Inversion Estimation of Soil Organic Matter in Songnen Plain Based on Multispectral Analysis

Siyu Tang, Chong Du and Tangzhe Nie
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Siyu Tang: School of Hydraulic and Electric Power, Heilongjiang University, Harbin 150080, China
Chong Du: School of Hydraulic and Electric Power, Heilongjiang University, Harbin 150080, China
Tangzhe Nie: School of Hydraulic and Electric Power, Heilongjiang University, Harbin 150080, China

Land, 2022, vol. 11, issue 5, 1-18

Abstract: Sentinel-2A multi-spectral remote sensing image data underwent high-efficiency differential processing to extract spectral information, which was then matched to soil organic matter (SOM) laboratory test values from field samples. From this, multiple-linear stepwise regression (MLSR) and partial least square (PLSR) models were established based on a differential algorithm for surface SOM modeling. The original spectra were subjected to basic transformations with first- and second-derivative processing. MLSR and PLSR models were established based on these methods and the measured values, respectively. The results show that Sentinel-2A remote sensing imagery and SOM content correlated in some bands. The correlation between the spectral value and SOM content was significantly improved after mathematical transformation, especially square-root transformation. After differential processing, the multi-band model had better predictive ability (based on fitting accuracy) than single-band and unprocessed multi-band models. The MLSR and PLSR models of SOM had good prediction functionality. The reciprocal logarithm first-order differential MLSR regression model had the best prediction and inversion results (i.e., most consistent with the real-world data). The MLSR model is more stable and reliable for monitoring SOM content, and provides a feasible method and reference for SOM content-mapping of the study area.

Keywords: soil organic matter; Sentinel-2A; remote sensing; differential algorithm; multispectral modeling; PLSR (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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