scholarly journals The Applicability of Remote Sensing Models of Soil Salinization Based on Feature Space

2021 ◽  
Vol 13 (24) ◽  
pp. 13711
Author(s):  
Jing Liu ◽  
Li Zhang ◽  
Tong Dong ◽  
Juanle Wang ◽  
Yanmin Fan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Salinity ◽  
Feature Space ◽  
Soil Salinization ◽  
Coastal Areas ◽  
Future Research ◽  
Landsat 8 ◽  
Distribution Maps ◽  
Inversion Model ◽  
Yanqi Basin

Soil salinization is a major challenge for the sustainable use of land resources. An optimal remote sensing inversion model could monitor regional soil salinity across diverse geographical areas. In this study, the feature space method was used to study the applicability of the inversion model for typical salt-affected soils in China (Yanqi Basin (arid area) and Kenli County (coastal area)), and to obtain soil salinity grade distribution maps. The salinity index (SI) surface albedo (Albedo)model was the most accurate in both arid and coastal regions with overall accuracy reaching 93.3% and 88.8%, respectively. The sensitivity factors for the inversion of salinity in both regions were the same, indicating that the SI-Albedo model is applicable for monitoring salinity in arid and coastal areas of China. We combined Landsat 8 Operational Land Imager image data and field data to obtain the distribution pattern of soil salinity using the SI-Albedo model and proposed corresponding countermeasures for soil salinity in the Yanqi Basin and Kenli County according to the degree of salinity. This study on soil salinity in arid and coastal areas of China will provide a useful reference for future research on soil salinity both in China and globally.

scholarly journals Soil Salinity Inversion in Coastal Corn Planting Areas by the Satellite-UAV-Ground Integration Approach

2021 ◽  
Vol 13 (16) ◽  
pp. 3100
Author(s):  
Guanghui Qi ◽  
Chunyan Chang ◽  
Wei Yang ◽  
Peng Gao ◽  
Gengxing Zhao
Keyword(s):  
Remote Sensing ◽  
Soil Salinity ◽  
Large Scale ◽  
Yellow River ◽  
Remote Sensing Data ◽  
Soil Salinization ◽  
Large Area ◽  
Sensing Data ◽  
Inversion Model ◽  
Uav Image

Soil salinization is a significant factor affecting corn growth in coastal areas. How to use multi-source remote sensing data to achieve the target of rapid, efficient and accurate soil salinity monitoring in a large area is worth further study. In this research, using Kenli District of the Yellow River Delta as study area, the inversion of soil salinity in a corn planting area was carried out based on the integration of ground imaging hyperspectral, unmanned aerial vehicles (UAV) multispectral and Sentinel-2A satellite multispectral images. The UAV and ground images were fused, and the partial least squares inversion model was constructed by the fused UAV image. Then, inversion model was scaled up to the satellite by the TsHARP method, and finally, the accuracy of the satellite-UAV-ground inversion model and results was verified. The results show that the band fusion of UAV and ground images effectively enrich the spectral information of the UAV image. The accuracy of the inversion model constructed based on the fused UAV images was improved. The inversion results of soil salinity based on the integration of satellite-UAV-ground were highly consistent with the measured soil salinity (R2 = 0.716 and RMSE = 0.727), and the inversion model had excellent universal applicability. This research integrated the advantages of multi-source data to establish a unified satellite-UAV-ground model, which improved the ability of large-scale remote sensing data to finely indicate soil salinity.

scholarly journals Detection and modeling of soil salinity variations in arid lands using remote sensing data

2021 ◽  
Vol 13 (1) ◽  
pp. 443-453
Author(s):  
Abduldaem S. Alqasemi ◽  
Majed Ibrahim ◽  
Ayad M. Fadhil Al-Quraishi ◽  
Hakim Saibi ◽  
A’kif Al-Fugara ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Salinity ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Prediction Models ◽  
Field Measurements ◽  
Soil Salinization ◽  
Landsat 8 ◽  
Salinity Variations

