Abstract
Soil surface roughness (SSR) is an important parameter affecting surface hydrology, erosion, gas exchange and other processes. The surface roughness of the farmland environment is directly related to the tillage process. In order to accurately characterize the random roughness (RR) parameters of the surface after ditching, a three-dimensional (3D) digital model of the surface was obtained by laser scanning under the conditions of an indoor ditching test, and the influence of oriented roughness components formed by removing ridge characteristics on the RR of the surface was analyzed by introducing the wavelet processing method. For this reason, four groups of ditching depths and two types of surface conditions (whether the surface was agglomerated or not) were designed in this paper. By comparing the root mean squared height (RMSH) and correlation length (CL) data calculated before and after wavelet processing under each group of tests, it was concluded that the RMSH values of the four groups before and after wavelet processing all change more than 200%, the change amplitude reached 271.02% under the treatment of 12 cm ditching depth, meanwhile, the average CL value of five cross-sections under each group of ditching depths decreased by 1.43–2.28 times, which proves that the oriented roughness component formed by furrows and ridges has a significant influence on the calculation of RR. By further analyzing the roughness value differences of clods and pits in different directions and local areas before and after wavelet transform, it was shown that the wavelet transform can effectively remove the surface anisotropy characteristics formed in the tillage direction and provide a uniform treatment method for the evaluation of surface RR at different ditching depths.
Highlights
Soil surface roughness (SSR), defined as the spatial variation of soil surface height, is one of the important soil surface characteristics affecting surface hydrology, erosion, gas exchange and other processes [1,2]
We proposed a method based on wavelet processing to remove the oriented roughness formed by rows in indoor soil bin test and provide a unified evaluation method for the random roughness (RR) formed by clods distribution under different ditching depths
We have studied the influence of different ditching depths and whether the surface is agglomerated on the RR of the soil surface, and obtained the following conclusions: (1) By comparing the root mean squared height (RMSH) and correlation length (CL) data calculated before and after the wavelet processing in each group of experiments, it can be seen that the SSR results before and after the wavelet processing have changed significantly
Summary
Soil surface roughness (SSR), defined as the spatial variation of soil surface height, is one of the important soil surface characteristics affecting surface hydrology, erosion, gas exchange and other processes [1,2]. It is an important parameter that affects the stable walking of small agricultural robots in the field [3,4]. Changes of SSR in farmland are mainly controlled by natural factors such as agricultural tillage and field management activities, wind, rain, and gravity, as well as the physical and chemical properties of soil itself [5]. Human agricultural activities can dramatically change surface roughness in a short time, while natural factors change SSR more slowly [6]. In order to quantitatively describe SSR changes, most previous studies were mainly represented by root mean squared height (RMSH), correlation length (CL) and autocorrelation function (ACF) of soil surface.
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