Soil salinity inversion based on differentiated fusion of satellite image and ground spectra

Chen, Hongyan; Ma, Ying-Rui; Zhu, A‐Xing; Wang, Zhuoran; Zhao, Gengxing; Wei, Yanan

doi:10.1016/j.jag.2021.102360

Cited by 26 publications

(16 citation statements)

References 46 publications

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“…Average ratio adjustment was performed to normalize the reflectance of the Landsat-8 image [ 23 ]. First, the reflectance ratio between the blue bands (Blue/B B ) of the Landsat-8 image and fitted UAV images was calculated, and then the reflectance correction coefficient of the blue band was calculated as the average of these ratios for all fitted UAV images.…”

Section: Methodsmentioning

confidence: 99%

“…Since the 1970s, a series of satellites (e.g., Landsat, SPOT, and Sentinel) equipped with multispectral sensors have been put into operation. From then, satellite remote sensing images have been widely used in large-scale SSC and SOM estimation for their convenient acquisition, easy processing, and large coverage area [ 22 , 23 ]. Ma et al used Sentinel-1A and Sentinel-2A data to retrieve the distribution map of soil salinization in the Ogan-Kuqa River Oasis located in the Tarim Basin in Xinjiang, China [ 24 ].…”

Section: Introductionmentioning

confidence: 99%

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Estimation of Soil Salt Content and Organic Matter on Arable Land in the Yellow River Delta by Combining UAV Hyperspectral and Landsat-8 Multispectral Imagery

Sun

Jiang

et al. 2022

Sensors

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Rapid and large-scale estimation of soil salt content (SSC) and organic matter (SOM) using multi-source remote sensing is of great significance for the real-time monitoring of arable land quality. In this study, we simultaneously predicted SSC and SOM on arable land in the Yellow River Delta (YRD), based on ground measurement data, unmanned aerial vehicle (UAV) hyperspectral imagery, and Landsat-8 multispectral imagery. The reflectance averaging method was used to resample UAV hyperspectra to simulate the Landsat-8 OLI data (referred to as fitted multispectra). Correlation analyses and the multiple regression method were used to construct SSC and SOM hyperspectral/fitted multispectral estimation models. Then, the best SSC and SOM fitted multispectral estimation models based on UAV images were applied to a reflectance-corrected Landsat-8 image, and SSC and SOM distributions were obtained for the YRD. The estimation results revealed that moderately salinized arable land accounted for the largest proportion of area in the YRD (48.44%), with the SOM of most arable land (60.31%) at medium or lower levels. A significant negative spatial correlation was detected between SSC and SOM in most regions. This study integrates the advantages of UAV hyperspectral and satellite multispectral data, thereby realizing rapid and accurate estimation of SSC and SOM for a large-scale area, which is of great significance for the targeted improvement of arable land in the YRD.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimation of Soil Salt Content and Organic Matter on Arable Land in the Yellow River Delta by Combining UAV Hyperspectral and Landsat-8 Multispectral Imagery

Sun

Jiang

et al. 2022

Sensors

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“…The determination coefficients for validation were more than 0.69. To improve regional retrieval precision, Chen et al (2021) presented a differentiated fusion method for calculating satellite and ground spectral variables of soil salinity based on sample differences [23]. Spectral parameters and correlation salinity indexes have been converted and filtered to retrieve soil salinity.…”

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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“…Hyperspectral data provide the most valuable information for surface monitoring. The fusion of hyperspectral data and satellite data can greatly improve the spectrum of satellite data and inversion accuracy of regional soil salinity (Chen et al, 2021; Sankey et al, 2021). UAV technology is developing rapidly and is widely used, providing a new high‐precision remote sensing data sources with excellent properties (Ivushkin et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

Soil salinity inversion in coastal cotton growing areas: An integration method using satellite‐ground spectral fusion and satellite‐UAV collaboration

Chang

Yang

et al. 2022

Land Degrad Dev

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Soil salinity is a crucial factor in agriculture, rising salinity undermines cotton (Gossypium spp.) production in coastal areas of China and damages crops in other countries. In this study, we propose an effective integration method using satellite‐ground spectral fusion and satellite‐unmanned arial vehicle (UAV) collaboration for soil salinity monitoring in cotton growing areas. Firstly, an extreme learning machine (ELM), random forest (RF), and extreme gradient boosting (XGBoost) models were constructed based on UAV images from test areas. The optimal model was selected for soil salinity inversion. Meanwhile, ground imaging hyperspectrum and SENTINEL‐2A multispectral images were differentially fused by nonnegative matrix factorization (NMF). Then, taking the inversion results of UAV as the training sample to build convolutional neural network (CNN) model of the fused SENTINEL‐2A, the soil salinity distribution map of cotton fields in the study area was obtained by inversion, and the satellite‐UAV‐ground integrated inversion of soil salinity in coastal cotton fields was realized. The results showed that the spectrum after satellite‐ground fusion was closer to the original ground hyperspectrum, the fusion improved the correlation between spectrum and soil salinity, and UAV inversion data possessed great potential for the reference data of satellite inversion. The soil salinity obtained by satellite‐UAV‐ground integration approach was highly consistent with the measured salinity in the study area (R2 = 0.805), and the integration approach is suitable for soil salinity inversion in the cotton seedling stage in the coastal area. The satellite‐UAV‐ground integration approach proposed in this study fully tap the advantages of remote sensing data from different platforms and improved the ability to obtain soil salinisation information in large‐scale quantitatively, accurately, and quickly.

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Soil salinity inversion based on differentiated fusion of satellite image and ground spectra

Cited by 26 publications

References 46 publications

Estimation of Soil Salt Content and Organic Matter on Arable Land in the Yellow River Delta by Combining UAV Hyperspectral and Landsat-8 Multispectral Imagery

Estimation of Soil Salt Content and Organic Matter on Arable Land in the Yellow River Delta by Combining UAV Hyperspectral and Landsat-8 Multispectral Imagery

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

Soil salinity inversion in coastal cotton growing areas: An integration method using satellite‐ground spectral fusion and satellite‐UAV collaboration

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