Salinity Monitoring Research Articles

Monitoring soil salinity in coastal wetlands with Sentinel-2 MSI data: Combining fractional-order derivatives and stacked machine learning models

Monitoring soil salinity is essential for understanding the behavior of coastal wetland ecosystems and implementing effective management strategies. Despite the advantages of the Multi-Spectral Instrument (MSI) data for large-scale, high-frequency soil salinity monitoring, challenges remain in data preprocessing and model construction. We combined fractional-order derivative (FOD) technology with stacked machine learning models to monitor and map soil salinity using Sentinel-2 MSI data. The base models included Elastic Net Regression, Support Vector Regression, Artificial Neural Network, Extreme Gradient Boosting, and Random Forest, with Non-Negative Least Squares as the meta-learner. The results showed that low-order FOD enhanced image gradients and maintained a high peak signal-to-noise ratio, thereby improving the correlation with soil salinity. Notably, the 0.25-order FOD showed the best performance, increasing the correlation coefficient with soil salinity by up to 13 %. The stacked machine learning models effectively combined the strengths of different base models, enhancing prediction accuracy by more than 8 % compared to single models. Furthermore, combining stacked models with FOD further improved prediction accuracy, with an increase in R² of up to 9 %. The combination of 0.25-order FOD and the stacked machine learning model achieved the best performance (R² = 0.82, RMSE = 10.19 ppt, RPD = 2.38, RPIQ = 4.69). This approach provides a reference for rapid and effective large-scale digital mapping of soil salinity in coastal wetlands.

Soil Salinity Inversion Based on a Stacking Integrated Learning Algorithm

Soil salinization is an essential risk factor for agricultural development and food security, and obtaining regional soil salinity information more reliably remains a priority problem to be solved. To improve the accuracy of soil salinity inversion, this study focuses on the Manas River Basin oasis area, the largest oasis farming area in Xinjiang, as the study area and proposes a new soil salinity inversion model based on stacked integrated learning algorithms. Firstly, we selected four machine learning regression models, namely, random forest (RF), back propagation neural network, support vector regression, and convolutional neural network, for performance evaluation. Based on the model performance, we selected the more effective RF and BPNN as the basic regression models and further constructed a stacking integrated learning model. This stacking integration learning model improved the prediction accuracy by training a secondary model to fuse the prediction results of these two basic models as new features. We compared and analyzed the stacking integrated learning model with four single machine learning regression models. Findings indicated that the stacking integrated learning regression model fitted better and had good stability; on the test set, the stacking integrated learning regression model showed a relative increase of 8.2% in R2, a relative decrease of 14.0% in RMSE, and a relative increase of 6.5% in RPD when compared to the RF model, which was the single most effective machine learning regression model, and the stacking model was able to achieve soil salinity inversion more accurately. The soil salinity in the oasis areas of the Manas River Basin tended to decrease from north to south from 2016 to 2020 from a spatial point of view, and it was reduced in April from a temporal point of view. The percentage of pixels with a high soil salinity content of 2.75–2.80 g kg−1 in the study area had decreased by 19.6% in April 2020 compared to April 2016. The innovatively constructed stacking integrated learning regression model improved the accuracy of soil salinity estimation on the basis of the superior results obtained in the training of the single optimal machine learning regression model. As a consequence, this model can provide technological backup for fast monitoring and inversion of soil salinity as well as prevention and containment of salinization.

Monitoring and assessment of spatiotemporal soil salinization in the Lake Urmia region

