Field-Scale Characterization of Spatio-Temporal Variability of Soil Salinity in Three Dimensions

Li, Hongyi; Liu, Xinlu; Hu, Bifeng; Biswas, Asim; Jiang, Qingsong; Liu, Weiyang; Wang, Nan; Peng, Jie

doi:10.3390/rs12244043

Cited by 19 publications

(14 citation statements)

References 55 publications

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“…Then, the CLQ at different scales was evaluated. The spatial scale transformation models obtained through model construction are shown as follows, and the determination coefficient (R 2 ) is used to measure the model accuracy [33]: (1) y = 0.722x + 0.262 (R 2 = 0.934) and y = 0.851x + 0.150 (R 2 = 0.843) are the transformation model from county-scale to city-scale and provincial-scale;…”

Section: Multi-scale Clq Evaluation Based On Gismentioning

confidence: 99%

Evaluation System Transformation of Multi-Scale Cultivated Land Quality and Analysis of Its Spatio-Temporal Variability

Chang

Zhao

et al. 2021

Sustainability

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To master the transformation method and spatio-temporal variation characteristics of cultivated land quality at multiple scales, this paper constructed three spatial scales (Laixi city, Qingdao city, and Shandong province) and two temporal scales (the second survey (2007) and the third survey (2020)), and used a linear model to transform the evaluation system. Descriptive statistics, area statistics, spatial distribution, and aggregation analysis were used to explore the spatial scale variability, and the dynamic variation characteristics were analyzed. The results showed that (1) the R2 of scale transformation models are more than 0.826, which has a simple structure and strong universality; (2) with the administrative scale increases, the evaluation units’ number decreases, the spatial distribution is generally similar but progressively approximate, the high and low land levels gradually change to medium-level land, and the spatial aggregation degree is county-scale > provincial-scale > city-scale, with significant scale effect; and (3) in the past ten years, the average grade has increased from 6.26 to 6.13 in Laixi city, but still has much room for development. This study puts forward a method of spatio-temporal scale transformation and scale effect analysis for cultivated land quality, which has positive significance for improving the evaluation system, promoting land protection, and regional sustainable development.

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Section: Multi-scale Clq Evaluation Based On Gismentioning

confidence: 99%

Evaluation System Transformation of Multi-Scale Cultivated Land Quality and Analysis of Its Spatio-Temporal Variability

Chang

Zhao

et al. 2021

Sustainability

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“…Most parts of North India show tremendous variation in temperature in terms of maximum, minimum, mean, monthly and daily range of temperature (Jiang et al, 2021). The current statistical data shows increase in temperature and economic damages in past forty years (H. Li et al, 2020). According to published sources of government information on climate change phenomena were detected a 0.13°C per decade rising trend (X. .…”

Section: Introductionmentioning

confidence: 99%

“…As a result of this climatic imbalance, few cold weather events have been recorded but most of the studies showed warming trend in global climate (Zhang et al, 2021). Due to anthropogenic activities the physical environment has been disturb and as a result both the northern and southern hemispheres are getting warm (R. Li et al, 2020). According to a research finding, increase in greenhouse gases and industrialization have increased the earth's surface temperature have raised up to 0.7°C in last fifty years, with the 1990s being the warmest decade (J. .…”

Section: Introductionmentioning

confidence: 99%

Spatio-temporal Fluctuation of Temperature Using Specific Climate Indices in South Xinjiang, China

Khan¹,

Zhao²,

Khan³

et al. 2023

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Temperature and precipitation are significant variables that can be used to determine the climatic variability in a region. This research study has focused on temperature, which is significant for the study of climatic variability over a long period in an area. The temperature has been assessed spatially and temporally in South Xinjiang, China using various climate indices. The temporal data about the temperature of seventeen meteorological stations were acquired from the Chinese Meteorology Administration (CMA) from 1980 to 2018. The Mann-Kendall (MK) trend test and Sen’s Slope (SS) statistical models were used to quantify the magnitude and temporal trend. The monthly and seasonal analysis of temperature reveals a significant increasing trend in mean maximum, mean minimum and average temperatures in the study area. The Sen’s Slope test results indicate an increasing monthly and seasonal temperature trend in almost all meteorological stations. The increasing trend in temperature is mainly due to the desert type of climate of the region that is gradually further aggravating with every passing year. This increasing temperature trend will smoothen the way for perpetual drought in the region in the future. Therefore, this research would be beneficial for future planning and management of water resources in the region and for making preventive measures to mitigate the impacts of climate change in the study area.

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“…The absence of studies in the study area based on these conditions leads to the complexity, when it comes to mapping the distribution of irrigation soil salinity and assessment [Ivushkin et al, 2019;Gebremeskel et al, 2018]. Large scales mapping spatial, temporal, and depth variability of soil salinity is one of the fundamental steps in salinity management at irrigation in arid and semi-arid areas [Li et al, 2020]. Usually, soil salinity monitoring is often carried out with intensive sampling from observation points and an appropriate interpolation method to estimate salinity values for unsampled locations [Laiskhanov et al, 2021].…”

Section: Introductionmentioning

confidence: 99%

Mapping of Cornfield Soil Salinity in Arid and Semi-Arid Regions

Smanov¹,

Laiskhanov²,

Poshanov³

et al. 2023

J. Ecol. Eng.

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Soil salinization and their annual increase in volume is not only one of the main problems of arid and subarid regions, but it is becoming global. Studying the problem of salinization and its spatial distribution using operational remote sensing methods is very important for Kazakhstan, where almost half of the agricultural land is exposed to salinization, but it is at the initial stage of development in the use of space technologies of research. The main goal of this study is to conduct a field study of soil salinity in corn fields, one of the most common crops in the arid region of the country, located in the Shaulder irrigated massif, using space-based methods, and to create algorithms for compiling a salinity map based on remote sensing data. For this purpose, firstly, using Sentinel-2 images, the method of separating corn from other dominant crops in the region by creating NDVI dynamics covering all phases of growth of agricultural crops was shown. Then, a regression analysis was performed on soil and vegetation indices calculated using satellite images and data on soil salinity obtained through field studies. As a result of the analysis, the main predictor of deciphering salinized soils was determined. By dividing the predictive image into quartiles, contours of salinized soils were determined and a soil salinity map was created. With the help of the soil salinity map, it was found that, non-saline soils -2912.2 ha; slightly saline soils -3288.4 ha, moderately saline soils -2615.2 ha, and strongly saline soils -1284.3 ha in the study area.

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Field-Scale Characterization of Spatio-Temporal Variability of Soil Salinity in Three Dimensions

Cited by 19 publications

References 55 publications

Evaluation System Transformation of Multi-Scale Cultivated Land Quality and Analysis of Its Spatio-Temporal Variability

Evaluation System Transformation of Multi-Scale Cultivated Land Quality and Analysis of Its Spatio-Temporal Variability

Spatio-temporal Fluctuation of Temperature Using Specific Climate Indices in South Xinjiang, China

Mapping of Cornfield Soil Salinity in Arid and Semi-Arid Regions

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