This paper examines the climate change impact on the spatiotemporal soil salinity dynamics during the last 30 years (1987–2017) in the arid landscape. The state of Kuwait, located at the northwest Arabian Peninsula, was selected as a pilot study area. To achieve this, a Landsat- Operational Land Imager (OLI) image acquired thereabouts simultaneously to a field survey was preprocessed and processed to derive a soil salinity map using a previously developed semi-empirical predictive model (SEPM). During the field survey, 100 geo-referenced soil samples were collected representing different soil salinity classes (non-saline, low, moderate, high, very high and extreme salinity). The laboratory analysis of soil samples was accomplished to measure the electrical conductivity (EC_-Lab) to validate the selected and used SEPM. The results are statistically analyzed (p ˂ 0.05) to determine whether the differences are significant between the predicted salinity (EC_-Predicted) and the measured ground truth (EC_-Lab). Subsequently, the Landsat serial time’s datasets acquired over the study area with the Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+) and OLI sensors during the last three decades over the intervals (1987, 1992, 1998, 2000, 2002, 2006, 2009, 2013, 2016 and 2017) were radiometrically calibrated. Likewise, the datasets were atmospherically and spectrally normalized by applying a semi-empirical line approach (SELA) based on the pseudo-invariant targets. Afterwards, a series of soil salinity maps were derived through the application of the SEPM on the images sequence. The trend of salinity changes was statistically tested according to climatic variables (temperatures and precipitations). The results revealed that the EC_-Predicted validation display a best fits in comparison to the EC_-Lab by indicating a good index of agreement (D = 0.84), an excellent correlation coefficient (R² = 0.97) and low overall root mean square error (RMSE) (13%). This also demonstrates the validity of SEPM to be applicable to the other images acquired multi-temporally. For cross-calibration among the Landsat serial time’s datasets, the SELA performed significantly with an RMSE ≤ ± 5% between all homologous spectral reflectances bands of the considered sensors. This accuracy is considered suitable and fits well the calibration standards of TM, ETM+ and OLI sensors for multi-temporal studies. Moreover, remarkable changes of soil salinity were observed in response to changes in climate that have warmed by more than 1.1 °C with a drastic decrease in precipitations during the last 30 years over the study area. Thus, salinized soils have expanded continuously in space and time and significantly correlated to precipitation rates (R² = 0.73 and D = 0.85).

Depending on the band position on the electromagnetic spectrum, optical and electronic characteristics, sensors collect the reflected energy by the Earth’s surface and the atmosphere. Currently, the availability of the new generation of medium resolution, such as the Multi-Spectral Instrument (MSI) on board the Sentinel-2 satellite, offers new opportunities for long-term high-temporal frequency for Earth’s surfaces observation and monitoring. This paper focuses on the analysis and the comparison of the visible, the near-infrared (VNIR), and the shortwave infrared (SWIR) spectral bands of the MSI for soil salinity discrimination in an arid landscape. To achieve these, a field campaign was organized, and 160 soil samples were collected with various degrees of soil salinity, including non-saline soil samples. The bidirectional reflectance factor was measured above each soil sample in a goniometric laboratory using an ASD (Analytical Spectral Devices) spectroradiometer. In the laboratory work, pHs, electrical conductivity (EC-_Lab), and the major soluble cations (Na⁺, K⁺, Ca²⁺+, and Mg²⁺) and anions (CO₃²⁻, HCO₃⁻, Cl⁻, and SO₄²⁻) were measured using extraction from a saturated soil paste, and the sodium adsorption