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. 2022 May 25;22(11):3990.
doi: 10.3390/s22113990.

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

Mingyue Sun  1 , Qian Li  2 , Xuzi Jiang  1 , Tiantian Ye  1 , Xinju Li  1 , Beibei Niu  1
Affiliations

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

Mingyue Sun et al. Sensors (Basel). .

Abstract

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.

Keywords: Landsat-8 satellite; UAV hyperspectral images; Yellow River Delta; soil organic matter; soil salt content.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Locations of the study area and test plots.
Figure 2
Figure 2
Reflectance of (a) original hyperspectral data and (b) S–G filter denoised hyperspectral data.
Figure 3
Figure 3
Correlation of original reflectance and S–G filter denoised reflectance for (a) SSC and (b) SOM.
Figure 4
Figure 4
Correlations between hyperspectral reflectance and (a) SSC and (b) SOM under different mathematical transformations.
Figure 5
Figure 5
Scatter diagram of the (a) SSC1 and (b) SOM1 model.
Figure 6
Figure 6
Distribution of (a) SSC and (c) SOM based on the hyperspectral estimation model and that of (b) SSC and (d) SOM based on the fitted multispectral estimation model in the three test plots.
Figure 7
Figure 7
Comparison (a) and scatter plot (b) of surface reflectance of the fitted multispectral bands of the UAV images and bands of the Landsat-8 image.
Figure 8
Figure 8
Spatial distribution of retrieved (a) SSC and (b) SOM values in the study area.
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