‐reflective spectral bands of EO‐1 ALI sensor. The results show that the SWIR region is a good indicator of and is more sensitive to different degrees of slight and moderate soil salinity and sodicity. In general, relatively high salinity soils show higher spectral signatures than do sodic soils and unaffected soils. Also, strongly sodic soils present higher spectral responses than moderately sodic soils. However, in spite of the improvement of EO‐1 ALI spectral bands by comparison to Landsat‐ETM+, this research shows the weakness of multispectral systems for the discrimination of slight and moderate soil salinity and sodicity. Although remote sensing offers good potential for mapping strongly saline soils (dry surface crust), slight and moderately saline and sodic soils are not easily identified, because the optical properties of the soil surfaces (color, brightness, roughness, etc.) could mask the salinity and sodicity effects. Consequently, their spatial distribution will probably be underestimated. According to the laboratory results, the proposed Soils Salinity and Sodicity Indices (SSSI) using EO‐1 ALI 9 and 10 spectral bands offers the most significant correlation (52.91%) with the ground reference (EC). They could help to predict different spatial distribution classes of slight and moderate saline and sodic soils using EO‐1 ALI imagery data. Keywords: Electrical conductivityEO‐1 Advanced Land Imaging sensorremote sensingsalinitysodicityspectral indicesspectroradiometric data ACKNOWLEDGMENTS The authors thank NATO, Agence Universitaire de la Francophonie (AUF), Natural Sciences and Engineering Research Council (NSERC), and the University of Ottawa for their financial support. We thank Mr. Hammou El‐Khamar, head of the ORMVAT, for his support and for the laboratory analysis. We are grateful to the Faculty of Science and Technology (FST) of Béni‐Mellal and Mr. M. Farhi and Mr. R. Amediaz from the Office Régional de Mise en Valeur Agricole du Tadla (ORMVAT) for their support and assistance in the field work.