ratio (SAR) was calculated using a standard procedure. These parameters, in addition to the field observations, were used to interpret and investigate the spectroradiometric measurements and their relevant transformations using the continuum removed reflectance spectrum (CRRS) and the first derivative (FD). Moreover, the acquired spectra over all the soil samples were resampled and convolved in the solar-reflective spectral bands using the Canadian Modified Herman transfer radiative code (CAM5S) and the relative spectral response profiles characterizing the Sentinel-MSI band filters. The statistical analyses conducted were based on the second-order polynomial regression (p < 0.05) between the measured EC-_Lab and the reflectances in the MSI convolved spectral bands. The results obtained indicate the limitation of VNIR bands and the potential of SWIR domain for soil salinity classes’ discrimination. The CRRS and the FD analyses highlighted a serious spectral-signal confusion between the salt and the soil optical properties (i.e., color and brightness) in the VNIR bands. Likewise, the results stressed the independence of the SWIR domain vis-a-vis these soil artifacts and its capability to differentiate significantly among several soil salinity classes. Moreover, the statistical fit between each MSI individual spectral band and EC-_Lab corroborates this trend, which revealed that only the SWIR bands were correlated significantly (R² of 50% and 64%, for SWIR-1 and SWIR-2, respectively), while the R² between the VNIR bands and EC-_Lab remains less than 9%. According to the convergence of these four independent analysis methods, it is concluded that the Sentinel-MSI SWIR bands are excellent candidates for an integration in soil salinity modeling and monitoring at local, regional, and global scales.

Soil management practices that maintain crop residue cover and reduce tillage improve soil structure, increase organic matter content in the soil, positively influence water infiltration, evaporation and soil temperature, and play an important role in fixing CO₂ in the soil. Consequently, good residue management practices on agricultural land have many positive impacts on soil quality, crop production quality and decrease the rate of soil erosion. Several studies have been undertaken to develop and test methods to derive information on crop residue cover and soil tillage using empirical and semi-empirical methods in combination with remote sensing data. However, these methods are generally not sufficiently rigorous and accurate for characterizing the spatial variability of crop residue cover in agricultural fields. The goal of this research is to investigate the potential of hyperspectral Hyperion (Earth Observing-1, EO-1) data and constrained linear spectral mixture analysis (CLSMA) for percent crop residue cover estimation and mapping. Hyperion data were acquired together with ground-reference measurements for validation purposes at the beginning of the agricultural season (prior to spring crop planting) in Saskatchewan (Canada). At this time, only bare soil and crop residue were present with no crop cover development. In order to extract the crop residue fraction, the images were preprocessed, and then unmixed considering the entire spectral range (427 nm–2355 nm) and the pure spectra (endmember). The results showed that the correlation between ground-reference measurements and extracted fractions from the Hyperion data using CLMSA showed that the model was overall a very good predictor for crop residue percent cover (index of agreement (D) of 0.94, coefficient of determination (R²) of 0.73 and root mean square error (RMSE) of 8.7%) and soil percent cover (D of 0.91, R² of 0.68 and RMSE of 10.3%). This performance of Hyperion is mainly due to the spectral band characteristics, especially the availability of contiguous narrow bands in the short-wave infrared (SWIR) region, which is sensitive to the residue (lignin and cellulose absorption features).