Soil salinization stands as a prominent global environmental challenge, necessitating enhanced assessment methodologies. This study is dedicated to refining soil salinity assessment in the Lake Urmia region of Iran, utilizing multi-year data spanning from 2015 to 2018. To achieve this objective, soil salinity was measured at 915 sampling points during the 2015–2018 timeframe. Simultaneously, remote sensing data were derived from surface reflectance data over the same study period. Four distinct scenarios were considered such as a newly developed spectral index (Scenario I), the newly developed index combined with other salt-based spectral indices from the literature (Scenario II), indirect spectral indices based on vegetation and soil characteristics (Scenario III), and the amalgamation of both direct and indirect spectral indices (Scenario IV). Linear Regression (LR), Support Vector Machine (SVM), and Random Forest (RF) were employed to assess soil salinity. The measured data divided to 75% of the data as the calibration dataset, while the remaining 25% constituted the validation dataset. The findings revealed a correlation between soil salinity and spectral indices from the literature, with a range of -0.53 to 0.51, while the newly developed spectral index exhibited a stronger correlation (r = 0.59). Furthermore, RF yielded superior results when using the newly developed spectral index (Scenario I). Overall, SVM emerged as the most effective model (ME = -9.678, R2 = 0.751, and RPIQ = 1.78) when integrating direct and indirect spectral indices (Scenario IV). This study demonstrates the efficacy of combining machine learning techniques with a blend of newly developed and existing spectral indices from the literature for the monitoring of soil salinity, particularly in arid and semi-arid regions.

The Link between Surface Visible Light Spectral Features and Water–Salt Transfer in Saline Soils—Investigation Based on Soil Column Laboratory Experiments

Monitoring soil salinity with remote sensing is difficult, but knowing the link between saline soil surface spectra, soil water, and salt transport processes might help in modeling for soil salinity monitoring. In this study, we used an indoor soil column experiment, an unmanned aerial vehicle multispectral sensor camera, and a soil moisture sensor to study the water and salt transport process in the soil column under different water addition conditions and investigate the relationship between the soil water and salt transport process and the spectral reflectance of the image on the soil surface. The observation results of the soil column show that the soil water and salt transportation process conforms to the basic transportation law of “salt moves together with water, and when water evaporates, salt is retained in the soil weight”. The salt accumulation phenomenon increases the image spectral reflectance of the surface layer of the soil column, while soil temperature has no effect on the reflectance. As the water percolates down, water and salt accumulate at the bottom of the soil column. The salinity index decreases instantly after the addition of brine and then tends to increase slowly. The experimental results indicate that this work can capture the relationship between the water and salt transport process and remote sensing spectra, which can provide theoretical basis and reference for soil water salinity monitoring.

Soil chemical characteristics, yield and technological quality of sugarcane under subsurface drip irrigation with treated sewage effluent

ABSTRACT Sugarcane cultivation requires sustainable strategies to meet water and nutritional needs. This study tested the hypothesis that irrigation with treated sewage effluent (TSE) can meet the water needs of sugarcane plants, improving their technological quality and yield. The objective of this study was to assess the effects of subsurface drip irrigation (SDI) with TSE or surface reservoir water (SRW) on soil chemical characteristics and technological quality and productivity. Thus, an SDI system was installed at soil depths of 0.20 and 0.40 m to apply SRW and TSE to sugarcane plants, composing four irrigation treatments (TSE0.2 m, TSE0.4 m, SRW0.2 m and SRW0.4 m) and a control (non-irrigated treatment). Analyses of soil chemical characteristics in different layers showed no negative effects for the use of SDI, which maintained favorable soil conditions for sugarcane plantations. The surface layer (0–0.2 m) irrigated with SRW or TSE showed higher accumulation of nutrients and organic matter. The results confirmed the hypothesis that irrigation with TSE or SRW improves the technological quality and yield of sugarcane; the treatment TSE0.2 m showed the best results regarding increases in sugar yield and alcohol production. However, continuous monitoring of soil salinity is necessary in wastewater-irrigated agricultural systems.

Evolution Patterns and Dominant Factors of Soil Salinization in the Yellow River Delta Based on Long-Time-Series and Similar Phenological-Fusion Images

