Soil salinization affects crop production and food security. Mapping spatial distribution and severity of salinity is essential for agricultural management and development. This study was aimed to test the effectiveness of machine learning algorithms for soil salinity mapping taking the Mussaib area in Central Mesopotamia as an example.A combined dataset consisting of Landsat 5 Thematic Mapper (TM) and ALOS L-band radar data acquired at the same time was used for fulfilling the task. Relevant biophysical indicators were derived from the TM images, and the soil component was retrieved by removing the vegetation contribution from the L-band radar backscattering coefficients. Field-measured salinity at the three corner plots of triangles were averaged to represent the salinity of these triangular areas. These averaged plots were converted into raster by either direct rasterization or buffering-based rasterization into different cell size to create the training set (TS). One of the three triangle corners was randomly selected to constitute a validation set (VS). Using this TS, the support vector regression (SVR) and random forest regression (RFR) algorithms were then applied to the combined dataset for salinity prediction. Results revealed that RFR performed better than SVR with higher accuracy (93.4-94.2% vs. 85.2-89.4%) and less normalized root mean square error (NRMSE; 6.10-7.69% vs. 10.29-10.52%) when calibrated with both TS and VS.In comparison, prediction by multivariate linear regression (MLR) achieved in our previous study using the same datasets also showed less NRMSE than SVR. Hence, both RFR and MLR are recommended for soil salinity mapping. KEYWORDScombined optical-radar dataset, field sample rasterization, random forest regression, soil salinity prediction, support vector regression

Soil salinization is a critical environmental problem for dryland agriculture. Mapping its distribution and severity in space and time is essential for agricultural management and development. Recently, remote sensing technology has been widely applied in such mapping but mostly using optical remote sensing data. In conjunction with the field surveys, this case study was aimed at developing an operational approach for this purpose by employing ALOS (Advanced Land Observing Satellite) L-band radar data with support of Landsat 5 TM (Thematic Mapper) imagery acquired at almost the same time. The test was conducted in the Mussaib site in Central Iraq. The innovative procedure involved was the removal or minimization of the impact of vegetation cover and moisture on the backscattering coefficients by Water Cloud Model. The results revealed a strong correlation between the corrected backscattering coefficients of soil and the measured soil salinity (R 2 =0.565-0.677). The radar-based salinity models developed through multivariate linear regression (MLR) analysis were able to predict salinity with reliability of 70.05%. In conclusion, it is possible to use radar data for soil salinity prediction and mapping in dry environment.

No abstract

No abstract

To study the genesis and developments of illuviation horizons in Iraqi arid region, three pedons were selected in the Pripj region bottom, which formed the larger region in Iraqi southern desert in Muthana governorate, the pedons were  ‫من‬ ‫مستل‬ ‫البحث‬ ‫أطروحة‬ ‫الثالث‬ ‫للباحث‬ ‫اه‬ ‫دكتور‬