Multispectral and Microwave Remote Sensing Models to Survey Soil Moisture and Salinity

Periasamy, Shoba; Shanmugam, Ramakrishnan S.

doi:10.1002/ldr.2661

Cited by 41 publications

(23 citation statements)

References 59 publications

(78 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Compared with conventional laboratory analysis methods, remote sensing technology has been widely used due to its rich information, continuity, high precision and low cost (Ben-Dor, 2002; Viscarra Rossel et al, 2006; Viscarra Rossel & Behrens, 2010; Viscarra Rossel & Webster, 2012). The various soil constituents (contents of water, salt, organic matter and so forth) can be acquired conveniently from remote sensing data (Gomez, Viscarra Rossel & McBratney, 2008; Yu et al, 2010; Periasamy & Shanmugam, 2017). Hence, with the abundant spectral reflection information within the VIS-NIR intervals of soil salinity, it is feasible to improve the accuracy of soil salinization inversion (Al-Khaier, 2003; Ben-Dor et al, 2009; Abbas et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Quantitatively estimating main soil water-soluble salt ions content based on Visible-near infrared wavelength selected using GC, SR and VIP

Wang

Chen

Zhang

et al. 2019

PeerJ

View full text Add to dashboard Cite

Soil salinization is the primary obstacle to the sustainable development of agriculture and eco-environment in arid regions. The accurate inversion of the major water-soluble salt ions in the soil using visible-near infrared (VIS-NIR) spectroscopy technique can enhance the effectiveness of saline soil management. However, the accuracy of spectral models of soil salt ions turns out to be affected by high dimensionality and noise information of spectral data. This study aims to improve the model accuracy by optimizing the spectral models based on the exploration of the sensitive spectral intervals of different salt ions. To this end, 120 soil samples were collected from Shahaoqu Irrigation Area in Inner Mongolia, China. After determining the raw reflectance spectrum and content of salt ions in the lab, the spectral data were pre-treated by standard normal variable (SNV). Subsequently the sensitive spectral intervals of each ion were selected using methods of gray correlation (GC), stepwise regression (SR) and variable importance in projection (VIP). Finally, the performance of both models of partial least squares regression (PLSR) and support vector regression (SVR) was investigated on the basis of the sensitive spectral intervals. The results indicated that the model accuracy based on the sensitive spectral intervals selected using different analytical methods turned out to be different: VIP was the highest, SR came next and GC was the lowest. The optimal inversion models of different ions were different. In general, both PLSR and SVR had achieved satisfactory model accuracy, but PLSR outperformed SVR in the forecasting effects. Great difference existed among the optimal inversion accuracy of different ions: the predicative accuracy of Ca2+, Na+, Cl−, Mg2+ and SO42− was very high, that of CO32− was high and K+ was relatively lower, but HCO3− failed to have any predicative power. These findings provide a new approach for the optimization of the spectral model of water-soluble salt ions and improvement of its predicative precision.

show abstract

Section: Introductionmentioning

confidence: 99%

Quantitatively estimating main soil water-soluble salt ions content based on Visible-near infrared wavelength selected using GC, SR and VIP

Wang

Chen

Zhang

et al. 2019

PeerJ

View full text Add to dashboard Cite

show abstract

“…In the last decades, multispectral and microwave RS data with varied spatio-temporal resolutions (e.g., Landsat, SPOT XS, IKONOS, ASTER, MODIS, IRS, and Radar, etc.) have been used for monitoring [ 9 , 10 , 11 ] and mapping [ 12 , 13 ] salt-affected soils and halophytic vegetation [ 3 , 14 ]. In most cases, multiple bands were transformed into one index that was more sensitive to soil salinity than using a single band [ 15 , 16 ].…”

Section: Introductionmentioning

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

“…There are several options. Optical multispectral imaging (Periasamy and Shanmugam, 2017) exploits the dependence of the optical reflectance of bare soil on moisture content, and thermal infrared (Hain et al, 2009) techniques exploit the thermal inertia of soil due to soil moisture. Both are confounded by vegetation obscuring the soil, cloud cover, and certain lighting conditions.…”

Section: Introductionmentioning

confidence: 99%

The significance of soil properties to the estimation of soil moisture from C-band synthetic aperture radar

Beale

Snapir

Waine

et al. 2019

Preprint

View full text Add to dashboard Cite

Abstract. Soil Moisture is a key variable in hydrology, weather and climate modelling. Research has been directed to the estimation of soil moisture over wide areas through a combination of modelling, in-situ measurement and remote sensing to improve the accuracy of hydrological and meteorological forecasting. For monitoring and controlling irrigation and other agricultural purposes, there is also a need to capture local variability. Significant soil moisture differences are observed between and within fields due to land use, soil properties, drainage, tillage, vegetation, solar radiation, air temperature, wind, rain and other factors. Taking the United Kingdom as an example, the average area of agricultural fields is about 12 hectares, requiring a mapping resolution of less than 100 m. Satellite-based remote sensing, including the use of C-band SAR (such as on Sentinel-1), has the potential to satisfy this requirement, but many current data products are aggregated to a spatial resolution of at least 1km and/or provide soil moisture in relative units or indices. Both strategies mitigate the uncertainties introduced by field-scale variability in soil hydrological and vegetation properties. Geospatial datasets of soil properties and land use, crop modelling and other remote sensing techniques may provide an alternative approach to mitigating this variability and allow finer scale products to be produced with acceptable errors. This paper looks at the role of soil properties in the estimation of soil moisture from C-band SAR. We show that information on the soil texture, organic matter content, surface temperature, land use and crop modelling should be important inputs to the success of retrieving soil moisture at the field scale. Previously published data provides guidance in setting soil roughness parameters, based on soil properties, following farming operations such as primary tillage. Beyond soil moisture retrieval, there is exciting potential in SAR remote sensing data to improve the spatial resolution and mapping accuracy of some soil properties.

show abstract

Multispectral and Microwave Remote Sensing Models to Survey Soil Moisture and Salinity

Cited by 41 publications

References 59 publications

Quantitatively estimating main soil water-soluble salt ions content based on Visible-near infrared wavelength selected using GC, SR and VIP

Quantitatively estimating main soil water-soluble salt ions content based on Visible-near infrared wavelength selected using GC, SR and VIP

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

The significance of soil properties to the estimation of soil moisture from C-band synthetic aperture radar

Contact Info

Product

Resources

About