Abstract Soil salinization is a ubiquitous global problem. The literature supports the integration of remote sensing (RS) techniques and field measurements as effective methods for developing soil salinity prediction models. The objectives of this study were to (i) estimate the level of soil salinity in Abu Dhabi using spectral indices and field measurements and (ii) develop a model for detecting and mapping soil salinity variations in the study area using RS data. We integrated Landsat 8 data with the electrical conductivity measurements of soil samples taken from the study area. Statistical analysis of the integrated data showed that the normalized difference vegetation index and bare soil index showed moderate correlations among the examined indices. The relation between these two indices can contribute to the development of successful soil salinity prediction models. Results show that 31% of the soil in the study area is moderately saline and 46% of the soil is highly saline. The results support that geoinformatic techniques using RS data and technologies constitute an effective tool for detecting soil salinity by modeling and mapping the spatial distribution of saline soils. Furthermore, we observed a low correlation between soil salinity and the nighttime land surface temperature.

Developing Logistic Regression Models to Identify Salt-Affected Soils Using Optical Remote Sensing

2019 ◽  
pp. 233-256
Author(s):  
Nirmal Kumar ◽  
S. K. Singh ◽  
G. P. Obi Reddy ◽  
R. K. Naitam
Keyword(s):  
Remote Sensing ◽  
Logistic Regression ◽  
Spatial Distribution ◽  
Soil Salinity ◽  
Regression Models ◽  
Landsat 8 ◽  
Gangetic Plain ◽  
Logistic Regression Models ◽  
Salt Affected Soils ◽  
Indo Gangetic Plain

A major part of Indo-Gangetic plain is affected with soil salinity/alkalinity. Information on spatial distribution of soil salinity is important for planning management practices for its restoration. Remote sensing has proven to be a powerful tool in quantifying and monitoring the development of soil salinity. The chapter aims to develop logistic regression models, using Landsat 8 data, to identify salt affected soils in Indo-Gangetic plain. Logistic regression models based on Landsat 8 bands and several salinity indices were developed, individually and in combination. The bands capable of differentiating salt affected soils from other features were identified as green, red, and SWIR1. The logistic regression model developed in the study area was found to be 81% accurate in identifying salt-affected soils. A total area of 34558.49 ha accounting to ~10% of the total geographic area of the district was found affected with salinity/alkalinity. The spatial distribution of salt-affected soils in the district showed an association of shallow ground water depth with salinity.

scholarly journals Soil salinity mapping and hydrological drought indices assessment in arid environments based on remote sensing techniques

2017 ◽  
Vol 6 (1) ◽  
pp. 149-158 ◽  
Author(s):  
Mohamed Elhag ◽  
Jarbou A. Bahrawi
Keyword(s):  
Remote Sensing ◽  
Soil Salinity ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Hydrological Drought ◽  
Drought Indices ◽  
Landsat 8 ◽  
Arid Environments ◽  
Crop Water ◽  
Sensing Applications

Abstract. Vegetation indices are mostly described as crop water derivatives. The normalized difference vegetation index (NDVI) is one of the oldest remote sensing applications that is widely used to evaluate crop vigor directly and crop water relationships indirectly. Recently, several NDVI derivatives were exclusively used to assess crop water relationships. Four hydrological drought indices are examined in the current research study. The water supply vegetation index (WSVI), the soil-adjusted vegetation index (SAVI), the moisture stress index (MSI) and the normalized difference infrared index (NDII) are implemented in the current study as an indirect tool to map the effect of different soil salinity levels on crop water stress in arid environments. In arid environments, such as Saudi Arabia, water resources are under pressure, especially groundwater levels. Groundwater wells are rapidly depleted due to the heavy abstraction of the reserved water. Heavy abstractions of groundwater, which exceed crop water requirements in most of the cases, are powered by high evaporation rates in the designated study area because of the long days of extremely hot summer. Landsat 8 OLI data were extensively used in the current research to obtain several vegetation indices in response to soil salinity in Wadi ad-Dawasir. Principal component analyses (PCA) and artificial neural network (ANN) analyses are complementary tools used to understand the regression pattern of the hydrological drought indices in the designated study area.

