Improving Satellite-Based Estimation and Mapping of Soil Lead by Using an Enhanced Spectral Feature Set and XGBoost Model.
Saved in:
| Title: | Improving Satellite-Based Estimation and Mapping of Soil Lead by Using an Enhanced Spectral Feature Set and XGBoost Model. |
|---|---|
| Authors: | Xu, Xibo1,2 (AUTHOR), Wang, Ying2,3 (AUTHOR), Dai, Xinrui3,4 (AUTHOR), Shen, Qi4 (AUTHOR), Wu, Quanyuan1,5 (AUTHOR), Wang, Zeqiang1,2,4 (AUTHOR), Cao, Jianfei1,2,5 (AUTHOR) caojianfei@sdnu.edu.cn |
| Source: | Remote Sensing. May2026, Vol. 18 Issue 9, p1446. 24p. |
| Subjects: | Lead in soils, Satellite-based remote sensing, Sustainability, Hyperspectral imaging systems, Ensemble learning, Soil pollution |
| Abstract: | Highlights: What are the main findings? An enhanced spectral feature set (ESFS) was constructed, and two new spectral indices for Pb-contaminated soils, named SPPI-2 and SPPI-3, were developed and incorporated into the ESFS. The XGBoost-based satellite hyperspectral mapping model achieved satisfactory performance (RPD = 2.06). What are the implications of the main findings? This study presents new spectral indices for effectively characterizing Pb-contaminated soils and supporting regional-scale soil management. This study provides a solution framework for estimating and mapping soil heavy metals, which assists in contamination hotspot identification and promotes environmental sustainability. Satellite hyperspectral remote sensing offers an efficient and cost-effective approach for estimating and mapping soil lead (Pb), thereby supporting pollution identification and environmental sustainability. However, the development of satellite-based spectral estimation models depends on the availability of a robust spectral feature set for soil Pb as input, which is difficult to obtain under field conditions due to interference from moisture, particle size, and light scattering. To address this issue, controlled spectral experiments were conducted on laboratory-prepared soil samples with varying Pb contamination levels. The spectral characteristics associated with Pb contamination were analyzed, and an enhanced spectral feature set (ESFS) was constructed using the successive projections algorithm–Shapley additive explanations method. Two new spectral indices for Pb-contaminated soils, named SPPI-2 and SPPI-3, were developed and incorporated into the ESFS. The ESFS was then applied to satellite hyperspectral data calibrated via direct standardization, with its spectral parameters used as input variables and measured Pb concentrations from field soil samples as the dependent variable. Finally, a satellite-based spectral model for soil Pb estimation was developed using the XGBoost (eXtreme Gradient Boosting) algorithm. Results showed that the spectral parameters in the ESFS included four characteristic bands (R840, R1013, R1215, and R2239) and two newly developed spectral indices (SPPI-2 and SPPI-3). The satellite-based spectral estimation model based on the ESFS and XGBoost algorithm achieved the best performance, with R2 (coefficient of determination) and RPD (ratio of performance to deviation) values of 0.78 and 2.10, respectively, representing a maximum improvement of 164.10% and a minimum improvement of 12.86% (in terms of RPD values) compared to common methods. Hotspot areas of Pb-contaminated soils were mainly found in the eastern part of the abandoned coal mining area, which is associated with improper coal mining and transportation activities. This study presents a satellite hyperspectral framework for effectively estimating the distribution pattern of soil Pb and supporting the regional-scale soil management and environmental sustainability. [ABSTRACT FROM AUTHOR] |
| Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
| Abstract: | Highlights: What are the main findings? An enhanced spectral feature set (ESFS) was constructed, and two new spectral indices for Pb-contaminated soils, named SPPI-2 and SPPI-3, were developed and incorporated into the ESFS. The XGBoost-based satellite hyperspectral mapping model achieved satisfactory performance (RPD = 2.06). What are the implications of the main findings? This study presents new spectral indices for effectively characterizing Pb-contaminated soils and supporting regional-scale soil management. This study provides a solution framework for estimating and mapping soil heavy metals, which assists in contamination hotspot identification and promotes environmental sustainability. Satellite hyperspectral remote sensing offers an efficient and cost-effective approach for estimating and mapping soil lead (Pb), thereby supporting pollution identification and environmental sustainability. However, the development of satellite-based spectral estimation models depends on the availability of a robust spectral feature set for soil Pb as input, which is difficult to obtain under field conditions due to interference from moisture, particle size, and light scattering. To address this issue, controlled spectral experiments were conducted on laboratory-prepared soil samples with varying Pb contamination levels. The spectral characteristics associated with Pb contamination were analyzed, and an enhanced spectral feature set (ESFS) was constructed using the successive projections algorithm–Shapley additive explanations method. Two new spectral indices for Pb-contaminated soils, named SPPI-2 and SPPI-3, were developed and incorporated into the ESFS. The ESFS was then applied to satellite hyperspectral data calibrated via direct standardization, with its spectral parameters used as input variables and measured Pb concentrations from field soil samples as the dependent variable. Finally, a satellite-based spectral model for soil Pb estimation was developed using the XGBoost (eXtreme Gradient Boosting) algorithm. Results showed that the spectral parameters in the ESFS included four characteristic bands (R840, R1013, R1215, and R2239) and two newly developed spectral indices (SPPI-2 and SPPI-3). The satellite-based spectral estimation model based on the ESFS and XGBoost algorithm achieved the best performance, with R2 (coefficient of determination) and RPD (ratio of performance to deviation) values of 0.78 and 2.10, respectively, representing a maximum improvement of 164.10% and a minimum improvement of 12.86% (in terms of RPD values) compared to common methods. Hotspot areas of Pb-contaminated soils were mainly found in the eastern part of the abandoned coal mining area, which is associated with improper coal mining and transportation activities. This study presents a satellite hyperspectral framework for effectively estimating the distribution pattern of soil Pb and supporting the regional-scale soil management and environmental sustainability. [ABSTRACT FROM AUTHOR] |
|---|---|
| ISSN: | 20724292 |
| DOI: | 10.3390/rs18091446 |
