The inversion of arid-coastal cultivated soil salinity using explainable machine learning and Sentinel-2

verfasst von

Pingping Jia, Junhua Zhang, Yanning Liang, Sheng Zhang, Keli Jia, Xiaoning Zhao

Abstract

The escalating salinization of cultivated soil poses a significant threat to the ecological environment. It is imperative to establish a monitoring system and mitigate the spread of salinization in arid and coastal areas through remote sensing, incorporating high-precision cross-regional models for soil salt content inversion. This study focuses on typical saline-alkali soils in arid and coastal regions of China. Using Sentinel 2 data (including 6 bands and 27 spectral indices), along with soil texture, moisture content, temperature, precipitation, and digital elevation model (DEM) data to establish an arid-coastal salinity inversion model. Variable selection methods such as pearson correlation coefficient (PCC), variable importance in projection (VIP), gray relational analysis (GRA), and gradient boosting machine (GBM) were used, while using 9 models including adaptive boosting (Adaboost), extremely randomized trees (ERT), and light gradient boosting machine (LightGBM). The best model was further elucidated using the Shapley additive explanations method. Results indicate that the common sensitive characteristic variables of arid-coastal areas were spectral indices and soil properties in PCC, the spectral variable bands and indices in VIP, and all variables in GRA and GBM. The best inversion model GBM-ERT (R² = 0.91, RMSE = 1.06) in arid areas exhibited higher accuracy than the best inversion model GBM-Adaboost (R² = 0.77, RMSE = 1.74) in coastal areas. The arid-coastal inversion model PCC-LightGBM demonstrated the best inversion performance (R² = 0.64, RMSE = 2.29) and simulation performance in arid (R² = 0.67) and coastal areas (R² = 0.63). Dead fuel index (DFI) had the most significant impact on model prediction (0.89) and the second ratio index (RI2) contributed the highest relative importance (18 %) to the model. Our analysis indicates that the arid-coastal model of PCC-LightGBM established using common characteristic variables, can effectively monitor large-scale soil salinity.

Organisationseinheit(en)

Institut für Bodenkunde

Externe Organisation(en)

Ningxia University
Nanjing University of Information Science and Technology
Chinese Academy of Sciences (CAS)

Typ

Artikel

Journal

Ecological indicators

Band

166

Anzahl der Seiten

14

ISSN

1470-160X

Publikationsdatum

09.2024

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Entscheidungswissenschaften (insg.), Ökologie, Evolution, Verhaltenswissenschaften und Systematik, Ökologie

Ziele für nachhaltige Entwicklung

SDG 15 – Lebensraum Land

Elektronische Version(en)

https://doi.org/10.1016/j.ecolind.2024.112364 (Zugang: Offen)