Salinization is a major soil degradation threat in irrigated systems worldwide. Irrigated systems in the Niger River basin are also affected by salinity, but its spatial distribution and intensity are not currently known. The aim of this study was to develop a method to detect salt-affected soils in irrigated systems. Two complementary approaches were tested: salinity assessment of bare soils using a salinity index (SI) and monitoring of indirect effects of salinity on rice growth using temporal series of a vegetation index (NDVI). The study area was located south of Niamey (Niger) in two irrigated systems of rice paddy fields that cover 6.5 km². We used remote-sensing and ground-truth data to relate vegetation behavior and reflectance to soil characteristics. We explored all existing Sentinel-2 images from January 2016 to December 2019 and selected cloud-free images on 157 dates that covered eight successive rice-growing seasons. In the dry season of 2019, we also sampled 44 rice fields, collecting 147 biomass samples and 180 topsoil samples from January to June. For each field and growing season, time-integrated NDVI (TI-NDVI) was estimated, and the SI was calculated for dates on which bare soil conditions (NDVI < 0.21) prevailed. Results showed that since there were few periods of bare soil, SI could not differentiate salinity classes. In contrast, the high temporal resolution of Sentinel-2 images enabled us to describe rice-growing conditions over time. In 2019, TI-NDVI and crop yields were strongly correlated (r = 0.77 with total biomass yield and 0.82 with grain yield), while soil electrical conductivity was negatively correlated with both TI-NDVI (r = −0.38) and crop yield (r = −0.23 with total biomass and r = −0.29 with grain yield). Considering the TI-NDVI data from 2016–2019, principal component analysis followed by ascending hierarchical classification identified a typology of five clusters with different patterns of TI-NDVI during the eight growing seasons. When applied to the entire study area, this classification clearly identified the extreme classes (i.e., areas with high or no salinity). Other classes with low TI-NDVI (i.e., during dry seasons) may be related to areas with moderate or seasonal soil salinity. Finally, the high temporal resolution of Sentinel-2 images enabled us to detect stresses on vegetation that occurred repeatedly over the growing seasons, which may be good indicators of soil constraints due to salinity in the context of the irrigated paddy systems of Niger. Further research will validate the ability of the method developed to detect moderate soil salinity constraints over large areas.

Enhancing the spatial resolution of pedological information is a great challenge in the field of digital soil mapping (DSM). Several techniques have emerged to disaggregate conventional soil maps initially and are available at a coarser spatial resolution than required for solving environmental and agricultural issues. At the regional level, polygon maps represent soil cover as a tessellation of polygons defining soil map units (SMUs), where each SMU can include one or several soil type units (STUs) with given proportions derived from expert knowledge. Such polygon maps can be disaggregated at a finer spatial resolution by machine-learning algorithms, using the Disaggregation and Harmonisation of Soil Map Units Through Resampled Classification Trees (DSMART) algorithm. This study aimed to compare three approaches of the spatial disaggregation of legacy soil maps based on DSMART decision trees to test the hypothesis that the disaggregation of soil landscape distribution rules may improve the accuracy of the resulting soil maps. Overall, two modified DSMART algorithms (DSMART with extra soil profiles; DSMART with soil landscape relationships) and the original DSMART algorithm were tested. The quality of disaggregated soil maps at a 50 m resolution was assessed over a large study area (6775 km²) using an external validation based on 135 independent soil profiles selected by probability sampling, 755 legacy soil profiles and existing detailed 1:25 000 soil maps. Pairwise comparisons were also performed, using the Shannon entropy measure, to spatially locate the differences between disaggregated maps. The main results show that adding soil landscape relationships to the disaggregation process enhances the performance of the prediction of soil type distribution. Considering the three most probable STUs and using 135 independent soil profiles, the overall accuracy measures (the percentage of soil profiles where predictions meet observations) are 19.8 % for DSMART with expert rules against 18.1 % for the original DSMART and 16.9 % for DSMART with extra soil profiles. These measures were almost 2 times higher when validated using 3×3 windows. They achieved 28.5 % for DSMART with soil landscape relationships and 25.3 % and 21 % for original DSMART and DSMART with extra soil observations, respectively. In general, adding soil landscape relationships and extra soil observations constraints allow the model to predict a specific STU that can occur in specific environmental conditions. Thus, including global soil landscape expert rules in the DSMART algorithm is crucial for obtaining consistent soil maps with a clear internal disaggregation of SMUs across the landscape.

