Effect of Open Soil Surface Patterns on Soil Detectability Based on Optical Remote Sensing Data

Abstract

Arable soils are subjected to the altering influence of agricultural and natural processes determining surface feedback patterns therefore affecting their ability to reflect light. However, remote soil mapping and monitoring usually ignore information on surface state at the time of data acquisition. Conducted research demonstrates the contribution of surface feedback dynamics to soil reflectance and its relationship with soil properties. Analysis of variance showed that the destruction surface patterns accounts for 71% of spectral variation. The effect of surface smoothing on the relationships between soil reflectance and its properties varies. In the case of organic matter and medium and coarse sand particles, correlation decreases with the removement of surface structure. For particles of fine sand and coarse silt, grinding changes spectral areas of high correlation. Partial least squares regression models also demonstrated variations in complexity, R 2 cv and RMSEPcv. Field dynamics of surface feedback patterns of arable soils causes 22-46% of soil spectral variations depending on the growing season and soil type. The directions and areas of spectral changes seem to be soil-specific. Therefore, surface feedback patterns should be considered when modelling soil properties on the basis of optical remote sensing data to ensure reliable and reproducible results.

Influence of Soil Background on Spectral Reflectance of Winter Wheat Crop Canopy

“…As the measurements were made in sunny weather (near midday time), optically active soil surface layer (1-2 mm) was dry. Therefore, the main factor of soil surface spectral variations within soil type was a transformation of the surface under the impact of rainfall which is known to result in changes of soil surface organic matter content and mineralogical composition or in the development of biological crust [49][50][51][52]65,67].…”

“…This result suggests that the ability of soil-line-based indices to correct for soil background variations is limited by temporal soil line variability. composition or in the development of biological crust [49][50][51][52]65,67].…”

“…The introduction of a temporal dimension into the studies requires the consideration of the soil surface dynamics. Soil surface properties was found to change under the impact of rainfall during the vegetative season (not only soil surface moisture or roughness, but also soil surface mineralogical composition, organic matter content, salts content and so on) [48][49][50][51][52]. Corresponding changes in soil surface spectral reflectance will create a site-specific soil line dynamics, introducing additional uncertainty in the assessment of the crop state from remote sensing data.…”

Utilizing Hyperspectral Remote Sensing for Soil Gradation

“…Using hyperspectral data, one can predict different features/characteristics of bare soil, and certain wavelengths may provide a means of identifying/distinguishing certain features [19,20]. The agriculture field has utilized soil spectral reflectance for many purposes, such as identifying reflectance effects based on moisture content, soil tilling, organic material, iron content, and minerals within the soil [21][22][23][24][25][26][27][28] and how the open soil surface patterns impact that reflectance [29]. Visible and near-infrared spectroscopy has already been used for identifying clay mineral contents within the soil [30], and shown ease of use and good agreement with identifying soil mineral composition to that of X-ray diffraction analysis [31].…”