Previous studies were mostly conducted based on sparse time series and different phenological images, which often ignored the dramatic changes in salinization evolution throughout the year. Based on Landsat and moderate-resolution-imaging spectroradiometer (MODIS) images from 2000 to 2020, this study applied the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) algorithm to obtain similar phenological images for the month of April for the past 20 years. Based on the random forest algorithm, the surface parameters of the salinization were optimized, and the feature space index models were constructed. Combined with the measured ground data, the optimal monitoring index model of salinization was determined, and then the spatiotemporal evolution patterns of salinization and its driving mechanisms in the Yellow River Delta were revealed. The main conclusions were as follows: (1) The derived long-time-series and similar phenological-fusion images enable us to reveal the patterns of change in the dramatic salinization in the year that we examined using the ESTARFM algorithm. (2) The NDSI-TGDVI feature space salinization monitoring index model based on point-to-point mode had the highest accuracy of 0.92. (3) From 2000 to 2020, the soil salinization in the Yellow River Delta showed an aggravating trend. The average value of salinization during the past 20 years was 0.65, which is categorized as severe salinization. The degree of salinization gradually decreased from the northeastern coastal area to the southwestern inland area. (4) The dominant factors affecting soil salinization in different historical periods varied. The research results could provide support for decision-making regarding the precise prevention and control of salinization in the Yellow River Delta.

Contested quantification for planetary health – A sociotechnical analysis of Bangladesh's water salinity monitoring infrastructure

In climate change and planetary health, the datafication of natural processes and their effects on human health is gaining importance, not least due to data's role in international funding mechanisms. In Bangladesh, water salinity has become the focus of much research and could emerge as an asset to access climate funding. Taking Bangladesh's water salinity monitoring infrastructure as a case study, this paper problematizes the “neutrality” of water salinity data. Adopting the methodological approach of an “infrastructural inversion”, we foreground the relational nature of data production. The study draws on ethnographic fieldwork in Bangladesh along the data production chain. We highlight how the involvement of a large variety of actors gave rise to vastly different data infrastructures and explicate the influence of actors' “problematization”. We further illustrate the importance of pre-existing materiality for the implementation of data collection systems on the ground and their power to influence whose reality is represented and whose is left out. Discussing these findings in the context of the international development sector reveals the complex interplay between the dual function of data as a mediator of knowledge and proof of legitimacy. Attention is paid to the tendencies of perpetuating patterns of exclusion and inclusion through data in a setting of project-based funding. We thereby provide global health researchers, policy makers and development practitioners with a detailed case study whilst stimulating reflection on the hegemony of datafication.

High spatiotemporal resolution vegetation index time series can facilitate enhanced remote sensing monitoring of soil salinization

High spatiotemporal resolution vegetation index time series can facilitate enhanced remote sensing monitoring of soil salinization

Real-Time Optical Fiber Salinity Interrogator Based on Time-Domain Demodulation and TPMF Incorporated Sagnac Interferometer.

A novel demodulation scheme for a point-type fiber sensor is designed for salinity concentration monitoring based on a Sagnac interferometer (SI) composed of a tapered polarization-maintaining fiber (TPMF) and optical time stretching technology. The SI, constructed using a PMF with a taper region of 5.92 μm and an overall length of 30 cm, demonstrated a notable enhancement in the evanescent field, which intensifies the interaction between the light field and external salinity. This enhancement allows for a direct assessment of salinity concentration changes by analyzing the variations in the SI reflection spectra and the experimental results indicate that the sensitivity of the sensor is 0.151 nm/‱. In contrast to traditional fiber optic sensors that depend on spectral demodulation with slower response rates, this work introduces a new approach where the spectral shift is translated to the time domain, utilizing a dispersion compensation fiber (DCF) with the demodulation rate reaching up to 50 MHz. The experimental outcomes reveal that the sensor exhibits a sensitivity of -0.15 ns/‱ in the time domain. The designed sensor is anticipated to play a pivotal role in remote, real-time monitoring of ocean salinity.

Assessment of Soil Salinity and Irrigation Water Quality at Tahtay-Adyabo District, Northwestern Tigray, Ethiopia

