Developing a Cross-Platform Transferable Spectral Index for Soda Saline–Alkali Soils: A Case Study in the Songnen Plain, Northeast China.
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| Title: | Developing a Cross-Platform Transferable Spectral Index for Soda Saline–Alkali Soils: A Case Study in the Songnen Plain, Northeast China. |
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| Authors: | Gu, He1,2 (AUTHOR), Shang, Kun1,2 (AUTHOR) shangkun@lasac.cn, Sun, Weichao3 (AUTHOR), Xiao, Chenchao1 (AUTHOR), Xie, Yisong2,3 (AUTHOR) |
| Source: | Remote Sensing. Mar2026, Vol. 18 Issue 5, p758. 24p. |
| Subjects: | Soil salinization, Remote sensing, Electric conductivity of soils, Plains, Soil salinity |
| Geographic Terms: | Manchuria (China) |
| Abstract: | Highlights: What are the main findings? A cross-platform transferable spectral index for soda saline–alkali soils was developed using laboratory spectra and hyperspectral satellite observations through a collaborative, cross-dataset spectral feature selection framework. The proposed Difference Index based on Square Root Reflectance at 520 nm and 900 nm (DISRR520900) showed consistent relationships with log-transformed soil electrical conductivity across datasets (R = 0.60 for hyperspectral satellite data; R = 0.82 for laboratory spectra). What are the implications of the main findings? The integration of multi-source remote sensing data enhances soil salinization monitoring sensitivity and continuity in large-scale applications. The resulting soil salinization maps can provide an operational tool for regional monitoring, agricultural management, and ecological restoration planning. Soil salinization is a widespread form of land degradation that severely constrains agricultural productivity and ecosystem stability. Efficient and transferable monitoring methods are therefore essential for large-scale salinization assessment. Remote sensing provides timely and synoptic observations, while the integration of multi-source datasets offers complementary spectral and spatial information. In this study, we developed a cross-platform spectral index specifically for soda saline–alkali (carbonate/bicarbonate-dominated) soils by integrating laboratory spectra and hyperspectral satellite observations through a collaborative, cross-dataset spectral feature selection framework. Dual-band spectral indices were constructed from transformed reflectance spectra, and a stepwise coupled correlation analysis was applied to identify representative candidates that consistently exhibited strong associations with log-transformed soil electrical conductivity (logEC) across datasets. An optimal central-wavelength analysis was then performed to determine a stable and transferable band pair. The study was conducted in the Songnen Plain of Northeast China using laboratory-measured soil spectra and Ziyuan-1 02D Advanced Hyperspectral Imager data, and the proposed index was further validated using Landsat-8 and Sentinel-2 Multispectral data. Results show that the proposed Difference Index based on Square Root Reflectance at 520 nm and 900 nm (DISRR520900) exhibited consistent relationships with logEC (R = 0.60 for hyperspectral satellite data and R = 0.82 for laboratory spectral data), outperforming commonly used salinity indices in terms of cross-sensor stability. The spatial distribution of soil salinization derived from DISRR520900 is highly consistent with true-color imagery, and multi-source data fusion further improves mapping continuity and spatial coverage. It should be noted that the proposed index is primarily applicable to bare or sparsely vegetated soil surfaces in soda saline–alkali regions. Under dense vegetation cover, substantial crop residue, or wet surface conditions, additional masking or correction may be required. These results demonstrate that DISRR520900 provides a stable cross-sensor solution for large-scale soil salinization mapping within comparable soil chemical contexts. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? A cross-platform transferable spectral index for soda saline–alkali soils was developed using laboratory spectra and hyperspectral satellite observations through a collaborative, cross-dataset spectral feature selection framework. The proposed Difference Index based on Square Root Reflectance at 520 nm and 900 nm (DISRR520900) showed consistent relationships with log-transformed soil electrical conductivity across datasets (R = 0.60 for hyperspectral satellite data; R = 0.82 for laboratory spectra). What are the implications of the main findings? The integration of multi-source remote sensing data enhances soil salinization monitoring sensitivity and continuity in large-scale applications. The resulting soil salinization maps can provide an operational tool for regional monitoring, agricultural management, and ecological restoration planning. Soil salinization is a widespread form of land degradation that severely constrains agricultural productivity and ecosystem stability. Efficient and transferable monitoring methods are therefore essential for large-scale salinization assessment. Remote sensing provides timely and synoptic observations, while the integration of multi-source datasets offers complementary spectral and spatial information. In this study, we developed a cross-platform spectral index specifically for soda saline–alkali (carbonate/bicarbonate-dominated) soils by integrating laboratory spectra and hyperspectral satellite observations through a collaborative, cross-dataset spectral feature selection framework. Dual-band spectral indices were constructed from transformed reflectance spectra, and a stepwise coupled correlation analysis was applied to identify representative candidates that consistently exhibited strong associations with log-transformed soil electrical conductivity (logEC) across datasets. An optimal central-wavelength analysis was then performed to determine a stable and transferable band pair. The study was conducted in the Songnen Plain of Northeast China using laboratory-measured soil spectra and Ziyuan-1 02D Advanced Hyperspectral Imager data, and the proposed index was further validated using Landsat-8 and Sentinel-2 Multispectral data. Results show that the proposed Difference Index based on Square Root Reflectance at 520 nm and 900 nm (DISRR520900) exhibited consistent relationships with logEC (R = 0.60 for hyperspectral satellite data and R = 0.82 for laboratory spectral data), outperforming commonly used salinity indices in terms of cross-sensor stability. The spatial distribution of soil salinization derived from DISRR520900 is highly consistent with true-color imagery, and multi-source data fusion further improves mapping continuity and spatial coverage. It should be noted that the proposed index is primarily applicable to bare or sparsely vegetated soil surfaces in soda saline–alkali regions. Under dense vegetation cover, substantial crop residue, or wet surface conditions, additional masking or correction may be required. These results demonstrate that DISRR520900 provides a stable cross-sensor solution for large-scale soil salinization mapping within comparable soil chemical contexts. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18050758 |