The present study focuses on the validation and comparison of eight different physical models for soil salinity mapping in an arid landscape using two independent Landsat-Operational Land Imager (OLI) datasets: simulated and image data. The examined and compared models were previously developed for different semi-arid and arid geographic regions around the world, i.e., Latino-America, the Middle East, North and East Africa and Asia. These models integrate different spectral bands and unlike mathematical functions in their conceptualization. To achieve the objectives of the study, four main steps were completed. For simulated data, a field survey was organized, and 100 soil samples were collected with various degrees of salinity levels. The bidirectional reflectance factor was measured above each soil sample in a goniometric laboratory using an analytical spectral device (ASD) FieldSpec-4 Hi-Res spectroradiometer. These measurements were resampled and convolved in the solar-reflective bands of the Operational Land Imager (OLI) sensor using a radiative transfer code and the relative spectral response profiles characterizing the filters of the OLI sensor. Then, they were converted in terms of the considered models. Moreover, the OLI image acquired simultaneously with the field survey was radiometrically preprocessed, and the models were implemented to derive soil salinity maps. The laboratory analyses were performed to derive electrical conductivity (EC_-Lab) from each soil sample for validation and comparison purposes. These steps were undertaken between predicted salinity (EC_-Predicted) and the measured ground truth (EC_-Lab) in the same way for simulated and image data using regression analysis (p ˂ 0.05), coefficient of determination (R²), and root mean square error (RMSE). Moreover, the derived maps were visually interpreted and validated by comparison with observations from the field visit, ancillary data (soil, geology, geomorphology and water table maps) and soil laboratory analyses. Regardless of data sources (simulated or image) or the validation mode, the results obtained show that the predictive models based on visible- and near-infrared (VNIR) bands and vegetation indices are inadequate for soil salinity prediction in an arid landscape due to serious signals confusion between the salt crust and soil optical properties in these spectral bands. The statistical tests revealed insignificant fits (R² ≤ 0.41) with very high prediction errors (RMSE ≥ 0.65), while the model based on the second-order polynomial function and integrating the shortwave infrared (SWIR) bands provided the results of best fit, with the field observations (EC_-Lab), yielding an R² of 0.97 and a low overall RMSE of 0.13. These findings were corroborated by visual interpretation of derived maps and their validation by comparison with the ground truthing.

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 the 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 ground water, coupled with agricultural intensification, generates secondary soil salinity. Knowing when, where, and how salinity may occur is very important to the sustainable development of any irrigated production system. Remedial actions require reliable information to help set priorities and to choose the type of action that is most appropriate in each situation. Ground-based electromagnetic measurements of soil electrical conductivity (EC) are generally accepted as the most effective method for quantification of soil salinity. Unfortunately, these methods are expensive, time consuming, and need considerable human resources for land surveying. Moreover, the dynamic nature of soil salinity in space and time makes it more difficult to use conventional methods for comparisons over large areas. A major challenge of remote sensing, as a potential alternative technique, is to detect different levels of soil salinity. The main aim of this research is to assess the potential of the Advanced Land Imager (ALI) sensor on board the Earth Observing-1 (EO-1) satellite, with its rich infrared bands, for the discrimination and mapping of slight and moderate soil salinity in the Tadla’s irrigated agricultural perimeter in Morocco. To achieve this goal, semi-empirical predictive models developed in a previous study using second order regression analysis between the EC of salt-affected soils and different spectral salinity indices were applied to the ALI image. This was atmospherically corrected and the radiometric sensor drift was calibrated. Visual comparisons and statistical validation of these models using ground truth were undertaken in order to identify the best semi-empirical model for slight and moderate salinity mapping. The obtained results show that the model based on the Normalized Difference Salinity Index (NDSI) does not give any results. The model based on the Salinity Index-1 (SI-1) and the SI-Advanced Space-borne Thermal Emission and Reflection Radiometer (SI-ASTER) confuses vegetation with high soil salinity, although the model does bring out areas of lower salinity. Both R² of 0.67 for the SI-1 and 0.65 for the SI-ASTER further reinforce that these models cause too much confusion to be used with accuracy for salt-affected soil detection. The semi-empirical model based on Soil Salinity and Sodicity Index-1 (SSSI-1) performs better than the two last models. However, there is a relative confusion between the classes in the slight and moderate salinity and in areas that are shown by the validation map; the higher class of salinity does not appear to contain higher levels of salinity. The statistical validation of this model reinforces what is seen on the derived map with only an R² = 0.68. The model based on the SSSI-2 clearly provides the best results in comparison to the ground truth. Its derived map gives the closest overall visual approximation of the EC map, with a whole range of values. With a statistical validation of R² = 0.97 to the ground truth, it is by far the best performance of any of the other models, and the different classes are statistically well separated, which further reinforces the accuracy of the visual analysis.