Estimation of Soil Salinity Under Various Soil Moisture Conditions Using Laboratory Based Thermal Infrared Spectra

Xu, Lu; Wang, Zhichun; Hu, Jiang; Wang, Shuguo; Nyongesah, John Maina

doi:10.1007/s12524-020-01271-9

Cited by 3 publications

(2 citation statements)

References 58 publications

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“…В [14] показано определение поверхностной температуры почвы на лесной территории на границе Вирджинии и северной Каролины (США) при помощи объедения данных тепловых каналов (10,5…12,5 мкм) спутников Landsat-8 и MODIS (MOD11). Коэффициент детерминации модели был равен 0,76.…”

Section: Introductionunclassified

Informative value of infrared survey data for detecting properties of arable soils

Grubina

Savin

2023

Vestn. Ross. univ. družby nar., Ser. Agron. životnovod.

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Possibility of detecting soil fertility parameters based on the use of thermal survey data was studied on the test area of Yasnogorsky District, Tula region, Russia. The test area has gray forest slightly eroded arable soils located in the flat part of the slope. During the field works, an open soil surface was photographed using a FLIR VUE 512 thermal imager (range 7.5-13.5 mkm), soil samples were also taken from a layer of 0-5 cm and soil moisture was measured in a layer of 15-20 cm. For almost all parameters of soil fertility (pH, humus content, potassium content, exchange cations - Mg++, K+, Na+), a statistically significant correlation was established (r =0.4-0.7) between them and the survey data in the thermal range of the spectrum. For moderate correlations, polynomial regression equations were compiled. Among the studied fertility parameters, the pH of the salt extract, the content of potassium oxide and potassium exchange cations had significant coefficient of determination (R2 0.60) with the thermal range of the spectrum - R2= 0.61, R2 =0.60 and R2 = 0.63, respectively. The obtained results have shown that thermal imaging can be used to map some parameters of soil fertility for the region. Nevertheless, it turned out to be impossible to reliably detect all the main parameters of soil fertility of the test field on the basis of thermal survey data. However, the thermal soil survey data can be used as auxiliary data when shooting in the visible and nearIR ranges, which helps to improve the accuracy of contactless soil monitoring.

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Section: Introductionunclassified

Informative value of infrared survey data for detecting properties of arable soils

Grubina

Savin

2023

Vestn. Ross. univ. družby nar., Ser. Agron. životnovod.

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“…Owing to its powerful data synthesis and high effectiveness, remote sensing technology is widely used to monitor soil salinity in large-scale and multi-temporal ways, which depends on the diagnostic spectroscopy of different salt minerals [12,13]. Many scholars have identified and classified saline-alkali soils using spectral measurements and the response of saline soils [14][15][16][17], band combinations of soil spectral features [18][19][20], and inversion modeling for soil salinity from different scales [21][22][23]. However, there are still many factors influencing the transmission process of electromagnetic waves, indicating that the estimation of soil salinity parameters using spectroscopy is often limited to qualitative descriptions [24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Quantitative Study on Salinity Estimation of Salt-Affected Soils by Combining Different Types of Crack Characteristics Using Ground-Based Remote Sensing Observation

Zhang

Zhou

et al. 2023

Remote Sensing

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Soil salinity is one of the parameters used for determining the extent of soil salinization. During water evaporation, the surface of salt-affected soils in the Songnen Plain, China, exhibits obvious shrinkage and cracking phenomena due to the high salt content. The aim of this current study is to quantify the influence of the salt content on the surface shrinkage–cracking process and to achieve quantitative extraction of soil salinity parameters based on different crack parameter types. In order to achieve the above objectives, a controlled shrinkage–cracking experiment was conducted. Subsequently, three kinds of crack characteristics such as crack length, box-counting dimension, and 12 gray-level co-occurrence matrix (GLCM) texture features were quantitatively extracted from the standard binary crack patterns. In order to predict the soil physical–chemical properties of salt-affected soils in the Songnen Plain, three models such as multiple linear regression (MLR), multiple stepwise regression (MSR), and artificial neural network (ANN) were developed and compared based on the crack length, box-counting dimension, and the first two principal components of GLCM texture features. The results show that the extent of desiccation cracks was determined by soil salinity since the water film caused by exchangeable cations and the thickness of DDL determined by soil salinity can promote desiccation cracking. Although the three methods have high prediction accuracy for Na+, electrical conductivity (EC), and total soil salinity, the ANN-based method showed the best prediction with R2 values for Na+, EC, and soil salinity as high as 0.91, 0.91, and 0.89, and ratio of performance to deviation (RPD) values for Na+, EC, and soil salinity corresponding to 2.96, 3.47, and 2.95.

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Soil Salinity and Soil Water Content Estimation Using Digital Images in Coastal Field: A Case Study in Yancheng City of Jiangsu Province, China

Wang

2022

Chin. Geogr. Sci.

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Estimation of Soil Salinity Under Various Soil Moisture Conditions Using Laboratory Based Thermal Infrared Spectra

Cited by 3 publications

References 58 publications

Informative value of infrared survey data for detecting properties of arable soils

Informative value of infrared survey data for detecting properties of arable soils

Quantitative Study on Salinity Estimation of Salt-Affected Soils by Combining Different Types of Crack Characteristics Using Ground-Based Remote Sensing Observation

Soil Salinity and Soil Water Content Estimation Using Digital Images in Coastal Field: A Case Study in Yancheng City of Jiangsu Province, China

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