Exploring the capability of Gaofen-5 hyperspectral data for assessing soil salinity risks

Ge, Xiangyu; Ding, Jianli; Teng, Dexiong; Xie, Boqiang; Zhang, Xianlong; Wang, Jinjie; Han, Lijing; Bao, Qingling; Wang, Jingzhe

doi:10.1016/j.jag.2022.102969

Cited by 25 publications

(23 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the size of the hyperspectral reflectance differed between the soils, because of the differences in soil formation conditions and the parent materials, i.e., the soil mineralogy factors that control the spectral characteristics. With the exception of the SSK, the size of the reflectance did not regularly change with the degree of salinity (Figure 4) and differed from the values reported from other locations, such as the Ebinur Lake oasis [18] and Zhenlai County in China [44] and the Urmia Lake in Iran [45]. This can be attributed to the relatively high pH but low EC values in some parts of our study area.…”

Section: Spectral Characteristics Of Different Types Of Soilcontrasting

confidence: 71%

See 1 more Smart Citation

Inversion of Different Cultivated Soil Types’ Salinity Using Hyperspectral Data and Machine Learning

Jia

Zhang

et al. 2022

Remote Sensing

View full text Add to dashboard Cite

Soil salinization is one of the main causes of global desertification and soil degradation. Although previous studies have investigated the hyperspectral inversion of soil salinity using machine learning, only a few have been based on soil types. Moreover, agricultural fields can be improved based on the accurate estimation of the soil salinity, according to the soil type. We collected field data relating to six salinized soils, Haplic Solonchaks (HSK), Stagnic Solonchaks (SSK), Calcic Sonlonchaks (CSK), Fluvic Solonchaks (FSK), Haplic Sonlontzs (HSN), and Takyr Solonetzs (TSN), in the Hetao Plain of the upper reaches of the Yellow River, and measured the in situ hyperspectral, pH, and electrical conductivity (EC) values of a total of 231 soil samples. The two-dimensional spectral index, topographic factors, climate factors, and soil texture were considered. Several models were used for the inversion of the saline soil types: partial least squares regression (PLSR), random forest (RF), extremely randomized trees (ERT), and ridge regression (RR). The spectral curves of the six salinized soil types were similar, but their reflectance sizes were different. The degree of salinization did not change according to the spectral reflectance of the soil types, and the related properties were inconsistent. The Pearson’s correlation coefficient (PCC) between the two-dimensional spectral index and the EC was much greater than that between the reflectance and EC in the original band. In the two-dimensional index, the PCC of the HSK-NDI was the largest (0.97), whereas in the original band, the PCC of the SSK400 nm was the largest (0.70). The two-dimensional spectral index (NDI, RI, and DI) and the characteristic bands were the most selected variables in the six salinized soil types, based on the variable projection importance analysis (VIP). The best inversion model for the HSK and FSK was the RF, whereas the best inversion model for the CSK, SSK, HSN, and TSN was the ERT, and the CSK-ERT had the best performance (R2 = 0.99, RMSE = 0.18, and RPIQ = 6.38). This study provides a reference for distinguishing various salinization types using hyperspectral reflectance and provides a foundation for the accurate monitoring of salinized soil via multispectral remote sensing.

show abstract

Section: Spectral Characteristics Of Different Types Of Soilcontrasting

confidence: 71%

“…Remote sensing has been successfully applied for the monitoring of EC, ranging from hyperspectral to multispectral analysis [16][17][18]. Hyperspectral remote sensing technology is an important method for monitoring the physical and chemical properties of soil because of its strong dynamics, high resolution, and continuous band.…”

Section: Introductionmentioning

confidence: 99%

Inversion of Different Cultivated Soil Types’ Salinity Using Hyperspectral Data and Machine Learning

Jia

Zhang

et al. 2022

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Traditional assessments of ecological quality are limited to small-scale areas, which are both time consuming and labor intensive (Li et al, 2020a). By contrast, remote sensing technology can be used to rapidly monitor the ecological environment on large scales (Ge et al, 2022a). Many remote sensing products have been developed and designed to effectively monitor changes in the eco-environment.…”

Section: Introductionmentioning

confidence: 99%

Assessment of ecological quality in Northwest China (2000–2020) using the Google Earth Engine platform: Climate factors and land use/land cover contribute to ecological quality

