Ioannis N. Daliakopoulos

The threat of soil salinity : A European scale review

Soil salinisation is one of the major soil degradation threats occurring in Europe. The effects of salinisation can be observed in numerous vital ecological and non-ecological soil functions. Drivers of salinisation can be detected both in the natural and man-made environment, with climate and the foreseen climate change also playing an important role. This review outlines the state of the art concerning drivers and pressures, key indicators as well as monitoring, modeling and mapping methods for soil salinity. Furthermore, an overview of the effect of salinisation on soil functions and the respective mechanism is presented. Finally, the state of salinisation in Europe is presented according to the most recent literature and a synthesis of consistent datasets. We conclude that future research in the field of soil salinisation should be focused on among others carbon dynamics of saline soil, further exploration of remote sensing of soil properties and the harmonization and enrichment of soil salinity maps across Europe within a general context of a soil threat monitoring system to support policies and strategies for the protection of European soils.

Tree crown detection on multispectral VHR satellite imagery.

A new method called Arbor Crown Enumerator (ACE) was developed for tree crown detection from multispectral Very High-resolution (VHR) satellite imagery. ACE uses a combination of the Red band and Normalized Difference Vegetation Index (NDVI) thresholding, and the Laplacian of the Gaussian (LOG) blob detection method. This method minimizes the detection shortcomings of its individual components and provides a more accurate estimation of the number of tree crowns captured in an image sample. The ACE was applied successfully to sample images taken from a four-band QuickBird (0.7m 0.7m) scene of Keritis watershed, in the Island of Crete. The method performs very well for different tree types, sizes and densities that may include non vegetation features such as roads and houses. Statistical analysis on the tree crown detection results from the sample images supports the agreement between the measurements and the simulations. The new method reduces considerably the effort of manual tree counting and can be used for environmental applications of fruit orchard, plantation and open forest population monitoring.

Soil Moisture Content Estimation Based on Sentinel-1 and Auxiliary Earth Observation Products. A Hydrological Approach

A methodology for elaborating multi-temporal Sentinel-1 and Landsat 8 satellite images for estimating topsoil Soil Moisture Content (SMC) to support hydrological simulation studies is proposed. After pre-processing the remote sensing data, backscattering coefficient, Normalized Difference Vegetation Index (NDVI), thermal infrared temperature and incidence angle parameters are assessed for their potential to infer ground measurements of SMC, collected at the top 5 cm. A non-linear approach using Artificial Neural Networks (ANNs) is tested. The methodology is applied in Western Crete, Greece, where a SMC gauge network was deployed during 2015. The performance of the proposed algorithm is evaluated using leave-one-out cross validation and sensitivity analysis. ANNs prove to be the most efficient in SMC estimation yielding R2 values between 0.7 and 0.9. The proposed methodology is used to support a hydrological simulation with the HEC-HMS model, applied at the Keramianos basin which is ungauged for SMC. Results and model sensitivity highlight the contribution of combining Sentinel-1 SAR and Landsat 8 images for improving SMC estimates and supporting hydrological studies.

Climate-Induced Shifts in Global Soil Temperature Regimes

Abstract Soil temperature is a key factor of plant growth and biological enzyme activities occurring in the soil, affected by the land cover, the evapotranspiration rate, the albedo, and the energy budget of the soil surface. In recent decades, efforts have been made to conserve soils against nonsustainable anthropogenic pressures. Changes in climate can impose additional threats on soil sustainability, as global scale soil temperature regime alterations are expected under global warming. Here, data from three well-established global climate models, spanning from 1981 to as far as 2120, are used to force the JULES (Joint UK Land Environment Simulator) model and produce simulations of soil temperature, calculating the water and energy budgets of the land surface. Modeled soil temperature data are used to estimate the climate-induced changes in the global soil temperature regimes at three different global warming levels. The results show significant shifts in the soil temperature regime for extended areas of the world, especially in the northern hemisphere. Pergelic and Cryic areas are reduced, whereas the Mesic and Thermic soils gain large areas in all three studied scenarios. Implications of the warming patterns might indicate the northward shift of various croplands in regions that until now their cultivation was not possible.

Assessing soil salinity using WorldView-2 multispectral images in Timpaki, Crete, Greece

Abstract Salinization is one of the major soil degradation threats occurring worldwide. This study evaluates the feasibility of operational surface soil salinity mapping based on state-of-the-art Earth Observation (EO) products captured by sensors on-board WorldView-2 (WV2) and Landsat 8 satellites. The proposed methods are tested in Timpaki, south-central Crete,Greece, where brackish water irrigation puts soil health at risk of soil salinization. In all cases, EO products are calibrated against soil samples collected from bare soil locations. Results indicate a moderate correlation of observed ECe values with the investigated remote sensing parameters. Regarding sensitivity to saline soil, the yellow band displays higher values. Comparison between methods used in the literature shows that those developed specifically for soil salinity, and especially index S5, perform better. The proposed ‘detection index’ and 3D PCA transformation methodology perform reasonably well in detecting areas with high ECe values and provide a simple and effective operational alternative for saline topsoil detection and mapping.