A study was conducted in Tahtay Adyabo northern Ethiopia to assess the salinity and irrigation water quality at farmer-level irrigation sites in the year of 2012. irrigation sites was identified and sampling units was assigned. 38 soil samples were collected from 19 plots (Dugub, Endaserawat, Mytewldish, Egum dima, and scattered wells in Mentebteb district ) at depths of 0-30cm and 30-50cm, along with, 21 water samples from 14 wells and 7 rivers. These samples were then analyzed at the Shire Soil Laboratory for salinity and fertility parameters. The results showed that the salinity status of surface soil at 0-30cm depth for dugub, scattered wells, and Myteweldish was 75% non-saline and 25% slightly saline. For Endaserawat, 50% was slightly saline, 25% moderately saline, and 25% strongly saline. Egum Dima showed 67% slightly saline and 33% moderately saline. In subsurface soil at 30-50cm depth, dugub, scattered wells, and Myteweldish were 75% non-saline and 25% slightly saline, while Endaserawat was 25% slightly saline and 75% moderately saline, and Egum Dima was 67% slightly saline, 16.5% moderately saline, and 16.5% strongly saline. Water analysis for the wells showed that out of 14 samples, 21% were non-saline, 21% were slightly to moderately saline, and 57% were severely saline. For the rivers, out of 7 samples, 43% were slightly to moderately saline and 57% were severely saline. Overall, out of 21 water samples from wells and rivers, 14% were non-saline, 29% were slightly to moderately saline, and 57% were severely saline. In general, the salinity status in Dugub, Endaserawat, Mytewldish, and Egum Dima varies from non-saline to strongly saline, but the severity is more pronounced in Endaserawat and Egum Dima. This salinity is attributed to the parent material and the water table. Crop selection, integration of organic matter, applying extra irrigation water and regular salinity monitoring is recommended, to optimize the productivity of the soils in the irrigation site.

Remote Water Quality Monitoring System In Shrimp Ponds With Photovoltaic (PV)-Based Energy Source

Introduction: Shrimp has great potential to be used as a business field in Indonesia. Especially in Parigi Regency itself, there are now many shrimp ponds that can be found because of the benefits obtained, so people are interested in making shrimp farming a livelihood. One of the main problems of shrimp ponds is pond water quality. There are several factors that affect pond water quality, namely water temperature, water pH, and water salinity, good pond water quality management can maintain quality standards and can increase shrimp yield and productivity Method: To facilitate the shrimp farmers, innovations need to be made in order to help the shrimp farmers manage their shrimp ponds, therefore using a remote water quality monitoring was system in shrimp ponds with photovoltaic (PV) based energy sources is expected to be an innovation in managing shrimp ponds, especially in monitoring water quality in shrimp ponds. The monitoring system designed using the NRF24L01 module as a remote communication module, at the research location the distance from the shrimp pond to the house is approximately 100 meters, so the tools that have been designed can facilitate shrimp farmers to monitor the shrimp pond Results and Disscussion: The results of testing the DS18B20 temperature sensor compared with a digital thermometer measuring instrument get an average error of 0.014%. The test results of the pH sensor compared to the pH meter get an average error of 0.026%. TDS sensor test results compared with the TDS meter get an average error of 0.04%. Conclusion: The temperature, pH, and salinity monitoring system uses transmitter and receiver modules, where the transmitter module reads and sends data to the receiver module wirelessly with NRF24L01. Using DS18B20 temperature, pH, and TDS sensors, as well as energy from photovoltaics, this system helps shrimp farmers monitor pond water quality remotely.

Complementarity of Sentinel-1 and Sentinel-2 Data for Soil Salinity Monitoring to Support Sustainable Agriculture Practices in the Central Bolivian Altiplano

Soil salinization will affect 50% of global cropland areas by 2050 and represents a major threat to agricultural production and food sovereignty. As soil salinity monitoring is costly and time consuming, many regions of the world undertake very limited soil salinity observation (in space and time), preventing the accurate assessment of soil salinity hazards. In this context, this study assesses the relative performance of Sentinel-1 radar and Sentinel-2 optical images, and the combination of the two, for monitoring changes in soil salinity at high spatial and temporal resolution, which is essential to evaluate the mitigation measures required for the sustainable adaptation of agriculture practices. For this purpose, an improved learning database made of 863 soil electrical conductivity (i.e., soil salinity) observations is considered for the training/validation step of a Random Forest (RF) model. The RF model is successively trained with (1) only Sentinel-1, (2) only Sentinel-2 and (3) both Sentinel-1 and -2 features using the Genetic Algorithm (GA) to reduce multi-collinearity in the independent variables. Using k-fold cross validation (3-fold), overall accuracy (OA) values of 0.83, 0.88 and 0.95 are obtained when considering only Sentinel-2, only Sentinel-1 and both Sentinel-1 and -2 features as independent variables. Therefore, these results highlight the clear complementarity of radar (i.e., Sentinel-1) and optical (i.e., Sentinel-2) images to improve soil salinity mapping, with OA increases of approximately 10% and 7% when compared to Sentinel-2 and Sentinel-1 alone. Finally, pre-sowing soil salinity maps over a five-year period (2019–2023) are presented to highlight the benefit of the proposed procedure to support the sustainable management of agricultural lands in the context of soil salinization on a regional scale.