Wang

Ding

et al. 2022

J. Arid Land

Self Cite

View full text Add to dashboard Cite

The ecological quality of inland areas is an important aspect of the United Nations Sustainable Development Goals (UN SDGs). The ecological environment of Northwest China is vulnerable to changes in climate and land use/land cover, and the changes in ecological quality in this arid region over the last two decades are not well understood. This makes it more difficult to advance the UN SDGs and develop appropriate measures at the regional level. In this study, we used the Moderate Resolution Imaging Spectroradiometer (MODIS) products to generate remote sensing ecological index (RSEI) on the Google Earth Engine (GEE) platform to examine the relationship between ecological quality and environment in Xinjiang during the last two decades (from 2000 to 2020). We analyzed a 21-year time series of the trends and spatial characteristics of ecological quality. We further assessed the importance of different environmental factors affecting ecological quality through the random forest algorithm using data from statistical yearbooks and land use products. Our results show that the RSEI constructed using the GEE platform can accurately reflect the ecological quality information in Xinjiang because the contribution of the first principal component was higher than 90.00%. The ecological quality in Xinjiang has increased significantly over the last two decades, with the northern part of this region having a better ecological quality than the southern part. The areas with slightly improved ecological quality accounted for 31.26% of the total land area of Xinjiang, whereas only 3.55% of the land area was classified as having a slightly worsen (3.16%) or worsen (0.39%) ecological quality. The vast majority of the deterioration in ecological quality mainly occurred in the barren areas Temperature, precipitation, closed shrublands, grasslands and savannas were the top five environmental factors affecting the changes in RSEI. Environmental factors were allocated different weights for different RSEI categories. In general, the recovery of ecological quality in Xinjiang has been controlled by climate and land use/land cover during the last two decades and policy-driven ecological restoration is therefore crucial. Rapid monitoring of inland ecological quality using the GEE platform is projected to aid in the advancement of the comprehensive assessment of the UN SDGs.

show abstract

“…The RF regression method was performed 100 times to react to the model bias. Three commonly used statistical indicators were used to evaluate the performance of the proposed model, jointly evaluating model performance from different perspectives and validating each other's results, including the following: (1) the coefficient of determination ( R 2 ); (2) root mean square error (RMSE); (3) and ratio of the performance to the interquartile distance (RPIQ) (Ge et al, 2022; Moura‐Bueno et al, 2019). R 2 is the fitting coefficient of the model; the closer this value is to 1, the higher the model accuracy is.…”

Section: Methodsmentioning

confidence: 99%

Soil salinity estimation: Effects of microwave dielectric spectroscopy and important frequencies

Zhao

Ding

et al. 2023

Land Degrad Dev

Self Cite

View full text Add to dashboard Cite

The microwave dielectric constant is a key bridge in establishing the relationship between microwave remote sensing and soil salinity (electrical conductivity, EC). However, the response between microwave dielectric spectrum type, frequency, and soil salinity is still unclear. The purpose of this study is to reveal the dielectric spectrum and frequency range closely related to soil salinity. In this study, 129 surface soil samples were collected, and soil EC and microwave dielectric spectrum (frequency range of 0.3–20 GHz) were measured. Four variable selection methods were used to select important spectral variables, and the relationship between these spectral variables and soil salt was constructed using a random forest model. The results showed that the combination of boldε′ (real part) and boldε′′ (imaginary part) revealed the most implicit information related to soil salinity. Considering the important spectral variables, the model accuracy was generally improved to the full‐dielectric spectrum model (the ratios of the performance to the interquartile distance (RPIQ) improved by 0.11–0.40); furthermore, the variable iterative space shrinkage (VISSA) method could most effectively select important spectral variables (RPIQ = 2.32). The selected spectral‐RF models could provide important frequencies for estimating soil salinity; moreover, the low‐frequency range (0.3–4 GHz) and new important frequency ranges (e.g., 7 and 15 GHz) able to respond to soil salinity were identified. This study provides a new strategy for monitoring the soil salinization risk using multiple frequencies microwave remote sensing techniques.

show abstract

Exploring the capability of Gaofen-5 hyperspectral data for assessing soil salinity risks

Cited by 25 publications

References 59 publications

Inversion of Different Cultivated Soil Types’ Salinity Using Hyperspectral Data and Machine Learning

Inversion of Different Cultivated Soil Types’ Salinity Using Hyperspectral Data and Machine Learning

Assessment of ecological quality in Northwest China (2000–2020) using the Google Earth Engine platform: Climate factors and land use/land cover contribute to ecological quality

Soil salinity estimation: Effects of microwave dielectric spectroscopy and important frequencies

Contact Info

Product

Resources

About