Evaluating Bare Soil Properties and Vegetation Indices for Digital Farming Applications from UAV-based Multispectral Images

Year 2023, Volume: 6 Issue: 1, 5 - 10, 30.06.2023

Sinan Demir Levent Başyiğit

Abstract

The possibilities of using unmanned aerial vehicles (UAVs)-based on multispectral sensors and data produced from images taken from agricultural areas in digital agriculture applications are being investigated. This research is to determine the effect of bare soil reflection on vegetation indices produced from UAV-based multispectral images in the sustainable management of agricultural lands and to reveal the relationship between soil texture and vegetation indices. In the study, clay, silt, and sand contents were determined by making texture analyses in soil samples obtained by using a random stratified sampling method. A multi-band orthophoto image was created from the UAV-based multispectral data for the study area. Visible Atmospheric Resistant Index (VARI), Normalized difference vegetation index (NDVI), Normalized Difference Red Edge Index (NDRE), Leaf Chlorophyll Index (LCI), Green-Red Vegetation Index (GRVI), which are widely used in digital agriculture, from the multispectral image of the study area. Soil Adjusted Vegetation Index (SAVI), and Green Normalized Difference Vegetation Index (GNDVI) vegetation indices were calculated. The relationships between vegetation indices data set and soil clay, silt, and sand contents were determined statistically (p < 0.001). It was determined that the highest correlated vegetation index GNDVI with soil texture. It was determined that there were 0.62, -0.72, and 0.73 correlation coefficients between the GNDVI vegetation index and clay, silt, and sand, respectively. The data produced from UAV-based multispectral images between the bare soil reflection and vegetation indices have been shown to have potential at the farmland scale.

Keywords

Digital Agriculture, UAV-Based Remote Sensing, Multispectral Image, Plant Vegetation Indices

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