A detailed mapping of soil organic matter content in arable land based on the multitemporal soil line coefficients and neural network filtering of big remote sensing data
Д. И. Рухович, П. В. Королева, Alexey Rukhovich, Mikhail Komissarov
Abstract
• A method for SOM content mapping based on a multitemporal soil line was developed. • Mapping detail was 30 m with an accuracy of R 2 = 0.8. • The neural network automated the selection of remote sensing data and bare soil. • The proposed approach can be applied to a wide range of soils and large areas. A new method for constructing detailed maps of the soil organic matter (SOM) distribution in the top layer of arable land has been developed and proposed. The method is based on the theory of spectral neighborhood of the soil line (SNSL) and the technology of constructing a multitemporal soil line (MSL). The method is based on the processing of big remote sensing data (BRSD) from 1984 to 2023. Filtering of BRSD and detection of bare soil surface (BSS) is carried out on the basis of neural networks. The method was implemented for BSS with an area about of 79,000 ha with a spatial resolution of 30 m in the Mtsensk district (Oryol Oblast, Russia). Verification was provided by four independent field surveys (which were carried out using three various methods) in different years (2022–2023). The regression is described by a polynomial of degree 2. The coefficient of determination (R 2 ) of the regression was 0.8. The proposed method can be widely used for mapping of SOM in the areas of transition from leached chernozems (Luvic Chernozems) to sod-podzolic (Albic Retisol) and gray forest (Luvic Phaeozems) soils or in similar nature conditions.