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.
Highlights
Remote sensing data are actively used to assess and monitor the state of crops [1,2,3,4,5]
The difference between these two phases lies in the intensity of the reflectance and in the pattern of the increase in reflectance in the NIR part of the spectrum. Such behavior of the spectral reflectance of gray forest soil at shooting and milky ripeness phases can be associated with the development of a biological crust on the soil surface [66,67]
Our findings showed that the dynamics of the surface of arable soil affects the contribution of the soil to the spectral reflectance of the winter wheat crop canopy in certain parts of the spectrum, but it is accompanied by a change in the values of vegetation indices for the soil in such a way that they become close to the values of the vegetation indices for winter wheat crops at certain stages of their development
Summary
Remote sensing data are actively used to assess and monitor the state of crops [1,2,3,4,5]. Modern systems for monitoring agricultural lands use satellite data to predict crop yields at various levels (global, national) [6,7,8]. Such information affects the pricing policy of agricultural producers and the situation on the global agricultural market. There are two main groups of approaches which can be used for crop assessment and monitoring on the basis of remote sensing data: statistical and physical approaches [10,11,12]. Physical approaches are based on canopy radiative transfer models (RTM) such as PROSAIL [12,15,16,17]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