scholarly journals Estimation of Soil Salinization by Machine Learning Algorithms in Different Arid Regions of Northwest China

2022 ◽  
Vol 14 (2) ◽  
pp. 347
Author(s):  
Xiaofang Jiang ◽  
Hanchen Duan ◽  
Jie Liao ◽  
Pinglin Guo ◽  
Cuihua Huang ◽  
...  
Keyword(s):  
Soil Salinity ◽  
Land Surface ◽  
Saline Soil ◽  
Soil Salinization ◽  
Landsat 8 ◽  
Hyperspectral Reflectance ◽  
Surface Parameters ◽  
Light Gradient ◽  
The Mean ◽  
Land Surface Parameters

Hyperspectral data has attracted considerable attention in recent years due to its high accuracy in monitoring soil salinization. At present, most existing research focuses on the saline soil in a single area without comparative analysis between regions. The regional differences in the hyperspectral characteristics of saline soil are still unclear. Thus, we chose Golmud in the cold–dry Qaidam Basin (QB–G) and Gaotai–Minghua in the relatively warm–dry Hexi Corridor (HC–GM) as the study areas, and used the deep extreme learning machine (DELM) and sine cosine algorithm–Elman (SCA–Elman) to predict soil salinity, and then selected the most suitable algorithm in these two regions. A total of 79 (QB–G) and 86 (HC–GM) soil samples were collected and tested to obtain their electrical conductivity (EC) and corresponding hyperspectral reflectance (R). We utilized the land surface parameters that affect the soil based on Landsat 8 and digital elevation model (DEM) data, selected the variables using the light gradient boosting machine (LightGBM), and built SCA–Elman and DELM from the hyperspectral reflectance data combined with land surface parameters. The results revealed the following: (1) The soil hyperspectral reflectance in QB–G was higher than that in HC–GM. The soils of QB–G are mainly the chloride type and those of HC–GM mainly belong to the sulfate type, having lower reflectance. (2) The accuracies of some of the SCA–Elman and DELM models in QB–G (the highest MAEv, RMSEv, and were 0.09, 0.12 and 0.75, respectively) were higher than those in HC–GM (the highest MAEv, RMSEv, and were 0.10, 0.14 and 0.73, respectively), which has flatter terrain and less obvious surface changes. The surface parameters in QB–G had higher correlation coefficients with EC due to the regular altitude change and cold–dry climate. (3) Most of the SCA–Elman results (the mean in HC-GM and QB-G were 0.62 and 0.60, respectively) in all areas performed better than the DELM results (the mean in HC–GM and QB–G were 0.51 and 0.49, respectively). Therefore, SCA–Elman was more suitable for the soil salinity prediction in HC–GM and QB–G. This can provide a reference for soil salinization monitoring and model selection in the future.

scholarly journals Potential of Landsat 8 OLI for mapping and monitoring of soil salinity in an arid region: A case study in Dushak, Turkmenistan

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0259695
Author(s):  
Elif Günal ◽  
Xiukang Wang ◽  
Orhan Mete Kılıc ◽  
Mesut Budak ◽  
Sami Al Obaid ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Regression Model ◽  
Soil Salinity ◽  
Multivariate Regression ◽  
Regression Models ◽  
Satellite Images ◽  
Remotely Sensed ◽  
Small Scale ◽  
Landsat 8 ◽  
Landsat 8 Oli