Comparison of an artificial neural network and a conceptual rainfall–runoff model in the simulation of ephemeral streamflow

ABSTRACT The rainfall–runoff process is governed by parameters that can seldom be measured directly for use with distributed models, but are rather inferred by expert judgment and calibrated against historical records. Here, a comparison is made between a conceptual model (CM) and an artificial neural network (ANN) for their ability to efficiently model complex hydrological processes. The Sacramento soil moisture accounting model (SAC-SMA) is calibrated using a scheme based on genetic algorithms and an input delay neural network (IDNN) is trained for variable delays and hidden layer neurons which are thoroughly discussed. The models are tested for 15 ephemeral catchments in Crete, Greece, using monthly rainfall, streamflow and potential evapotranspiration input. SAC-SMA performs well for most basins and acceptably for the entire sample with R2 of 0.59–0.92, while scoring better for high than low flows. For the entire dataset, the IDNN improves simulation fit to R2 of 0.70–0.96 and performs better for high flows while being outmatched in low flows. Results show that the ANN models can be superior to the conventional CMs, as parameter sensitivity is unclear, but CMs may be more robust in extrapolating beyond historical record limits and scenario building. EDITOR M.C. Acreman; ASSOCIATE EDITOR not assigned

Modeling Soil Salinity in Greenhouse Cultivations Under a Changing Climate With SALTMED: Model Modification and Application in Timpaki, Crete

Abstract Soil salinity is a major soil degradation threat especially for arid coastal environments where it hinders agricultural production, thus imposing a desertification risk. In the prospect of a changing climate, soil salinity caused by brackish water irrigation introduces additional uncertainties regarding the viability of deficit irrigation and intensive cultivation practices such as greenhouse cropping. Here, we propose a modification of the SALTMED leaching requirement model to account for greenhouse cultivation conditions. The model is applied in the RECARE Project Case Study of Timpaki, a semiarid region in south-central Crete, Greece, where greenhouse horticulture is an important land use. Excessive groundwater abstractions toward irrigation have resulted in a drop of the groundwater level in the coastal part of the aquifer, thus leading to seawater intrusion and in turn to soil salinization. Crop yield and soil profile electrical conductivity (EC) sensitivity to initial soil EC (up to 2 dS m−1) and irrigation water EC (up to 3 dS m−1) are modeled for the locally popular horticultural crops of Solanum lycopersicum, Solanum melongena, and Capsicum annuum. Climate model data obtained from nine general circulation models for the “worst case” representative concentration pathway of 8.5 W m−2 of the fifth phase of the Coupled Model Intercomparison Project are corrected for bias against historical observations with the Multisegment Statistical Bias Correction method and used to estimate crop yield and soil profile EC sensitivity in a warmer future. Results show that the effects of climate change on S. lycopersicum greenhouse cultivations of Timpaki will be detrimental, whereas S. melongena and C. annuum cultivations may show greater resilience.

Spatial Downscaling of Alien Species Presences Using Machine Learning

Large scale, high-resolution data on alien species distributions are essential for spatially explicit assessments of their environmental and socio-economic impacts, and management interventions for mitigation. However, these data are often unavailable. This paper presents a method that relies on Random Forest (RF) models to distribute alien species presence counts at a finer resolution grid, thus achieving spatial downscaling. A sufficiently large number of RF models are trained using random subsets of the dataset as predictors, in a bootstrapping approach to account for the uncertainty introduced by the subset selection. The method is tested with an approximately 8×8 km2 grid containing floral alien species presence and several indices of climatic, habitat, land use covariates for the Mediterranean island of Crete, Greece. Alien species presence is aggregated at 16×16 km2 and used as a predictor of presence at the original resolution, thus simulating spatial downscaling. Potential explanatory variables included habitat types, land cover richness, endemic species richness, soil type, temperature, precipitation, and freshwater availability. Uncertainty assessment of the spatial downscaling of alien species’ occurrences was also performed and true/false presences and absences were quantified. The approach is promising for downscaling alien species datasets of larger spatial scale but coarse resolution, where the underlying environmental information is available at a finer resolution than the alien species data. Furthermore, the RF architecture allows for tuning towards operationally optimal sensitivity and specificity, thus providing a decision support tool for designing a resource efficient alien species census.

A method to preserve trends in quantile mapping bias correction of climate modeled temperature

Bias correction of climate variables is a standard practice in climate change impact (CCI) studies. Various methodologies have been developed within the framework of quantile mapping. However, it is well known that quantile mapping may significantly modify the long-term statistics due to the time dependency of the temperature bias. Here, a method to overcome this issue without compromising the day-to-day correction statistics is presented. The methodology separates the modeled temperature signal into a normalized and a residual component relative to the modeled reference period climatology, in order to adjust the biases only for the former and preserve the signal of the later. The results show that this method allows for the preservation of the originally modeled long-term signal in the mean, the standard deviation and higher and lower percentiles of temperature. To illustrate the improvements, the methodology is tested on daily time series obtained from five Euro CORDEX regional climate models (RCMs).

Climate-induced catastrophic shifts in pastoralism systems managed under the maximum sustainable yield model

The effects of the foreseen change in precipitation and temperature on dynamic grazing systems that are managed under the hypothesis of the maximum sustainable yield (MSY) are assessed. The standard Gordon Schaefer approach that relates the rate of above-ground vegetation production to biomass consumption by herbivores is adopted to simulate the grazing system. In order to account for future climate variability, the model is modified using principles from water balance hydrology, thus introducing vegetation growth limitations due to climatic aridity. The model is applied for an equilibrium established under the much criticized MSY hypothesis that assumes the optimum herbivore density for maximum biomass removal. Sensitivity analysis results indicate that as climate changes towards a warmer and drier future, the probability of a low ecological stability grazing system to collapse increases, especially in arid environments where water is a limited resource. Such sudden shifts that lead to undesirable stable states of arid ecosystems are investigated by the CASCADE EU project.