Effects of Seasonal Freezing and Thawing on Soil Moisture and Salinity in the Farmland Shelterbelt System in the Hetao Irrigation District

Water resources are scarce, and secondary soil salinization is severe in the Hetao Irrigation District. Farmland shelterbelt systems (FSS) play a critical role in regulating soil water and salt dynamics within the irrigation district. However, the understanding of soil water and salt migration within FSS during the freeze–thaw period remains unclear due to the complex and multifaceted interactions between water and salt. This study focused on a typical FSS and conducted comprehensive monitoring of soil moisture, salinity, temperature, and meteorological parameters during the freeze–thaw period. The results revealed consistent trends in air temperature and soil temperature overall. Soil freezing durations exceeded thawing durations, and both decreased with an increasing soil depth. At the three critical freeze–thaw nodes, the soil moisture content at a 0–20 cm depth was significantly lower than at a 40–100 cm depth (p < 0.05). The soil water content increased with time and depth at varying distances from the shelterbelt, with an average increase of 7.63% after freezing and thawing. The surface water content at the forest edge (0.3H, 4H) was lower than inside the farmland (1H, 2H, 3H). Soil salt accumulation occurred during both freezing stable periods and melting–thawing periods in the 0–100 cm soil layer near the forest edge (0.3H, 4H), with the highest soil salinity reaching 0.62 g·kg−1. After the freeze–thaw period, the soil salt content in each layer increased by 11.41–47.26% compared to before the freeze–thaw period. Salt accumulation in farmland soil near the shelterbelt was stronger than in the far shelterbelt. The multivariate statistical model demonstrated goodness of fit for soil water and salt as 0.94 and 0.72, respectively, while the BP neural network model showed goodness of fit for soil water and salt as 0.82 and 0.85, respectively. Our results provide an efficient theoretical basis for FSS construction and agricultural water management practices.

Monitoring soil salinity based on Sentinel-1/2 remote sensing parameters and two-dimensional space theory

Multi-spectral satellite remote sensing is widely used in soil salinity monitoring by virtue of its wide coverage, fast update and low cost, but it is susceptible to cloudy weather, limiting the accuracy and application conditions of the inversion, while SAR (synthetic aperture radar) is hardly affected by cloud with strong penetration ability and enables almost round-the-clock, climate-wide monitoring of soil salinity. Two-dimensional feature space model, being easily interpretive and widely used in soil salinity monitoring, requires only some easily extracted parameters and can reduce other influences on soil salinity inversion. Therefore, in order to take full advantages of the SAR and multi-spectral satellite, achieve monitoring of soil salinity in more climatic conditions and longer time, and further improve soil salinity monitoring accuracy, this paper combine SAR-Spectral data with two-dimensional space theory to construct two-dimensional models for monitoring soil salinity. In this study, two-dimensional space models are constructed using Sentinel-1 and Sentinel-2 feature parameters, and models are evaluated with R2 with measured soil salinity data of the Shahaoqu irrigation area in Hetao irrigation district, Inner Mongolia. The results show that it is feasible to use the parameters extracted from Sentinel-1 after H/a polarization for constructing two-dimensional feature space models for the soil salinity inversion(R2parameters of Sentinel-1 = 0.467,0.587,0.517), and the two-dimensional space models combining Sentinel-1/2 parameters are more accurate and stable in soil salinity monitoring than those using Sentinel-1 or Sentinel-2 alone((Rparameters of Sentinel-2 = 0.320,0.517,0.341),(R2parameters of Sentinel-1/2 = 0.507,0.545,0.562,0.512,0.557,0.568)).

