Estimation of soil salinity in semi‐arid land using a geostatistical model

Pedreño, José Navarro; Jordán, Manuel M.; Melendez-Pastor, Ignacio; Gómez, Ignacio; Paz, Juan Pablo; Mateu, Jorge

doi:10.1002/ldr.781

Cited by 21 publications

(6 citation statements)

References 25 publications

(22 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Then, we verified that Landsat 8 OLI imagery was appropriate and economic for extracting the information of soil salinity and sodicity over a large area. Generally, the advantages of RS technology involve quick access and saving time, wider coverage, and constant time series provision for long term monitoring [ 6 , 16 , 44 ]. By contrast, OLI imagery owns broader views, comparable spatial resolution relative to satellite hyperspectral data (e.g., Hyperion [ 45 ]), and abundant band/spectral information competent with higher resolution imagery like QuickBird, IKONOS, and GeoEye-1 [ 46 ].…”

Section: Discussionmentioning

confidence: 99%

Mapping Soil Salinity/Sodicity by using Landsat OLI Imagery and PLSR Algorithm over Semiarid West Jilin Province, China

Liu

et al. 2018

Sensors

View full text Add to dashboard Cite

Soil salinity and sodicity can significantly reduce the value and the productivity of affected lands, posing degradation, and threats to sustainable development of natural resources on earth. This research attempted to map soil salinity/sodicity via disentangling the relationships between Landsat 8 Operational Land Imager (OLI) imagery and in-situ measurements (EC, pH) over the west Jilin of China. We established the retrieval models for soil salinity and sodicity using Partial Least Square Regression (PLSR). Spatial distribution of the soils that were subjected to hybridized salinity and sodicity (HSS) was obtained by overlay analysis using maps of soil salinity and sodicity in geographical information system (GIS) environment. We analyzed the severity and occurring sizes of soil salinity, sodicity, and HSS with regard to specified soil types and land cover. Results indicated that the models’ accuracy was improved by combining the reflectance bands and spectral indices that were mathematically transformed. Therefore, our results stipulated that the OLI imagery and PLSR method applied to mapping soil salinity and sodicity in the region. The mapping results revealed that the areas of soil salinity, sodicity, and HSS were 1.61 × 106 hm2, 1.46 × 106 hm2, and 1.36 × 106 hm2, respectively. Also, the occurring area of moderate and intensive sodicity was larger than that of salinity. This research may underpin efficiently mapping regional salinity/sodicity occurrences, understanding the linkages between spectral reflectance and ground measurements of soil salinity and sodicity, and provide tools for soil salinity monitoring and the sustainable utilization of land resources.

show abstract

Section: Discussionmentioning

confidence: 99%

Mapping Soil Salinity/Sodicity by using Landsat OLI Imagery and PLSR Algorithm over Semiarid West Jilin Province, China

Liu

et al. 2018

Sensors

View full text Add to dashboard Cite

show abstract

“…We employed the GSTAT package (Pebesma & Wesseling, 1998) for modeling the experimental variograms of our soil properties. Based on our previous experience on mapping soil properties with geostatistics in the study area (Bas-Niñerola et al, 2017;Juan et al, 2011;Navarro-Pedreño et al, 2007), we adopted the following procedure:…”

Section: Soil Property Mapping With Geostatisticsmentioning

confidence: 99%

Environmental factors influencing DDT–DDE spatial distribution in an agricultural drainage system determined by using machine learning techniques

Melendez-Pastor

Lopez-Granado

Pedreño

et al. 2023

Environ Geochem Health

View full text Add to dashboard Cite

The presence and persistence of pesticides in the environment are environmental problems of great concern due to the health implications for humans and wildlife. The persistence of DDT–DDE in a Mediterranean coastal plain where pesticides were widely used and were banned decades ago is the aim of this study. Different sources of analytical information from water and soil analysis and topography and geographical variables were combined with the purpose of analyzing which environmental factors are more likely to condition the spatial distribution of DDT–DDE in the drainage watercourses of the area. An approach combining machine learning techniques, such as Random Forest and Mutual Information (MI), for classifying DDT–DDE concentration levels based on other environmental predictive variables was applied. In addition, classification procedure was iteratively performed with different training/validation partitions in order to extract the most informative parameters denoted by the highest MI scores and larger accuracy assessment metrics. Distance to drain canals, soil electrical conductivity, and soil sand texture fraction were the most informative environmental variables for predicting DDT–DDE water concentration clusters.

show abstract

“…The problem of soil salinity can be solved by leaching soluble salts out of the root zone. Conventionally, data of soil properties are based on averages of soil analyses collected soil samples with no consideration of the spatial variations either at macro or micro scales within-field (Navarro-Pedreño et al, 2007;Webster and Oliver, 2007). Geostatistics is an effective tool to assess within field spatial variations of soil analyses used to delineate different management zones (Oliver and Webster, 2015).…”

Section: Introductionmentioning

confidence: 99%

Site-Specific Reclamation Maps of a Salt Affected Soil in Ismailia Governorate

Mohammed

Shaddad

Nasrallah

et al. 2019

Zagazig Journal of Agricultural Research

View full text Add to dashboard Cite

Delineation of soil management zones is essential for precision agriculture applications, to know agricultural inputs added to soil. Geostatistics provides an effective tool for soil spatial variability and delineating management zones. Spatial variability was done on soil properties to prepare prescription maps for leaching and gypsum requirements (GR) of a salt affected soil. The performance of the spatial model was evaluated by calculating two different statistics. Mean error (ME) as a measure of precision and mean square standardized error (MSSE) as a measure of accuracy were calculated. The developed spatial maps of investigated soil parameters [electrical conductivity (ECe), exchangeable sodium percentage (ESP) and cation exchange capacity (CEC)] were used to specify 4 zones-prescription maps that need leaching and gypsum requirements. Results showed that the three models were precise and accurate with ME and MSSE values (-0.110,-0.210, 0.002 and 0.937, 1.033, 1.010), respectively. Applying site-specific management for leaching proved costeffective and beneficial effects compared with traditional management which is based on an average value over the experimental field. The (GR) on basis of traditional management was cost-effective but was not more beneficial because zones 1 and 2 showed GR more than needed and vice versa for zones 3 and 4. The high cost in this case could be counterbalanced by the expected increased production in zones with high ESP.

show abstract

Estimation of soil salinity in semi‐arid land using a geostatistical model

Cited by 21 publications

References 25 publications

Mapping Soil Salinity/Sodicity by using Landsat OLI Imagery and PLSR Algorithm over Semiarid West Jilin Province, China

Mapping Soil Salinity/Sodicity by using Landsat OLI Imagery and PLSR Algorithm over Semiarid West Jilin Province, China

Environmental factors influencing DDT–DDE spatial distribution in an agricultural drainage system determined by using machine learning techniques

Site-Specific Reclamation Maps of a Salt Affected Soil in Ismailia Governorate

Contact Info

Product

Resources

About