Spatiotemporal Distribution of Soil Moisture and Salinity in the Taklimakan Desert Highway Shelterbelt

Spatial heterogeneity of soil nutrients in Yili River Valley

Soil nutrients are a vital reference index of soil fertility and are essential in studying spatial variability for the development of land resources. The traditional statistical methods including correlation analysis and geostatistical analysis, were used to explore the spatial variability of nutrients and its influencing factors in the Yili River Valley. The results showed that soil total potassium (STK) had a weak variation, soil organic carbon (SOC), soil total nitrogen (STN) and soil total phosphorus (STP) showed a moderate degree of variation. Correlation analysis showed that SOC had a significant correlation with STN, STP, STK, silt, soil water content (SWC), Cos a and altitude (p < 0.01). In contrast, negative correlations were found between the SOC and sand, soil bulk density (SBD) and pH (p < 0.01), the same as STN. STP had a significant correlation with STK, silt (p < 0.01) and Cos a (p < 0.05), whereas negative correlations were found between the STP, sand and SBD (p < 0.01). STK had a significant correlation with silt, whereas negative correlations were found between the STK, sand and SBD. Ordinary Kriging interpolation showed that the distribution of SOC and STN had a high value in the northeast, northwest and southeast, and a low value in the central and southwest. STP was high in the northwest and southeast and low in the northeast and southwest. STK was high in the northwest and northeast and low in the central and southeast regions. This is helpful for the rational exploitation of land resources in ecological economy development in the Yili River Basin.

Monitoring the Variations of Soil Salinity in a Palm Grove in Southern Algeria

Soil salinity is considered the most serious socio-economic and environmental problem in arid and semi-arid regions. This study was done to estimate the soil salinity and monitor the changes in an irrigated palm grove (42 ha) that produces dates of a high quality. Topsoil samples (45 points), were taken during two different periods (May and November), the electrical conductivity (EC) and Sodium Adsorption Ratio (SAR) were determined to assess the salinity of the soil. The results of the soil analysis were interpolated using two geostatistical methods: inverse distance weighting (IDW) and ordinary Kriging (OK). The efficiency and best model of these two methods was evaluated by calculating the mean error (ME) and root mean square error (RMSE), showing that the ME of both interpolation methods was satisfactory for EC (−0.003, 0.145) and for SAR (−0.03, −0.18), but the RMSE value was lower using the IDW with both data and periods. This can explain the accuracy of the IDW interpolation method. This model showed a dominance of soil salinity distribution in the South and South-East of the study area during the first season, and for the second season, the salts were concentrated in the middle of the area. Several factors could interact in this variation such as the topographic direction of the water flow and the aridity of the climate (evaporation). From this study emerges the need to maintain a better management of agricultural water and soils, avoiding salt accumulation, to ensure a good yield and the sustainability of agriculture in arid environments.

Spatial Distribution Characteristics of Soil Salinity and Moisture and Its Influence on Agricultural Irrigation in the Ili River Valley, China

Soil salinization is a global problem, which threatens agricultural productivity and sustainability, especially in arid and semi-arid regions. Soil salinity and moisture are important factors affecting agricultural production in arid regions. However, few studies have considered the influence of topographic factors on the spatial distribution patterns of soil salinity and moisture. This research aims to explore the spatial distribution characteristics and its influencing factors of soil salinity and moisture in the oasis farmland of arid areas. In this paper, GIS and geostatistics methods were applied to analyze the spatial distribution characteristics and variability of soil salinity and moisture, and then the corresponding proxy variables were used to quantitatively study the influence factors by using the geographical detector model. The results showed the coefficients of the variation of soil salinity and moisture to be 71.25% and 31.89%, respectively. There was moderate spatial autocorrelation of soil salinity and moisture. Soil salinity in the southwest was higher than in the northeast, and soil moisture in the northwest and southeast were lower than in the center and the northeast edge. The main influencing factors were available phosphorus, roughness of terrain, alkaline nitrogen, available potassium, and elevation. Combined action of topographic factors and soil nutrients has a major influence on the spatial distribution of soil salinity and moisture. Therefore, developing a suitable fertilizer regime under different topographic conditions could be an effective way to promote the sustainability of oasis agriculture in arid areas.

Effects of Alternating Irrigation with Fresh and Saline Water on the Soil Salt, Soil Nutrients, and Yield of Tomatoes

Saline water irrigation has become extremely important in arid and semi-arid areas in northwestern China. To study the effect of alternating irrigation models on the soil nutrients, soil salts, and yield of tomatoes with fresh water (total dissolved solids of 0.50 g·L−1) and saline water (total dissolved solids of 3.01 g·L−1), a two-year field experiment was carried out for tomatoes in the Hetao Irrigation District (HID), containing six drip irrigation models: T1 (all freshwater irrigation), T2 (saline water used in the seedling and flowering stages; fresh water in the fruit-set and breaker stages), T3 (saline water in the flowering and fruit-set stages; fresh water in the seedling and breaker stages), T4 (saline water in the fruit-set and breaker stages; fresh water in the seedling and flowering stages), T5 (saline water in the flowering and breaker stages; fresh water in the seedling and fruit-set stages), T6 (saline water in the seedling and fruit-set stages; fresh water in the flowering and breaker stages). The study found that saline water irrigation tends to have a positive effect on soil total nitrogen and a negative influence on soil total phosphorus at each growth stage of the tomato. Soil Na+, Mg2+, Ca2+, K+, and Cl− increased over the growth period, soil HCO3− decreased gradually by growth stage, and the salt ions increased with the amount of saline water applied in alternating irrigation. Though the soil salt accumulated in all experimentally designed alternating irrigation models, soil alkalization did not occur in the tomato root zone under the soil matric potential threshold of −25 kPa. The utilization of saline water resulted in about a 1.9–18.2% decline in fruit yield, but the total soluble solids, lycopene, and sugar in the tomato fruits increased. Ultimately, drip irrigation with fresh water at the seedling to flowering stages and saline water at the fruit-set to breaker stages was suggested for tomato cultivation in HID.

A Case Study of the Forced Invariance Approach for Soil Salinity Estimation in Vegetation-Covered Terrain Using Airborne Hyperspectral Imagery

Soil spectroscopy is a promising technique for soil analysis, and has been successfully utilized in the laboratory. When it comes to space, the presence of vegetation significantly affects the performance of imaging spectroscopy or hyperspectral imaging on the retrieval of topsoil properties. The Forced Invariance Approach has been proven able to effectively suppress the vegetation contribution to the mixed image pixel. It takes advantage of scene statistics and requires no specific a priori knowledge of the referenced spectra. However, the approach is still mainly limited to lithological mapping. In this case study, the objective was to test the performance of the Forced Invariance Approach to improve the estimation accuracy of soil salinity for an agricultural area located in the semi-arid region of Northwest China using airborne hyperspectral data. The ground truth data was obtained from an eco-hydrological wireless sensing network. The relationship between Normalized Difference Vegetation Index (NDVI) and soil salinity is discussed. The results demonstrate that the Forced Invariance Approach is able to improve the retrieval accuracy of soil salinity at a depth of 10 cm, as indicated by a higher value for the coefficient of determination (R2). Consequently, the vegetation suppression method has the potential to improve quantitative estimation of soil properties with multivariate statistical methods.