Understanding the role of vegetation in the interface between the atmosphere and groundwater is the most decisive key in analyzing the processes involved in water transfer. The main effect of vegetation is its root water uptake, which significantly modifies the processes involved in water transfer in the vadose zone. This paper focuses on mapping temporal and spatial changes in soil moisture using electrical resistivity tomography (ERT). The main objective is to assess how electrical resistivity (ER) is useful for mapping water distribution along a heterogeneous toposequence crossed by a hedgerow. Ten ERT were performed over the studied period for a 28 m long toposequence and compared to matric potential and groundwater level measurements. Soil volumetric water content (VWC) was predicted with two methods: (i) from ER using the Waxman and Smits model (ii) and from matric potential using an experimental retention curve fitted by a Van Genuchten model. Probability density functions (PDFs) of our set of data show that the largest change in mean ER and matric potential was observed in the topsoil layer. We then analyzed the consistency between ER and point measurements in this layer by extracting the arrays at the junction of ER grids and point measurements. PDFs of ER maps at each monitoring time (from T01 to T10) were also calculated to select the most contrasting distributions, corresponding to the wettest (T06) and driest states (T10). Results of ER were consistent with matric-potential measurements, with two different behaviors for locations inside and outside the root zone. A consistent correlation between VWC values from the Waxman and Smits model and those obtained from the retention curve was observed outside the root zone. The heterogeneous soil system inside the root zone shows a different pattern in this relationship. A shift in the relationship between ER and soil moisture for the locations outside and inside the root zone highlights the nonstationarity between wet and dry periods inside the root zone. The equivocal behavior of this relationship shows the limitation of using ER to predict soil moisture in a heterogeneous soil system. Such systems were actually related to the high hedgerow root density and also to a particular topographical context (ditch and bank) that is encountered in Brittany and throughout northwestern Europe.

Monitoring soil salinity over time is a crucial issue in Saharan oases to anticipate salinisation related to insufficient irrigation management. This project tested the ability of electromagnetic conductivity surveys to describe, by means of regression-tree inference models, spatiotemporal changes in soil salinity at different depths within a complex 10-ha pattern of irrigated plots in an Algerian oasis. Soils were sandy Aridic Salic Solonchaks with a fluctuating saline watertable at less than 2 m. Apparent electrical conductivity (ECa) was measured by an EM38 device at fixed 10- or 20-m intervals (2889 points) at four sampling dates between March 2009 and November 2010. For calibration and validation purposes, soil salinity was measured from a 1 : 5 diluted extract (EC1:5) in three layers (0–10, 10–25, 25–50 cm) at 30 of these points randomly chosen at each date. ECa measurements were used to predict EC1:5 using calibration regression trees created with the software Cubist, including either parameters specific to the study site (specific model) or more general parameters (general model), allowing extrapolation to other sites. Performance of regression tree predictions was compared with predictions derived from a multiple linear regression (MLR) model adjusted for each date using the software ESAP. Salinity was better predicted by Cubist regression tree models than MLR models. For the deep layer (25–50 cm), Cubist models were more accurate with the specific model (r2 = 0.8, RMSE = 1.6 dS/m) than the general model (r2 = 0.4, RMSE = 2.5 dS/m). Prediction accuracy of both models decreased from the bottom to the top of the soil profile. Salinity maps showed high inter-plot variability, which was captured better by the more flexible regression-tree inference models than the classic MLR models, but they need to build site-specific prediction models. Overall, the monitoring surveys, combined with the Cubist prediction tool, revealed both the seasonal dynamics and spatial variability of salinity at different depths.