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

Abstract

The spectral reflectance of crop canopy is a spectral mixture, which includes soil background as one of the components. However, as soil is characterized by substantial spatial variability and temporal dynamics, its contribution to the spectral reflectance of crops will also vary. The aim of the research was to determine the impact of soil background on spectral reflectance of crop canopy in visible and near-infrared parts of the spectrum at different stages of crop development and how the soil type factor and the dynamics of soil surface affect vegetation indices calculated for crop assessment. The study was conducted on three test plots with winter wheat located in the Tula region of Russia and occupied by three contrasting types of soil. During field trips, information was collected on the spectral reflectance of winter wheat crop canopy, winter wheat leaves, weeds and open soil surface for three phenological phases (tillering, shooting stage, milky ripeness). The assessment of the soil contribution to the spectral reflectance of winter wheat crop canopy was based on a linear spectral mixture model constructed from field data. This showed that the soil background effect is most pronounced in the regions of 350–500 nm and 620–690 nm. In the shooting stage, the contribution of the soil prevails in the 620–690 nm range of the spectrum and the phase of milky ripeness in the region of 350–500 nm. The minimum contribution at all stages of winter wheat development was observed at wavelengths longer than 750 nm. The degree of soil influence varies with soil type. Analysis of variance showed that normalized difference vegetation index (NDVI) was least affected by soil type factor, the influence of which was about 30%–50%, depending on the stage of winter wheat development. The influence of soil type on soil-adjusted vegetation index (SAVI) and enhanced vegetation index (EVI2) was approximately equal and varied from 60% (shooting phase) to 80% (tillering phase). According to the discriminant analysis, the ability of vegetation indices calculated for winter wheat crop canopy to distinguish between winter wheat crops growing on different soil types changed from the classification accuracy of 94.1% (EVI2) in the tillering stage to 75% (EVI2 and SAVI) in the shooting stage to 82.6% in the milky ripeness stage (EVI2, SAVI, NDVI). The range of the sensitivity of the vegetation indices to the soil background depended on soil type. The indices showed the greatest sensitivity on gray forest soil when the wheat was in the phase of milky ripeness, and on leached chernozem when the wheat was in the tillering phase. The observed patterns can be used to develop vegetation indices, invariant to second-type soil variations caused by soil type factor, which can be applied for the remote assessment of the state of winter wheat crops.

From Laboratory to Proximal Sensing Spectroscopy for Soil Organic Carbon Estimation—A Review

“…Attempts to create samples with variable soil moisture content have also been made, though they are somewhat time consuming [73,80,143]. Additional factors that could interfere with in situ measurements are ambient conditions such as temperature, wind, and precipitation [144,145], soil roughness that could be altered by field operations, stones, plant residues and movement during spectral acquisition, i.e., vibrations [112,113,121]. Moreover, it should be noted that in-field measurements are usually performed in the top layer of the soil, with an exception being studies where the sensor penetrates the soil, which may impact the model's quality because laboratory reference measurements concern samples taken from a layer.…”

Remote Sensing Energy Balance Model for the Assessment of Crop Evapotranspiration and Water Status in an Almond Rootstock Collection

“…On the other hand, it is well-established that VIs are strongly influenced by canopy architecture, optical properties, sun illumination angle, viewing properties and soil background ( Huete, 1988 ; Guillen-Climent et al, 2012 ; Xie et al, 2018 ; Prudnikova et al, 2019 ). In addition, saturations at moderate-to-dense canopies, leaf area distribution, and clumping effect are three of the most important issues influencing the accuracy of optical LAI estimates in row crops ( Delalieux et al, 2008 ; Shafian et al, 2018 ; Yan et al, 2019 ).…”

Assessing Leaf Biomass of Agave sisalana Using Sentinel-2 Vegetation Indices

“…With Sentinel's 20-m resolution, this means that the signal is always a mixture of sisal and either soil, trees or ground vegetation, or all. Spectral regions used in the red-edge and NIR to G ratios, which showed the best sensitivity to biomass, have exhibited low sensitivity to soil background effects [63,64]. However, it should be acknowledged that the sensitivity of VIs to soil depends on the soil type and wider evaluation of soil-adjusted indices could therefore be relevant [65].…”