A field experiment was carried out to study the effect of drain depth on the drainage water quality and flax productivity. The flax crop was planted during winter season. The field experiment was conducted at the Bahteem Research Station, Qaliubiya Governorate, from November 2015 till April 2016. Two design systems were selected, the first was conventional drainage system (CDS), with drain depth 1.5 m, the second was modified drainage system (MDS), three lateral drain lines were installed to main drain directly. The depth of the lateral drain was (0.90 - 1.0) m.   The obtained results revealed that the salinity of the average soil profile decreased after the second irrigation onward. The soil salinity percentages of (MDS) decreased by (47, 30 and 9.5) for (2nd, 3rd and 4th) irrigation, respectively. On the other hand, soil salinity percentages of (CDS) decreased by (40, 32 and 9) for (2nd, 3rd and 4th) irrigation, respectively. The chloride percentages for average soil profile of (MDS) decreased by (77, 82 and 54) for (2nd, 3rd and 4th) irrigation, respectively. On the other hand, the chloride percentages of (CDS) decreased by (70, 75 and 35) for (2nd, 3rd and 4th) irrigation, respectively. The EC values of drainage water of (MDS) decreased from first irrigation onwards. The EC percentages of drainage water salinity of (MDS) decreased by (10.6, 18.2 and 22.7) for (2nd, 3rd and 4th) irrigation, respectively. On the other hand, the EC percentages of drainage water salinity of (CDS) decreased by (6.3, 5.6 and 24.6) for (2nd, 3rd and 4th) irrigation, respectively. The chloride percentages of drainage water  salinity of (MDS) decreased by (22.6, 43 and 14.2) for (2nd, 3rd and 4th) irrigation, respectively. On the other hand, the chloride percentages of (CDS) decreased by (14.7, 32 and 16.4) for (2nd, 3rd and 4th) irrigation, respectively. The piezometer reading showed that the water table levels reaching the soil surface upon irrigation reached low level before the next irrigation. The average values of water table after first irrigation were (14.5, 11 cm) for (MDS) and (29.5, 24 cm) for (CDS) for (L/4, L/2 distance from drain line), respectively. Also the results indicated that the water table level continue decreasing for both systems before 2nd and 3rd irrigation. The results recorded were (91, 82 cm), (140.5, 132 cm) before 2nd irrigation; (75.5, 60 cm), (133.5, 125 cm) before 3rd irrigation for (L/4, L/2 distance from drain line) for both systems (MDS) and (CDS), respectively. On the other hand data showed that the water table was higher after 2nd irrigation on ward. The results recorded were (16.5, 10 cm), (33.5, 25 cm) after 2nd irrigation; (7.5, 3 cm), (28, 21 cm) after 3rd irrigation for (L/4, L/2 distance from drain line) for both systems (MDS) and (CDS) respectively. So (MDS) produced drainage water with higher quality and lower salts concentration than the (CDS). At the end of the season the flax productivity was 3.5 ton/fed for both systems. It can be recommended to be used (MDS) with shallow drain depth.

The objectives of the current study were to investigate the opportunity of estimating soil salinity from hyperspectral data and identifying the most informative spectral zones for estimation. Electrical conductivity (EC) measurements of ninety topsoil samples (0–30 cm) collected from Toshka, Egypt, were used as data set. Analytical spectral device was employed to collect the reflectance spectral signatures of soil samples. Both linear regression and HSD Tukey’s analyses displayed that the SWIR1 and SWIR2 zones are the most suitable for soil salinity prediction while, blue, green and NIR were the wickedest. Moreover, EC estimation was better in case of lower soil salinity (0-2 dS m-1) than higher levels (8-1). Partial-least-squares-regression (ΡLSR) was employed to establish soil salinity prediction model using the training set of soil samples (n=75). The PLSR model was set up using the most informative wave bands (SWIR1 and SWIR2). The result showed that PLSR linear model gave a precise prediction of soil salinity (R2 = 0.93). The results revealed that employing reflectance values in SWIR in the model variables increases the precision of soil EC prediction.