Soil salinity is the most common land degradation agent that impairs soil functions, ecosystem services and negatively affects agricultural production in arid and semi-arid regions of the world. Therefore, reliable methods are needed to estimate spatial distribution of soil salinity for the management, remediation, monitoring and utilization of saline soils. This study investigated the potential of Landsat 8 OLI satellite data and vegetation, soil salinity and moisture indices in estimating surface salinity of 1014.6 ha agricultural land located in Dushak, Turkmenistan. Linear regression model was developed between land measurements and remotely sensed indicators. A systematic regular grid-sampling method was used to collect 50 soil samples from 0–20 cm depth. Sixteen indices were extracted from Landsat-8 OLI satellite images. Simple and multivariate regression models were developed between the measured electrical conductivity values and the remotely sensed indicators. The highest correlation between remote sensing indicators and soil EC values in determining soil salinity was calculated in SAVI index (r = 0.54). The reliability indicated by R2 value (0.29) of regression model developed with the SAVI index was low. Therefore, new model was developed by selecting the indicators that can be included in the multiple regression model from the remote sensing indicators. A significant (r = 0.74) correlation was obtained between the multivariate regression model and soil EC values, and salinity was successfully mapped at a moderate level (R2: 0.55). The classification of the salinity map showed that 21.71% of the field was non-saline, 29.78% slightly saline, 31.40% moderately saline, 15.25% strongly saline and 1.44% very strongly. The results revealed that multivariate regression models with the help of Landsat 8 OLI satellite images and indices obtained from the images can be used for modeling and mapping soil salinity of small-scale lands.

scholarly journals A Quantifying Approach to Soil Salinity Based on a Radar Feature Space Model Using ALOS PALSAR-2 Data

2022 ◽  
Vol 14 (2) ◽  
pp. 363
Author(s):  
Nuerbiye Muhetaer ◽  
Ilyas Nurmemet ◽  
Adilai Abulaiti ◽  
Sentian Xiao ◽  
Jing Zhao
Keyword(s):  
Soil Salinity ◽  
Soil Management ◽  
Feature Space ◽  
Soil Salinization ◽  
Synthetic Aperture ◽  
Alos Palsar ◽  
Space Model ◽  
Sustainable Soil Management ◽  
Comparison Results ◽  
Extract Information

In arid and semi-arid areas, timely and effective monitoring and mapping of salt-affected areas is essential to prevent land degradation and to achieve sustainable soil management. The main objective of this study is to make full use of synthetic aperture radar (SAR) polarization technology to improve soil salinity mapping in the Keriya Oasis, Xinjiang, China. In this study, 25 polarization features are extracted from ALOS PALSAR-2 images, of which four features are selected. In addition, three soil salinity inversion models, named the RSDI1, RSDI2, and RSDI3, are proposed. The analysis and comparison results of inversion accuracy show that the overall correlation values of the RSDI1, RSDI2, and RSDI3 models are 0.63, 0.61, and 0.62, respectively. This result indicates that the radar feature space models have the potential to extract information on soil salinization in the Keriya Oasis.

scholarly journals Quantitative Retrieval of Soil Salinity Using Landsat 8 OLI Imagery

2021 ◽  
Vol 11 (23) ◽  
pp. 11145
Author(s):  
Ruolin Dong ◽  
Xiaodong Na
Keyword(s):  
Soil Moisture ◽  
Soil Salinity ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Soil Salinization ◽  
Landsat Tm ◽  
Landsat 8 ◽  
Entire Study ◽  
Field Samples ◽  
Spatial Differences

Soil salinization is the main reason for declining soil quality and a reduction in agricultural productivity. We derive the spatial distribution of soil moisture from the temperature vegetation dryness index (TVDI) of Landsat TM-8 OLI images to analyze the effect of spatial heterogeneity of soil moisture on the retrieval accuracy of soil salinity. We establish five soil salinity inversion models for different soil moisture levels (drought levels) based on the canopy response salinity index (CRSI), normalized difference vegetation index (NDVI), and automatic water extraction index (AWEI) derived from Landsat TM-8 OLI images. The inversion accuracy of soil salinity is assessed using 42 field samples. The results show that the average accuracies of the five inversion models are higher than that of the traditional soil salinity inversion model of the entire study area. The proposed model underestimates soil salinity in high-moisture areas and overestimates it in drought areas. Therefore, inversion models of soil salinization should consider spatial differences in soil moisture to improve the inversion accuracy.

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