Leaching of soil salinity in irrigated and non-irrigated agriculture is crucial to ensure sustainable food production. Soil salinity is considered a global problem, since more than 800 million hectares (6% of the world's total area) worldwide are salt-affected. Generally, the regions affected by soil salinity also face water scarcity problems. Consequently, in these regions, even saline waters are considered to be essential resources for agriculture. The use of inefficient irrigation practices in regions that experience water scarcity, especially those using saline water, aggravates soil salinity problems. Therefore, in the context of these constraints and challenges, increasing the efficiency of water use for irrigation and leaching is necessary. An increasing interest in using drip irrigation systems (DI) is noticed in the current research that deals with water scarcity due to the benefits of applying water precisely in time and space. However, the traditional leaching guidelines, based on steady-state conditions, are not appropriate for DI. Therefore, the use of DI for leaching practices is still under question. The objectives of this study are (i) to better understand and assess the leaching process for common DI, and (ii) to develop a simulation-based optimization approach considering site-specific conditions for optimal DI design for salinity management. Accordingly, a new two-stage framework for optimizing leaching practices has been developed focusing on DI. The transient-state-based numerical model HYDRUS-2D was used for simulating water movement and solute transport processes for both stages. In the first stage, a general assessment tool for leaching practices in the form of “irrigation atlas” was created using two-dimensional (2D) numerical experiments. The atlas displays and compares the reclamation leaching results of (i) surface drip irrigation (SI), (ii) subsurface irrigation drip irrigation (SDI), (iii) sprinkler irrigation (S), and (iv) flood irrigation. The results are introduced graphically, showing the final 2D salinity distribution in the soil profile, and numerically giving the water and salt mass balances. The SI and SDI results show the high potential of these systems for improving the efficiency of salinity leaching. However, because of the resultant localized leaching patterns, the performance of these systems depends on the location of the drip line relative to the targeted leaching area. In the second stage of the study a site-specific optimization framework for leaching design and management was developed through a combination of simulation and optimization. The objective was to achieve optimal leaching results with a given limited amount of water. The framework was applied in synthetic field conditions for reclamation leaching in two and silt). Moreover, the framework was based on running numerous simulations of possible DI setups (scenarios). The optimization approach made it possible to derive values for the optimal irrigation design parameters for salinity management under the given field conditions. The new optimization framework was implemented in a case study focusing on the impact of different DI designs on yield and the leaching of salts. The framework aims to make the most effective use of an existing DI system by optimizing the combination of irrigation frequency, duration, and discharge. The optimization framework was successfully employed: (i) to increase the relative yield of the considered crop, and (ii) to reduce the subsurface drainage towards the saline and shallow groundwater, which contributes to better control of the saline groundwater levels in the study area. The seasonal results confirmed the leaching pattern of a dripper presented in the irrigation atlas. The optimal seasonal solutions are presented as site and crop specific leaching and water productivity functions for a wide range of applied water and different irrigation water salinities. The developed optimization framework proved to be a suitable approach for assessing and improving leaching management of DI under salinity conditions and deriving optimal design parameter values. The suitability and flexibility of the framework were verified through implementation in a case study with new field conditions and objectives, and it was shown that the reliability of the proposed framework depends on the quality of the collected and measured data required for the model inputs.

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.

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.