Estimating Soil Salinity with Different Levels of Vegetation Cover by Using Hyperspectral and Non-Negative Matrix Factorization Algorithm

Jianfei Cao, Han Yang, Jianshu Lv, Quanyuan Wu () and Baolei Zhang
Additional contact information
Jianfei Cao: College of Geography and Environment, Shandong Normal University, Jinan 250014, China
Han Yang: College of Geography and Environment, Shandong Normal University, Jinan 250014, China
Jianshu Lv: College of Geography and Environment, Shandong Normal University, Jinan 250014, China
Quanyuan Wu: College of Geography and Environment, Shandong Normal University, Jinan 250014, China
Baolei Zhang: College of Geography and Environment, Shandong Normal University, Jinan 250014, China

IJERPH, 2023, vol. 20, issue 4, 1-15

Abstract: Hyperspectral technology has proven to be an effective method for monitoring soil salt content (SSC). However, hyperspectral estimation capabilities are limited when the soil surface is partially vegetated. This work aimed to (1) quantify the influences of different fraction vegetation coverage (FVC) on SSC estimation by hyperspectra and (2) explore the potential for a non-negative matrix factorization algorithm (NMF) to reduce the influence of various FVCs. Nine levels of mixed hyperspectra were measured from simulated mixed scenes, which were performed by strictly controlling SSC and FVC in the laboratory. NMF was implemented to extract soil spectral signals from mixed hyperspectra. The NMF-extracted soil spectra were used to estimate SSC using partial least squares regression. Results indicate that SSC could be estimated based on the original mixed spectra within a 25.76% FVC (R 2 cv = 0.68, RMSE cv = 5.18 g·kg −1 , RPD = 1.43). Compared with the mixed spectra, NMF extraction of soil spectrum improved the estimation accuracy. The NMF-extracted soil spectra from FVC below 63.55% of the mixed spectra provided acceptable estimation accuracies for SSC with the lowest results of determination of the estimation R 2 cv = 0.69, RMSE cv = 4.15 g·kg −1 , and RPD = 1.8. Additionally, we proposed a strategy for the model performance investigation that combines spearman correlation analysis and model variable importance projection analysis. The NMF-extracted soil spectra retained the sensitive wavelengths that were significantly correlated with SSC and participated in the operation as important variables of the model.

Keywords: partially vegetated surfaces; mixed hyperspectra; non-negative matrix factorization; soil salt content (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1660-4601/20/4/2853/pdf (application/pdf)
https://www.mdpi.com/1660-4601/20/4/2853/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:20:y:2023:i:4:p:2853-:d:1059270

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().