Soil Salinity Inversion in Yellow River Delta by Regularized Extreme Learning Machine Based on ICOA

Soil salinization has seriously affected agricultural production and ecological balance in the Yellow River Delta region. Rapid and accurate monitoring of soil salinity has become an urgent need. Traditional machine learning models tend to fall into local optimal values during the learning process, which reduces their accuracy. This paper introduces Circle map to enhance the crayfish optimization algorithm (COA), which is then integrated with the regularized extreme learning machine (RELM) model, aiming to improve the accuracy of soil salinity content (SSC) inversion in the Yellow River Delta region. We employed Landsat5 TM remote sensing images and measured salinity data to develop spectral indices, such as the band index, salinity index, vegetation index, and comprehensive index, selecting the optimal modeling variable group through Pearson correlation analysis and variable projection importance analysis. The back propagation neural network (BPNN), RELM, and improved crayfish optimization algorithm–regularized extreme learning machine (ICOA-RELM) models were constructed using measured data and selected variable groups for SSC inversion. The results indicate that the ICOA-RELM model enhances the R2 value by an average of about 0.1 compared to other models, particularly those using groups of variables filtered by variable projection importance analysis as input variables, which showed the best inversion effect (test set R2 value of 0.75, MAE of 0.198, RMSE of 0.249). The SSC inversion results indicate a higher salinization degree in the coastal regions of the Yellow River Delta and a lower degree in the inland areas, with moderate saline soil and severe saline soil comprising 48.69% of the total area. These results are consistent with the actual sampling results, which verify the practicability of the model. This paper’s methods and findings introduce an innovative and practical tool for monitoring and managing salinized soils in the Yellow River Delta, offering significant theoretical and practical benefits.

Quantitative Inversion of Soil Salinity in Different Seasons in Huinong District, Ningxia based on Sentinel-2 satellite

Salinisation of agricultural soils poses an important constraint to sustainable agricultural development, especially in the Huinong region of Ningxia, northwestern China, where salinised soils are widely distributed. However, due to the limitations of monitoring technology, the detailed situation of soil salinisation in this region is not known. Currently, the multispectral instrument (MSI) on board the Sentinel-2 satellite provides a good opportunity for monitoring soil salinity dynamics. Therefore, in this study, the feasibility of using the multispectral instrument (MSI) on board the Sentinel-2 satellite, combined with a machine learning model, to accurately monitor the soil salinity content during the spring and summer seasons was explored. And three additional red-edge bands (B5-B7) were used instead of the traditional red band (B4) to generate potential soil salinity indices. A screening method based on the PLS-VIP criterion was used to screen the spectral covariates, and three machine learning methods, namely, Random Forest (RF), Support Vector Machine (SVM) and Extreme Learning Machine (ELM), were employed to build the inverse model of soil salt content. The results showed that the Random Forest model based on Sentinel-2 imagery performed the best in the inversion with good prediction results, with R2 and RE of 0.825 and 0.207 and 0.711 and 0.271 in spring and summer, respectively.The study also revealed that soil salinity varied significantly between seasons, and was higher in spring than in summer. This result is of great significance as a guide for soil salinity monitoring and land reclamation in arid or semi-arid regions.

Evaluation of Different Soil Salinity Indices Using Remote Sensing Techniques in Siwa Oasis, Egypt

Detecting and monitoring changes in soil salinity through remote sensing provides an opportunity for field assessment in regions where on-site measurements are limited. This research, conducted in Siwa Oasis, Egypt, aimed to assess the effectiveness of remote sensing techniques in detecting and monitoring changes in soil salinity. Using Landsat 5 and Landsat 7 satellite images, the researchers evaluated various soil salinity indices based on 56 on-site ground measurements. The study aimed to improve the correlation between electrical conductivity (EC) and index values and explore the relationship between salinity and changes in land cover. Eleven spectral indices were calculated for nine scenes captured in different months. Different approaches were employed, including stacking the data, categorizing EC measurements into salinity levels, analyzing data temporally, and conducting spatial correlation analysis. The initial approach revealed a weak correlation, due to substantial variation in EC values. However, the salinity index SI demonstrated the highest correlation coefficient of 0.38. In the second scenario, the salinity index 2 S2 index exhibited the highest correlation of 0.96 for moderate salinity samples. The third scenario showed that the salinity index 1S1 achieved the highest correlation value of 0.99 for moderately saline areas. In the fourth scenario, the SI index exhibited the strongest correlation among all four ponds, with correlation coefficients of 0.23, 0.23, 0.18, and 0.61. Notably, the correlations observed in the second and third scenarios demonstrated higher correlation coefficients than those of both the first and fourth scenarios. Additionally, remote sensing methods detected a 48% increase in total vegetated area over 17 years, showing the potential of remote sensing techniques in salinity monitoring for expanding agriculture and improving land management.

