Optical remote-sensing data based research on detecting soil salinity at different depth in an arid-area oasis, Xinjiang, China

Jiang, Hongnan; Hong, Su Young

doi:10.1007/s12145-018-0358-2

Cited by 37 publications

(21 citation statements)

References 29 publications

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“…However, spectral reflectance of soil provides a comprehensive reflection of soil physical and chemical properties, which is influenced by soil nutrients (Hengl et al, 2017), atmospheric water vapor (song & Grejner-Brzezinska, 2009), ground-surface temperature (Anderson & Johnson, 2016), and so on. Therefore, it is difficult to accurately estimate the soil salt content based on the original spectral reflection characteristics (Jiang & Shu, 2019;Tilley et al, 2007). After some mathematical transformation of the soil spectral curve, the reflection and absorption characteristics of the spectrum can be better highlighted, so as to distinguish it from other spectral characteristics, and screen-sensitive bands.…”

Section: Introductionmentioning

confidence: 99%

“…(Chuangye et al, 2016;Wang et al, 2018). In soil salinity monitoring using Landsat7 remote sensing image, Jiang and Shu (2019) added vegetation index NDVI and soil salinity index DSI significantly related to soil salinity based on the original band index, thus effectively improving the accuracy of salt monitoring. In the study on soil salinity of three oases in Xinjiang, China, Fei Wang et al (2013) found that accuracy of the salt prediction model would gradually increase as near-infrared band index NIR, soil sand index, vegetation index EVI, slope and surface temperature LST were gradually introduced.…”

Section: Introductionmentioning

confidence: 99%

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RETRACTED ARTICLE: Monitoring soil salinization in Manas River Basin, Northwestern China based on multi-spectral index group

Shi

Song

Wang

et al. 2020

European Journal of Remote Sensing

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Large-scale and accurate monitoring soil salinization is essential for controlling soil degradation and sustainable agricultural development. The agricultural irrigation area of the Manas River Basin in the arid area of Northwest China was selected as the test area. The soil salinization monitoring model based on spectral index group was constructed by comparing the accuracy of PCR, PLSR and MLR models using the transformation of multi-spectral index group and index screening. The results showed that there was a certain correlation between the 28 spectral index groups, with a maximum correlation coefficient -0.3689 between the original spectral group and the soil salt content was B10 band. After the transformation of original data for the logarithm Ln(R), exponential e R and square root R 1/2 respectively, the correlation between each index and soil salinity was significantly improved, with the maximum correlation coefficient was up to -0.7564 of R 1/2 . The salt content estimation models were constructed by different data transformation using PLSR, PCR and MLR methods, respectively. This study provides a fast and accurate method for monitoring regional soil salinity content and the results can provide a reference for soil salinity grading management in arid and semi-arid areas.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Monitoring soil salinization in Manas River Basin, Northwestern China based on multi-spectral index group

Shi

Song

Wang

et al. 2020

European Journal of Remote Sensing

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“…9, only 30% to 70% of rainfall are consumed for crop use and the effective precipitation efficiency in arid and semi-arid regions are generally larger than those in humid and semi-humid regions. The arid and semi-arid regions are generally suffering from severe soil salinization (Jiang and Shu, 2018;Peng et al, 2019;Qian et al, 2019) and a series of ecological environment problems caused by it (Besser et al, 2017; https://doi.org/10.5194/hess-2021-80 Preprint. Discussion started: 22 April 2021 c Author(s) 2021.…”

Section: Water Use Efficiency In Irrigation Districtsmentioning

confidence: 99%

The application of Budyko framework to irrigation districts in China under various climatic conditions

Chen

Huo

Zhang

et al. 2021

Preprint

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Abstract. Budyko's framework has been widely used to study basin-scale water balance. In this study, we focus on the extended application of Fu's equation (one formulation of the Budyko-type curves) to 371 large irrigation districts in China over a period of 2010–2017. Water balance method was used to estimate actual evapotranspiration (ET) in the irrigated areas. Considering the contribution of shallow groundwater to ET, the water availability in the Budyko framework defined as equivalent precipitation (Pe) for irrigation areas is the sum of irrigation water (I), precipitation (P) and groundwater evaporation (ETgw). Results showed that the relationships between evapotranspiration (ET), water availability (Pe) and energy supply (ET0) can be accurately described by the Budyko's curves. The Fu's equation performed better in humid and semi-humid regions than arid and semi-arid regions. The comparison between δET/δPe and δET/δET0 confirmed the relative effect of water availability and energy supply on ET according to the variation of climatic conditions. The optimal values of Budyko parameter ω for each irrigation district was obtained with multi-annual data using least square method. Normalized Difference Vegetation Index (NDVI) and soil property (denoted by the proportion of clay and sand) were selected to develop empirical equation for parameter ω using multiple linear regression analysis method. This study showed that the Budyko framework can be extended to irrigation areas and provide useful information on evapotranspiration to assist in water management in irrigation areas.

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“…Traditional methods rely on field surveys and electrical conductivity measurements, which are accurate but time and labor-intensive [5,6], and do not allow for monitoring of the spatial distribution pattern of soil salinity content. Multi-and hyperspectral satellite RS technology has been used in soil salinity monitoring since the 1990s [7,8]. Azabdaftari et al (2016), for instance, computed vegetation indexes in the Adana region of Turkey using Landsat multispectral images from four different times [9].…”

Section: Introductionmentioning

confidence: 99%

Precise Monitoring of Soil Salinity in China’s Yellow River Delta Using UAV-Borne Multispectral Imagery and a Soil Salinity Retrieval Index

Chang

Song

et al. 2022

Sensors

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Monitoring salinity information of salinized soil efficiently and precisely using the unmanned aerial vehicle (UAV) is critical for the rational use and sustainable development of arable land resources. The sensitive parameter and a precise retrieval method of soil salinity, however, remain unknown. This study strived to explore the sensitive parameter and construct an optimal method for retrieving soil salinity. The UAV-borne multispectral image in China’s Yellow River Delta was acquired to extract band reflectance, compute vegetation indexes and soil salinity indexes. Soil samples collected from 120 different study sites were used for laboratory salt content measurements. Grey correlation analysis and Pearson correlation coefficient methods were employed to screen sensitive band reflectance and indexes. A new soil salinity retrieval index (SSRI) was then proposed based on the screened sensitive reflectance. The Partial Least Squares Regression (PLSR), Multivariable Linear Regression (MLR), Back Propagation Neural Network (BPNN), Support Vector Machine (SVM), and Random Forest (RF) methods were employed to construct retrieval models based on the sensitive indexes. The results found that green, red, and near-infrared (NIR) bands were sensitive to soil salinity, which can be used to build SSRI. The SSRI-based RF method was the optimal method for accurately retrieving the soil salinity. Its modeling determination coefficient (R2) and Root Mean Square Error (RMSE) were 0.724 and 1.764, respectively; and the validation R2, RMSE, and Residual Predictive Deviation (RPD) were 0.745, 1.879, and 2.211.

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Optical remote-sensing data based research on detecting soil salinity at different depth in an arid-area oasis, Xinjiang, China

Cited by 37 publications

References 29 publications

RETRACTED ARTICLE: Monitoring soil salinization in Manas River Basin, Northwestern China based on multi-spectral index group

RETRACTED ARTICLE: Monitoring soil salinization in Manas River Basin, Northwestern China based on multi-spectral index group

The application of Budyko framework to irrigation districts in China under various climatic conditions

Precise Monitoring of Soil Salinity in China’s Yellow River Delta Using UAV-Borne Multispectral Imagery and a Soil Salinity Retrieval Index

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