Hyperspectral remote sensing for soil heavy metal inversion: insights and applications
Researchers worldwide have increasingly recognised hyperspectral remote sensing (HRS) for its extensive coverage and high efficiency in monitoring the spatiotemporal distribution of soil heavy metal (SHM). However, accurately estimating SHM concentrations using HRS remains a significant challenge be...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-08-01
|
| Series: | International Journal of Digital Earth |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/17538947.2025.2520474 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849224300259704832 |
|---|---|
| author | Yi Su Boyan Li Jing Li Bin Guo Qi Feng |
| author_facet | Yi Su Boyan Li Jing Li Bin Guo Qi Feng |
| author_sort | Yi Su |
| collection | DOAJ |
| description | Researchers worldwide have increasingly recognised hyperspectral remote sensing (HRS) for its extensive coverage and high efficiency in monitoring the spatiotemporal distribution of soil heavy metal (SHM). However, accurately estimating SHM concentrations using HRS remains a significant challenge because of data complexity, spatial heterogeneity, and scale variability. In this review, we critically examine recent advancements in HRS for SHM monitoring, beginning with an overview of the mechanisms underlying the hyperspectral inversion (HI) of SHM content. We then discuss the application of HI technologies in SHM research, summarise key findings, and identify persistent challenges, including those related to inversion accuracy and large-scale mapping. Finally, implementation strategies are outlined to provide valuable insights and guidance for advancing soil pollution monitoring and regulation applications. This review aims to support further developments in the field and foster more effective monitoring and management of soil pollution. |
| format | Article |
| id | doaj-art-79efc26cf73d4aa2a11a40c216bd7a8a |
| institution | Kabale University |
| issn | 1753-8947 1753-8955 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Digital Earth |
| spelling | doaj-art-79efc26cf73d4aa2a11a40c216bd7a8a2025-08-25T11:25:10ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552025-08-0118110.1080/17538947.2025.2520474Hyperspectral remote sensing for soil heavy metal inversion: insights and applicationsYi Su0Boyan Li1Jing Li2Bin Guo3Qi Feng4School of Geography and Tourism, Shaanxi Normal University, Xi’an, People’s Republic of ChinaSchool of Geography and Tourism, Shaanxi Normal University, Xi’an, People’s Republic of ChinaSchool of Geography and Tourism, Shaanxi Normal University, Xi’an, People’s Republic of ChinaCollege of Geomatics, Xi’an University of Science and Technology, Xi’an, People’s Republic of ChinaSchool of Geography and Tourism, Shaanxi Normal University, Xi’an, People’s Republic of ChinaResearchers worldwide have increasingly recognised hyperspectral remote sensing (HRS) for its extensive coverage and high efficiency in monitoring the spatiotemporal distribution of soil heavy metal (SHM). However, accurately estimating SHM concentrations using HRS remains a significant challenge because of data complexity, spatial heterogeneity, and scale variability. In this review, we critically examine recent advancements in HRS for SHM monitoring, beginning with an overview of the mechanisms underlying the hyperspectral inversion (HI) of SHM content. We then discuss the application of HI technologies in SHM research, summarise key findings, and identify persistent challenges, including those related to inversion accuracy and large-scale mapping. Finally, implementation strategies are outlined to provide valuable insights and guidance for advancing soil pollution monitoring and regulation applications. This review aims to support further developments in the field and foster more effective monitoring and management of soil pollution.https://www.tandfonline.com/doi/10.1080/17538947.2025.2520474Hyperspectral remote sensingSoil heavy metalMachine learningAir-space-ground integration |
| spellingShingle | Yi Su Boyan Li Jing Li Bin Guo Qi Feng Hyperspectral remote sensing for soil heavy metal inversion: insights and applications International Journal of Digital Earth Hyperspectral remote sensing Soil heavy metal Machine learning Air-space-ground integration |
| title | Hyperspectral remote sensing for soil heavy metal inversion: insights and applications |
| title_full | Hyperspectral remote sensing for soil heavy metal inversion: insights and applications |
| title_fullStr | Hyperspectral remote sensing for soil heavy metal inversion: insights and applications |
| title_full_unstemmed | Hyperspectral remote sensing for soil heavy metal inversion: insights and applications |
| title_short | Hyperspectral remote sensing for soil heavy metal inversion: insights and applications |
| title_sort | hyperspectral remote sensing for soil heavy metal inversion insights and applications |
| topic | Hyperspectral remote sensing Soil heavy metal Machine learning Air-space-ground integration |
| url | https://www.tandfonline.com/doi/10.1080/17538947.2025.2520474 |
| work_keys_str_mv | AT yisu hyperspectralremotesensingforsoilheavymetalinversioninsightsandapplications AT boyanli hyperspectralremotesensingforsoilheavymetalinversioninsightsandapplications AT jingli hyperspectralremotesensingforsoilheavymetalinversioninsightsandapplications AT binguo hyperspectralremotesensingforsoilheavymetalinversioninsightsandapplications AT qifeng hyperspectralremotesensingforsoilheavymetalinversioninsightsandapplications |