A Novel Approach to Detecting the Salinization of the Yellow River Delta Using a Kernel Normalized Difference Vegetation Index and a Feature Space Model

Using the kernel normalized difference vegetation index (KNDVI) to monitor soil salinization has great advantages; however, approaches using KNDVI and a feature space model to monitor salinization have not yet been reported. In this study, the KNDVI, normalized difference vegetation index (NDVI), extended difference vegetation index (EDVI), green normalized difference vegetation index (TGDVI), modified soil-adjusted vegetation index (MSAVI), and salt index (SI) were used to establish five feature space monitoring indices for salinization. The spatio-temporal evolution pattern of soil salinization in the Yellow River Delta from 2000 to 2020 was analyzed based on the optimal monitoring index. The remote sensing monitoring index model based on KNDVI-SI’s point-to-point mode had the best applicability with R2 = 0.93, followed by EDVI-SI’s salinization monitoring index model with R2 = 0.90. From 2000 to 2020, soil salinization in the Yellow River Delta followed an exacerbating then improving trend. Soil salinization was more severe in the northern and eastern coastal areas of the Yellow River Delta. These results are conducive to salinization restoration and control in the Yellow River Delta.

Organizational Problems of Soil Salinization Monitoring on Irrigated Lands

Organizational Problems of Soil Salinization Monitoring on Irrigated Lands

UAV hyperspectral analysis of secondary salinization in arid oasis cotton fields: effects of FOD feature selection and SOA-RF.

Secondary salinization is a crucial constraint on agricultural progress in arid regions. The specific mulching irrigation technique not only exacerbates secondary salinization but also complicates field-scale soil salinity monitoring. UAV hyperspectral remote sensing offers a monitoring method that is high-precision, high-efficiency, and short-cycle. In this study, UAV hyperspectral images were used to derive one-dimensional, textural, and three-dimensional feature variables using Competitive adaptive reweighted sampling (CARS), Gray-Level Co-occurrence Matrix (GLCM), Boruta Feature Selection (Boruta), and Brightness-Color-Index (BCI) with Fractional-order differentiation (FOD) processing. Additionally, three modeling strategies were developed (Strategy 1 involves constructing the model solely with the 20 single-band variable inputs screened by the CARS algorithm. In Strategy 2, 25 texture features augment Strategy 1, resulting in 45 feature variables for model construction. Strategy 3, building upon Strategy 2, incorporates six triple-band indices, totaling 51 variables used in the model's construction) and integrated with the Seagull Optimization Algorithm for Random Forest (SOA-RF) models to predict soil electrical conductivity (EC) and delineate spatial distribution. The results demonstrated that fractional order differentiation highlights spectral features in noisy spectra, and different orders of differentiation reveal different hidden information. The correlation between soil EC and spectra varies with the order. 1.9th order differentiation is proved to be the best order for constructing one-dimensional indices; although the addition of texture features slightly improves the accuracy of the model, the integration of the three-waveband indices significantly improves the accuracy of the estimation, with an R2 of 0.9476. In contrast to the conventional RF model, the SOA-RF algorithm optimizes its parameters thereby significantly improving the accuracy and model stability. The optimal soil salinity prediction model proposed in this study can accurately, non-invasively and rapidly identify excessive salt accumulation in drip irrigation under membrane. It is of great significance to improve the growing conditions of cotton, increase the cotton yield, and promote the sustainable development of Xinjiang's agricultural economy, and also provides a reference for the prevention and control of regional soil salinization.